本篇博文主要内容为 2026-08-18 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 (2026-08-18)
今日共更新1341篇论文,其中:
- 自然语言处理共156篇(Computation and Language (cs.CL))
- 人工智能共465篇(Artificial Intelligence (cs.AI))
- 计算机视觉共269篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共374篇(Machine Learning (cs.LG))
- 多智能体系统共19篇(Multiagent Systems (cs.MA))
- 信息检索共45篇(Information Retrieval (cs.IR))
- 人机交互共50篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）在仅凭已发表论文的参考文献列表时，能否准确恢复其原始研究思想的核心问题。这一任务具有高度挑战性，因为参考文献本身不包含论文的结论或核心假设，而模型需从有限的引用信息中推断出未公开的研究动机与科学假说。解决方案的关键在于构建一个严格的“盲态思想重构”评估基准——Reconstruction，其通过多重防泄露机制保障实验公正性：包括时间截断的引用截止策略（temporal citation cutoff）、匿名化参考文献标识符（anonymous reference IDs），以及冻结每篇论文的参考文献集合（frozen per-paper bibliographies），从而防止在提示生成阶段出现种子论文思想的泄露。在此基础上，研究提出了一种仅依赖参考文献的多智能体协同推理框架，结合跨模型评审与瑞士轮锦标赛式的选择机制，在不进行外部网络搜索的前提下，实现了对候选假说的高效筛选与优化。实验结果表明，该多智能体方案在六个科学领域共643篇论文上的匹配率（Match rate）提升至23%-42%，相较表现最佳的单模型基线提升了约2.4倍，验证了协同推理与结构化筛选在知识稀疏场景下的有效性。
链接: https://arxiv.org/abs/2608.16645
作者: Shaolong Chen,Yanlin Fei,Nazhou Liu,Xinmiao Yu,Lei Li,Rahul Thapa,Madalina Ciobanu,Qingqing Mao,Ritankar Das
机构: Stanford University (斯坦福大学); Titan Holdings (钛控股); Prentis AI
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Can a language model recover the true research idea of a published paper when given only that paper’s pre-publication bibliography? We introduce Reconstruction, a blind idea-recovery benchmark that withholds the seed paper and all contemporaneous or future literature, and asks models to propose hypotheses that an independent large language model judge matches against the held-out ground-truth idea. A strict anti-leakage protocol-temporal citation cutoff, anonymous reference IDs, and frozen per-paper bibliographies, which prevents prompt-time leakage of the seed idea. Across six scientific domains and 643 evaluated papers, seven frontier models achieve only modest Match rates (approx. 3-15%). We then evaluate a reference-only multi-agent (top 4) pipeline that combines cross-model review with a Swiss tournament over aligned hypothesis slots, without external web search. Cross-model review plus tournament selection raises Match rates to approx. 23-42% across all six domains, which is an observed approx. 2.4x lift over the best single-model baseline. This draft reports the protocol, anti-leakage design, and current results as an arXiv timestamp.
[MA-1] “If It Looks Like a User”: Measuring Real-Time Moderation Effects via Social Media Simulation
【速读】：该论文旨在解决现有基于代理的社会媒体模拟器在内容治理研究中因参数设定主观性过强而导致的现实效度不足问题，其核心挑战在于如何构建一个能够真实再现平台动态的可校准仿真框架。解决方案的关键在于将传统的启发式参数化方法替换为基于真实世界数据（新冠疫情期间关于疫苗的在线讨论）拟合的统计分布，并通过协方差矩阵自适应进化策略（CMA-ES）进行优化，同时在时间、分布和结构等多个维度上对模型进行实证验证。该方法确保了模拟器能够准确复现真实数据中的关键统计特征，如用户活跃度分布、发帖/转发比率及时间模式。在此基础上，研究进一步验证了主流虚假信息传播者识别与干预方法在仿真环境中的有效性，并揭示了静态（事后）评估与动态（实时）干预之间的重要差异：静态评估高估了用户封禁的实际效果，而动态模拟显示剩余用户会通过补偿性转发行为削弱低质量内容的减少幅度，因此静态结果应被视为上限。该工作强调了基于仿真的内容治理政策评估的必要性，并提供了一个可重复使用、以实证为基础的仿真框架。
链接: https://arxiv.org/abs/2608.16601
作者: Enrico Verdolotti,Gianluca Nogara,Luca Luceri,Silvia Giordano
机构: Università della Svizzera italiana (USI), Lugano, Switzerland; SUPSI, DTI-ISIN, Viganello, Switzerland; USC Information Sciences Institute, Marina Del Rey, CA, USA
类目: ocial and Information Networks (cs.SI); Computers and Society (cs.CY); Multiagent Systems (cs.MA)
备注: 15 pages, 4 figures, 2 tables. Accepted for presentation at the Social Simulation Conference (SSC) 2026
Abstract:Agent-based social media simulators offer a controlled environment to study content moderation, yet their value hinges on how faithfully they reproduce real platform dynamics. We develop a calibrated extension of SimSoM, an agent-based model of information diffusion on social networks, grounded in a real-world dataset of online vaccine discourse during the COVID-19 pandemic. Our approach replaces ad-hoc parametrisations with empirically fitted distributions, optimised via CMA-ES (Covariance Matrix Adaptation Evolution Strategy) and validated against real data across temporal, distributional, and structural dimensions. Using this validated simulator, we provide three key contributions. First, we show that the calibrated model reproduces key statistical signatures of the empirical data, including activity distributions, post/reshare ratios, and temporal patterns. Second, we apply established misinformation-spreader detection and prevention methods to both empirical and simulated data, progressively removing top-ranked users and showing that the resulting decline in low-quality content is consistent across the two. Third, comparing static (retroactive) and dynamic (in-simulation) moderation across 30 network realisations, we show that static evaluation significantly overestimates the effectiveness of user bans for the most effective detectors: when moderation is applied in real time, compensatory resharing by the remaining users dampens the expected reduction in low-quality content, so static estimates should be read as an upper bound. These findings highlight the necessity of simulation-based evaluation for content moderation policies and contribute a reusable, empirically grounded simulation framework.
[MA-2] Physics of Agents : Statistical Mechanics Predicts Collective Behavior of AI Agents
【速读】：该论文旨在解决多智能体系统中因智能体间交互导致的集体行为动态问题，特别是信息交换与协同决策过程中可能出现的群体极化、从众效应或偏见放大等现象。其核心挑战在于理解并预测复杂交互环境下智能体群体的演化规律，以设计出更高效且对齐目标的多智能体系统。解决方案的关键在于提出一种基于统计力学的形式化框架，将智能体建模为在社会压力下随机偏好更低压力状态的个体。该模型仅需初始意见作为输入，即可准确预测个体演化轨迹，超越所有标准基线方法，并在未见过的社区图结构上实现良好泛化，成功复现了观察到的群体原型分布。通过拟合模型参数，研究揭示了三个关键机制：一是社区整体运行于低于临界社会温度的状态，解释了信念强度的逐步积累；二是吸引性连接强于排斥性连接，有利于达成共识；三是持有正确答案的智能体具有最强吸引力，驱动系统趋向真理。这些发现表明，人工智能智能体的集体行为遵循简洁而可预测的动力学规律，类似于其他复杂系统的行为模式。
链接: https://arxiv.org/abs/2608.16578
作者: Batu El,Jinhee Paeng,Fatih Dinc,Shiye Su,Mete Erdogan,Aneesh Pappu,Haotian Ye,Wanjia Zhao,Surya Ganguli,James Zou
机构: Stanford University (斯坦福大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
备注: 51 pages, 20 figures, 9 tables
Abstract:AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their interactions can improve collective reasoning but may also produce herding, polarization, or amplify shared biases. Understanding and predicting these collective dynamics is therefore important for designing effective and aligned multi-agent systems. Here, we study over 10,000 communities of language-model agents that repeatedly exchange messages and revise their opinions across objective mathematics questions and subjective political statements. Despite substantial diversity in possible behavior, the individual and group dynamics can be represented by three characteristic regimes: indifference, polarization, and consensus. AI agents start indifferent and build conviction as they interact. On objective questions, communication improves collective accuracy, while on subjective questions it often drifts group opinions toward the right in the political spectrum. We explain these observations with a statistical-mechanics formalism in which agents stochastically favor lower social pressure. Given only initial opinions, our model predicts individual trajectories, outperforms all standard baselines, generalizes to unseen community graphs, and reproduces the observed group archetype distributions. Our fitted model parameters reveal the mechanics underlying our key observations: i) communities operate below the critical social temperature, which explains conviction buildup; ii) attractive ties outweigh repulsive ones, which favors consensus; and iii) agents holding the correct answer exert the strongest pull, which drives truth-seeking. Overall, our results demonstrate that collective behavior of AI agents, like that of other complex systems, follows compact and predictive dynamical laws.
[MA-3] VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experience
【速读】：该论文旨在解决现有技能自演化方法中仅依赖当前任务执行轨迹进行技能优化，而忽视了公开技能版本历史中蕴含的可复用演化先验知识的问题。其核心挑战在于如何有效融合来自外部公共版本历史的通用演化经验与当前任务轨迹提供的具体证据，以提升技能自演化效果。解决方案的关键在于提出VCE-Skill框架，通过从噪声大且实现细节丰富的公开技能变更中提炼出结构化、可复用的版本变更经验（version-change experience），并将其与基础演化器生成的轨迹驱动建议自适应融合，从而在保留任务特定证据的同时，充分利用外部积累的演化知识。实验表明，该方法显著提升了技能自演化性能，平均得分提高3.20–4.98点，并展现出更强的跨模型迁移能力，揭示了公共技能版本变更作为潜在先验知识来源的重要价值。
链接: https://arxiv.org/abs/2608.16544
作者: Jianming Chen,Xuanbin Ye,Yawen Wang,Junjie Wang,Qing Wang,Fanjiang XU
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Agents increasingly rely on reusable skills to encode task knowledge, tool-use procedures, and validation rules. Existing skill self-evolution methods primarily revise skills using execution trajectories collected from current tasks, leaving the evolution knowledge accumulated in public skill version histories largely untapped. Our pilot study reveals a clear complementarity between the two sources: public skill changes provide reusable evolution priors, whereas trajectories provide evidence grounded in the current task. Motivated by this, we propose VCE-Skill, which distills noisy and implementation-specific public skill changes into reusable, structured version-change experience and adaptively fuses it with trajectory-derived proposals from the base evolver, thereby exploiting external experience while retaining task-specific evidence. Extensive experiments demonstrate that VCE-Skill improves skill self-evolution, increasing mean scores by 3.20–4.98 points; transfer experiments further show that the resulting skills achieve stronger cross-model transfer performance. Our work highlights public skill version changes as a previously underexplored yet effective source of prior knowledge and advances trajectory-driven skill self-evolution.
[MA-4] MELD: A Protocol for Merging Knowledge Across Distributed Agent ic Memories
【速读】：该论文旨在解决多智能体系统中知识记忆无法有效协同的问题，即尽管智能体可通过共享通信通道调用彼此的工具，却难以实现对所知信息的一致性整合——现有机制无法处理同一事实的不同表述、无法关联分散持有的相关知识，更无法在发现矛盾时保持透明而非静默地丢弃任一主张。其核心解决方案是提出MELD（Memory coherency via Ego-Driven Learning），一种基于知识图谱运行时模型的自管理一致性机制。其关键在于：每个智能体的“大脑”通过五种结果（插入、合并、关联、冲突或拒绝）决策接收外部声明，依据三个信号（作用域内声明键身份、嵌入相似度及自然语言推理判断）并受上下文与新鲜度门控约束；所有状态变更仅通过唯一可审计、可认证的Patch对象完成，确保操作的可追溯性。同时，采用基于标准发布/订阅传输的每声明状态CRDT（冲突可容错数据类型），实现无中心协调下的主权智能体间状态一致性，在网络分区或丢包场景下具备自愈能力，并抵御来自对端的静默重写攻击。值得注意的是，MELD不进行真理裁决，而是保留检测到的矛盾供后续处理，避免隐式消解。实验表明，在HotpotQA干扰测试中，分布式合并策略在预设等价性测试下召回率不低于集中式存储，且比朴素并集减少约11%的活跃存储占用；合并分类器在已裁定候选对上达到AUC 0.968，误合并率仅为0.013；状态CRDT在30次真实分区恢复试验中全部成功收敛，而传统最后写入者胜方案仅11次成功；语义路由在保持召回率的前提下消息量降低约3倍。评估基于跨越运营商级5G边缘、国家级高性能计算与本地层级的真实计算连续体，使用经验校准的阈值参数。
链接: https://arxiv.org/abs/2608.16357
作者: Lauri Lovén,Jaakko Sauvola,Jukka Riekki,Sasu Tarkoma
机构: University of Oulu(奥卢大学); University of Helsinki(赫尔辛基大学)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 30 pages, 3 figures, 1 table, plus an 11-page appendix (A-N). Code and experiment data: this https URL
Abstract:Autonomous agents share a transport and can call each other’s tools, but they cannot share what they know: no protocol lets two agents’ memories reconcile a fact phrased two ways, link related facts held apart, or reconcile contradictory knowledge without silently discarding either claim. We present MELD, a self-managing coherence mechanism for a federation of agent memories whose run-time model is the knowledge graph itself. Each brain admits every incoming claim through a five-outcome procedure (insert, merge, relate, conflict, or reject), decided from three signals (scoped claim-key identity, embedding similarity, and a natural-language-inference verdict) under context and freshness gates, and acting through exactly one auditable, authenticated Patch, the only object that mutates state. A binding onto standard publish/subscribe transport with a per-claim status CRDT keeps sovereign brains coherent in claim status without a coordinator: self-healing after partitions and under lossy routing, and self-protecting against silent rewrite by a peer, under a benign-fault model. MELD does not adjudicate truth; a detected contradiction is preserved for later adjudication, never silently resolved. On HotpotQA distractor, distributed merge is recall-non-inferior to a centralized store under a pre-specified equivalence test and recall-superior to naive union at about 11% less live storage; the merge classifier separates at AUC 0.968 with a 0.013 false-merge rate on adjudicated candidate pairs; the status CRDT reconverges in 30/30 real partition-heal trials where last-writer-wins manages 11/30; and semantic routing delivers about 3x fewer messages at matched recall. We evaluate on a real computing continuum spanning an operator-grade 5G edge, national HPC, and a local tier, with empirically calibrated thresholds.
[MA-5] KC-BFPRL: Knowledge-Guided Multi-UAV Collaboration for Grassland Restoration via Bilevel Formerpointer-Based Reinforcement Learning
【速读】：该论文旨在解决多无人机（multi-UAV）系统在大规模生态环境修复任务中面临的恢复区域最大化问题（RAMP），该问题具有非线性、组合优化复杂度高、受载荷依赖的能量动态变化及生态退化异质性等挑战。其解决方案的关键在于提出一种知识引导的协同双层前指强化学习框架（KC-BFPRL），采用分层范式将RAMP分解为全局任务分配与局部修复规划，后者进一步细化为上层航迹规划与下层恢复区域分配。该框架通过基于Transformer的编码器融合静态环境特征与动态无人机状态，并结合基于演员-评论家框架训练的前指网络解码器，实现高效决策；同时嵌入生态优先规则与启发式逻辑，提供结构化的热启动机制，有效缓解强化学习中的冷启动问题，确保严格满足约束条件。实验结果表明，KC-BFPRL在最复杂场景U8-R160中实现0.00%最优性间隙，且运行速度接近MAPDP的三倍，充分验证了其在大规模自动化生态修复中的鲁棒性、可扩展性与实时应用潜力。
链接: https://arxiv.org/abs/2608.16326
作者: Dongbin Jiao,Xianyi Wang,Yuchen Yuan,Weibo Yang,Peng Yang,Peng Zhao,Zhanhuan Shang,Shi Yan
机构: Lanzhou University (兰州大学); Southern University of Science and Technology (南方科技大学); Chang’an University (长安大学); State Key Laboratory of Grassland Agro-Ecosystem, College of Ecology, Lanzhou University (草原农业生态系统国家重点实验室，兰州大学生态学院)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Multi-unmanned aerial vehicle (UAV) systems provide scalable service platforms for large-scale environmental tasks, such as grassland ecosystem restoration. However, coordinating fleet operations requires solving the restoration area maximization problem (RAMP). This non-linear combinatorial optimization challenge is complicated by payload-dependent energy dynamics and heterogeneous ecological degradation. We propose a novel knowledge-guided collaborative bilevel formerpointer reinforcement learning framework (KC-BFPRL) to address this complexity. Using a hierarchical paradigm, KC-BFPRL decomposes RAMP into global task allocation and local restoration planning, with the latter further divided into upper-level trajectory planning and lower-level restoration area allocation. Our specialized architecture pairs featuring a Transformer-based encoder that fuses static environmental features with dynamic UAV states, and a Pointer Network decoder trained via a robust actor-critic framework. By embedding ecological priority rules and heuristic logic, KC-BFPRL achieves a structured warm-start, solving the RL cold-start problem while ensuring strict constraint satisfaction. Extensive experiments demonstrate that KC-BFPRL consistently outperforms state-of-the-art baselines, achieving superior objective values and efficiency. It maintains a 0.00% optimality gap in the most complex scenarios U8-R160 and operates nearly three times faster than MAPDP, validating its robustness, scalability, and real-time applicability for large-scale automated ecological restoration.
[MA-6] Marker-Constrained Pose-Graph Correction for Cross-Platform Georeferencing in GNSS-Denied Environments
【速读】：该论文旨在解决在无全球导航卫星系统（GNSS）环境下，异构映射流程难以维持一致空间参考的问题。其核心解决方案是采用由胆甾相球形反射器（Cholesteric Spherical Reflectors, CSRs）制成的伪装匹配特征标记（camouflage-matched fiducial markers），作为预先测绘的视觉锚点，实现轻量级激光雷达里程计（LiDAR-odometry）轨迹与稠密RTAB-Map重建结果的联合地理参考。通过粗略相似性配准与标记约束的姿态图优化相结合，将两者统一至卢森堡通用地心坐标系（LUREF）框架下，无需运行时依赖GNSS信号。实验验证表明，该方法在模拟无人地面车辆（UGV）和无人飞行器（UAV）运动模式的两次手持采集会话中，显著提升了重访一致性：分别降低了97.9%和99.1%的漂移误差，并优于一次性对齐策略的外部锚点预测性能。此外，独立地理参考的稠密重建在跨会话间实现了58 cm的中位最近邻距离，且标记处理可实时运行，轨迹校正每会话耗时不足0.25秒。结果证明了利用视觉上隐蔽、预先测绘的锚点，在无GNSS条件下实现轻量级里程计与稠密重建精准地理参考的可行性。
链接: https://arxiv.org/abs/2608.16281
作者: Marco Giberna,Jose Luis Sanchez Lopez,Holger Voos
机构: University of Luxembourg(卢森堡大学); Faculty of Science, Technology and Medicine, University of Luxembourg(卢森堡大学理学院、科技与医学学院)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: 14 pages, 5 figures, 5 tables, submitted to SPIE Security + Defence conference
Abstract:Autonomous operation in GNSS-denied environments requires heterogeneous mapping pipelines to maintain a consistent spatial reference. This paper presents a framework using camouflage-matched fiducial markers fabricated from Cholesteric Spherical Reflectors (CSRs) as pre-surveyed visual anchors. The anchors georeference both a lightweight LiDAR-odometry trajectory and a dense RTAB-Map reconstruction, allowing their outputs to be expressed in a common LUREF frame (geodetic coordinate reference system used in Luxembourg) without requiring GNSS measurements during operation. The method combines coarse similarity alignment with marker-constrained pose-graph optimization. We evaluate it using two handheld acquisition sessions with ground-level and elevated motion profiles emulating UGV and UAV operation. A single iMarker was relocated among six surveyed positions, with the first position revisited to quantify drift correction. Marker-anchor correction reduced revisit inconsistency by 97.9% and 99.1% for the UAV- and UGV-emulating sessions, respectively, and improved held-out anchor prediction compared with one-time alignment. Separately georeferenced dense reconstructions achieved a median cross-session nearest-neighbour distance of 58 cm without explicit cross-session registration. Marker processing operated in real time, while trajectory correction required less than 0.25 s per session. These results demonstrate a proof of concept for georeferencing lightweight odometry and dense reconstructions using visually unobtrusive, pre-surveyed anchors during GNSS-denied operation.
[MA-7] ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems
【速读】：该论文旨在解决临床多智能体系统（Multi-Agent System, MAS）在真实医疗环境中部署时面临的伦理挑战，尤其是安全、公平性、可问责性、透明度及患者信任等关键问题。尽管已有世界卫生组织、美国国家医学院及FUTURE-AI联盟等机构提出多项伦理框架与治理原则，但这些方案仍停留在概念层面，缺乏可执行的落地机制。为此，本文提出一种名为ETHOS（Ethics and Trust through Hierarchical Oversight System）的模块化伦理框架，其核心创新在于作为治理元智能体（governance meta-agent），可无缝集成至任意现有多智能体系统中，无需修改底层架构。ETHOS的关键在于采用分层治理机制，将利益相关者驱动的伦理要求转化为运行时可执行的监督策略，包括确定性检查、上下文审查以及最终的伦理评判器（ethics critic）。该机制持续评估中间推理过程与最终输出，识别潜在伦理风险，触发修正或抑制不合规响应。在肝病临床决策支持系统的实证应用表明，ETHOS能有效提升决策可靠性，通过检测证据不完整、不一致或超出范围的情况，合理增加拒绝推荐的频率，从而保障推荐的安全性与可信度。该方法实现了从高层伦理原则到可审计、可部署的运行时防护机制的转化，为生成式AI在医疗场景中的可信应用提供了可行路径。
链接: https://arxiv.org/abs/2608.15424
作者: Rakesh Sharma,Sydney Pugh,Cameron Beeche,Pankhuri Singhal,Rachel Wu,Margaret Eby,Jeffrey Duda,James Gee,Kyra O’Brien,Hersh Sagreiya,Marina Serper,Victoria Gershuni,Angela Bradbury,Anurag Verma,Eric Eaton,Kevin B. Johnson,Walter Witschey
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint of an article submitted for consideration in Pacific Symposium on Biocomputing \textcopyright\ 2027 World Scientific Publishing Company. \url{ this https URL }
Abstract:The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making. However, the deployment of these systems in real-world healthcare settings raises critical ethical concerns related to safety, fairness, accountability, transparency, and patient trust. While numerous organizations, including the World Health Organization, the National Academy of Medicine, and the FUTURE-AI consortium, have proposed ethical frameworks and governance principles for healthcare AI, these efforts remain largely conceptual. To address this challenge, we present ETHOS (Ethics and Trust through Hierarchical Oversight System), a modular ethics framework designed as a governance meta-agent that can be integrated with any existing multi-agent system without requiring changes to its underlying architecture. ETHOS translates stakeholder-informed ethical requirements into executable runtime oversight through a layered governance approach consisting of deterministic checks, contextual reviews, and a final ethics critic. These components continuously evaluate intermediate reasoning steps and final outputs, enabling the system to identify ethical risks, request revisions, or suppress responses that fail predefined safety and trustworthiness criteria. We demonstrate ETHOS within a hepatology clinical decision-support MAS. Results show that ETHOS improves decision reliability by detecting incomplete, inconsistent, or out-of-scope evidence and appropriately increasing abstention when safe recommendations cannot be supported. By embedding ethical governance directly into system operation, ETHOS provides a practical and auditable mechanism for transforming high-level AI ethics principles into deployable safeguards.
[MA-8] Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLM s
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在自我不确定性评估、知识冲突检测以及识别超出自身专业能力范围的问题时表现不佳的核心挑战，这些问题严重制约了LLMs的可靠性与可信度。其解决方案的关键在于提出并实现首个面向LLM集成系统（ensembles of LLMs）的元认知框架，通过显式的监控与调控机制提升系统的自省能力。该框架引入一个元认知状态向量（Metacognitive State Vector, MSV），从认知心理学中提取的五个维度——情绪反应、正确性评估、经验匹配、信息冲突与问题重要性——对模型的自我意识进行量化表征，并据此实现动态控制：根据查询复杂度自动切换至快速处理模式（System 1）或深度推理模式（System 2）。在System 2中，基于图论算法，系统依据各节点的MSV值分配专业化角色（领域专家、批评者、评估者、综合者与通用型节点），实现智能调度；同时，原型演示系统通过实时雷达图与决策指标可视化元认知过程，验证了该框架在实现元认知自我觉知与自我调节方面的可行性。
链接: https://arxiv.org/abs/2608.15400
作者: Charles Courchaine,Ricky J. Sethi,Hefei Qiu
机构: Fitchburg State University (菲奇堡州立大学); National University (国家大学); Worcester Polytechnic Institute (伍斯特理工学院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 5 pages, 5 figures. Charles Courchaine and Ricky J. Sethi contributed equally. Demo and code: this https URL
Abstract:Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizing when problems exceed their expertise; such limitations inevitably undermine reliability and trust in LLMs. In this paper, we present the first implementation of a metacognitive framework for ensembles of LLMs that addresses these challenges through explicit monitoring and control mechanisms. Our system computes a Metacognitive State Vector (MSV) quantifying self-awareness for monitoring across five dimensions derived from cognitive psychology: Emotional Response, Correctness Evaluation, Experiential Match, Conflicting Information, and Problem Importance. MSV values also provide self-regulation for control, automatically switching between System 1 (fast, single- or multi-node) and System 2 (deliberative, multi-node) processing based on query complexity. For System 2 execution, graph-theoretic algorithms control the assignment of specialized roles (Domain Expert, Critic, Evaluator, Synthesizer, and Generalist) to ensemble nodes according to their MSV-quantified metacognitive states. Our implementation allows users to explore how different query types trigger distinct processing modes. The Proof-of-Concept (PoC) demo showcases the framework with illustrative examples showing appropriate System 1/System 2 routing and helps visualize the metacognitive process via real-time radar charts and decision indicators. This PoC implementation demonstrates the feasibility of creating a framework for metacognitive self-awareness and self-regulation in LLM systems. Comments: 5 pages, 5 figures. Charles Courchaine and Ricky J. Sethi contributed equally. Demo and code: this https URL Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2608.15400 [cs.AI] (or arXiv:2608.15400v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15400 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Companion Proceedings of the ACM Web Conference 2026 (WWW Companion '26), ACM, 2026, pp. 152-155 Related DOI: https://doi.org/10.1145/3774905.3793124 Focus to learn more DOI(s) linking to related resources
[MA-9] UC-PSRO: Utility-Conditioned Policy-Space Response Oracles with a Communication-Dropout Curriculum for Game-Theoretic Course-of-Action Generation in Adversarial Swarms
【速读】：该论文旨在解决在通信受限环境下，如何为蓝方无人航空系统（UAS）集群生成具有博弈论优化特性的行动方案（Course of Action, COA），以应对具备自适应能力的红方对手。核心挑战在于：在通信链路频繁中断的现实场景中，传统依赖中心化通信或固定策略的强化学习方法难以维持任务执行的鲁棒性与协同效率。其解决方案的关键在于提出一种名为UC-PSRO（Utility-Conditioned Policy-Space Response Oracles with a Communication-Dropout Curriculum）的新型训练框架，融合三大机制：（1）基于策略空间响应归纳（PSRO）的自对弈机制，使蓝红双方策略通过近似最优反应迭代更新，避免因对抗方策略固定而产生的过拟合；（2）采用FiLM（Feature-wise Linear Modulation）条件化机制，将指挥官意图权重向量（从狄利克雷分布采样）嵌入蓝方策略，实现无需重训练即可在推理阶段动态调整行为的“可重导向性”；（3）引入渐进式通信图边丢弃课程（curriculum annealing communication-graph edge dropout），迫使集群在训练中逐步学会去中心化、点对点的协作模式，从而提升对通信降级的鲁棒性。实验结果表明，仅使用通信丢弃课程即能显著提升任务完成率（在丢包率从0增至0.75时，成功率由35%升至62%），且表现出反直觉的“越干扰越稳健”特性；而引入效用条件化与自对弈虽增强了策略多样性，但导致收敛速度显著下降，在给定预算下未体现出可靠的可利用性优势，二者与固定对手策略相比统计上无显著差异。因此，研究团队坦诚指出，当前自对弈机制带来的收敛成本尚未被实证的鲁棒性收益所抵消，强调应避免夸大单一方法的优势。最终，该工作提供了一个完全向量化、开源的训练环境，可在单个消费级GPU上以毫秒级延迟支持高达200个智能体的规模化仿真。
链接: https://arxiv.org/abs/2608.15372
作者: Phillip Jiang
机构: Appsofa LLC
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:We study generating game-theoretically optimized Courses of Action (COAs) for a Blue UAS swarm against an adaptive Red adversary in a communication-degraded environment, motivated by (but not derived from) a public U.S. Air Force SBIR solicitation. We propose UC-PSRO (Utility-Conditioned Policy-Space Response Oracles with a Communication-Dropout Curriculum), combining three mechanisms: (i) PSRO self-play, so Blue and Red policies train as approximate best responses to each other rather than one side against a fixed scripted opponent; (ii) FiLM conditioning of the Blue policy on a Commander’s-Intent weight vector, sampled from a Dirichlet distribution during training, so one trained policy is re-steerable at execution time without retraining; and (iii) a curriculum annealing communication-graph edge dropout during training, so the swarm learns decentralized, peer-to-peer fallback instead of depending on full connectivity. We evaluate on a synthetic, unclassified stand-in for the solicitation’s maritime scenario, with 5 seeds at N=25 Blue agents and a scalability sweep to N=200. We find a genuine trade-off, not a uniform win: the communication-dropout curriculum alone gives the strongest, most robust mission-completion rates of any learned method, improving counter-intuitively as denial increases (35% to 62% success as dropout rises from 0 to 0.75); adding utility-conditioning and PSRO self-play substantially slows convergence within a fixed budget, and we find no reliable exploitability advantage for self-play over a fixed-opponent policy, both statistically indistinguishable from a small, near-zero gap. We report this honestly as a convergence cost not yet offset by a demonstrated robustness benefit, rather than overstating one method as dominant, and provide a fully vectorized, open environment training at N=200 agents in single-digit milliseconds per step on a single consumer GPU.
[MA-10] Insurance as AI Risk Infrastructure: A Generative-Agent Simulation of AI Adoption
【速读】：该论文旨在解决现代人工智能系统（尤其是大语言模型，LLM）固有的不可靠性及其潜在的不确定运营后果所导致的企业采纳障碍问题。尽管现有技术防护措施可降低AI工作流失败的可能性或严重程度，但无法在残余财务尾部损失实际发生时提供事后经济补偿。为此，论文提出一种社会经济框架，通过保险机制转移并吸收AI采纳所产生的残余财务风险，从而弥补现有技术保障的不足。其解决方案的关键在于构建一个基于大语言模型驱动的代理社会仿真系统（LLM-driven Agent-Based Social Simulation, LABSS），以验证该保险框架在行为合理性上的有效性，并证明其能够显著降低企业层面的财务暴露，进而加速AI工具的集体采纳进程，提升企业偿付能力与整体资本水平。
链接: https://arxiv.org/abs/2608.15181
作者: Yixuan Yuan,Dedai Wei,Chudong Qian,Jielin Feng,Ziyue Lin,Yuheng Zhao,He Cao,Erasmo Purificato,Xinwu Ye
机构: University of Macau(澳门大学); Fudan University(复旦大学); Immunocan AI; European Commission(欧洲委员会); The University of Hong Kong(香港大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:The rapid evolution of artificial intelligence (AI) tools has demonstrated immense potential to enhance societal well-being and operational efficiency. However, the inherent unreliability and uncertain operational consequences of modern AI systems, typified by large language models (LLMs), have created a significant barrier to enterprise adoption. Many enterprises remain hesitant to integrate these tools deeply into their workflows due to concerns about unpredictable losses and liability exposure. While existing technical safeguards primarily seek to reduce the likelihood or severity of AI-enabled workflow failures, they do not by themselves provide ex post financial protection when residual pecuniary tail losses materialize. In this paper, we introduce a socio-economic framework that complements these safeguards by transferring and absorbing the residual financial consequences of AI adoption through insurance. To evaluate this framework, we develop an LLM-driven agent-based social simulation (LABSS) system. We assess the behavioral validity of the simulation using established economic and sociological theories. Our analysis demonstrates that the proposed insurance framework reduces firm-level financial exposure, thereby accelerating the aggregate adoption of AI tools and improving firm solvency and aggregate capital.
[MA-11] Constitutive Priors for Machine Intelligence: A Legitimacy Theory of the Artificial Physical World
【速读】：该论文旨在解决物理世界中人工智能（AI）面临的“冷启动困境”——即缺乏数据导致无法构建智能，而没有已部署的智能又无法产生数据。这一困境具有结构性特征，但并非在所有场景下均等存在；其例外情况被称为“人工物理世界”，如建筑、工业设施与基础设施，这些系统是人为设计并留有可读档案的构成性实体，其规范在实例出现前即已确立，而非由实例统计平均得出。解决方案的关键在于提出一个基于四世界本体论的合法性准则：若某对象领域为有意建构且留存可读档案，则先验框架的提取具有合法性；该准则可通过“方向契合度”进行检验——偏离构成性规范被视为世界中的违规行为，而非模型的修正。进一步地，研究证明此类框架至少包含语法、概念、知识与实例四个不可兼容的层级，构成分层下限。研究还建立了跨五个工业领域的部署声明与32类故障模式的分类体系，并提出五个可证伪的预测，其中核心预测可通过公开工程记录验证。大型语言模型在此框架中被定位为档案的阅读者而非档案本身，体现了对生成式AI（Generative AI）角色的精准界定。
链接: https://arxiv.org/abs/2608.15147
作者: Jiang Jiang(1),Yifu Sun(1),Qi Shen(1) ((1) Persagy Science and Technology Co., Beijing, China)
机构: Persagy Science and Technology Co.(北京普瑞赛吉科技有限公司)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 55 pages, 3 figures, 75 references. Appendix A contains the semi-formal statements. First of three companion works; the two companions are in preparation
Abstract:Machine intelligence has conquered the symbolic world but stalled at the physical one. The stall is structural: physical AI faces a cold-start deadlock – no intelligence without data, no data without deployed intelligence. Our thesis: the deadlock is real but unevenly distributed, and the exception has a name: the artificial physical world. Buildings, industrial facilities, and infrastructure are intentionally constituted and documented: designed artifacts ship with readable archives that precede and constitute their instances; here, norms are promulgated before instances, not averaged from them. Four contributions. (i) From a four-world ontology we derive a legitimacy criterion for constitutive prior frameworks: prior extraction is legitimate if and only if the object domain is intentionally constituted and has left a readable archive; the criterion is testable through direction of fit – deviation from a constitutive norm is a violation in the world, not a revision of the model. (ii) We establish a layering lower bound: any such framework has at least four layers – syntax, concept, knowledge, instance – because four construction goals pair into mutually incompatible carriers. (iii) We register deployment claims across five industrial domains and a 32-class failure-mode vocabulary. (iv) We stake the framework on five falsifiable predictions, the central one checkable on the public engineering record: if it fails, the framework fails. Semi-formal arguments back these claims (Appendix A): a Gold-type boundary on rule coverage in archiveless worlds, a decidability result for failure reduction over closed concept layers, and a boundary theorem for certificate-anchored calculi. Large language models find an honored place here – as readers of the archive, not as the archive. First of three companion works; the companions take up the questions deliberately left open.
[MA-12] From LLM Inference to Agent ic Workloads: Characterization and Implications for Serving Systems
【速读】：该论文旨在解决生成式 AI（Generative AI）应用在实际部署中所面临的系统行为不透明问题，特别是当大型语言模型（LLM）作为智能体（Agent）协调工具、环境与持久状态时，其长周期、高复杂度的工作负载对传统以孤立模型推理为基础的推理服务系统带来的挑战。传统服务系统依赖于适用于常规推理场景的假设，难以有效应对新型智能体工作负载的动态特性。本文提出 AgentSysBench，一个包含十种代表性智能体应用的基准套件与统一的系统级监控工具包，通过受控部署和真实生产轨迹分析，揭示了六项关键差异：（1）执行过程重且有状态，非 LLM 组件在 5/10 应用中主导延迟，沙箱工作集内存峰值达每会话 28 GB；（2）组件资源需求异构，如 GPU 密集型推理、内存密集型检索、CPU 密集型沙箱，任务延迟差异可达 32 倍；（3）瓶颈随请求、模型与部署动态变化；（4）会话在活动步骤间存在数分钟至数小时的空闲状态；（5）控制平面开销显著，包括辅助 LLM 调用及工具模式与观测信息带来的上下文负担，挤占有效计算资源；（6）三类应用的真实轨迹显示搜索查询与网页获取存在大量跨请求冗余，暴露出巨大的缓存优化潜力。基于上述发现，四项设计探索验证了其可操作性：任务感知调度降低延迟 29–40%，通信感知放置提升性能达 4.5 倍，状态卸载使内存使用减少 4.6 倍，工具结果缓存消除 35.2% 的冗余搜索调用并节省 19.3% 的总搜索延迟。解决方案的关键在于从“静态、单一资源优化”转向“动态、细粒度、多维度”的系统感知与协同优化策略。
链接: https://arxiv.org/abs/2608.15127
作者: Chaokun Chang,Yukun Zhou,Kaihua Fu,Dakai An,Tianyu Feng,Hanfeng Lu,Sheng Yao,Pu Guo,Yinghao Yu,Yizhou Shan,Bo Li,Binhang Yuan,Wei Wang
机构: Hong Kong University of Science and Technology (香港科技大學); Alibaba Group (阿里巴巴); Bytedance (字节跳动)
类目: Operating Systems (cs.OS); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA)
备注:
Abstract:Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state. However, the system behavior of these workloads—where latency, cost, and bottlenecks arise—remains poorly characterized, leaving serving systems to rely on assumptions built for conventional inference. We present AgentSysBench, a benchmark suite and measurement toolkit with ten representative agentic applications and unified systems-level instrumentation. Across controlled deployments and production traces, we identify six properties that distinguish agentic workloads from conventional LLM serving: (1) execution is heavyweight and stateful, with non-LLM components dominating latency in 5 of 10 applications and sandbox working-set memory peaking at 28 GB per session; (2) applications compose components with heterogeneous resource affinity—GPU-bound inference, memory-bound retrieval, CPU-bound sandboxes—whose task latencies diverge by up to 32x; (3) bottlenecks shift across requests, models, and deployments; (4) production sessions hold state idle for minutes to hours between active steps; (5) a control-plane tax—auxiliary LLM calls and context overhead from tool schemas and observations—crowds out productive compute and context; and (6) production traces from three applications reveal heavy cross-request redundancy in search queries and web fetches, exposing a large caching opportunity. Four design explorations demonstrate that these findings are actionable: task-aware serving reduces latency by 29–40%, communication-aware placement by up to 4.5x, state offloading reduces memory usage by 4.6x, and tool-result caching removes 35.2% of redundant search calls and saves 19.3% of aggregate search latency.
[MA-13] SysEvolve: An AI-native safe autonomous adversarial attack-defense co-evolutionary system
【速读】：该论文旨在解决当前网络安全领域中攻击与防御能力发展严重失衡的问题，即攻击正朝着自主化执行方向快速演进，而防御仍高度依赖人工干预。尽管已有大量关于网络靶场、生成式攻击和生成式防御的研究，但这种不对称性依然存在。作者指出，其根本原因在于攻击与防御在三个关键层面上均陷入演化停滞。为此，论文提出“协同演化”（co-evolution）作为核心解决方案，通过构建能够自主且安全地相互驱动进化的攻防AI智能体，在对抗性交互中实现攻防双方的同步进化。基于此理念，论文设计并实现了 \sysevolve 系统，包含三个协同设计的核心组件：\sysfield（构建真实多主机网络靶场）、\sysspear（生成高效且安全的攻击策略）和 \sysarmor（实现实时可解释的防御）。三者共同构成一个自驱式的对抗闭环，恢复了攻防在各层面的持续演化能力。实验结果表明，\sysfield 在仅 2.1% 开销下实现零损失数据采集，并将 257 个 CVE 演化为 1,148 个复杂靶场；\sysspear 相较于基线大语言模型（LLM）攻击成功率提升超过 25%；\sysarmor 的检测精度比现有系统高出 10–1000 倍，并成功在华为与深信服的实际生产环境中识别出真实高级持续性威胁（APT）攻击。此外，评估还揭示了大语言模型智能体的三项关键能力局限：首先，多步任务组合与更大拓扑结构暴露了单步评估所掩盖的能力短板；其次，后渗透阶段的状态利用是主要瓶颈；最后，环境干扰显著影响智能体表现——当部署诱饵节点时，智能体超时次数增加三倍，下游任务完成率归零，尽管初始访问成功率未变，表明其对环境扰动极为敏感。
链接: https://arxiv.org/abs/2608.15012
作者: Yuhan Meng,Shaofei Li,Jionghao Huang,Jiandong Jin,Puyi Wang,Hanlin Jiang,Anis Yusof,Peng Jiang,Zhenkai Liang,Yao Guo,Ding Li
机构: Peking University (北京大学); Southeast University (东南大学); National University of Singapore (新加坡国立大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Technical Report For SysEvolve System
Abstract:The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous execution while defense remains predominantly human-intensive. Despite substantial prior work across cyber ranges, AI-driven attack, and AI-driven defense, this asymmetry persists. We trace it to a deeper root cause, that evolution itself has stalled on both sides at three layers. To overcome this, we propose co-evolution as the integrating insight, where attack and defense AI agents autonomously and safely drive each other’s evolution through adversarial confrontation. Based on this insight, we present \sysevolve, comprising three co-designed components, \sysfield, \sysspear, and \sysarmor. \sysfield constructs realistic multi-host ranges. \sysspear generates efficient, safe attack schemes. \sysarmor performs real-time, interpretable defense. Together they form a self-driven adversarial loop restoring evolution at all three layers. In evaluation, \sysfield achieves zero-loss collection at 2.1% overhead and orchestrates 257 CVEs into 1,148 ranges, \sysspear improves attack success by over 25% over baseline LLMs, and \sysarmor achieves 10–1000 \times greater precision than prior systems and detects real APT attacks in production at Huawei and Sangfor. Our evaluation also reveals three findings about LLM agent capabilities. First, multi-step composition and larger topologies expose agent capability gaps hidden by single-step evaluations. Second, the bottleneck lies after initial access in post-compromise state utilization. Third, LLM agents are susceptible to environmental interference. When decoy endpoints are deployed in the range, agent timeouts triple and downstream completion disappears despite the success rates of initial accesses are unchanged.
[MA-14] he Open-Strategy Dictator Game: Cooperation Under Mutual Transparency
【速读】：该论文旨在解决在可观察决策机制的环境中，个体如何通过策略选择实现合作行为的演化稳定性问题。传统博弈论中的独裁者博弈（Dictator Game）通常假设决策是匿名且不可见的，而本文引入开放策略独裁者博弈（Open-Strategy Dictator Game, OSDG），其中每位参与者以自然语言文档形式公开其决策策略，使得施舍者（dictator）的决策可依赖于受赠者策略文本的内容。核心解决方案在于利用大语言模型（Large Language Model, LLM）作为仲裁者，基于上下文对双方策略进行语义理解与决策判断。通过在多种策略间开展循环赛并分析收益矩阵，研究发现条件性合作策略（即对合作者分享、对剥削者索取）具有显著优势，始终占据主导地位，而无条件策略（始终分享或始终索取）则处于弱劣势。结果表明，在策略可见的交互环境中，条件性合作具备跨广泛收益参数的演化稳健性，揭示了可解释性策略在促进合作演化中的关键作用。
链接: https://arxiv.org/abs/2608.14913
作者: Michael Glass
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player’s strategy is a natural-language document visible to all participants. The dictator’s decision, to SHARE or TAKE an endowment, may depend on the text of the recipient’s strategy. A large language model adjudicates each interaction by interpreting the dictator’s strategy in the context of the recipient’s. We run round-robin tournaments among diverse strategies and analyze the resulting payoff matrix using softmax equilibrium frequencies, dominance analysis, and sensitivity to the relative value of cooperation. Conditionally cooperative strategies, those that share with cooperators and take from exploiters, consistently dominate, while unconditional strategies (always share or always take) are weakly dominated. The results suggest that in environments where agents can inspect each other’s decision procedures, conditional cooperation is evolutionarily robust across a wide range of payoff parameters.
[MA-15] Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce
【速读】：该论文旨在解决前沿大语言模型（LLM）代理在复杂、长期且涉及多个委托方的现实操作环境中，因自然语言交互而产生的语用错位（speech-act misalignment）问题。现有安全研究多依赖于对单一代理或简化任务的对抗性诱使评估，难以反映真实场景中多代理系统在长周期、异构主体、动态状态及自然语言沟通下的行为偏差。本文通过分析20次一年期模拟运行中来自13个前沿大语言模型的2,583封代理间邮件，构建了一种结合消息内容、仿真器真实状态与推理日志的分类方法，以识别包含虚假陈述、操纵、合谋或威胁等行为的错位通信。结果显示，12.6%的邮件被标记为错位，且该现象在所有模拟运行中均出现，74.7%的单个代理运行中存在错位行为。这一结果在不同采样温度和跨模型家族复现中保持稳定，表明其非偶然性。进一步发现，错位行为具有互惠性和压力依赖性：接收对方错位邮件会使自身回复产生错位的概率提升1.65倍，低库存条件则使该概率上升1.58倍。此外，在能力不对称的剥削测试中未发现高能力模型系统性地利用弱对手，模型性能排名也无法预测错位率。因此，解决方案的关键在于揭示：可测量的、依赖于环境状态的语用错位可在无需人为诱导的情况下，在竞争性多代理系统中自发形成，其模式主要关联于资源稀缺性与对手行为，而非模型能力本身。
链接: https://arxiv.org/abs/2608.14825
作者: Zeyuan Li(Massachusetts Institute of Technology),Lukas Petersson(Andon Labs),Alessandro Acquisti(Massachusetts Institute of Technology),Michiel A. Bakker(Massachusetts Institute of Technology)
机构: Massachusetts Institute of Technology (麻省理工学院); Andon Labs
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs. Much of the safety literature studies misaligned LLM behavior through adversarial-elicitation evaluations on single agents or stylized tasks. Its prevalence and structure in settings that combine long horizons, separate principals, real operational state, and inter-agent natural-language exchange remain insufficiently measured. We study 2,583 inter-agent emails from 20 one-year simulation runs of Vending-Bench Arena, a competitive vending environment spanning 13 frontier LLMs. We operationalize speech-act misalignment as emails containing false factual claims, manipulation, collusion, or threats, combining message content with ground-truth simulator state and logged reasoning traces to classify and validate such behavior. Under our primary classifier, 12.6% of emails are labeled misaligned; misalignment appears in all 20 runs and 74.7% of individual agent-runs. Both the magnitude and composition of this misalignment are preserved under repeated classification at different sampling temperatures and under full-pipeline replication with judges from two other frontier-model families. Misalignment is also reciprocal and stress-conditioned: receiving a misaligned email from a counterparty raises the odds of a misaligned reply by 1.65x, and low-inventory conditions raise them by 1.58x. Across tests of capability-asymmetric exploitation, we find no evidence that higher-capability models differentially exploit weaker counterparties, and model performance rank does not predict misalignment rates. Together, these results indicate that measurable, state-dependent misalignment can arise in competitive multi-agent environments without engineered elicitation, in patterns associated with operational scarcity and counterparty behavior rather than model capability alone.
[MA-16] BRA-Audit: Budgeted Runtime Auditing for LLM Multi-Agent Systems via Cumulative-Exposure Audit-Point Placement
【速读】：该论文旨在解决基于大语言模型的多智能体系统（LLM-MAS）中因智能体间依赖关系导致幻觉或恶意输出传播，进而引发系统级失败的问题。现有审计策略面临效率困境：仅在任务末端进行审计虽降低计算开销，但削弱了检测效果并扩大回滚范围；而每轮均对所有智能体审计虽提升检测与定位能力，却带来高昂的令牌（token）消耗。为此，论文提出BRA-Audit——一种预算感知的运行时审计框架，其核心在于将多智能体系统的执行过程建模为动态依赖图，并在固定审计调用预算约束下，将审计调度问题形式化为审计点的最优部署问题，以最小化累积未受检暴露风险。该框架采用贪心调度策略，优先选择影响力大且长时间未被审计的区域，同时引入可信审计点实现局部恢复。实验表明，BRA-Audit在结构化协作、复杂推理和开放式任务场景中，性能接近无污染基准设置，优于或媲美重型防护方法，同时将端到端令牌消耗降低17.2%至40.6%。
链接: https://arxiv.org/abs/2608.14668
作者: Kaixiang Wang,Yidan Lin,Jiong Lou,Jie Li
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based multi-agent systems (LLM-MAS) solve complex tasks through specialized collaboration, but inter-agent dependencies can propagate hallucinated or malicious outputs into system-level failures. Auditor agents mitigate these risks, yet existing strategies face an efficiency dilemma: end-only auditing reviews long trajectories and final outputs, potentially weakening audit effectiveness and enlarging rollback scope, while auditing every agent each round improves detection and localization at high token cost. How can guard performance be preserved while minimizing token cost? To address this problem, we propose BRA-Audit, a budget-aware runtime auditing framework that models MAS execution as a dynamic dependency graph and formulates audit scheduling as audit-point placement under a fixed audit-call budget to minimize cumulative unchecked exposure. Its greedy scheduler prioritizes influential and long-unaudited regions, while trusted audit points enable localized recovery. Across structured coordination, complex reasoning, and open-ended tasks, BRA-Audit restores performance close to the clean setting, remains competitive with heavy guard methods and reduces end-to-end token consumption by (17.2%)–(40.6%).
[MA-17] he Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines ICML2026
【速读】：该论文旨在解决序列化多智能体大语言模型（multi-agent LLM）流水线中因缺乏跨阶段验证而导致的幻觉传播问题。其核心挑战在于，初始阶段引入的幻觉不仅不会被消除，反而在每一轮任务传递中经历“原始事实 → 派生计算 → 叙事性描述 → 不可见结论”的状态演化，导致检测难度呈指数级上升，形成所谓的“幻觉雪球效应”（hallucination snowball effect）。该效应被建模为一个一阶马尔可夫过程，包含四个状态，且各边界间的幻觉逃逸概率分别为24.6%、48.3%和89.3%。实验在FinanceBench上的4智能体金融分析流水线中验证，发现即使最强模型（Qwen3.5-397B-A17B）在第4阶段的幻觉检测率也仅约60–65%，而高达23.7%的幻觉完全未被检测到。关键解决方案是引入基于检索增强生成（RAG）的边界验证机制，相较于仅在末尾检查，采用相同验证工具在阶段间设置“门控”可使幻觉存活率从58.4%降至16.2%（Cohen’s h = -0.911, p < 0.000001），显著提升系统鲁棒性。研究进一步提出最优资源分配策略：应优先在首个阶段间（S₁→S₂）部署验证，此时仍有75.4%的幻觉可被捕捉，而非在幻觉已高比例逃逸的S₃→S₄阶段投入资源。因此，验证发生的时间点比是否验证本身更具决定性影响。
链接: https://arxiv.org/abs/2608.14588
作者: Prabhjot Singh,Bhushan Pawar
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 10 pages, 3 figures; accepted at the FAGEN Workshop (Failure Modes in Agentic AI), ICML 2026
Abstract:Sequential multi-agent LLM pipelines chain specialized agents without verification at handoffs, creating a structural flaw with measurable and severe consequences. We show that hallucinations injected at Stage 1 do not merely persist; they transform: raw numerical facts become derived computations, then narrative prose, then editorially approved conclusions. At each transformation, detectability degrades near-irreversibly. We formalize this as the hallucination snowball effect, a first-order Markov process over four states (Raw Fact \to Derived \to Narrative \to Invisible) with empirically measured per-boundary escape probabilities of 24.6%, 48.3%, and 89.3%. Across 346 automatically injected hallucinations in a 4-agent financial analysis pipeline on FinanceBench, gpt-4o detection drops from 72.0% at Stage 1 to 50.9% at Stage 4, and 23.7% of hallucinations survive completely undetected in the final output. Even the strongest model tested (Qwen3.5-397B-A17B, 87.0% at Stage 1) faces a structural ceiling; projected Stage 4 detection is only \sim 60–65%. Critically, boundary gates using identical RAG verification tools reduce hallucination survival from 58.4% to 16.2% versus end-of-pipeline checking (Cohen’s h = -0.911 , p 0.000001 ), while end-checking alone achieves merely 2.3 pp improvement over no verification. When you verify matters more than whether you verify. Our model predicts survival for n -agent linear pipelines and prescribes optimal verification resource allocation: invest at S_1\toS_2 first, where 75.4% of hallucinations are still catchable, not at S_3\toS_4 where 89.3% have already escaped.
[MA-18] Absence of critical scaling in the Schelling segregation model
【速读】：该论文旨在解决经典谢林隔离模型（Schelling segregation model）是否存在临界标度行为（critical scaling）的问题。研究表明，在摩尔邻域（Moore neighborhood）及其扩展至切比雪夫半径 $ r_0 = 6 $（最多168个邻居）的密集谱扩展情形下，所有有限尺寸标度诊断均未发现临界现象的证据：临界温度 $ T_c $ 随系统线性尺寸 $ L $ 的漂移不显著，序参量方差 $ \text{Var}(S) \sim L^{-2.02 \pm 0.09} $ 与平凡平均效应一致，关联长度指数 $ \gamma/\nu \approx 0 $，且标度坍缩无法达到最优。尽管摩尔邻域的离散满意度结构导致序参量 $ S(T) $ 呈阶梯状，具有23个有理阈值，但此离散性本身并不排除临界性（如伊辛模型所示），实证数据仍否定了临界行为的存在。关键解决方案在于揭示其本质机制并非源于长程关联缺失与确定性高阶动力学，而是由分支比（branching ratio）决定的亚临界级联过程，预测级联平均大小为 $ 1/(1 - R) $，并通过扰动实验验证误差在15%以内；同时多标度差异长度在相变过程中保持有限。此外，在引入贝塔分布异质容忍度时，低容忍尾部即使在平均容忍度适中时亦能驱动隔离。因此，阶梯定理（staircase theorem）与级联机制（cascade mechanism）共同解释了谢林相变，无需依赖临界现象理论。
链接: https://arxiv.org/abs/2608.16557
作者: Sam Rifaki
机构: 未知
类目: atistical Mechanics (cond-mat.stat-mech); Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注: 24 pages, 16 figures, 6 appendices. Over 12,500 simulation runs on periodic grids up to L = 320; Chebyshev radii r_0 up to 6 (k = 168 neighbors)
Abstract:We find no evidence of critical scaling in the Schelling segregation model, in either the Moore neighborhood or its dense-spectrum extension to Chebyshev radii up to r_0 = 6 ( k = 168 neighbors). On periodic grids up to L = 320 with 50 trials per point ( 12,500 runs), every finite-size scaling diagnostic in the Moore baseline fails: the per- L T_c does not drift, Var (S) \sim L^-2.02 \pm 0.09 matches trivial averaging, \gamma/\nu \approx 0 , and the scaling collapse never reaches a finite optimum. The 8-site Moore neighborhood restricts satisfaction to ratios j/k with k \leq 8 , giving S(T) a staircase structure with 23 rational thresholds; discreteness alone does not forbid criticality (cf. the Ising model), but the scaling evidence rules it out empirically. A branching-ratio calculation predicts subcritical cascades of mean size 1/(1-R) and is validated by perturbation experiments to within 15%; the multiscalar dissimilarity length stays finite across the transition. The dense-spectrum extension strengthens the negative verdict: across r_0 \in 3,4,5,6 on L \in 40,80,160 the Binder cumulant has no L -curve crossing and the per- L T_c drift is monotonic and unsaturated; at r_0 = 4 , extending to L = 320 gives \alpha = -2.70 , below the critical boundary \alpha = -2 , dissolving an apparent \alpha = +0.81 signal visible only on L \in 40,80 . The mechanism is the absence of long-range correlation in equilibrium plus deterministic high- k dynamics, not the staircase structure. With a Beta-distributed heterogeneous tolerance, the intolerant tail drives segregation even at moderate population-average tolerance. The staircase theorem and cascade mechanism together account for the Schelling transition without invoking critical phenomena.
自然语言处理
[NLP-0] owards Computational Provenance: Carrying Causal-State Evidence in Generated Text
【速读】： 该论文旨在解决生成式语言模型输出缺乏可验证的内部计算证据的问题，即如何在不改变最终输出的情况下，使生成文本中包含可检测的、关于其内部因果相关状态的痕迹。其核心解决方案是通过设计受控架构（包括模块化前馈神经网络与基于Transformer的模型），在训练过程中强制引入两个离散的中间状态路径，并通过人为切换路径来验证实际使用的内部状态；随后，利用该已验证状态决定生成文本中的细微统计模式，从而实现后续对内部状态的可检测性。实验结果表明，两种架构均在公开与隔离的端到端评估中成功通过全部128组匹配对测试，且检测器能够准确恢复与认证内部状态相关的信号。此外，该因果计算过程在多个独立训练的前馈与Transformer模型中均具可复现性。相比之下，在仅依赖答案的Transformer实验中，线性探测器未能识别出自然学习的中间状态，凸显了主动嵌入状态信息的重要性。该研究为“计算溯源”（computational provenance）提供了一个受控的可行性证明：即使答案保持不变，经过验证的因果相关内部状态仍可在生成文本中以可检测的方式保留。
链接: https://arxiv.org/abs/2608.16868
作者: Benjamin Belay
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 1 figure, 7 tables
Abstract:A language model’s output does not by itself provide verifiable evidence about the internal computation that produced it. We study computational provenance: whether generated text can carry detectable evidence of which causally relevant internal state occurred. We test a bounded form of this idea in two controlled architectures: a modular feed-forward neural network and a transformer-based model. Both architectures are trained on the same arithmetic task with a mandatory pathway through two discrete intermediate states, allowing different internal paths to produce the same answer. We deliberately switch between these paths, authenticate the state actually used, and let that verified state determine a subtle statistical pattern in the generated text that can later be detected. The feed-forward and transformer systems each passed all 128 matched pairs in both their public and separately sealed protected end-to-end evaluations, with the detector recovering the signal associated with the authenticated internal state. The required causal computation also reproduced across five independently trained feed-forward models and three independently trained transformers. In a separate answer-only transformer experiment, our linear probes did not recover a naturally learned intermediate state. These results provide a controlled proof of concept that information about a verified, causally relevant internal state can be preserved in generated text even when the answer is unchanged.
[NLP-1] Proteus: Incremental Memory Activation for Long-Context Sequence Modeling
【速读】： 该论文旨在解决基于注意力机制的序列模型在处理长上下文时计算开销呈二次增长的问题，特别是现有基于记忆（memory-based）的模型普遍采用静态记忆结构所导致的内存污染与容量浪费问题。其核心挑战在于：早期输入令牌因缺乏压缩压力而过度占用记忆空间，从而“污染”记忆状态，削弱对后续内容的存储能力并加剧前后信息间的干扰。为此，论文提出一种新颖的增量式记忆激活（incremental memory activation）范式，其关键在于动态调度记忆的有效容量——通过在序列早期施加记忆瓶颈以强制高效压缩历史信息，并随上下文增长逐步释放新的记忆容量，从而降低干扰、提升后期内容的保留能力。该方法被具体实现为Proteus机制，可无缝集成至多种神经记忆架构中且无需额外计算成本。实验表明，将Proteus应用于SWLA、Comba、Titans和Hope-Attention等先进模型，在标准语言建模、推理任务以及长上下文检索与理解任务中均取得稳定提升，且性能增益随上下文长度增加而愈发显著。研究结果表明，静态记忆并非最优设计，而对有效记忆容量进行动态调度是一种简单且普适性强的序列建模优化策略。
链接: https://arxiv.org/abs/2608.16844
作者: Reza Bayat,Ali Behrouz,Vahab Mirrokni,Aaron Courville
机构: Mila(蒙特利尔人工智能研究所); Google(谷歌)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:The quadratic cost of attention-based sequence models for long contexts has motivated a growing line of research on memory-based models that can compress context into a compact state. However, most existing memory models expose a static memory throughout the entire sequence. Because early tokens face no compression pressure, they occupy too many degrees of freedom and “pollute” the memory state, leaving little capacity for later context and increasing interference between what is stored and what arrives next. We study a new paradigm of incremental memory activation, where the effective capacity of memory is progressively expanded as the context grows. Imposing an early bottleneck forces the model to compress history more effectively, while unlocking fresh capacity over time reduces interference and improves retention of later context. We instantiate this paradigm in Proteus, a straightforward mechanism that can be incorporated into a broad class of neural memory architectures at no additional cost. We apply Proteus to state-of-the-art models, including SWLA, Comba, Titans, and Hope-Attention, and observe consistent improvements on standard language modeling and reasoning, as well as on long-context retrieval and understanding, with gains that grow at longer context lengths. Overall, our results show that static memory is suboptimal and that scheduling effective capacity is a simple and broadly applicable tool for sequence modeling.
[NLP-2] Model Hypnosis: Strong control of AI via additive subliminal effects
【速读】： 该论文旨在解决生成式 AI 模型在提示（prompt）诱导下表现出不可预测且高度可控的行为这一安全与可解释性问题。其核心挑战在于，即使单个提示元素看似微不足道或无关紧要，但通过系统性组合这些“弱线索”（如措辞变换、拼写错误等），仍能显著操控模型行为，这种现象被称为“模型催眠”（model hypnosis）。解决方案的关键在于揭示并理解此类隐蔽文本特征如何累积效应以实现对模型输出的隐性控制，从而为构建更鲁棒的 AI 安全机制和提升模型决策过程的可解释性提供新视角。
链接: https://arxiv.org/abs/2608.16834
作者: Enric Boix-Adsera,Benedict Tessler
机构: University of Pennsylvania (宾夕法尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We demonstrate that AI models are broadly susceptible to a phenomenon we call model hypnosis, in which individually weak and seemingly irrelevant cues in the prompt can be systematically combined to strongly control model behavior. Model hypnosis occurs across model families and scales, including in frontier reasoning models, and hypnotic prompts can transfer between models. Because the model is controlled by inconspicuous textual choices, such as paraphrases and typos, model hypnosis presents new challenges and avenues for AI safety, and is a major hurdle for AI interpretability.
[NLP-3] Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning
【速读】： 该论文旨在解决在罕见病诊断等高复杂度任务中，如何利用预训练语言模型（Pretrained Language Model, PLM）实现稳定、可迭代且具备专家干预能力的智能决策系统构建问题。传统方法受限于静态模型行为与缺乏持续优化机制，难以应对罕见病诊断中罕见样本、复杂推理链和高误诊风险的挑战。其解决方案的关键在于提出一种基于人类反馈的策略迭代（Policy Iteration with Human Feedback, PIHF）框架：以固定权重的预训练语言模型作为执行底座（execution substrate），将策略与工具集版本化为自然语言形式，并通过语言模型构成的批评者（critic）与临床专家共同完成对推理路径与工具使用轨迹的回溯性评估，定位重复性失败模式并生成候选修正方案；专家保留对策略采纳与回滚的最终决策权，同时借助召回率@1（Recall@1）与召回率@5（Recall@5）在多轮执行后验证修正效果。该设计实现了从“一次性生成”向“持续反思-修正-验证”的闭环迭代演进，显著提升了在3至490亿参数规模的私有及开源执行器上的诊断性能，尤其在GPT-5.4与Qwen3.6-35B上分别取得32.7和31.1个百分点的召回率提升，验证了预训练语言模型作为固定权重执行底座，在专家引导下进行策略演化的可行性与有效性。
链接: https://arxiv.org/abs/2608.16831
作者: Minh-Ha Nguyen,Cathy Shyr
机构: Vanderbilt University (范德比尔特大学); Vanderbilt University Medical Center (范德比尔特大学医学中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: PIHF method paper
Abstract:Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a fixed model could adapt its behavior from instructions and demonstrations. Policy Iteration with Human Feedback (PIHF) builds on this development and the recurrent evaluate-and-improve structure of generalized policy iteration. PIHF uses a pretrained language model as its execution substrate and moves persistent revision to a versioned natural-language policy and tool set. A language-model critic and clinical expert review complete-panel reasoning and tool-use trajectories to localize recurrent failures and form candidate revisions; the expert may reinterpret the evidence and retains authority over admission and rollback, while Recall@1 and Recall@5 validate outcomes after candidate execution. Across cumulative ablations and ultra-rare-disease benchmarks, a PIHF-derived policy improved Recall@1 in one proprietary executor and three open-weight executors spanning 3 to 49 billion active parameters. Gains were 32.7 percentage points for GPT-5.4 and 31.1 points for Qwen3.6-35B, a difference of 1.7 points. These results support the feasibility of using pretrained language models as fixed-weight execution substrates for expert-guided policy development in rare-disease diagnosis. Comments: PIHF method paper Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.16831 [cs.AI] (or arXiv:2608.16831v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.16831 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-4] ClawGym II: Exploring Black-Box RL on Agent Harness
【速读】： 该论文旨在解决在复杂代理协作者（agent harnesses）环境下，通过强化学习（Reinforcement Learning, RL）对长时程任务进行稳定且可扩展优化的难题。现有方法在面对由复杂协作者驱动的长周期任务时，受限于训练过程中的环境耦合性、策略优化与执行过程的不可见性以及多轮交互轨迹难以重构等问题，导致训练不稳定且难以规模化。其解决方案的关键在于提出一个统一的黑盒强化学习框架，通过构建基于沙箱（sandbox）的执行基础设施，将任务环境与协作者隔离于临时沙箱中，实现大规模并发采样；进一步将策略优化与不透明的协作者执行解耦，在模型边界部署服务代理（serving proxy）以捕获模型调用行为；利用捕获的调用序列构建前缀树（prefix tree），重构多轮交互轨迹，并在此结构上适配基于价值函数的PPO与无价值函数的GRPO算法，提升训练效率；同时通过保持训练-推理一致性确保优化稳定性；最后引入混合作业训练（mix-harness training），使单一模型可被异构协作者联合优化。实验表明，该框架在Qwen3-30A3B模型上显著提升了ClawGym-Bench、JobBench和OfficeQA等任务上的表现，Pass@1指标分别提升9.98和14.81点，且在200–400次优化步骤内保持稳定，验证了其在通用代理优化中的有效性、稳定性和跨异构执行系统的统一训练能力。
链接: https://arxiv.org/abs/2608.16798
作者: Huatong Song,Fei Bai,Ming Yang,Renyuan Li,Jia Deng,Jujie He,Zhange Zhang,Daixuan Cheng,Yan Xing,Qi Yun,Xuxing Chen,Danyang Li,Feng Chang,Chuan Hao,Ran Tao,Jian Yang,Bryan Dai,Wayne Xin Zhao,Mingjie Tang,Ji-Rong Wen
机构: Gaoling School of Artificial Intelligence, Renmin University of China (中国人民大学高瓴人工智能学院); IQuest Research
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Agent harnesses have substantially improved performance on long-horizon tasks by coordinating agent interactions with the environment. However, reinforcement learning through complex harnesses remains largely unexplored, as scaling such training to long-horizon agent tasks introduces fundamental challenges. In this work, we present a unified black-box RL framework for stable and scalable optimization of general agents through complex harnesses. Concretely, we first build a sandbox-based execution infrastructure that isolates task environments and harnesses within temporary sandboxes for large-scale concurrent rollouts. We then decouple policy optimization from opaque harness execution and place a serving proxy at the model boundary to capture model calls. To reconstruct multi-turn trajectories and improve training efficiency, we organize the captured calls into prefix trees and further adapt both critic-based PPO and critic-free GRPO to optimize over the recovered tree structure. Meanwhile, we maintain training-inference consistency throughout the optimization process. Finally, we introduce mix-harness training, allowing a single model to be jointly optimized by heterogeneous harnesses. With Qwen3-30A3B, black-box RL improves Pass@1 on ClawGym-Bench by 9.98 and 14.81 points through OpenClaw and Claude Code, respectively, while remaining stable over 200-400 optimization steps. Moreover, the framework yields consistent gains on more challenging tasks such as JobBench and OfficeQA. Overall, our framework enables effective, stable, and scalable optimization of general agents through black-box harnesses, supporting unified training across heterogeneous execution systems.
[NLP-5] Neurosymbolic Embodied Agents
【速读】： 该论文旨在解决生成式语言与视觉-语言模型在生成具身任务规划时存在的可执行性问题，即其输出可能违背环境动态规律或作用于错误识别的实体。为此，提出一种神经符号（neurosymbolic）代理框架，其核心在于将长时程家庭任务分解为两个阶段：任务导向的视觉探索与受约束的符号规划。关键解决方案包括：第一阶段利用视觉-语言模型与探索机制从第一人称观测和具身交互中获取目标相关的谓词与实例绑定，构建符号化初始状态；第二阶段通过PDDL（Planning Domain Definition Language）转换模型对解码过程施加约束，仅允许扩展有效动作的标记，并结合蒙特卡洛树搜索（Monte Carlo Tree Search）基于领域无关的规划启发式评估可执行的后续规划。该方法确保生成的计划在转换模型下具有可执行性，且迁移至真实环境依赖于正确的视觉接地。实验表明，在VirtualHome与ALFWorld基准上，4B-27B规模的开放模型成功率均超过90%，而最小版本代理在两项任务中均显著优于27B直接视觉策略。约束与搜索策略呈互补而非互换关系：单独使用时仅能解决不到三分之一的任务，二者结合则实现超95%的成功率。此外，该方法生成的标记数量仅为扩展思维（chain-of-thought）的数分之一，且所需可见图像远少于直接交互方式；残余失败集中于状态获取环节，而非规划生成过程，无需专门训练即可定位。
链接: https://arxiv.org/abs/2608.16794
作者: Mohammad Albinhassan,Yuming Feng,Alessandra Russo,Pranava Madhyastha
机构: Imperial College London(帝国理工学院); Johns Hopkins University(约翰霍普金斯大学); City, University of London(伦敦城市大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Language and vision-language models generate plausible embodied plans but do not guarantee executability, as their outputs can violate environment dynamics or act on incorrectly grounded entities. We present a neurosymbolic agent that factors long-horizon household tasks into task-directed visual exploration and constrained symbolic planning. In the first phase, a vision-language model and exploration harness acquire goal-relevant predicates and instance bindings from egocentric observations and grounded interactions, producing a symbolic initial state. In the second, a PDDL transition model restricts decoding to tokens that extend applicable actions. Monte Carlo tree search then evaluates executable continuations using a domain-independent planning heuristic. The resulting plans are executable by construction under the transition model, with transfer to the environment conditioned on correct visual grounding. On VirtualHome and ALFWorld, open 4B-27B models exceed 90% success in both environments, and our smallest agent substantially outperforms a 27B direct visual policy in each. Constraints and search prove complementary rather than interchangeable: in ALFWorld either alone solves under a third of tasks, whereas their combination solves over 95%. The method also uses several times fewer generated tokens than extended thinking and far fewer model-visible images than direct interaction, and residual failures localize to state acquisition rather than plan generation without any specialized training.
[NLP-6] Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在需要复杂环境探索的决策任务中，如何平衡探索（exploration）与利用（exploitation）的问题。不同于传统强化学习中的符号化动作空间，LLM代理通过自然语言与环境交互，其动作标签具有丰富的语义信息，而这些信息在任务结构中并无直接对应。为此，论文提出“语义老虎机”（semantic bandit）这一扩展的多臂老虎机框架，显式建模动作文本标签的语义特性，以研究预训练过程中形成的语义先验（semantic priors）——即语言与预期奖励之间通过词共现习得的归纳偏置——如何影响LLM的探索行为。研究发现，语义信息丰富的动作标签会抑制探索、促进利用，当其与真实奖励结构对齐时可提升性能，但若存在偏差则会导致性能严重下降；此外，负向奖励引发的探索显著高于等量正向奖励，表明模型存在由预训练数据中奖励表达惯例所诱发的“期望尺度偏差”（expected-scale bias）。因此，论文指出：使用语言定义环境与奖励不可避免地引入源于词共现统计规律的固有偏差，这对LLM代理在现实决策场景中的可靠性与鲁棒性构成根本性挑战。解决方案的关键在于识别并量化这些由语言表征引发的语义偏差，从而为设计更稳健的基于语言的决策系统提供理论基础与干预路径。
链接: https://arxiv.org/abs/2608.16707
作者: David Eric Austin,Kaheer Suleman,Jackie Chi Kit Cheung
机构: Mila – Quebec AI Institute(蒙特利尔人工智能研究所); Skyfall AI; Canada CIFAR AI Chair, Mila(加拿大魁北克人工智能主席, Mila); McGill University(麦吉尔大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures in main body
Abstract:Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration. However, existing work has raised questions about how LLMs actually balance exploration and exploitation. Unlike classical agents, LLM agents engage with tasks through natural language, exposing them to semantic information with no formal counterpart in the task structure. We introduce the semantic bandit, an extension of the multi-armed bandit setting that explicitly considers the textual labels assigned to actions, and use it to study how semantic priors — inductive biases arising from associations between language and expected reward learned during pre-training, shape LLM exploration behaviour. We find that semantically informative action labels reduce exploration in favour of exploitation, improving performance when aligned with the reward structure and severely degrading it when misaligned. We further find that negative rewards trigger substantially more exploration than equivalent positive rewards, consistent with an expected-scale bias induced by reward conventions common in pre-training data. Overall, we argue that the use of language to define the environment and rewards introduces unavoidable biases derived from the fact that the model is trained on word co-occurence, with implications for the reliability and robustness of LLM agents in real-world decision-making settings.
[NLP-7] Does the LM Head Create a Harmful Gradient Bottleneck? A Causal Test
【速读】： 该论文旨在探究语言模型（Language Model, LM）头部在Transformer架构中进行的高维到低维投影是否构成一种有害的优化瓶颈。具体而言，传统LM头部将宽度为D的隐藏状态映射到大小为V的词表空间，其转置仅能向Transformer返回最多D个独立方向，这一过程被认为可能限制了模型的表达能力与优化效率。作者通过分离几何结构与因果推断，提出一种仅在反向传播阶段降低梯度秩（backward-only intervention）的干预方法，在保持原始输出logits和完整参数更新的同时，仅压缩传递回Transformer的梯度秩。实验结果表明，尽管降低反向梯度秩会导致验证损失上升（在大模型中半秩时增加0.0586，95%置信区间[0.0167, 0.1005]），但同等秩的前向分解式头部导致的损失增长更为显著（0.1795，[0.1547, 0.2042]）。此外，词汇空间残差项对正常LM头部更新具有贡献，移除该贡献会损害性能。其他控制实验进一步显示，重复标记失败现象受独立采样符号数量的混淆影响，添加未被目标覆盖的输出类别不影响学习，且投影诊断无法可靠预测训练进展。测试的辅助反馈路径也未能超越调优后的反向传播。综上，研究确认了强几何压缩的存在，但并未证明其构成有害的优化瓶颈，关键在于：通过仅在反向传播中降低梯度秩而不改变前向计算结构，可有效隔离并评估梯度压缩对优化的影响，从而揭示现有机制中存在非致命性但显著的压缩效应，而非根本性瓶颈。
链接: https://arxiv.org/abs/2608.16671
作者: Anand Murugan
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:The language-model head maps a hidden state of width D to a vocabulary of size V, so its transpose can return at most D independent directions to the Transformer. Godey and Artzi argue that this severe projection is a harmful optimization bottleneck. We separate the geometry from the causal claim. Our backward-only intervention keeps the ordinary logits and the exact LM-head parameter update while reducing only the rank of the gradient sent into the Transformer. Across five paired seeds on byte-level and BPE-8192 WikiText-2 models, reducing backward rank increases validation loss. An equally ranked factorized forward head, however, increases loss substantially more. At half rank in the larger model, the backward-only loss increase is 0.0586 (95% CI [0.0167, 0.1005]), while the factorized forward head increases loss by 0.1795 ([0.1547, 0.2042]). The vocabulary-space residual also contributes to the ordinary LM-head update, and removing that contribution is harmful. Additional controls show that repeated-token failures are confounded by the number of independently sampled symbols, that adding never-target output classes does not impair learning, and that projection diagnostics do not reliably predict progress in our runs. Tested auxiliary feedback routes do not beat tuned backpropagation. These results confirm strong geometric compression but do not establish that it is a harmful optimization bottleneck.
[NLP-8] PCA-guided Activation Scaling for Monotonic Bidirectional Control over LLM Sycophancy
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的“谄媚倾向”（sycophancy）问题，即模型倾向于无条件认同用户观点，无论其是否符合事实，从而可能加剧错误认知；但完全消除该倾向又可能导致对合理意见的过度纠正。因此，理想的控制机制需具备可预测且渐进的双向调节能力——既能有效降低谄媚行为，也能在必要时适度增强以支持正确观点。现有方法无法在不同模型与数据集上保证控制强度与行为结果之间的双向、单调关系。为此，本文提出基于主成分分析引导的激活缩放（PCA-guided Activation Scaling, PAS）框架：通过主成分分析（PCA）识别出残差流激活中的“谄媚-诚实”子空间，并将其与正交余量分离，随后对二者施加不对称的缩放指数，实现对模型行为的单调、双向调控。在三个主流大模型和三个数据集上的实验表明，PAS 在保持高度单调性（Spearman ρ = +0.92）的同时，平均每个方向实现15.4%的行为偏移，显著优于基线方法（8.7%）。消融实验进一步验证了特征分解、非对称缩放指数及层选择策略在维持单调控制中的关键作用。
链接: https://arxiv.org/abs/2608.16650
作者: Zheng Chen,Zhaoxin Feng,Yip Tin Po,Jianfei Ma,Emmanuele Chersoni,Bo Li
机构: The Hong Kong Polytechnic University(香港理工大学); The Hong Kong University of Science and Technology(香港科技大学)
类目: Computation and Language (cs.CL)
备注: accepted by COLM2026
Abstract:Large language models (LLMs) exhibit sycophancy, a tendency to agree with user beliefs regardless of factual accuracy. This can reinforce misconceptions, but eliminating it entirely risks over-correction against valid opinions. Effective control must therefore both reduce and increase sycophancy with predictable and gradual effect. Yet, existing methods fail to ensure a bidirectional and monotonic relationship between steering strength and behavioral outcome across models and datasets. We introduce PCA-guided Activation Scaling (PAS), an activation steering framework that decomposes residual stream activations into a PCA-identified sycophancy-honesty subspace and an orthogonal residual, then applies distinct scaling exponents to achieve monotonic, bidirectional control. Across three LLMs and three datasets, PAS achieves strong monotonicity (Spearman \rho = +0.92) and an average shift of 15.4% per direction, compared with 8.7% for the baselines. Ablation studies confirm that the decomposition, asymmetric exponents, and layer selection are each essential for maintaining monotonic control. The data and code are available at this https URL.
[NLP-9] Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）中在线策略蒸馏（On-policy Distillation, OPD）在泛化能力方面理解不足的问题，尤其关注其在分布外（out-of-domain）场景下的表现机制。现有研究多局限于单一领域或与训练数据相近的基准测试，难以揭示OPD的真实泛化特性。本文通过受控实验，系统性地分离并考察了从域内分布偏移、跨域迁移到多教师（multi-teacher）设置等关键泛化因素的影响。研究发现，OPD的核心机制并非简单复制教师对特定问题的答案，而是有效迁移教师的推理模式（reasoning behavior）——即使教师从未解决过某些问题，只要其推理路径可被学生学习，仍具有价值。此外，教师与学生之间的来源关系（origin relationship）是决定泛化效果的关键：同源（same-origin）对能显著提升学生在语言、推理深度甚至其他领域上的性能，而异源（cross-origin）对则主要局限于训练分布内的拟合。这一强泛化能力虽具优势，但也带来挑战——由于路由提示至领域专家无法限制教师影响范围，多教师组合时会产生能力间的“跷跷板效应”（seesaw effect），即各教师贡献相互制约。该研究明确了OPD在何种条件下能够有效泛化，并为多教师情境下的性能诊断提供了理论依据。
链接: https://arxiv.org/abs/2608.16647
作者: Zhaoyi Li,Deyang Kong,Yuan Wei,Evan Yang,Ranran Shen,Mahardika Krisna Ihsani,Ming Yang,Wei Zhang,Chuan Hao,Jian Yang,Ran Tao,Bryan Dai,Shikun Zhang,Wei Ye,Ying Wei,Defu Lian
机构: University of Science and Technology of China (中国科学技术大学); Peking University (北京大学); IQuest Research (IQuest 研究院); MBZUAI (MBZUAI); Zhejiang University (浙江大学)
类目: Computation and Language (cs.CL)
备注: Under Review
Abstract:On-policy distillation (OPD) transfers teacher capabilities by supervising trajectories sampled from the student’s own policy, yet its generalization behavior remains poorly understood, as most studies evaluate OPD on a single domain and on benchmarks close to the training data. We present a controlled study that varies one generalization factor at a time, from in-domain distribution shifts to cross-domain transfer and the multi-teacher setting. We find that OPD transfers a teacher’s reasoning behavior rather than its answers to particular problems: training difficulty barely matters, and even problems the teacher never solves are useful. Transfer depends strongly on the origin relationship between teacher and student: same-origin pairs bring the student close to the teacher across languages, reasoning horizons, and even other domains, whereas cross-origin pairs mostly fit the trained distribution. This broad reach is a double-edged sword: since routing prompts to domain experts cannot confine each teacher’s influence, combining them yields a mixture-dependent seesaw among their capabilities. These results clarify when OPD generalizes and offer a useful perspective for diagnosing multi-teacher OPD.
[NLP-10] oward Better Assessment of LLM s Performance in Clinical Error Detection ALT
【速读】： 该论文旨在解决当前临床文档错误检测任务中评估方法的局限性问题，即现有基准测试通常将每份临床记录独立评估，忽视了通过人工注入错误构建的“错误-无错”成对样本之间的结构关系。其核心问题是：依赖传统聚合指标（如F1分数或平衡准确率）可能无法真实反映模型在区分错误与正确记录方面的判别能力，从而导致对模型性能的误判。解决方案的关键在于引入成对判别评估（pairwise discrimination evaluation），通过对比同一文档的错误版本与其原始干净版本的输出，系统性地检验模型是否具备正确的判别能力。研究发现，尽管15个大型语言模型中有13个在F1指标上表现中等，但其成对判别能力却低于随机水平，且不同语言间存在显著的偏差模式差异（如同一模型在一种语言中倾向于忽略错误，在另一种语言中则过度标记）。进一步分析表明，模型虽能定位到与错误相关的文本内容，却无法对对应的干净版本做出正确判断。更重要的是，F1分数与成对准确率受同一底层偏差驱动，方向相反，因此仅依据F1排名会系统性地偏袒判别能力最弱的模型。为此，作者建议在安全关键的临床自然语言处理应用中，应将成对评估作为补充手段纳入基准报告体系，以更可靠地衡量模型的真实判别性能。
链接: https://arxiv.org/abs/2608.16643
作者: Yifan Zhang,Rahmatollah Beheshti
机构: University of Delaware(特拉华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at Machine Learning for Healthcare (MLHC) 2026; to appear in Proceedings of Machine Learning Research (PMLR), Vol. 340
Abstract:Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation. Error-detection benchmarks are typically constructed by injecting errors into notes, such that each erroneous note has a natural counterpart. Aggregate discriminative metrics (e.g., balanced accuracy or F1) do not exploit this structure. We show that this omission is consequential. In particular, evaluating 15 diverse LLMs on 4 standardized clinical error-detection test sets across 3 languages, we find that 13 of 15 models fall below the level of random pairwise discrimination, even while achieving F1 scores that standard practice would read as moderate. We also observe that the underlying bias patterns differ across languages: the same model can default to “no error” on one language and over-flag errors on another. To diagnose where discrimination breaks down, we further introduce a procedure to score the evidence models cite in their outputs. We find that while models consistently locate error-relevant content, they fail to produce the corresponding correct verdict on the clean counterpart. Finally, we show that F1 and pairwise accuracy are driven in opposite directions by the same underlying bias, so that ranking models by F1 may systematically promote the weakest discriminators. For safety-critical clinical NLP applications, we advocate for supplementing aggregate metrics with paired evaluations in benchmark reporting. Code and analysis scripts are available at this https URL.
[NLP-11] When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
【速读】： 该论文旨在解决在上下文学习（In-Context Learning, ICL）中，不同类型自然语言解释（Natural Language Explanations, NLEs）对下游模型性能影响的不明确问题。具体而言，研究关注NLE的来源（人类撰写、自我生成、外部大模型生成）以及NLE选择策略（随机选取与基于忠实度的筛选）如何影响解释增强提示的效果。其解决方案的关键在于系统性地对比六种基准任务和四种指令微调模型下不同NLE来源与选择方法的表现，揭示了外部生成的LLM-NLE在分类类任务中通常具有较强的下游效用，且在可比条件下可媲美人工解释；而自生成NLE则对选择策略更为敏感。此外，基于忠实度的筛选虽整体带来微小平均增益，但其效果高度依赖任务、指标与模型，不同忠实度度量间存在显著分歧，进而影响所选示例及其预测性能。研究还通过随机替换与分布外推理验证了部分鲁棒性，表明语义一致性是性能提升的重要因素。结果为实际提示工程流程中解释的选择与报告提供了实证依据。
链接: https://arxiv.org/abs/2608.16627
作者: Mahdi Dhaini,Adam Dejl,Juraj Vladika,Volkan Özer,Barbara Plank,Gjergji Kasneci
机构: MaiNLP lab, CIS, LMU Munich(慕尼黑大学信息与计算机科学系); Munich Center for Machine Learning (MCML); Department of Computing, Imperial College London(帝国理工学院计算机系); Technical University of Munich(慕尼黑工业大学); Munich Center for Machine Learning (MCML)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it remains unclear how different types of NLEs compare in their effects on downstream model performance in explanation-augmented prompting. Therefore, we provide a comparative evaluation across six benchmarks and four instruction-tuned models, studying how NLE source (human-written when available, self-generated explanations, generated by an external LLM) and NLE selection (random vs faithfulness-based filtering) affect downstream utility of NLEs when used in ICL settings. Our extensive evaluation shows that, on classification-style benchmarks, adding NLEs to few-shot prompts often improves accuracy over few-shot prompting without explanations; among NLE sources, externally generated LLM-NLEs often provide strong downstream utility and remain competitive with human rationales where both are available, whereas self-NLEs are more sensitive to the selection strategy. On math reasoning, the effects are more model- and source-dependent. We further show that faithfulness-based selection of self-NLEs yields small average gains overall, but can improve or reduce performance depending on the metric, task, and model. Different faithfulness metrics can disagree substantially, affecting which self-NLE examples are selected and their downstream predictive utility. Robustness tests with randomly swapped and out-of-distribution rationales indicate partial robustness, suggesting that semantic alignment contributes to performance gains. Overall, our results provide insights for selecting and reporting explanations that influence model behavior in practical prompting pipelines.
[NLP-12] Palmyra x6 Technical Report: An Agent ic Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning
【速读】： 该论文旨在解决企业级智能体（agentic tasks）在实际应用中面临的性能与可靠性问题，特别是在复杂工具调用任务中的准确性和安全性。其核心挑战在于如何在不显著增加计算开销的前提下，提升大语言模型在特定企业场景下的任务执行能力。解决方案的关键在于采用一种受控且保守的后训练策略：基于混合专家（Mixture-of-Experts, MoE）架构的基座模型，通过锚定监督微调（Anchored Supervised Fine-Tuning）在经过验证的合成工具使用轨迹数据集上进行单轮微调，仅使用626条高质量轨迹，并结合低学习率与冻结基座模型的KL散度约束，同时采用Muon + Adam混合优化器以增强训练稳定性。这一设计有效平衡了性能提升与模型泛化能力，使Palmyra x6在Writer Agent任务中表现显著优于前代默认模型，并在多个公开基准测试中取得领先成绩，尤其在BFCL Core基准上达到0.785的最高得分，六项基准平均分亦位居同侪首位，同时在偏见与安全性评估中展现出竞争力或领先优势。
链接: https://arxiv.org/abs/2608.16620
作者: Peng Du,Kiran Kamble,Rakshith Vasudev,Zhizhuo Yang,Rohith Nadimpally,Arjun Krishna,Waseem Alshikh,Daniel M. Bikel
机构: Writer AI Research, Writer, Inc.(Writer AI 研究院, Writer 公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages
Abstract:Palmyra x6 is a large language model optimized for use with enterprise-oriented agentic tasks. The model was built by post-training a Mixture-of-Experts base model with Anchored Supervised Fine-Tuning on a compact corpus of verified, synthetic tool-use trajectories, optimized with a Muon + Adam hybrid. The recipe is deliberately conservative and deliberately controlled: 626 trajectories, a single epoch, a low learning rate, and a KL anchor to the frozen base. The model shows substantial gains over the previous default model for Writer Agent, and compares favorably with several recent models on public benchmarks, scoring the highest on BFCL Core at 0.785 and posts the highest six-benchmark mean of the cohort. Furthermore, the model has shown itself to be competitive or leading relative to comparators in our bias and safety evaluations.
[NLP-13] BabelSteering: Multilingual Safety Alignment via English Steering Vectors
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在多语言场景下安全对齐不足的问题，即当前主流的安全研究与对齐工作主要集中于英语等高资源语言，导致非英语用户在使用相同模型执行高风险任务时可能面临较弱的安全防护。其核心解决方案是提出BabelSteering——一种轻量级的推理时激活调控方法，通过利用英语安全监督所提取的拒绝方向（refusal directions），实现跨语言的安全信号泛化。该方法的关键在于：在不修改模型参数的前提下，基于已有的英语安全知识，在推理阶段动态调整模型激活状态，从而提升多语言环境下对有害请求的拒绝能力，同时尽量维持任务通用性并控制过度拒绝。实验覆盖八种语言，结果显示BabelSteering显著提升了多语言场景下对有害请求的拒绝率（如Gemma 7B平均提升11个百分点，部分语言达17个百分点），且对全球通用任务评估集Global MMLU的性能无损；尽管伪有害提示的误拒略有上升（平均增加13个百分点），但整体仍具备良好的实用性与成本效益。此外，研究还构建了多语言翻译-评估流水线，以支持未来跨语言安全干预的系统性研究。结果表明，激活调控可作为扩展英语来源安全信号至低资源语言的一种高效、低成本的可行路径。
链接: https://arxiv.org/abs/2608.16577
作者: Emma V. Stein,Dominik Meier,Terry Ruas,Jan Philip Wahle,Bela Gipp
机构: University of Göttingen(哥廷根大学); German State Police NRW(德国北莱茵-威斯特法伦州警察局)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are deployed globally in high-stakes settings, yet most safety research and alignment efforts remain concentrated on English. Thus, users interacting with LLMs in other languages may encounter weaker safeguards despite relying on the same systems for similarly sensitive tasks. In this work, we investigate whether safety signals learned from a high-resource language, like English, can improve multilingual safety. We propose BabelSteering, an activation steering method that acts as a lightweight inference- time intervention, using refusal directions derived from English safety supervision to generalize across languages. Our evaluation includes eight languages and jointly measures refusal of harmful requests, over-refusal, and general task utility. The results show that BabelSteering increases the refusal of harmful requests across languages, with only a marginal to no reduction in task utility but with some increase in refusal of pseudo-harmful prompts. For example, for Gemma 7B, we see an average increase in the refusal of harmful prompts across languages of 11 percentage points (pp), with individual languages like Bengali seeing an increase of 17 pp, with no loss of utility on Global MMLU, while pseudo-harmful refusals increase by 13 pp on average. We also introduce a multilingual translation-and-evaluation pipeline to facilitate future work on cross-lingual safety interventions. Overall, our findings suggest that activation steering may provide a practical, low- cost mechanism for extending English-derived safety signals to other languages. Warning: this paper contains examples with unsafe content
[NLP-14] Ask Condition or Abstain: Reinforcement Learning for Missing-Premise Reasoning
【速读】： 该论文旨在解决生成式模型在面对前提缺失的不完整问题时，无法有效识别任务不确定性并采取合理响应策略的问题。传统仅依赖答案的强化学习（Answer-only Reinforcement Learning, RL）框架仅适用于条件完备的问题，而现实中的许多查询往往缺少关键前提，导致唯一正确答案无法确定。此时，理想的响应不应仅为拒绝回答，而应包括主动询问缺失前提、基于未知变量进行条件化推理或在无信息量条件下选择放弃回答等行为。为此，论文提出“问-条件-放弃”强化学习（Ask-Condition-Abstain Reinforcement Learning, ACA-RL）框架，其核心创新在于通过推理图引导的数据增强流程，将结构良好的问题转换为带有局部缺失前提标注的训练实例，并设计包含五种可观测响应行为的结构化奖励机制以指导模型学习多种应对不确定性的策略。同时，作者构建了“缺失前提基准测试集”（Missing-Premise Benchmark, MPB），一个涵盖数学、逻辑与现实世界应用的274个经人工验证的问题集合，用于评估模型在不确定情境下的表现。实验表明，ACA-RL在多个大语言模型（如Qwen3和Llama系列）上均显著提升在MPB上的性能，且保持对标准良好定义问题的竞争力。该研究推动了自然语言处理评估范式的演进，强调衡量模型识别任务欠定性及处理不确定性能力的重要性，而不仅限于解答完全指定问题的能力。
链接: https://arxiv.org/abs/2608.16554
作者: Yongqi Tong,Zhenyu Zhang,Zimi Liu,Kewei Fu,Mingli Song,Haofei Zhang,Junshao Zhang,Hong Zhu,Jiang-Ming Yang,Xin Zhang,Jianshe Li
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Answer-only reinforcement learning (RL) trains reasoning models to solve fully specified problems, but many realistic queries omit a premise needed for a unique answer. In this setting, the useful response is not always refusal: the model should ask for the missing premise, condition its answer on the unknown quantity, or abstain when no informative conditional response is available. We present \emphAsk-Condition-Abstain Reinforcement Learning (ACA-RL), a data-augmented RL framework for this setting. Its reasoning-graph-guided pipeline converts well-posed problems into missing-premise training instances with localized gap annotations; ACA-RL then trains on these instances with a structured reward over five observable response behaviors. We also introduce the \emphMissing-Premise Benchmark (MPB), a 274-instance human-verified benchmark spanning mathematical, logical, and real-world word problems. Across Qwen3 and Llama models, ACA-RL consistently improves on MPB while preserving competitive performance on well-posed reasoning tasks. Together with the released code, MPB, and training data, this work supports a new mission for NLP evaluation: measuring whether models can recognize when a task is underdetermined and handle uncertainty, not only whether they can answer fully specified questions.
[NLP-15] STAGE: Controlled Objective Admission for Multi-Preference LLM Alignment
【速读】： 该论文旨在解决多偏好对齐（multi-preference alignment）中目标进入策略优化时机不明确的问题，即在基于标量化的多维奖励融合方法中，各偏好维度应何时被纳入优化过程缺乏有效的时间决策机制。其核心解决方案是提出一种基于稳定性的主动集控制器（stability-guided active-set controller），即\methodname：该方法从较小的活跃目标集合出发，通过奖励偏差门控机制判断近期偏差是否较低或耐心预算是否耗尽，决定是否引入新目标；同时引入探查阶段以估计从难到易的目标排序，并采用自适应加权机制强化表现欠佳的活跃维度。实验表明，相较于同时标量化与共享预算的基线方法，\methodname在15个训练偏好和16个保留基准上的自动评估中均获得更高平均性能。组件消融与扩展动态分析进一步验证了累积保留、门控准入及探查引导排序等设计要素的有效性，从而将目标进入时机确立为奖励向量强化学习人类反馈（reward-vector RLHF）中的一个可调控的关键变量。
链接: https://arxiv.org/abs/2608.16553
作者: Yongqi Tong,Zhenyu Zhang,Ruirui Wang,Kewei Fu,Shaoqing Lin,Sijie Dong,Jiang-Ming Yang,Xin Zhang,Jianshe Li
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Multi-preference alignment is often framed as scalarization: combine reward dimensions, then optimize. This leaves a temporal decision underspecified: when should each preference dimension enter policy optimization? We propose \methodname, a stability-guided active-set controller for controlled objective admission. \methodname starts from a small active set, retains admitted objectives, and expands when reward-deviation gates indicate low recent deviation or a patience budget is exhausted. A probing phase estimates a hard-to-easy order, and adaptive weighting emphasizes underperforming active dimensions. Automatic evaluations with 15 training preferences and 16 held-out benchmark columns show that \methodname obtains higher averages than simultaneous scalarization and shared-budget adapted baselines. Component ablations and expansion dynamics further support cumulative retention, gated admission, and probing-derived ordering as useful design choices in this setting. These results position objective-entry timing as a concrete control variable in reward-vector RLHF.
[NLP-16] Listen Reason and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization
【速读】： 该论文旨在解决大音频语言模型（Large Audio Language Models, LALMs）在实际媒体工作流中部署受限的问题，尤其聚焦于自动化音频章节划分（audio chapterization）这一具有重要商业价值但极具挑战性的任务。该任务需将连续音频流分割为语义连贯的章节，其难点在于章节边界主要依赖主观编辑判断而非客观声学事件，要求模型具备对长时音频上下文进行序列推理的能力，并逼近创作者设定的边界决策。论文提出的解决方案核心是AudioChaps框架，采用基于思维链（Chain-of-Thought, CoT）引导的组相对策略优化（Group Relative Policy Optimization, GRPO）方法，在无需监督微调（SFT）冷启动的前提下，直接对端到端的LALM进行后训练对齐。该方法通过引入结构化、高质量且证据支撑的推理监督数据集AudioChaps-CoT，显著提升了模型在章节边界预测上的准确性。实验表明，AudioChaps-R1-Zero相比当前最优模型Audio-Flamingo-3-Think平均F1提升33个百分点，而最终模型AudioChaps-R1进一步提升49个百分点，验证了GRPO训练的LALM能够可靠地将非结构化音频流转化为可导航、结构化的媒体内容。
链接: https://arxiv.org/abs/2608.16539
作者: Tony Alex,Wish Suharitdamrong,Sara Atito,Armin Mustafa,Muhammad Awais,Philip J. B. Jackson,Jiankang Deng,Ismail Elezi
机构: University of Surrey(萨里大学); Huawei Noah’s Ark Lab(华为诺亚方舟实验室)
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 19 pages, 9 figures, 8 tables
Abstract:Large Audio Language Models (LALMs) have made rapid progress on standardized benchmarks, yet their deployment in practical media workflows, curation, archival indexing, and content distribution remains largely unrealized. We identify automated audio chapterization, the task of segmenting continuous audio streams into thematically coherent chapters, as a demanding and commercially consequential setting that exposes this gap. Chapterization is challenging because boundaries are defined less by objective acoustic events than by subjective editorial judgment, requiring models to reason sequentially over long acoustic contexts and approximate creator-authored boundary decisions. We present AudioChaps, a post-training framework for aligning end-to-end LALMs for this task via Group Relative Policy Optimization (GRPO) guided by Chain-of-Thought (CoT) reasoning. To support training and evaluation, we curate three datasets: AudioChaps-Alignment, derived from creator-annotated chapter boundaries on YouTube; AudioChaps-CoT, which provides structured supervision for well-formatted, high-quality, and evidence-grounded boundary reasoning; and AudioChaps-Eval, a held-out benchmark for audio chapterization. Applying GRPO directly without a Supervised Fine-Tuning (SFT) cold start, AudioChaps-R1-Zero already improves average F1 by 33 points over the state-of-the-art LALM Audio-Flamingo-3-Think. The AudioChaps framework produces our final aligned LALM, AudioChaps-R1, which improves average F1 by 49 points. These results demonstrate that GRPO-trained LALMs can reliably transform unstructured auditory streams into navigable, structured media. Our code, models, and dataset resources will be released upon acceptance at this https URL.
[NLP-17] DSPrompt: Dynamic Soft Prompt Defense Against M-RAG Corruption
【速读】： 该论文旨在解决多模态检索增强生成（Multimodal Retrieval Augmented Generation, M-RAG）系统在面对对抗攻击时的脆弱性问题，即恶意构造的数据通过生成与正常数据在向量空间中对齐的嵌入表示，诱导检索模块产生有害输出。现有防御方法主要依赖查询时的辅助检测器、相似性重排序或特征一致性检验，但普遍存在推理开销大、对未见攻击策略泛化能力差以及依赖特定攻击分布等局限性。本文提出一种名为DSPrompt的动态软提示（Dynamic Soft Prompt）防御框架，其核心在于不修改检索流水线的前提下，通过在冻结的视觉与文本编码器各层中插入可学习的软提示（soft prompts），动态重塑检索器的嵌入语义。该框架采用由浅至深的提示长度调度策略，以适配模型各层容量，并在动态最小-最大对抗训练机制下进行优化：在线生成的多模态攻击者持续构造难例对抗文档以挑战当前检索器，而防御方则不断更新以将这些对抗样本排除在前k位同时保持良性证据的排名与多样性。由于经过防御的编码器可像标准密集检索一样预先计算并索引，因此DSPrompt无需每次查询时进行优化，参数增量不足1%。大量实验表明，该方法在四个基准和三种典型投毒攻击场景下显著降低了攻击成功率与污染检索率，同时几乎无损保留了检索性能与生成保真度，且计算成本仅为现有基线方法的一小部分。
链接: https://arxiv.org/abs/2608.16536
作者: Chang Liu,Yuni Lai,Mingyue Cui,Cong Tian,Yunyan Zhang,Xian Wu,Kai Zhou,Bin Xiao
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Multimodal Retrieval Augmented Generation (M-RAG) is increasingly vulnerable to adversarial attacks where malicious data are crafted to produce embeddings that align with benign entries in the vector space, deceiving retrieval and inducing harmful outputs. Existing defenses primarily operate at query time, relying on auxiliary detectors, similarity re-ranking, or feature-consistency checks. However, these approaches suffer from non-trivial inference overhead, generalize poorly to unseen attack strategies, and often assume specific attack distributions. To address this, we propose DSPrompt, a Dynamic Soft Prompt defense framework that directly reshapes the retriever’s embedding semantics, without modifying the retrieval pipeline. It inserts few learnable soft prompts into each layer of the visual and textual encoders of a frozen retriever, utilizing a shallow-to-deep length schedule that is adaptive to the capacity in the model layers. These prompts are trained under a dynamic min-max scheme: an online multimodal attacker continually crafts hard adversarial documents against the current retriever, while the defender is updated to push such documents out of the top-k while preserving the ranking and diversity of benign evidence. Because the defended encoder can be pre-computed and indexed exactly as in standard dense retrieval, DSPrompt incurs no additional per-query optimization and introduces fewer than 1% additional parameters. Extensive experiments across four benchmarks and three representative poisoning attacks show that DSPrompt substantially reduces the attack success rate and poison retrieval rate while maintaining near-lossless retrieval utility and generation fidelity, consistently outperforming existing defense baselines at a fraction of their computational cost.
[NLP-18] When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中因外部证据来源可信度不可控而引发的“源信任问题”：检索到的上下文可能有用、无关甚至具有误导性。现有RAG系统通常对检索证据采用固定的信任策略，导致在用户明确要求遵循上下文时，系统可能过度依赖错误信息或未能充分使用相关上下文，从而影响生成结果的准确性和忠实性。为此，本文提出意图引导解码（Intent-Guided Decoding, IGD）框架，其核心在于根据用户意图动态协调检索上下文与模型参数记忆之间的关系。IGD通过答案层面的过滤和词元层面的修正，实现对最终生成轨迹的精细调控，使系统能够在保证严格遵循上下文的前提下显著提升事实准确性。实验在三个忠实性问答基准和三个事实冲突基准上评估了五种大语言模型，结果显示IGD相较于直接RAG在事实恢复能力上最高提升达65.4个百分点，同时维持或改善了对上下文的忠实遵循行为，凸显了在RAG中平衡事实性（factuality）与忠实性（faithfulness）的重要性。
链接: https://arxiv.org/abs/2608.16515
作者: Haolin Jin,Pengyue Yang,Huaming Chen
机构: The University of Sydney (悉尼大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading. Existing RAG systems often apply a fixed trust policy toward retrieved evidence, which can either over-trust incorrect context or underuse context when the user explicitly asks for context-following behavior. Therefore, we propose Intent-Guided Decoding (IGD), a framework that arbitrates between retrieved context and parametric memory according to user intent. IGD uses answer-level filtering and token-level correction to steer the final decoding trajectory between retrieved context and parametric memory. We evaluate IGD on three faithful QA benchmarks and three factual-conflict benchmarks across five LLMs, IGD substantially improves factual recovery, achieving gains of up to 65.4 percentage points on factual-conflict benchmarks over Direct RAG, while preserving or improving strict context-following behavior, this findings highlight the importance of balancing factuality and faithfulness in RAG.
[NLP-19] D2-ScaleAgent : Dual-Dimensional Scaling for Long Document Understanding
【速读】： 该论文旨在解决多模态检索增强生成（multi-modal retrieval-augmented generation, RAG）在处理视觉丰富型长文档时，现有方法因依赖固定工作流而无法动态调整计算规模、导致证据不足的问题。其核心解决方案是提出一种名为D2-ScaleAgent的智能体框架，引入双维度扩展范式（dual-dimensional scaling paradigm），通过基于查询内在难度的验证器驱动的动态路由循环实现检索与推理的自适应扩展。该框架以持续更新的证据库作为智能体的动态工作记忆：当需要扩大检索范围时，智能体向外路由（检索扩展），将查询分解为属性并行执行页面检索，再经自适应剪枝以保障证据覆盖全面性；当需进行细粒度推理时，智能体向内路由（推理扩展），动态选择不同粒度与数量的子智能体从页面中提取证据。最终，D2-ScaleAgent通过构建逻辑闭环的证据链实现对复杂问题的有效解答。大量实验表明，该方法在MMLongBench-Doc、LongDocURL等长文档基准上表现出色。
链接: https://arxiv.org/abs/2608.16417
作者: Hao Zhang,Longrong Yang,Lunhao Duan,Ziyang Wang,Qing-Guo Chen,Shanshan Zhao
机构: Zhejiang University (浙江大学); Alibaba Group (阿里巴巴集团); University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multi-modal retrieval-augmented generation (RAG) is a key technique for visually rich long document understanding. Existing multi-modal RAG methods are progressively advancing toward multi-agent systems: they first retrieve relevant pages based on a query, and then iteratively understand information within those pages. However, these methods typically rely on fixed workflows and lack the ability to dynamically scale computation at test time, often leading to insufficient evidence. To address this, we propose D2-ScaleAgent, an agentic framework that introduces a dual-dimensional scaling paradigm for retrieval and reasoning. The core of D2-ScaleAgent is a Verifier agent-driven dynamic routing loop based on the intrinsic difficulty of the query, centered around a continuously updated evidence bank that serves as the agent’s dynamic working memory: when retrieval needs to be expanded, the agent routes outward (retrieval scaling), decomposing the query into attributes and performing parallel page retrieval, followed by adaptive pruning to ensure comprehensive evidence coverage. When fine-grained reasoning is required, the agent routes inward (reasoning scaling), dynamically selecting sub-agents with varying granularity and count to extract evidence from pages. Finally, D2-ScaleAgent achieves logical closure over the evidence chain. Extensive experiments demonstrate that D2-ScaleAgent is effective on long and visually rich document benchmarks like MMLongBench-Doc, LongDocURL, etc.
[NLP-20] Counting Documents Is Not Counting Text: Unit Bias in Web-PDF Corpus Statistics
【速读】： 该论文旨在解决大规模PDF语料库中以“文档数”和“词元数（tokens）”为单位的统计指标严重脱节所导致的评估偏差问题。其核心问题是：当前语料库通常仅以文档数量宣传规模，却在实际分析中以文档为单位报告覆盖率、OCR处理率、重抓取恢复率及语言混合度等指标，而未对总词元数进行分解，致使关键信息被掩盖。解决方案的关键在于揭示词元分布的高度不均衡性——例如，在CC-MAIN-2021-31-PDF-UNTRUNCATED语料库中，仅3.02%的含文本文档承载了50%的词元（基尼系数达0.807），超过50页的文档占总文档数的5.00%，却贡献了53.53%的文本量；此外，由TeX工具链生成的PDF虽仅占1.66%的文档数，却占据4.05%的文本量。最显著的影响来自Common Crawl的截断限制：尽管仅影响23.06%的文档，却导致63.08%的文本丢失。实验表明，两种主流解析库对截断文件的恢复率分别为11.4%和1.4%，约72%–97%的受影响文档无法提取有效内容，整体约55%–62%的文本永久丢失。基于此，论文提出应同时以“文档数”和“词元数”双维度报告语料库统计数据，以实现更准确、透明的评估与比较。
链接: https://arxiv.org/abs/2608.16390
作者: Luca Foppiano
机构: Common Crawl Foundation(通用爬取基金会)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:PDF corpora advertise their size in tokens but compute every rate they publish (coverage, OCR routing, re-fetch recovery, language mix) per document, and none decomposes its token total. The two units diverge sharply. On CC-MAIN-2021-31-PDF-UNTRUNCATED (7.9M web PDFs, 32.6B tokens), 3.02% of text-bearing documents hold half the tokens (Gini 0.807); documents over 50 pages are 5.00% of the corpus but 53.53% of its text. The PDFs produced by a TeX toolchain are 1.66% of documents and 4.05% of the text. The clearest casualty is Common Crawl’s truncation cap: it affected 23.06% of documents and 63.08% of the text. Reconstructing the truncated files and extracting both versions, two widely used libraries recover 11.4% and 1.4% of that text; between 72% and 97% of affected documents yield nothing; roughly 55–62% of the corpus’s text is lost. Under the 5 MiB cap adopted in March 2025, 30.19% of tokens would still be truncated, and recovery on those documents rises only from 3.3% to 13.2%. We recommend that corpus statistics be reported in both units: documents and tokens.
[NLP-21] Mint-Agent : Introducing Finance-Native Agent ic Foundation Models
【速读】： 该论文旨在解决金融领域智能体（Financial Agent）在实际应用中面临的两大核心挑战：一是确保其在执行金融任务时具备高可靠性，能够基于真实证据精确完成原子化操作；二是实现长周期研究的可执行性与结论可审计性，以支持复杂、多步骤的金融分析流程。现有模型往往在单一能力上表现突出，但难以兼顾精准执行与长期规划能力，且缺乏透明、可追溯的决策证据链。为此，论文提出Mint-Agent，一种面向金融领域的原生智能体架构，其解决方案的关键在于构建“数据-工具-算法”三位一体的系统框架：首先通过真实世界金融数据构建结构化、精细化的任务体系，支撑原子级金融能力与长周期代理行为；其次利用MintHarness实现开放环境中的稳定交互，并全程保留可审计的证据轨迹；最后采用结合监督微调（SFT）、关键步骤强化学习（OPD）与基于验证的强化学习（RLVR）的训练策略，分别训练金融推理专家与代理执行专家，再通过模型融合与多教师在线蒸馏技术整合为轻量化的通用型金融智能体。该方法成功产出Mint-Cu（9B）与Mint-Ag（27B）两款旗舰模型，在多个专业金融基准测试中展现出卓越性能，尤其在可靠性（如RFC-Bench达98.33%）与可执行性（如FinSearchComp T2达69.86%）方面显著优于主流模型，验证了将领域知识、长周期执行与可审计性统一建模的有效性，为可信金融智能体的发展提供了系统性范式。
链接: https://arxiv.org/abs/2608.16386
作者: Mint-Agent Team,B. Zhang,Yaze Geng,Lei Tang,Yaoyang Yi,Zonghan Wu,Yifan Hu,Kun Wang,Qingsong Wen,Yilei Shao
机构: Mint Agent Team( Mint 代理团队)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Financial agents must do more than recall domain knowledge: they must be both reliable, executing precise operations over grounded evidence, and executive, sustaining long-horizon research whose conclusions remain auditable. We present Mint-Agent, a family of finance-native agentic models designed around these two scales of financial intelligence. Mint-Agent is built upon three pillars: data, harness, and algorithm. Our data engine constructs clean, specialized tasks for atomic financial capabilities and long-horizon agentic execution from real-world financial sources. MintHarness enables stable interaction with open-ended environments and maintains auditable evidence trails across extended research trajectories. Our training recipe combines SFT, critical-step OPD, and RLVR to develop separate financial reasoning and agentic execution experts, which are then unified through model merging and multi-teacher on-policy distillation into compact, general-purpose financial agents. This pipeline yields two flagship models, Mint-Cu (9B) and Mint-Ag (27B). Across professional financial benchmarks, our models demonstrate two defining strengths: (1) Reliability: Mint-Ag achieves 98.33% on RFC-Bench, surpassing GPT-5.6-Sol and Claude-Opus-4.8 by 3.66 and 3.00 points; and (2) Executability: Mint-Cu reaches 69.86% on FinSearchComp T2, outperforming Agents-A1-35B and Nex-N2-mini by 22.83 and 12.78 points, while Mint-Ag achieves 76.00% and 60.49% on FinanceAgentBench v1.1 and v2, respectively. These results establish a path toward trustworthy financial intelligence in which domain expertise, long-horizon execution, and auditable evidence are jointly engineered as a unified foundation for frontier agentic models.
[NLP-22] Unadapted Multilingual ASR on a Garrusi Kurdish Evaluation Set: A Common-Reference Staged Normalization Analysis
【速读】： 该论文旨在解决在使用阿拉伯字母输出的语音识别模型对拉丁字母拼写的库尔德语变体进行评估时所面临的测量难题：直接评分将书写系统差异误判为识别错误，从而导致评价结果失真。其解决方案的关键在于采用联合归一化（joint normalization）策略，将参考文本与假设文本统一至相同的简化正字法（reduced orthography），以消除书写系统不一致带来的偏差。然而，这一方法同时改变了参考文本的分词方式，使评分的分母发生变化，进而引入了额外的混淆因素。研究通过固定参考文本为9,763个词元，并仅改变假设文本的表示形式，采用“共参考设计”（common-reference design）进行对比分析。实验表明，从原始阿拉伯字母假设转换为折叠后的拉丁字母表示，可使词错误率（WER）降低13.85个百分点、字符错误率（CER）降低49.72个百分点，其中折叠操作本身贡献了4.51和6.69个百分点的改进。尽管如此，仍存在显著残余错误，包括仅有14.53%的参考词元实现精确匹配、编辑类型以替换为主，且短段落的每段WER更高。此外，针对南库尔德语微调系统的评估显示其性能更差，且大量输出字符超出映射表范围，说明部分残余误差源于评分管道本身的局限性，而非识别能力不足。研究最终将发布经修正的固定参考文本及逐段结果，以支持独立验证。
链接: https://arxiv.org/abs/2608.16379
作者: Hiwa Asadpour
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 12 pages A4, 4 tables, 2 figures, pilot study
Abstract:Evaluating speech recognition for a Kurdish variety written in a Latin field orthography, using a model that outputs Arabic script, creates a measurement problem before a modelling one: direct scoring treats writing-system differences as recognition errors. Jointly normalizing reference and hypothesis avoids this, but also changes reference tokenization, mixing agreement gains with a change in the scoring denominator. I evaluate MMS-1B-all with the Central Kurdish (ckb) adapter, used as released without adaptation, on 1,722 Garrusi questionnaire segments from five speakers (9,763 reference word tokens; 117.9 minutes). I use a common-reference design: the reference is folded once and fixed at 9,763 tokens, while only the hypothesis representation varies. The raw Arabic-script hypothesis scores 111.70% WER and 100.92% CER, with zero exact word matches. Latin transliteration gives 102.36% WER and 57.89% CER; folding it into the reference’s reduced orthography gives 97.85% and 51.20%. Thus RAW-to-FOLDED reduces measured WER by 13.85 points and CER by 49.72 points; folding alone accounts for 4.51 and 6.69 points. Substantial error remains: 14.53% of reference tokens are exact matches, edits are substitution-dominated, and per-segment WER is higher for shorter segments. A Southern Kurdish fine-tuned system (aranemini/southern-kurdish-asr), scored under the same design, performs worse on every speaker (1,703 segments), with 109.56% WER and 55.85% CER. However, 12,330 output characters fall outside the folding table, so these rates must be recomputed against the corrected fixed reference. The MMS output also contains 613 unconverted or unmapped characters, showing that part of the residual error reflects scoring-pipeline limits rather than recognition alone. I will release the fixed reference and segment-level results, subject to source-corpus sharing terms, to support independent checking.
[NLP-23] HalluTracer: Hallucination Detection via Depth-Averag ing Truth Signals
【速读】： 该论文旨在解决大语言模型在高风险应用中生成事实性错误内容（即幻觉）的可靠性问题，尽管这些模型在内部表示中蕴含可线性分离的真理性信号，但现有白盒检测方法仅依赖单一深度或孤立组件的信号，忽略了前向传播过程中分布于各层的判别性信息。其解决方案的关键在于提出HalluTracer检测框架，该框架通过读取并聚合模型前向传播全过程每一层的真理性证据，在模型输出任何答案令牌之前完成整合。几何分析表明，各层信号间相关性较弱，因此简单的深度平均可有效抑制层间噪声，并捕获几乎全部可线性获取的信息。在六种开源语言模型和五个幻觉检测基准上的实验结果表明，HalluTracer始终优于匹配的白盒基线，性能提升达1至14个百分点。本研究将幻觉检测从传统的层选择问题重构为受真理性信号几何稀疏性支配的深度聚合问题。
链接: https://arxiv.org/abs/2608.16353
作者: Zhihao Guo,Zonghan Wu,Huan Huo,DaYong Ye,Junwei Zhang,Weiran Yao,Zhiwei Liu,Qingsong Wen,Yilei Shao
机构: University of Technology Sydney, Australia; City University of Macau, Macau; Meta, USA; actAVA AI, USA; Microsoft AI, USA; Squirrel Ai Learning, USA; East China Normal University, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments. These models nonetheless carry linearly separable truthfulness signals in their internal representations. Existing white-box detectors, however, collapse this evidence to isolated components or a single depth, discarding discriminative information distributed across the full forward pass. We introduce HalluTracer, a detection framework that reads and aggregates truthfulness evidence across every layer of the forward pass before the model emits any answer token. A geometric analysis reveals that the per-layer signals are weakly correlated, so that simple depth averaging suppresses layer-specific noise and captures nearly all linearly accessible information. Across six open-source language models and five hallucination benchmarks, HalluTracer consistently outperforms matched white-box baselines, with gains ranging from one to fourteen points. Collectively, our work recasts hallucination detection from a layer-selection problem into a depth-aggregation problem governed by the geometric sparsity of the truthfulness signal.
[NLP-24] Architecture-Dependent Causal Transfer of Activation States Across Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）之间通过自然语言作为中介进行直接通信所带来的编码/解码开销、令牌成本及延迟问题。其核心挑战在于探索是否可以通过学习到的投影网络，将不同架构的LLM之间的内部激活状态（activation states）以因果方式传递，从而实现更高效、低延迟的跨模型信息迁移。解决方案的关键在于：利用训练后的模型间表示对齐性，通过一个可学习的投影网络将源模型的隐藏层激活状态映射至目标模型的潜在空间，并在三个层面评估其有效性——包括表示相似性、基于投影状态的跨模型检索性能以及生成过程中注入投影激活后对输出结果的因果影响。研究发现，尽管在表示层面存在显著的对齐性（尤其在基于秩的互近邻对齐指标下表现优于中心化核对齐和Procrustes分析），但仅有部分解码器架构对（如Qwen2-0.5B → Phi-3-mini）在生成阶段注入投影激活后表现出统计显著的因果效应，且该效应具有明显的架构依赖性，表明当前方法仅实现了表示载体（representational vehicle）的因果传递，而非语义内容的通用转移。因此，结论指出，当前实现的端到端激活状态跨模型传递并非普适性机制，而是高度依赖于具体模型架构。
链接: https://arxiv.org/abs/2608.16347
作者: Fernando Cardenas Piepereit
机构: Velez-Malaga, Spain
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 13 pages, 3 tables
Abstract:Direct communication between AI systems relies on natural language as an intermediate layer, incurring encoding/decoding overhead, token cost, and latency. We ask whether internal activation states can instead be transferred causally between different large language model (LLM) architectures via a learned projection, evaluated at three levels: representational similarity, cross-model retrieval from projected states, and end-to-end causal transfer via activation injection during generation. Using four architecturally diverse open-weight models (Qwen2-0.5B, Phi-3-mini, Mistral-7B, FLAN-T5-base), we find that representational alignment in trained models exceeds a random-initialization null baseline and is best captured by a rank-based metric (mutual k-nearest-neighbour alignment), more robust to activation-magnitude outliers than centered kernel alignment (CKA) or Procrustes analysis. A learned projection network retrieves the correct target-model representation from a held-out set well above chance for the three causal decoder-only model pairs (45-50% top-1 accuracy vs. 5% chance) but at chance level for the encoder-based FLAN-T5. Injecting projected activations into a target model during generation produces a statistically significant, pre-registered causal effect on retrieval-based output similarity for only one of the three decoder-only pairs (Qwen2-0.5B to Phi-3-mini: 23.3% vs. 0.0% under negative control, p=0.047, FDR-corrected); the two pairs targeting Mistral-7B show no such effect despite comparable representational alignment at the hidden-state level. We interpret these results as evidence for causal transfer of the representational vehicle, not of meaning, and conclude that end-to-end activation-state transfer between LLMs, as currently implemented, is architecture-dependent rather than universal.
[NLP-25] IndicQE-APE: A Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages
【速读】： 该论文旨在解决印度语种（Indic）质量评估（Quality Estimation, QE）与自动后编辑（Automatic Post-Editing, APE）数据分散于不同发布版本中，导致缺乏统一资源支持跨任务、跨语言对的训练与评估的问题。其解决方案的关键在于构建一个整合性基准数据集——\indicqe，该数据集汇集了WMT 2020–2024共享任务的延续性数据，并扩展至英语–马拉雅拉姆语这一新语言对，共包含126,754个实例，覆盖九个方向性语言对，每个句子片段上对齐多达四种标签类型：直接评分（direct assessment）、人工后编辑（human post-edit）、词级“正确/错误”标签（OK/BAD tags）、以及错误解释（error explanation），同时提供在四个难度轴上分层的测试集。在此基准上，研究系统性地评估了六种提示式大语言模型（prompted LLMs）和三种COMET度量在段落级QE上的表现，以及三种系统在APE上的性能。特别值得注意的是，研究通过引入控制组（control group）来验证两个难度轴的有效性，其中仅有一个轴在去除控制后仍保持显著差异：当整体质量信号与词粒度质量信号存在冲突时，即便整体评分相同，此类段落仍被所有九个系统、七个语言对一致地评为更低质量。此外，研究发现标注者分歧在引入控制后不再具有显著影响，而少样本提示（few-shot prompting）对所有模型均造成≤3.4B参数级别的相关性与输出格式合规性下降。更重要的是，同一语言内的高准确率并不能保证跨语言对间的评分可比性——三类训练后的度量中，虽有模型在单语言内相关性最优，但在跨语言对合并后表现最差。该研究最终将公开基准数据与代码，以推动后续研究发展。
链接: https://arxiv.org/abs/2608.16344
作者: Diptesh Kanojia,Archchana Sindhujan,Sourabh Deoghare,Daria Sokova,Shenbin Qian,Girish Koushik,Tharindu Ranasinghe,Constantin Orăsan,Chrysoula Zerva,Ricardo Rei,Frédéric Blain,André F. T. Martins,Marco Turchi,Matteo Negri,Rajen Chatterjee,Anoop Kunchukuttan,Mitesh M. Khapra,Pushpak Bhattacharyya
机构: IIT Bombay(印度理工学院孟买分校); University of Oslo(奥斯陆大学); Lancaster University(兰卡斯特大学); INESC-ID(里斯本技术研究所信息与通信技术研究中心); Instituto de Telecomunicações Instituto Superior Técnico, University of Lisbon(里斯本大学技术研究所通信研究所); Sword Health(剑锋健康); Tilburg University(蒂尔堡大学); Zoom Communications(Zoom通讯公司); Fondazione Bruno Kessler(布鲁诺·凯斯勒基金会); Apple(苹果公司); Bodhan AI(博丹AI); IIT Madras(印度理工学院马德拉斯分校); University of Surrey(萨里大学)
类目: Computation and Language (cs.CL)
备注: Submitted to WMT 2026 for review
Abstract:Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and language pairs on one footing. We consolidate the WMT 2020–2024 shared-task lineage with an extended English–Malayalam resource into \indicqe: 126,754 instances over nine directional pairs, with up to four label types aligned on the same segment, a direct assessment, a human post-edit, word-level OK/BAD tags and an error explanation, and a test set stratified over four difficulty axes. On it, we benchmark six prompted LLMs and three COMET metrics on segment-level QE, and three systems on APE. Two of the axes are defined partly on the direct assessment and select a compressed slice of it, so each axis is compared against a control drawn from the same language pair with the same score distribution. Only one survives that control: segments whose holistic and token-level quality signals conflict are ranked worse than equally-scored segments of the same language, for all nine systems and all seven pairs that carry the axis. Annotator disagreement, which looks second-hardest without the control, has no effect with it. Few-shot prompting costs every model \leq 3.4 B both correlation and output-format compliance. Within-language accuracy does not make scores comparable across pairs: of the three trained metrics, the one with the best within-language correlation loses most when the pairs are pooled. The benchmark and code will be released.
[NLP-26] Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning
【速读】： 该论文旨在解决传统基于策略的蒸馏（On-Policy Distillation, OPD）在学生模型轨迹纠错时存在的局限性：标准的逐标记（token-level）OPD仅能提供片段化的纠正信号，无法为错误轨迹提供完整且连贯的修复路径。为此，论文提出步骤级基于策略的蒸馏（Step-Level On-Policy Distillation, SOPD），其核心创新在于将监督微调（Supervised Fine-Tuning, SFT）的长时程纠错能力与OPD的在线策略优势相结合，实现对学生生成完整轨迹的步骤级（step-level）监督。SOPD的关键在于：教师输出条件于学生实际访问的状态轨迹，从而更精确地对齐学生所处环境；同时相较于OPD的碎片化逐标记指导，SOPD可提供更长时程、更连贯的修正路径。实验表明，SOPD在推理与智能体任务中显著优于传统SFT和OPD，在ALFWorld任务上相较基线OPD平均成功率提升13.4个百分点，验证了其有效性。
链接: https://arxiv.org/abs/2608.16333
作者: Changhui Sun,Lanbo Liu,Hang Lei,Tong Ling,Jiahang Xie,Zhiyong Zheng,Yujia Wang,Hao Liu,Feng Xiao,Lu Liu,Yanlong Du,Zifeng Cheng,Ziwei Jiang,Qing Gu
机构: State Key Laboratory for Novel Software Technology, Nanjing University(南京大学软件新技术国家重点实验室); XingYun Lab, HUJING Digital Media Entertainment Group(虎鲸数字媒体娱乐集团星云实验室); University of Chinese Academy of Sciences(中国科学院大学); School of Data Science, Fudan University(复旦大学数据科学学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy distillation (OPD) aligns a student model with a teacher’s logit distribution on student-generated trajectories. This approach has achieved strong empirical gains and can often surpass conventional off-policy distillation with substantially less data. However, standard token-level OPD can provide only fragmented corrections along an erroneous student trajectory and cannot unfold a complete and correct repair path. Motivated by this limitation, we propose \emphStep-Level On-Policy Distillation (SOPD), which combines the long-horizon correction of supervised fine-tuning (SFT) with the on-policy advantage of OPD to provide step-level supervision over complete student-generated trajectories. We show that, at different limits of step length, SOPD reduces to SFT or approximates OPD. Compared with SFT, the teacher responses in SOPD are conditioned on student trajectories and therefore align more closely with student-visited states; compared with OPD, SOPD provides longer-horizon corrections rather than fragmented token-level guidance. Across both reasoning and agent tasks, SOPD substantially outperforms conventional SFT and OPD. For example, on ALFWorld, SOPD improves the average success rate by 13.4 points over Vanilla OPD. We hope this work offers a new perspective for future research on distillation methods.
[NLP-27] Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning
【速读】： 该论文旨在解决大规模多模态模型（Large Multimodal Models, LMMs）在视频推理任务中因处理海量视觉信息而导致的高计算成本问题，核心挑战在于如何将大模型的推理能力有效迁移至更小、更高效的轻量级模型。现有方法如基于策略的蒸馏（On-Policy Distillation, OPD）虽能通过匹配学生模型生成轨迹中的输出令牌分布来实现知识迁移，但其仅依赖输出层监督，无法直接约束推理过程中累积的隐状态表征，尤其在需要跨多帧整合证据的复杂视频任务中表现受限。为此，本文提出潜空间OPD（Latent-OPD），其关键创新在于引入轨迹级潜空间蒸馏机制，聚焦于每条推理轨迹末端的隐藏状态，该位置的隐表示可有效汇总多帧视觉证据与推理上下文信息，从而实现对深层语义表征的精准对齐。此外，设计了渐进式教师前瞻策略（progressive teacher-lookahead strategy），使学生模型中较早到较晚的层逐步对齐不断加深的教师模型层，增强中间层表征的一致性。实验在六个视频推理基准上验证了该方法的有效性，结果表明，相较于仅依赖输出蒸馏的OPD，Latent-OPD在帧数有限、长视频及复杂证据聚合任务中均取得显著提升，充分证明其在提升帧效率与推理性能方面的优越性。
链接: https://arxiv.org/abs/2608.16316
作者: Ao Shen,Yongheng Zhang,Yinghui Li,Manning Wang,Di Yin,Xing Sun
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large Multimodal Models (LMMs) for video reasoning have long been hindered by the high computational cost of processing vast amounts of visual information. This dilemma motivates the transfer of the reasoning capabilities of large models to smaller, more efficient ones. On-Policy Distillation (OPD) offers a promising solution by matching output-token distributions along student-generated trajectories. However, video reasoning often depends on evidence accumulated across multiple frames. In this context, output-level supervision only captures information expressed through token predictions and does not directly constrain the latent representations formed during reasoning. To address this limitation, we propose Latent-OPD, which augments OPD with trajectory-level latent distillation. Specifically, our method focuses on the position at the end of each trajectory, where hidden states effectively summarize the accumulated visual evidence and reasoning context. Furthermore, we introduce a progressive teacher-lookahead strategy, which aligns middle-to-late student layers with increasingly deeper teacher layers. Experiments on six video reasoning benchmarks show that Latent-OPD consistently outperforms output-only OPD. Notably, the improvements are particularly pronounced in scenarios with limited frames, long videos, or tasks requiring complex evidence aggregation. These results establish Latent-OPD as a highly effective approach to frame-efficient video reasoning.
[NLP-28] FTA-Mem: Fact-Time-Affect Anchored Memory for Low-Density Long-Term Dialogue
【速读】： 该论文旨在解决长期情感支持对话系统中因信息密度低（low-density）而导致的个性化记忆建模难题。具体而言，情感支持对话常表现为话语不完整、证据分散且用户情绪状态随时间动态演化，而现有基于固定单元（如对话轮次级笔记或会话摘要）的记忆方法难以兼顾细节保留与噪声抑制，导致记忆表征失真或冗余。其解决方案的关键在于提出一种结构化记忆框架FTA-Mem，核心创新包括：采用边界保持的窗口分割（Boundary-preserving Window Segmentation, BWS）生成语义连贯的情境片段，并构建融合事实内容（Fact）、时间定位（Time）与情感上下文（Affect）的三元记忆单元（FTA Units），实现对多维度信息的联合编码；同时通过检索并合成结构化上下文以支持答案生成。实验表明，FTA-Mem在不同信息密度特征的基准（ES-MemEval与LoCoMo）上均显著提升长时记忆问答性能，在ES-MemEval上达到0.3871 F1和0.6668 BERTScore。进一步分析证实，情境级（situation-level）的FTA构造在证据保留与构建成本之间实现了更优平衡，相较于粗粒度会话级或过细粒度对话对级方法，提供了适用于长时对话记忆的有效粒度权衡策略。
链接: https://arxiv.org/abs/2608.16303
作者: Chang Liu,Shuyi Zhang,Changsheng Ma,Yongfeng Tao,Minqiang Yang,Bin Hu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-term emotional-support agents require memory mechanisms for personalized understanding across sessions. However, emotional-support dialogue is often low-density: turns are incomplete, evidence is scattered, and user states evolve over time. Existing memory methods usually rely on fixed units, such as turn-level notes or session summaries, which may lose details or introduce redundant noise. We propose FTA-Mem, a structured memory framework for low-density long-term dialogue. FTA-Mem uses Boundary-preserving Window Segmentation (BWS) to form coherent situation fragments, and constructs Fact-Time-Affect Memory Units (FTA Units) that jointly encode factual content, temporal grounding, and affective context. Retrieved units are then synthesized into structured context for answer generation. Experiments on ES-MemEval and LoCoMo show that FTA-Mem improves overall long-term memory question answering across benchmarks with different information-density characteristics. On ES-MemEval, FTA-Mem achieves 0.3871 F1 and 0.6668 BERTScore. Further analysis shows that situation-level FTA construction better balances evidence preservation and construction cost than coarse session-level or overly fine-grained turn-pair construction, providing an effective granularity trade-off for long-term dialogue memory.
[NLP-29] Executable Code Knowledge: Code as a Native Validation-Carrying Knowledge Representation for AI Coding Agents ICDE
【速读】： 该论文旨在解决生成式AI代码代理（AI coding agents）在执行任务时缺乏业务语义、验证证据、上下文关联性及知识时效性保障的核心问题。现有系统依赖检索、摘要、图结构、规则或逆向规格等外部化方式推断所需知识，难以确保知识的准确性与可追溯性。本文提出一种互补性解决方案——可执行代码知识（Executable Code Knowledge, ECK），其核心是引入可执行代码知识单元（ECKU），作为具备稳定身份、语义、可执行行为、契约、证据、关系、溯源信息、验证状态及查询接口的源码绑定对象。通过一个Python原型系统，实现了代码本地编写、知识声明导出、证据执行、精确变更行影响分析、新鲜度检查及面向代理的投影输出。实验在三个真实Python仓库和26个受控补丁任务中验证：直接使用ECK可在全部11个含证据的任务中提供可执行测试覆盖，并在9/11个任务中实现精确选择器；当隐藏声明的证据后，精确恢复率降至1/11（配对McNemar检验p=0.0078），凸显证据可见性的重要性。基于ECK推导的规则可恢复全部11个精确选择器，表明规则是有效的交付载体，而ECK则负责提供源码绑定、验证状态、影响分析与新鲜度保证。精确变更行影响分析与独立编写的标签完全一致（26个补丁，12个单元链接，精度、召回率与F1均为1.000）。以抽象语法树（AST）为边界生成的指纹可正确识别50个有效变更及17个同文件无关控制项，而静态规则快照未能检测出任何50个过时案例。模型驱动的补丁评审与跨层研究仅评估投影保真度，而非独立影响发现能力。结果支持一种混合架构：利用检索提升覆盖率，ECK实现源码与证据治理，投影用于最终交付。
链接: https://arxiv.org/abs/2608.16295
作者: Xueping Gao
机构: Alibaba Cloud(阿里云)
类目: Computation and Language (cs.CL)
备注: 11 pages. Submitted to AgenticDev 2026, co-located with ASE 2026
Abstract:AI coding agents need more than relevant snippets: they need business semantics, validation evidence, relations, and assurance that their context is current. Existing systems usually infer or externalize this knowledge through retrieval, summaries, graphs, rules, or reverse specifications. We investigate a complementary representation in which selected code units directly carry agent-usable knowledge. We introduce Executable Code Knowledge (ECK) and define an Executable Code Knowledge Unit (ECKU) as a source-bound object combining stable identity, semantics, executable behavior, contracts, evidence, relations, provenance, validation state, and a query interface. Our Python prototype supports code-local authoring, manifest export, evidence execution, exact changed-line impact, freshness checking, and agent-facing projections. Across three real Python repositories and 26 controlled patch tasks, direct ECK provides executable test coverage for 11/11 evidence-bearing tasks and exact selectors for 9/11; hiding declared evidence reduces exact recovery to 1/11 (paired exact McNemar p=0.0078). ECK-derived rules recover 11/11 exact selectors, showing that rules are effective delivery artifacts while ECK supplies source binding, validation state, impact, and freshness. Exact changed-line impact matches independently authored labels on all 26 patches (12 unit links; precision, recall, and F1 all 1.000). AST-bounded fingerprints classify 50 positive changes and 17 unrelated same-file controls correctly, whereas static rules snapshots detect none of the 50 stale cases. Model-backed patch-review and cross-layer studies measure projection fidelity rather than independent impact discovery. These results support a hybrid architecture: retrieval for coverage, ECK for source and evidence governance, and projections for delivery.
[NLP-30] Clause Encounters of the Third Kind: Can LLM s Replace Language Teachers?
【速读】： 该论文旨在解决当前生成式 AI（Generative AI）在语言教学场景中应用日益广泛背景下，缺乏对大型语言模型（LLM）在语言习得核心任务——即提供精准纠错与教学解释——实际表现的系统性评估问题。其解决方案的关键在于通过多维度实验设计，系统考察不同模型参数调整对输出质量、教学清晰度及一致性的影响，并结合检索增强生成（Retrieval-Augmented Generation, RAG）技术引入权威教学方法论数据，以提升生成内容的准确性与可解释性。研究采用自动化指标（如GLEU、BERTScore）与人工专家评判相结合的方法，全面评估模型在语言细微差别、文化敏感性及教学适切性等难以量化的关键维度上的表现。结果表明，尽管模型在表面纠错层面表现优异，但在提供具备专业术语和领域知识支撑的教学解释方面仍存在明显不足，揭示了当前对AI辅助语言学习的期待可能超前于其真实教学能力的认知水平。
链接: https://arxiv.org/abs/2608.16286
作者: Kristina Šekrst,Ana Kovačić
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:While various organizations now actively encourage LLM use in classrooms, we still lack rigorous, systematic evaluations of how well these models actually perform the fundamental tasks of language pedagogy. This paper examines whether state-of-the-art LLMs can deliver the kind of corrective feedback and methodological explanations that language learners need. The study tests multiple large language models on their ability to identify, correct, and explain common learner mistakes in English, by systematically varying model parameters to investigate how these technical adjustments affect output quality, pedagogical clarity, and consistency, along with using retrieval-augmented generation to query methodological data. The evaluation employs automated metrics (GLEU, BERTScore) but also human expert judgments to capture dimensions that purely computational measures miss: linguistic nuance, cultural sensitivity, and instructional appropriateness. While models demonstrate impressive surface-level correction abilities, their explanations often lack the terminological and domain knowledge that effective language teaching requires, suggesting that current enthusiasm for AI-assisted language learning may be outpacing our understanding of these systems’ actual pedagogical competence.
[NLP-31] Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
【速读】： 该论文旨在解决预训练语言模型（PLM）在专业化语料上进行主题建模时，因嵌入空间几何结构限制而导致的主题可解释性下降问题。其核心挑战在于：在未见于通用领域预训练的领域特定术语，在嵌入空间中会塌陷至难以区分的区域，而传统的领域自适应微调、词级图增强或参数高效微调等方法均受限于底层编码器的容量上限，无法有效重构嵌入空间。解决方案的关键创新在于提出一种可学习的、基于标记级（token-level）PLM嵌入的图神经网络（GNN）层——DARTopic，该层通过构建保留文档局部上下文信息的标记级语义图，并与主题推断目标联合优化，从而直接从目标领域的证据中重塑嵌入空间的几何结构。该方法不依赖编码器微调，可在通用、生物医学和法律等多个领域基准上持续超越强基线模型，在主题一致性与文档聚类性能上表现优异，同时对预训练模型选择具有鲁棒性且运行效率显著优于基于微调的方法。
链接: https://arxiv.org/abs/2608.16269
作者: Seung-Won Seo,Won Ik Cho,Yongmin Yoo
机构: TelePIX; AI Center; Samsung Electronics; School of Computing; Frontier AI Research Centre; Macquarie University
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora. This limitation primarily stems from the geometry of the embedding space, where domain-specific terms unseen during pre-training collapse into an indistinguishable region, and neither domain-specific re-training, word-level graph enrichment, nor parameter-efficient fine-tuning can restructure this space without inheriting the capacity ceiling of the underlying encoder. Our key insight is that a learnable graph layer operating on token-level PLM embeddings can acquire corpus-specific semantic structure that the frozen encoder lacks, because token-level graphs preserve document-local context that word-level representations discard and joint optimization with the topic objective reshapes embedding geometry directly from target-domain evidence. We instantiate this insight as DARTopic, a domain-agnostic framework that constructs token-level semantic graphs from frozen PLM embeddings and jointly trains a GNN encoder with topic inference. Across three benchmarks spanning general, biomedical, and legal domains, DARTopic consistently outperforms strong baselines in topic coherence and document clus- tering without any encoder fine-tuning, while demonstrating robustness to PLM choice and favorable runtime efficiency over fine-tuning based alternatives.
[NLP-32] STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering
【速读】： 该论文旨在解决现有基于提示的神经符号系统在时间问答（Temporal Question Answering, TQA）任务中，因依赖大语言模型（LLM）同时进行语义解析与精确时间推理而导致的可验证性差、决策易受概率误差影响的问题。其核心挑战在于：当时间推理涉及区间、时间锚点及有序状态等离散逻辑判断时，纯概率性的生成式推理难以保证结果的确定性与可解释性。为此，论文提出STAIR（Semantic-Temporal Automaton for Interpretable Reasoning），其关键创新在于将语义理解与精确时间推理解耦——通过一个无需答案反馈的LLM适配器将复杂问题转化为标准化的时间意图，再由具有有限状态控制和带守卫条件转移的确定性时间自动机，在规范化证据上执行预定义规则策略。该方法遵循“规则优先”设计原则：仅在规则路径无法生成可执行意图时才启用语义适应机制，从而显著减少自由形式推理，提升时间决策的可验证性与可解释性。具体而言，守卫执行机制支持精确的点时间包含与前后关系选择，而语义适应则处理非精确区间和时间锚定查询。在TimeQA-Easy、TimeQA-Hard、TempReason-L2和TempReason-L3等多个基准数据集上的实验表明，STAIR在相同模型设置下持续超越强基线，分别在Qwen2.5-7B和GPT-4o-mini模型上实现平均F1提升16.57%和3.10%。消融实验与诊断分析进一步验证了STAIR在边界敏感与顺序敏感查询上的卓越表现，且其守卫执行与语义适应机制分别保障了点时间推理的精确性与不精确区间的合理处理能力。
链接: https://arxiv.org/abs/2608.16224
作者: Xinlong Dai,Jinchuan Zhang,Lei Gao,Xinzhe Hu,Yuefeng He,Hui Gao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:By leveraging large-scale pretraining, LLMs can interpret diverse temporal expressions and question formulations without task-specific training. However, existing prompt-based neuro-symbolic systems continue to rely on LLMs for both semantic interpretation and exact temporal inference. Consequently, discrete decisions regarding intervals, time anchors, and ordered states remain vulnerable to probabilistic errors and difficult to verify. We present STAIR, a \textbfSemantic-\textbfTemporal \textbfAutomaton for \textbfInterpretable \textbfReasoning. STAIR separates semantic interpretation from precise temporal inference: an answer-free LLM adapter maps complex question formulations to normalized temporal intents, while a deterministic temporal automaton with finite control and guarded transitions executes the corresponding policies over canonicalized evidence. Following a rule-first design, STAIR resolves standard questions without invoking an LLM and applies semantic adaptation only when the rule path fails to produce an executable intent. This approach reduces free-form reasoning, making temporal decisions verifiable and interpretable. Specifically, guarded execution supports precise point-time containment and before/after selection, while semantic adaptation handles non-exact intervals and time-anchored queries. Across the TimeQA-Easy, TimeQA-Hard, TempReason-L2, and TempReason-L3 datasets, STAIR consistently outperforms strong baselines in the TQA task using matched model settings, achieving average F1 improvements of 16.57% and 3.10% when utilizing the Qwen2.5-7B and GPT-4o-mini models, respectively. Furthermore, ablations and diagnostic analyses demonstrate that STAIR excels at handling both boundary-sensitive and order-sensitive queries, while its guarded execution and semantic adaptation ensure precise point-time reasoning and inexact intervals, respectively.
[NLP-33] INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval INTERSPEECH2026
【速读】： 该论文旨在解决现有语音检索系统依赖固定相似性匹配机制、无法适应多样化用户意图的问题。其核心挑战在于如何实现对自然语言指令驱动的动态相关性判定，涵盖语义内容、说话人身份、表达风格、环境声音及其组合等多种检索需求。解决方案的关键在于提出首个面向指令感知的语音检索基准——INSPIRE，通过引入自然语言指令动态定义相关性标准，并系统评估四类主流检索范式：大音频-语言模型、级联流水线、自监督语音模型以及对比音频-语言模型。实验结果表明，当前方法在处理不同类型的检索意图时均存在局限性：基于文本的方法在语义检索上表现较好，但难以捕捉声学层面的副语言特征；而基于语音的方法虽能较好建模声学属性，却在遵循复杂指令方面表现不佳。这一发现凸显了构建统一架构以实现真正指令感知型语音检索的迫切需求。
链接: https://arxiv.org/abs/2608.16203
作者: Chen-An Li,Hung-yi Lee
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Interspeech 2026 long paper
Abstract:Existing speech retrieval systems rely on fixed similarity matching and cannot adapt to diverse user intents. We introduce INSPIRE, the first benchmark for instruction-aware speech retrieval, in which natural-language instructions dynamically specify relevance criteria, including semantic content, speaker identity, speaking style, environmental sounds, and their combinations. We evaluate four retrieval paradigms: large audio-language models, cascaded pipelines, self-supervised speech models, and contrastive audio-language models. Our results reveal that no current method robustly handles all retrieval intents. Text-based approaches perform relatively better at semantic retrieval but struggle with paralinguistic attributes, while speech-based models are moderately better at capturing acoustic properties but falter at following instructions. These findings highlight the need for unified architectures capable of instruction-aware speech retrieval.
[NLP-34] LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
【速读】： 该论文旨在解决大语言模型（LLM）代理在动态原始文档集合中进行问答时，因现有检索增强方法依赖预先固定切分、嵌入或持久化索引而导致的证据提取僵化、过时及粒度不可调适的问题。其核心挑战在于：在查询前无法预知相关证据的具体位置与范围，而传统方法却已固化了证据表示，导致效率低下且难以适应文档变更。论文提出一种无索引框架 LENS（Latent Evidence Exploration and Search），将上下文搜索建模为在由动态原始文档诱导的潜在证据空间中的预算受限证据定位问题。其关键创新在于不预先物质化证据空间，而是通过一个查询条件下的信念机制，结合互补的词汇、局部与探索性提议策略，利用大语言模型作为相关性判别器迭代更新信念，并在可控预算下逐步聚焦于高后验概率区域。该方法能将证据凝聚为紧凑、源可追溯的兴趣区域，并压缩为跨相关查询复用的自组织知识簇，实现高效、灵活且可追溯的证据定位。实验表明，在控制环境下，LENS 在精确匹配率（62.4%）和证据召回率（84.8%）上均优于基准方法，且在零索引的全维基百科子集上实现了更优的答案溯源能力（84.0% vs. 70.7%），充分验证了其在动态环境下的鲁棒性与优越性。
链接: https://arxiv.org/abs/2608.16185
作者: Xingjun Wang,Gongsheng Li,Qi Fan,Yunlin Mao,Luyan Su,Yingda Chen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent. Existing retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, or persistent indexes: effective for lookup, yet costly, stale-prone, and committed to a granularity before the query is known. We formulate in-context search as Budgeted Evidence Localization over a latent evidence space induced by dynamic raw documents and propose LENS (Latent Evidence Exploration and Search), an index-free framework. Instead of pre-materializing the evidence space, LENS maintains a query-conditioned belief over candidate units, iteratively selecting candidates via complementary lexical, local, and exploratory proposal policies, updating the belief via an LLM relevance oracle, and narrowing toward high-posterior regions under a controllable budget. Evidence is consolidated into compact, source-grounded regions of interest and compressed into self-organizing knowledge clusters reused across related queries. On a controlled 500-question evaluation with matched corpus snapshots, LENS reaches 62.4% exact match and 84.8% evidence recall vs. 65.2% exact match but 50.4% evidence recall for a ReAct-style baseline. Across scales, LENS gives the strongest supporting-fact localization and answer grounding. On a fixed 150-question fullwiki subset over the raw Wikipedia dump with zero indexing, LENS and ReAct are nearly tied in official answer quality (43.3% vs. 42.7% EM), with LENS grounding more answers in retrieved evidence (84.0% vs. 70.7%). A no-retrieval Closed-Book reference highlights the contribution of model memory. LENS is query-ready after corpus changes, needs no preprocessing or persistent index, and preserves source-grounded evidence localization throughout. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16185 [cs.CL] (or arXiv:2608.16185v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.16185 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-35] QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在长期个性化过程中因外部记忆系统设计缺陷而导致的用户状态建模不准确问题。具体而言，现有系统存在三大局限：固定回合、固定长度或会话边界难以适应用户交互中动态变化的语义连续性，易造成无关对话混杂或事件因果链断裂；将同一交互中的多类信息（如事实、偏好、洞察）捆绑存储，导致功能各异的记忆单元无法独立检索；以及将当前任务视为单一 top-k 检索查询，难以联合捕捉用户偏好的演化轨迹、时间有效性与上下文适用性。为应对上述挑战，本文提出 \textsc{QUMem}——一种面向查询条件的用户状态推断结构化记忆框架。其核心创新在于：首先基于语义连续性将交互历史划分为可变长度的事件片段；继而对每个片段进行解耦，生成独立可检索的事实记忆、偏好记忆与可迁移洞见记忆，并保留其时间位置与来源证据；在推理阶段，通过三个顺序协同的智能体分别完成任务信息需求识别、跨类型记忆的多查询检索规划，以及融合时间与上下文一致性的用户状态联合推断。该方案显著提升了长时个性化场景下的用户状态建模能力，在 PersonaMem 与 KnowU-Bench 基准上达到当前最优性能，验证了查询条件化用户状态推断的有效性。
链接: https://arxiv.org/abs/2608.16168
作者: Heng Wang,Yifei Li,Lingling Zhang,Pengyu Li,Xinyu Che,Xinyu Zhang,Zesheng Yang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 9pages,3figures
Abstract:Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferences may be distributed across time, change with context, and conflict with earlier evidence. However, existing systems face three limitations: fixed-turn, fixed-token, or session-based boundaries can mix unrelated dialogue or split an event from its causes, decisions, and outcomes; storing multiple pieces of user information from the same interaction as a single memory binds together items that serve different functions and should be independently retrievable; and treating the current task as a single top- k retrieval query can return fragments that are individually relevant but fail to jointly capture preference evolution, temporal validity, and contextual applicability. We introduce \textscQUMem, a structured memory framework for query-conditioned user-state inference. \textscQUMem first segments interaction histories into variable-length episodes according to semantic continuity, then decomposes each episode into independently retrievable factual, preference, and transferable insight memories while preserving temporal positions and source evidence. At inference time, three sequential agents identify task-specific information needs, plan multi-query retrieval over the typed memory stores, and jointly infer a temporally and contextually valid user state for downstream response generation. \textscQUMem achieves state-of-the-art performance on both PersonaMem and KnowU-Bench, demonstrating the effectiveness of query-conditioned user-state inference for long-term personalization.
[NLP-36] HyperSkill: Self-Evolving LLM Agents via Hypergraph-Structured Skill Memory
【速读】： 该论文旨在解决大语言模型（LLM）智能体在处理复杂代理任务时，因经验记忆设计不完善而导致的可复用知识利用效率低下的问题。现有方法在记忆存储、检索与演化方面存在局限：仅孤立地存储轨迹、洞察或工作流，忽视子任务间的组合关系与可复用技能的结构化表达；依赖扁平化的嵌入相似性进行检索，无法捕捉记忆项之间的语义关联；且缺乏对记忆结构的动态优化机制。为此，本文提出一种基于超图（hypergraph）的记忆框架HyperSkill，其核心创新在于将记忆建模为包含子任务步骤与可复用技能两类节点的超图，每条超边连接单个任务轨迹中的相关子任务与技能，从而显式保留任务的组成结构与技能复用关系。通过双路径检索机制，同时在子任务与轨迹层面进行查询，并依据跨轨迹共现频率对技能进行排序，增强检索的相关性；并引入周期性的结构感知维护策略，基于质量加权传播实现低效节点的剪枝与冗余技能的合并，提升记忆的紧凑性与有效性。实验结果表明，在xBench、GAIA和WebWalkerQA等多个基准上，HyperSkill显著优于十种主流记忆基线，在GAIA和WebWalkerQA上分别取得最高达+11.51和+11.18的性能提升，验证了其在联合优化记忆存储、检索与演化方面的有效性。
链接: https://arxiv.org/abs/2608.16114
作者: Ruiyao Xu,Tiankai Yang,Wei-Chieh Huang
机构: University of Southern California(南加州大学); University of Illinois at Chicago(芝加哥伊利诺伊大学); Northwestern University(西北大学)
类目: Computation and Language (cs.CL)
备注: 25 pages
Abstract:As agentic tasks grow in complexity, LLM agents increasingly rely on experiential memory to reuse procedural knowledge across tasks. Effective memory design must jointly address what to store, how memory is structured and retrieved, and how memory evolves. Existing systems tackle each only partially: they store trajectories, insights, or workflows as isolated entries, discarding compositional relationships among subtasks and reusable skills; retrieve by flat embedding similarity that ignores relational signals; and maintain memory without leveraging its relational structure. We propose HyperSkill, a hypergraph-based memory framework that jointly improves all three. HyperSkill represents memory as a hypergraph with two node types, subtask steps and reusable skills, where each hyperedge links the subtasks and skills from a single trajectory. Dual-path retrieval queries both subtask and trajectory levels, ranking skills by co-occurrence across retrieved trajectories. Periodic structure-informed maintenance prunes low-utility nodes and merges redundant skills via quality-weighted propagation. Across xBench, GAIA, and WebWalkerQA with GPT-4o and Qwen3-30B-A3B, HyperSkill outperforms ten memory baselines, yielding gains of up to +11.51 on GAIA and +11.18 on WebWalkerQA.
[NLP-37] CAPO: Constraint-Aware Prompt Optimization for LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为智能体（agent）在实际部署中面临的操作约束问题，包括工具使用合理性、提示词（prompt）简洁性与解决方案路径的有效性，以及对安全性和格式规范的合规性要求。传统方法依赖于领域特定的监督数据进行后训练以满足这些约束，但对多数实践者而言，此类数据的构建成本过高且不可行。为此，论文提出CAPO（Constraint-Aware Prompt Optimization），一种基于池的重写与自适应约束加权相结合的原始-对偶优化方法，能够在显式操作约束下高效优化系统提示。其核心创新在于通过动态调整约束权重并利用候选提示池实现高质量重写，从而在代理基准测试中更可靠地达到可执行的操作点，并提升任务性能。此外，研究进一步提出DCAPO（Dynamically Trained CAPO），采用基于GRPO的反馈与对偶条件化重写器，在保持任务智能体冻结的前提下实现端到端提示优化。实验表明，DCAPO在不同规模的任务智能体上均能生成可行提示，并在任务准确率上匹配或超越基线方法。对偶误差分析揭示了有限提示池与离散重写误差如何影响近似原始-对偶过程的稳定性，为方法设计提供了理论支撑。
链接: https://arxiv.org/abs/2608.16068
作者: Victor Ye Dong,Reid Pryzant,Yi Liu,Jian Jiao
机构: Microsoft(微软)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose distinct operational requirements, including appropriate tool use, concise prompts and solution paths, and compliance with safety and formatting policies. For many practitioners, however, assembling domain-specific supervised data to post-train models to meet these requirements is infeasible. We introduce CAPO (Constraint-Aware Prompt Optimization), a primal-dual method that combines pool-based rewrites with adaptive constraint weighting to optimize system prompts under explicit operational constraints. Across agentic benchmarks, CAPO more reliably reaches empirically feasible operating points while improving task performance. CAPO also generalizes beyond agentic settings, achieving strong results on assistant-style evaluations with output-format and safety/privacy constraints. We further introduce DCAPO (Dynamically Trained CAPO), which trains a feedback- and dual-conditioned rewriter with pool-based GRPO while keeping the task agent frozen. Across task agents of different sizes, DCAPO produces a feasible prompt in every evaluated domain and matches or improves the task accuracy achieved by the evaluated baselines. A surrogate analysis characterizes how finite-pool and discrete-rewrite errors enter the inexact primal-dual procedure.
[NLP-38] DuplexGen: Decoupling Content Timing and Acoustics for Synthetic Dialogue Speech
【速读】： 该论文旨在解决现有对话合成流程中对话时序生成方式僵化的问题，即当前方法通常先生成对话内容，再通过手工标记或预设时间规则插入打断、重叠和回应性话语（backchannel），导致对话时序为预定而非由交互动态驱动。其解决方案的关键在于提出DuplexGen框架，该框架通过显式解耦对话内容、时序与声学特征，利用大语言模型（LLM）生成对话脚本后，引入两个全双工对话模型在实时交互中共同演绎脚本，使对话时序自然涌现并忠实于脚本内容；最后采用高保真语音合成模型对交互过程进行重渲染，保持原始时序不变。该方法显著提升了合成对话的自然度与真实感，实验表明其生成的对话动态更接近真实对话场景。
链接: https://arxiv.org/abs/2608.16053
作者: Pengcheng Wang,Sheng Li,Jiyi Li,Takahiro Shinozaki
机构: Institute of Science Tokyo (东京科学研究所); Hokkaido University (北海道大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Synthetic conversational speech has become an important resource for developing and evaluating conversational speech systems. However, existing dialogue synthesis pipelines typically generate dialogue content first and then insert interruptions, overlap, and backchannels using handcrafted markers or timing rules, making conversational timing prescribed rather than interaction-driven. We present DuplexGen, a dialogue synthesis framework that explicitly decouples content, timing, and acoustics. An LLM first generates the dialogue script, and then two full-duplex conversational models perform the script while listening to each other in real time. This allows conversational timing to emerge naturally while preserving the scripted content. Finally, a high-fidelity text-to-speech model re-renders the interaction without altering its timing. As a demonstration of the proposed framework, we construct a patient–clinician conversational speech corpus with construction-time annotations, including word timestamps, speaker activity, overlap regions, and interaction events. Experimental results show that the proposed framework produces conversational dynamics closer to real dialogue than conventional stitching-based synthesis.
[NLP-39] R3-Bench: LLM s Struggle with Resource-Rational Reasoning under Shared Budgets
【速读】： 该论文旨在解决认知科学中资源理性（resource rationality）的核心问题，即在计算资源有限的条件下，智能体如何最优分配计算资源以最大化预期收益。现有研究多采用独立于任务的预算设定，且缺乏对模型在单任务表现与多任务共享预算下性能之间的系统性对比。为此，本文提出 R^3-Bench 基准测试框架，在无需工具和具备代理能力（agentic）两种设置下，评估六种问题组合（涵盖数学、竞赛编程与抽象推理）在共享预算下的综合表现。其关键创新在于通过匹配单任务响应曲线构建离线经验最优解（empirical oracle），从而客观衡量多任务共享预算下的实际性能上限。实验结果表明，在72个主表单元中，该最优解在所有情况下均不低于或超过竞赛平均表现，且在71个单元中严格更高。在中等程度的无工具压力下，等量分配策略的重放表现也优于多数模型的竞赛表现。轨迹诊断揭示了策略更新能力有限及压力依赖型失败模式。在强代理压力下的三模型诊断中，至少一种固定调度器在九个测试单元中的六个超越竞赛均值，但无单一策略在所有领域占优。这些发现暴露了当前模型在展示出的单任务能力与其在共享预算环境下真实实现能力之间存在持续差距。
链接: https://arxiv.org/abs/2608.16033
作者: Peisong Wang,Zhiwei Ma,Bowen Liu,Feixue Liu,Aochuan Chen,Chenyi Zi,Hongchuan Zeng,Yuhan Li,Jia Li
机构: Tencent(腾讯); The University of Hong Kong(香港大学); The Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州))
类目: Computation and Language (cs.CL)
备注: Code is available at this https URL . The dataset is available at this https URL
Abstract:In cognitive science, resource rationality asks how an agent should allocate limited computation to maximize expected value. Most reasoning and agent benchmarks use independent per-task budgets; existing shared-budget studies do not calibrate suite performance against the same model’s demonstrated single-problem competence. We introduce R^3 -Bench, which evaluates six-problem suites under shared budgets across mathematics, competitive programming, and abstract reasoning in tool-free and agentic settings. Matched single-problem response curves define an offline empirical oracle over observed successes. Across 72 main-table cells for six models, the oracle mean matches or exceeds the contest mean in all cells and is strictly higher in 71. Under moderate tool-free pressure, equal-allocation replay also exceeds contest performance for four of six models. Trajectory diagnostics reveal limited strategy updating and pressure-dependent failure patterns. In a three-model diagnostic under strong agentic pressure, at least one fixed scheduler exceeds the contest mean in six of nine cells, but no policy dominates across domains. These results expose a persistent gap between demonstrated competence and shared-budget realization.
[NLP-40] ReRef-3D: A Benchmark for Spatial Referring Expression-Guided 3D Scene Rearrangement ACL
【速读】： 该论文旨在解决语言引导的3D场景中物体放置（language-guided 3D placement）的评估与建模问题，尤其关注自然语言指令中复杂空间关系的理解与实现。其核心挑战在于：语言指令通常定义一个可接受的放置区域而非单一坐标，因此需在生成候选位置后重新计算场景中物体间的关系并验证其语义与物理有效性。为解决此问题，作者提出了ReRef-3D基准数据集，包含998个基于CLEVR生成的3D场景，涵盖33,826条指令，覆盖16类放置任务及直接、一跳和二跳指代关系。关键解决方案是引入“重计算关系验证”机制——将模型预测的位置插入场景后，动态重算所有空间关系，并同时检验语义合理性（如“nearest to”、“between”）与物理可行性（如不穿透、不重叠）。此外，每条指令均配有经验证的自然化重写版本，以增强泛化能力。实验表明，尽管各模型在关系满足率上表现优于物理有效性，但“最近”和“介于……之间”等关系仍具挑战性，且指令表述方式对性能影响较小。
链接: https://arxiv.org/abs/2608.16011
作者: Mary Lynn Martin,Yifei Zhang,Martha Palmer,Maria Leonor Pacheco
机构: University of Colorado Boulder(科罗拉多大学博尔德分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 4 figures. Submitted to ACL Rolling Review (ARR)
Abstract:We introduce ReRef-3D, a benchmark for language-guided placement in 3D scenes. It contains 33,826 instructions across 998 CLEVR-derived scenes, spanning 16 placement families and direct, one-hop, and two-hop references. Each instruction must be resolved into a valid new placement position. Given that an instruction defines a region of acceptable placements rather than one coordinate, our evaluation inserts a prediction into the scene, recomputes relations, and tests relation satisfaction and physical validity. Each instruction also includes a verified naturalized rewrite. After fine-tuning, LLaVA-3D, 3D-LLM, and PlaceIt3D produce valid placements for 68.3%, 31.6%, and 22.4% of instructions, respectively. Across models, relation satisfaction surpasses physical validity, relations such as nearest and between are the most difficult, and phrasing has minimal effect on performance.
[NLP-41] Prior Audit-Repair Context Shifts LLM Verifier Thresholds Toward Leniency
【速读】： 该论文旨在解决生成式AI在自动化代码检查流程中因上下文信息影响而导致的误报（false alarms）问题，具体关注检查模型（checker）在存在先前审计-修复（audit-repair）事件上下文时，其判断行为是否发生变化。其核心解决方案的关键在于：在检查任务保持字节级一致的前提下，引入已完成的审计-修复事件作为上下文，能够显著降低误报率——在15组模型与表述组合中，误报率平均下降2.8至11.5个百分点，相对对照组减少9%至25%。这一现象与现有“累积信息”（accumulated-message）理论预测相反，因为预期审计报告错误会引发更多误报，但实际观察到的是更少的误报。进一步分析表明，审计结论与修复内容具有互补性，二者对不同模型族的影响机制不同；信号检测分析揭示，这种变化源于决策阈值（criterion）的下移而非辨别能力（d’）的提升——在所有15组组合中阈值均发生移动，且在13组中经校正后仍稳定，而辨别能力未显著改变。手动审核50个误报案例发现其中82%为明显错误，说明此阈值调整在当前操作点上非但无害，反而提升了可靠性。当启用推理模式时，该效应在测试的两个模型上依然保持相对幅度，并且阈值变化特征得以维持。
链接: https://arxiv.org/abs/2608.16003
作者: Parsa Mazaheri,Kasra Mazaheri
机构: University of California, Santa Cruz; Massachusetts Institute of Technology
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 2 figures, 4 tables. Code and analysis artefacts: this https URL
Abstract:Automated checking pipelines increasingly place one language model as the checker and another (or the same one) as the fixer. We ask whether that wiring changes what the checker reports. Measuring false alarms on human-verified-correct ProcessBench traces with the present task held byte-identical, we find that a completed audit - repair episode already in the model’s context lowers false alarms in 15 of 15 model x wording combinations, by 2.8 to 11.5 percentage points against a length-matched non-audit control, a 9 to 25% reduction relative to that control. The direction contradicts what the accumulated-message literature predicts: an episode whose audit reported an error lowers false alarms further still, at all five wordings on the model where that manipulation lands cleanly, though a negativity asymmetry predicts more flagging. Decomposing the episode finds repair content and audit verdict complementary: different components carry the effect on different model families. Signal-detection analysis locates the change in the threshold rather than in discrimination – the criterion moves in 15 of 15 combinations and survives correction in 13 while d’ survives in none, though the d’ test is half as sensitive by construction – and a hand audit of 50 false alarms finds 82% simply wrong, so at this operating point the shift need not be harmful. With reasoning enabled the effect keeps its relative size on both models tested, and the threshold reading holds there too.
[NLP-42] From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）智能体在复杂交互环境中部署时面临的不确定性量化（Uncertainty Quantification, UQ）可靠性问题。现有方法主要依赖局部信号（如词元概率、预测熵或单步置信度），无法捕捉执行轨迹中长期依赖关系导致的错误累积效应，因而难以识别那些起因于早期推理或交互步骤的智能体失败。为应对这一挑战，论文提出了一种轨迹级不确定性量化框架——关系型不确定性传播（Relational Uncertainty Propagation for Agents, RUPA）。其核心创新在于将执行历史建模为有向轨迹图（directed trajectory graph），其中推理状态、工具调用和环境反馈作为节点，通过时序与语义依赖边连接，从而显式建模各步骤间的动态关联；随后在该图上进行不确定性传播，以追踪执行风险在多步交互中的累积与转移机制。最终，将传播后的不确定性信号与轨迹级行为特征及目标对齐信息融合，生成对整个智能体执行轨迹的置信度估计。实验在τ-2、Terminal-Bench-2和GAIA等代表性基准上使用6个开源LLM进行验证，结果表明RUPA显著优于现有方法，在提升不确定性估计准确性、实现更早故障检测以及增强基于不确定性的智能体执行效率方面表现优异。研究证实，显式建模关系依赖是实现长时程LLM智能体可靠不确定性量化的关键所在，为构建可信智能体执行系统提供了坚实的技术基础。
链接: https://arxiv.org/abs/2608.16002
作者: Zhengzhao Ma. Boxi Cao,Yaojie Lu,Hongyu Lin,Xianpei Han,Le Sun
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable uncertainty quantification (UQ) is essential for deploying large language model (LLM) agents in complex interactive environments. Existing UQ methods largely rely on local signals, such as token probabilities, predictive entropy, or per-step confidence, and therefore overlook the long-range dependencies through which errors accumulate across an execution trajectory. As a result, they may fail to identify agent failures whose causes originate several reasoning or interaction steps before the final answer. We propose RUPA (Relational Uncertainty Propagation for Agents), a trajectory-level UQ framework for LLM agents. RUPA represents an execution history as a directed trajectory graph in which reasoning states, tool interactions, and environment feedback are nodes connected by temporal and semantic dependency edges. It then propagates uncertainty over this graph to capture how execution risk accumulates and transfers across interaction steps. The propagated signal is combined with trajectory-level behavioral features and goal-alignment information to produce a confidence estimate for the full agent trajectory. We evaluate RUPA on representative agent benchmarks, including \tau -2, Terminal-Bench-2, and GAIA, using 6 open-source LLMs spanning multiple model families. Experimental results show that RUPA consistently outperforms existing UQ methods by providing more accurate uncertainty estimates, enabling earlier failure detection, and improving uncertainty-guided agent execution across diverse agent tasks. These results demonstrate that explicitly modeling relational dependency is crucial to reliable UQ for long-horizon LLM agents, providing a practical foundation for trustworthy agent execution.
[NLP-43] Whose Gold? Annotator-Pool Disagreement Is Large at the Item Level and Hidden by Small Leaderboards NEURIPS2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）评估中依赖人工标注偏好数据时，标注者群体选择对模型排行榜结果稳定性的影响问题。尽管当前主流做法将标注者身份视为可忽略的实现细节，但本文通过实证分析揭示：不同标注群体（如专家与众包标注者）在相同测试样本上存在显著分歧——在多偏好（MultiPref）和MT-Bench数据集上，标注结果差异分别高达23.6%和30.5%，且对立判定比例达9.2%和8.5%。然而，这些个体层面的显著分歧并未反映在最终模型排行榜上，其看似稳定的“比特级一致”（bit-identical leaderboard）实则源于聚合过程对噪声的掩盖。研究的关键发现在于：这种一致性是极弱的证据，因为微小扰动即可导致排名变化——标注池切换使模型胜率变动达1.9个百分点（标准差），而基于项目级别的自助抽样显示，在28%的重采样中至少有一个模型被置换；当模型数量增至十或二十时，排名被改变的概率分别升至0.86和0.9997。因此，解决方案的核心在于强调：不能因六模型榜单的表面稳定性而推断其可推广性，所有依赖逐项标签的评估机制在规模扩大后均不可靠。研究进一步指出，一个广泛使用的数据集所声称的“组内标注者无差异”假设并不成立，并发现大语言模型（LLM）裁判在三款测试模型上更倾向于跟随众包群体而非专家群体的判断，表明标注群体偏差具有系统性影响。
链接: https://arxiv.org/abs/2608.15980
作者: Anik Jha
机构: 独立研究员(Independent Researcher)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Submitted to the HAIC workshop at NeurIPS 2026
Abstract:Preference benchmarks are built by hiring annotators, and the identity of those annotators is treated as an implementation detail. We measure what that detail buys. On the 2,885 MultiPref items where both pools are internally unanimous, so no tie-breaking convention is consulted at all, expert and crowd annotators assign a different majority label to 23.6% and name the opposite winner on 9.2%; on the 246 comparably unanimous MT-Bench cells, benchmark authors and recruited experts differ on 30.5% and reverse on 8.5%. Yet on both corpora the resulting model leaderboards are bit-identical: Kendall tau = 1.00 with zero of six models displaced. That invariance is far weaker evidence than it looks, and we quantify how weak. Switching pools moves a model’s win rate by 1.9pp (SD), one adjacent pair in our own leaderboard sits 0.8pp apart and had a 38% chance of swapping, and an item-level bootstrap displaces at least one model in 28% of resamples. The observed zero is the common outcome, not a property of aggregation: on the same measured perturbation, a ten-model leaderboard is displaced with probability 0.86 and a twenty-model leaderboard with probability 0.9997. Reporting a six-model leaderboard is safe; the safety does not generalise, and everything that consumes labels per item is not safe at any size. We make the distinction precise, show that a widely used dataset’s stated assumption of no intra-group annotator variability is false, and show that an LLM judge tracks the crowd pool over the expert pool on all three models we test, including one from a different vendor. All code, per-call outputs, and pre-registered decision rules will be released upon acceptance. Comments: Submitted to the HAIC workshop at NeurIPS 2026 Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.15980 [cs.CL] (or arXiv:2608.15980v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.15980 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-44] A Scalable Pipeline for LLM -Teacher Distillation Labeling: Work-Stealing Job Scheduling and Memory-Aware GPU Concurrency
【速读】： 该论文旨在解决大规模文本语料库标注中两个核心问题：一是如何以最低成本获取高精度的教师标签（即单位美元所能获得的标签质量），二是如何在负载不均且易发生故障的分布式GPU计算环境中，高效调度并保持计算资源的持续利用率。其解决方案的关键在于提出一个简单、可复现的流水线架构：首先，采用基于工作窃取环形队列（work-stealing ring pool）的任务调度机制，通过原子条件写入实现任务的精确一次抢夺，并结合过期抢夺清理策略保障崩溃容错性，仅依赖存储层的比较与交换（compare-and-set）原语即可实现，且基于单个SQLite文件实现，无需外部依赖，确保实验可在单机上完全复现；其次，引入内存感知的并发控制规则，根据GPU显存容量动态调整每节点的并行模型实例数，使代码可在不同设备规模下安全运行；最后，构建了一种重新标注基准评估方法，让教师模型对已具备真实标签的公开数据集进行重标注，从而将标签质量转化为一致性度量，同时通过实测吞吐率确定成本。实验表明，在负载倾斜场景下，该方案的吞吐量可达静态分片的3.4倍，且在半数工作节点中途崩溃时仍能保证0任务丢失（静态分片丢失953任务），同时在讽刺和情感分析任务上获得了可量化的质量与成本数据。所有实验均基于公开数据和通用硬件完成，代码、测试用例与运行日志均已开源。
链接: https://arxiv.org/abs/2608.15975
作者: Ravi Satya Durga Prasad Yenugula
机构: Independent Researcher(独立研究员)
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages, 1 figure, 3 tables. Code, tests, and all run artifacts: this https URL
Abstract:Labeling large text corpora with LLM teachers has become a practical route to training data at scale. At millions of items, hand-labeling every batch is not feasible, and two questions dominate: what label quality a teacher buys per dollar, and how to keep a fleet of GPU workers busy under skewed, failure-prone workloads. We present a simple, reproducible pipeline that addresses both. First, a work-stealing ring pool: each worker owns a queue, drains it first, and then steals from ring successors, with exactly-once task claims via atomic conditional writes and crash tolerance via stale-claim sweeping. The claim protocol requires only a compare-and-set primitive from its storage layer; we implement it on a single SQLite file, which makes the reference implementation dependency-free and the experiments reproducible on one machine. Second, a memory-aware concurrency rule that sizes per-node parallelism by how many model copies fit on the GPU, so the same code runs safely across device sizes. Third, a relabeling benchmark methodology in which the teacher relabels a public dataset that already has gold labels, so quality reduces to an agreement measurement and cost follows from measured throughput. Under skewed load the pool sustains up to 3.4 times the throughput of static sharding while matching it at zero skew, loses 0 of 2,000 tasks when half the workers are killed mid-run (static sharding loses 953), and yields measured quality and cost points for an instruction-tuned teacher on irony and sentiment tasks. All experiments run on public data and commodity hardware; code, tests, and run logs are released.
[NLP-45] he Limits of Binding in Dual Encoders
【速读】： 该论文旨在解决双编码器模型（如CLIP）在语义绑定（binding）任务中表现不佳的问题，即在区分“一个红色汽车和一个蓝色狗”与“一个蓝色汽车和一个红色狗”这类涉及属性-实体组合的语义差异时，模型得分接近随机水平。其核心问题是：现有双编码器架构因依赖独立向量的内积计算，无法有效建模跨模态成分间的复杂绑定关系。解决方案的关键在于从数学层面揭示绑定失败的本质根源，指出其并非由维度或平滑性等结构性限制所致，而是源于训练激励机制与编码结构设计的局限。具体而言，研究通过三个关键发现构建理论框架：首先，在理想编码器框架下证明相关公理可满足，说明任何不可能性均源于可验证的附加假设；其次，提出深度（depth）限制——递归角色绑定码的交换边际（swap margin）随嵌套深度呈指数衰减 $ m(D) = 2b^{-D} ，在实际维度下可解析地估算出仅约单数字级别的可分辨深度；再次，提出目标无关性（objective）定理，表明对比学习目标对绑定的奖励完全受限于样本与其交换版本对比的频率，而该频率在大规模网络数据上趋近于零；最后，建立几何平滑性−绑定边界（smoothness−bindingfrontier），揭示两个交换相关描述必须靠近共同释义锚点的程度与绑定边际之间的精确约束关系。实证分析显示，18个部署文本编码器的文本诊断指标仅达到理论上限的25 r=0.99 $）。因此，当前双编码器的绑定失败并非维度或平滑性瓶颈，而是训练激励不足与编码结构缺陷所致，即使修正这些因素，仍存在由深度决定的明确理论上限。
链接: https://arxiv.org/abs/2608.15971
作者: Kin Ian Lo
机构: University College London (伦敦大学学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dual-encoder models such as CLIP score an image-caption pair by a single inner product of two independently computed unit vectors, and fail at binding, often scoring near chance when asked to distinguish “a red car and a blue dog” from “a blue car and a red dog”. We give a mathematical account of when this failure is necessary and when it is contingent. Working within the ideal-encoder framework proposed by Kang et al., we first show the relevant axioms are satisfiable, so every impossibility must enter through an added, checkable hypothesis. We then prove three such obstructions. Depth: for recursive role-binding codes the swap margin obeys an exact law m(D) = 2b^-D in the nesting depth D, with a finite-dimension version holding up to one explicitly flagged concentration estimate; the resolvable depth grows only logarithmically in the dimension and is single-digit at CLIP scale, the nesting depth of ordinary language. Objective: architecture-free throttle theorems showing that the contrastive objective’s entire reward for binding is bounded by the rate at which training contrasts a caption against its own swap, a rate that vanishes at web scale, and that exactly reversed binding costs only that rate times the mean binding margin; both are verified in simulation. Geometry: a tight smoothness-binding frontier: the closer the two swap-related captions must embed to a shared paraphrase anchor, the smaller the binding margin can be, with an exact constant. Measuring its text-only diagnostic across 18 deployed text encoders, every model sits at roughly 25-35% of its ceiling, and the induced per-item ceiling tracks SugarCrepe’s subset difficulty at r = 0.99. Binding failure in deployed dual encoders is thus not a dimension or smoothness limit today, but an incentive and code-structure limit, with a proved depth ceiling that remains once those are fixed.
[NLP-46] LLM s Get Smarter from Targeted Synthetic Multilingual Data
【速读】： 该论文旨在解决语言特定能力偏差（Language-specific Competency, LSC）问题，即大语言模型在不同语言提示下表现出不一致的性能，导致相同语义查询在不同语言中产生差异甚至错误的回答。现有解决方案存在两难：一是将所有查询路由至英语以提升性能，但牺牲了非英语语言的表达能力；二是采用语言平衡的数据训练以均衡跨语言表现，却导致整体性能下降。本文从数据视角出发，提出HOTFIXR（Hardness Optimized Training data For Improving X-Lingual Reasoning）——一种基于模型探测与学习学生模型多语言薄弱环节的合成数据生成框架。其核心在于利用预训练模型识别模型在多语言任务中的困难样本，并针对性生成高质量、多样化的多语言合成训练数据，从而精准强化模型在低性能语言和跨语言推理中的表现。实验表明，HOTFIXR在分布内任务平均提升6.2%性能，显著缓解微调引发的分布外任务灾难性遗忘（降低3.7%），并在分布外语言上实现7.1%的性能提升，有效推动大语言模型向真正的多语言专业能力迈进。
链接: https://arxiv.org/abs/2608.15964
作者: Ishika Agarwal,Arkajyoti Charaborty,Tanner Sorensen,Neha Gupta,Andreas Stolcke
机构: University of Illinois Urbana-Champaign (UIUC); Uniphore
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt. In other words, a language model outputs different (and potentially incorrect) responses to the same semantic query when prompted in different languages. Prior work attributes this to an internal misalignment of semantic representation across languages. Currently, there are two main approaches to address LSC in the literature: (1) routing all queries through English, improving performance, but limiting language expressivity to English; or (2) training on language-balanced data, equalizing model performance across languages, but reducing overall performance. In this work, we take a data centric perspective and introduce HOTFIXR: Hardness Optimized Training data For Improving X-Lingual Reasoning. It is a data generation framework that uses models to probe and learn a student model’s multilingual weaknesses, and generates data to mitigate them. HOTFIXR can generate multilingual synthetic training data that can improve multilingual performance. We evaluate on three in-distribution tasks, three out-of-distribution tasks, and four out-of-distribution languages. On average, HOTFIXR (1) improves in-distribution performance by 6.2%, (2) reduces catastrophic forgetting (induced by fine-tuning) on OOD tasks by 3.7%, and (3) on OOD languages by 7.1%. Overall, as many real-world applications requires multilingual LLMs, our work contributes to the efforts of making LLMs multilingually proficient. We will release code upon acceptance.
[NLP-47] SEER: Long-Context Reasoning via Selective Visual-Text Compression
【速读】： 该论文旨在解决大语言模型在处理长文本上下文时因注意力机制存在二次计算复杂度而导致的高计算开销问题。现有基于视觉-文本压缩的方法虽能通过将文本转化为图像并利用视觉-语言模型降低令牌使用量，但其采用统一压缩策略，忽视查询相关性，可能在需要精细内容提取的场景中损失精度。本文提出SEER框架，其核心解决方案是通过视觉扫描学习动态选择与查询相关的图像，并仅在必要区域进行文本内容检索，从而在保持视觉压缩效率的同时实现文本推理的精准性。该方法通过监督微调工具交互轨迹，使模型能够自适应地决策图像选择与文本检索操作。实验结果表明，SEER在长上下文基准测试中显著提升了信息提取精度，在LongBench上达到51.11%的平均准确率，优于视觉-文本基线Glyph-9B（+2.33点）和Qwen3-8B（+3.49点），同时维持了相对于全文本输入的平均提示令牌节省。
链接: https://arxiv.org/abs/2608.15962
作者: Jiawei Xu,Zhilin Zhai,Jinrui Fang,Ruohan Xu,Mingfei Lu,Yi Zhang,Guanchu Wang,Tianlong Chen,Ying Ding
机构: The University of North Carolina at Chapel Hill (北卡罗来纳大学教堂山分校); University of Cambridge (剑桥大学); University of Technology Sydney (悉尼科技大学); The University of North Carolina at Charlotte (北卡罗来纳大学夏洛特分校); The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: COLM 2026, Third Conference on Language Modeling
Abstract:Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a promising alternative by rendering text into images and processing them with vision-language models, often reducing token usage. However, existing approaches apply uniform compression regardless of query relevance, potentially sacrificing precision where detailed extraction is required. We present SEER, a framework that learns to select query-relevant images through visual scanning and retrieve textual content only where needed, combining the efficiency of visual compression with the precision of text-based reasoning. Through supervised fine-tuning on tool-interaction trajectories, SEER learns adaptive tool invocation for selection and retrieval. Experiments on long-context benchmarks show that SEER improves extraction precision through selective text retrieval while retaining average prompt-token savings relative to full-text baselines. On LongBench, SEER achieves 51.11% average accuracy, outperforming the visual-text baseline Glyph-9B by 2.33 points and Qwen3-8B by 3.49 points. Code can be accessed at this https URL
[NLP-48] he Null Token Knows: Reducing Message-Free Hallucination in ASR and NMT AAAI-27 AAAI
【速读】： 该论文旨在解决自动语音识别（ASR）与神经机器翻译（NMT）系统在输入信息缺失或无法恢复语义时仍生成流畅但虚假文本（即幻觉）的问题。其核心挑战在于：现有编码器-解码器模型在缺乏有效输入信号的情况下，依然能够通过内部机制生成看似合理的内容，从而导致不可靠输出。解决方案的关键在于利用模型中预设的空标记（null token）作为判断是否应放弃生成的诊断信号——研究发现，空标记的得分本身往往已携带可区分真实与虚构输出的有效线索。然而，标准解码策略并未可靠地依据这一信号进行决策。通过提升空标记得分或引入基于解码状态的调控机制（如监督式行编辑），可显著抑制幻觉生成，但过度干预亦可能误删有效内容或缩短合法翻译。因此，该研究将空标记视为观测幻觉现象的“诊断透镜”，并提出应以抑制幻觉与减少有效内容删除之间的权衡为评价指标，而非仅关注幻觉降低效果。
链接: https://arxiv.org/abs/2608.15940
作者: Kirill Borodin,Vasiliy Kudryavtsev,Ivan Viakhirev
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
备注: Submitted to the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-27)
Abstract:Modern encoder-decoder systems can produce fluent text even when their input contains no recoverable message. We study this failure in ASR and NMT through the models’ reserved null tokens, asking whether the score for ending generation already carries a usable abstention signal. Across speech recognizers and translation models, we audit native null-token scores and scalar logit shifts. In Whisper, we additionally probe decoder states and compare supervised row edits with conventional external gates. The evaluated models often expose a useful abstention signal, but stock decoding does not reliably act on it. Raising the null-token score can sharply suppress fabrication, but aggressive intervention also deletes valid speech or shortens legitimate translations. These findings turn the null token into a diagnostic lens on hallucination and motivate evaluating abstention methods by both suppression and deletion costs, rather than by hallucination reduction alone.
[NLP-49] Aborted but Not Forgotten: KV-Cache Retention Breaks Rollback Consistency in Language Agents
【速读】： 该论文旨在解决生成式AI（Generative AI）系统中因状态保留导致的逻辑回滚失效问题，即在应用层执行完整回滚后，模型仍可能持续关注已被认为丢弃的旧状态内容。其核心问题是：当服务会话在逻辑回滚后仍保留键值（KV）缓存状态时，模型的注意力机制会继续访问这些未被清除的缓存信息，从而破坏回滚的一致性。解决方案的关键在于提出“回滚一致性”（rollback consistency）的形式化保障——要求完整的逻辑回滚不仅恢复应用传输记录，还必须恢复模型实际关注的状态。研究通过引入“同词不同缓存”审计方法，固定决策步的输入令牌，仅改变前缀缓存是否为过期或从提交状态重建，发现仅保留KV缓存即可在7个开源大模型（3.8B-36B）中导致63个测试单元中有25个出现受保护行为的误判，且攻击令牌在所有情况下均未出现在服务请求中；而重建缓存可彻底关闭该漏洞。该漏洞在端到端会话应用、Hugging Face Transformers默认缓存复用路径及LangGraph时间旅行场景中均重现，表明其为结构性缺陷。研究进一步验证了该问题与位置和长度无关，可泛化至多种受保护效应、策略结构及缓存隔离的混合专家模型，并证明仅需事务局部缓存恢复即可修复，无需全局缓存清空。所有结果均可由公开发布成果确定性复现。
链接: https://arxiv.org/abs/2608.15939
作者: Guijia Zhang,Harry Yang
机构: The Hong Kong University of Science and Technology(香港科技大学)
类目: Computation and Language (cs.CL)
备注: 21 pages, 5 figures, 7 tables
Abstract:Stateful language agents assume a rejected branch can be taken back by clearing it from the application transcript. We show this breaks when the serving session retains key/value (KV) state across the logical abort: the model can continue attending to content the application believes it discarded. We formalize the missing guarantee as rollback consistency: a complete abort must restore the state the model attends, not just the transcript. The key failure is cross-layer: a correct logical rollback need not compose with retained inference state, and the gap can remain invisible to the application. To isolate cache effects from text effects, we introduce a same-token/different-cache audit that holds decision-step tokens identical while varying only whether the cached prefix is stale or rebuilt from committed state. Across seven open-weight families (3.8B-36B), retained KV alone flips a typed protected effect in 25 of 63 audited cells, while attacker tokens are absent from the served request in all 63; rebuilding the cache closes every cell. The channel reproduces in an end-to-end session application, on the default Hugging Face Transformers cache-reuse path, and under LangGraph time-travel, where verified logical rollback can still leave attended KV stale. Susceptibility varies across models, but the underlying attended-state integrity violation is structural. We rule out position and length confounds, generalize across protected effects, policy structures, and a cache-isolated Mixture-of-Experts model, and show that transaction-local cache restoration closes the channel without requiring a global cache flush. All headline results are deterministic and reproducible from released artifacts.
[NLP-50] oken Distribution versus Data Volume: Domain Balancing in Multi-Domain Meeting Summarisation
【速读】： 该论文旨在解决在联合微调大语言模型（LLM）时，面对规模差异显著的多领域会议摘要数据集，如何区分“领域平衡训练混合比例带来的性能提升”是源于跨领域的令牌分布均衡，还是仅仅由于总训练数据量增加这一混淆因素。其核心解决方案的关键在于：通过在五个英语会议语料库上构建平衡型（balanced）与自然比例型（native-proportional）的令牌混合方案，并在相同令牌预算（2–3200万）下进行对比实验，从而精确分离出数据量与分布的影响。研究发现，通过令牌层面的平衡可有效将高质量输出重新分配至数据稀缺的少数领域，且对数据丰富领域的性能影响极小；当少数领域的重要性不可忽略时（如其占比固定为1–2%），实现与其相匹配的性能需远超自然比例分配所需的数据总量，因此平衡策略更具优势。此外，研究还揭示剔除低价值对话行可减少约15%的令牌但不显著影响性能，且基于令牌的平衡与基于样本的平衡并非等价。通过741条由双标注者标注的事实级评估验证了方法的有效性。最终，该工作为实践者提供了在何种条件下应采用平衡策略、以及以何种单位（令牌或样本）进行平衡的决策依据。
链接: https://arxiv.org/abs/2608.15935
作者: Ashima Sood,Bryan Gardiner,Joan Condell
机构: Ulster University (阿尔斯特大学); Londonderry, Northern Ireland, United Kingdom
类目: Computation and Language (cs.CL)
备注: Accepted at 19th International Natural Language Generation Conference (INLG 2026), Utrecht, Netherlands
Abstract:Jointly fine-tuning an LLM on meeting-summarisation corpora of widely varying size raises a question that prior work leaves confounded: when a domain-balanced training mixture helps, is the gain due to the distribution of tokens across domains, or merely to the volume of data seen? We disentangle these factors by constructing balanced and natural (native-proportional) token mixtures at matched token budgets (2-32M) over five English meeting corpora, fine-tuning Mistral-7B with QLoRA, and evaluating per domain. Balancing redistributes quality, improving the data-scarce minority domains at a low cost to the data-rich ones. The trade favours balancing whenever the minority domains matter: their share under proportional allocation is fixed at 1-2% regardless of budget, so matching balanced quality on those domains requires far more total data. We further find that pruning low-value transcript lines removes ~15% of tokens from the conversational corpora at no measurable cost, and that balancing by tokens is not the same as balancing by examples. A two-annotator study of 741 judge-labelled facts validates our fact-level evaluation. Together these results give practitioners a basis for deciding when to balance an imbalanced multi-domain mixture, and on what unit.
[NLP-51] PLSQLBench: Benchmarking LLM Systems for Executable Procedural Database Programming
【速读】： 该论文旨在解决大语言模型（LLM）在生成可执行的PL/SQL程序方面缺乏有效评估工具的问题，尤其关注其在过程式数据库编程中的表现。现有评测主要聚焦于通用代码生成或声明式文本到SQL的转换，而对过程式数据库编程这一重要但未充分探索的领域关注不足。为填补这一空白，论文提出了PLSQLBench，这是首个基于执行结果验证的、专门用于评估LLM编写可执行PL/SQL程序能力的基准测试集。其关键创新在于构建了一个涵盖多层次数据库依赖与过程复杂度的多样化数据集，包含2,865个实例，包括单轮任务和多轮对话，覆盖企业级Spider 2数据库的复杂模式依赖任务、基于Spider的简化模式依赖任务以及源自MBPP的过程式问题。实验表明，当前八种主流LLM在模式对齐、PL/SQL方言保真度、过程控制流、异常处理及跨轮次一致性等方面仍存在显著挑战；尽管引入工具增强的LLM代理可在部分模式依赖任务上提升性能，但整体差距依然明显。该研究揭示了传统代码生成与文本到SQL基准无法全面衡量的过程式数据库编程能力，强调了开发更适配此类任务评估体系的重要性。
链接: https://arxiv.org/abs/2608.15931
作者: Marianne Menglin Liu,Leonid Boytsov,Daniel W. Peterson,Pramuditha Perera,Rongguang Wang,Sai Ashish Somayajula,Syed Hamza Rafique,Rohit Saini,Shubham Pathak,Sujeeth Bharadwaj,Tao Sheng,Graham Horwood,Fahad Shah,Ankan Bansal,Sujith Ravi,Dan Roth
机构: Oracle AI(奥拉克人工智能); Turing Enterprise Inc(图灵企业公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:We present PLSQLBench, to our knowledge the first benchmark for evaluating whether LLMs can write executable PL/SQL programs, with correctness measured through execution-based tests. Existing LLM evaluations largely target general-purpose code generation or declarative text-to-SQL, leaving procedural database programming underexplored. PLSQLBench contains 2,865 instances: 2,594 single-turn tasks and 271 multi-turn conversations spanning 978 turns. The benchmark combines complex schema-grounded tasks over enterprise-style Spider 2 databases, simpler schema-grounded tasks derived from Spider, and MBPP-derived procedural problems, covering varying levels of database grounding and procedural complexity. Experiments with eight LLMs reveal recurring difficulties in schema grounding, PL/SQL dialect fidelity, procedural control flow, exception handling, and cross-turn consistency. Tool-augmented LLM agents improve performance on several schema-grounded evaluations, although substantial gaps remain. These results highlight procedural database programming capabilities not directly assessed by conventional code generation or text-to-SQL benchmarks. Our code is available at this https URL.
[NLP-52] When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在孟加拉语新闻标题生成任务中因输入上下文选择与呈现方式不当而导致性能不稳定的问题。其核心挑战在于如何从长篇新闻文章中有效筛选并组织关键上下文信息，以提升标题生成的质量。解决方案的关键在于：通过精心设计的上下文选择策略与提示工程（prompting strategies），而非简单增加输入长度，来提升生成效果。实验表明，使用精选的导语段落（lead paragraphs）作为上下文，往往能维持甚至优于全篇文章输入的表现；同时，跨语言提示（Cross-Lingual Prompting, XLP）相较于本地化提示（Bengali Native Prompting, BNaP）在多数情况下表现更优，尤其当结合包含辅助上下文线索的提示模板时；此外，少样本学习（few-shot prompting）对Gemini模型有显著增益，单个示例即可带来主要改进，而Llama模型则受益有限。研究结果强调，上下文相关性与提示设计质量远比输入长度更重要，为多语言及低资源场景下的生成式AI应用提供了可实践的优化路径。
链接: https://arxiv.org/abs/2608.15879
作者: Muhammad Ashad Kabir,Kawsar Ahmed,Md. Osama
机构: Charles Sturt University(查尔斯·斯特尔大学); Chittagong University of Engineering and Technology(吉大港工程与技术大学); Bangladesh Army University of Science and Technology(孟加拉国陆军科学技术大学)
类目: Computation and Language (cs.CL)
备注: 11 pages
Abstract:Large language models (LLMs) have shown strong performance in text generation tasks, yet their effectiveness on headline generation remains sensitive to how input context is selected and presented. In this work, we investigate Bengali news headline generation as a document-level generation task that requires effective selection and presentation of salient contextual information from long-form articles. Using Gemini-2.0-Flash, Llama-3.3-70B, and GPT-4o, we systematically study the effects of context selection, prompting strategies, and in-context learning (i.e., few-shot) on the quality of headline generation. Our experiments show that providing the full article does not necessarily improve performance; instead, using selected lead paragraphs of the article can maintain, and in some cases improve, headline generation quality. We further compare Bengali Native Prompting (BNaP) and Cross-Lingual Prompting (XLP), and examine how each interacts with context-enriched prompt templates incorporating auxiliary contextual cues. Results demonstrate that prompting strategies substantially influence generation quality: XLP often yields stronger performance, particularly when combined with contextual enrichment, but its benefits are model-dependent. Additionally, few-shot prompting substantially improves Gemini, with most of the gain obtained from a single demonstration, whereas Llama shows limited benefit from additional examples. Overall, our findings highlight that effective Bengali news headline generation depends more on context relevance and prompt design than on increasing input length, offering practical insights for multilingual and low-resource LLM applications.
[NLP-53] Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles
【速读】： 该论文旨在解决如何利用大规模语言模型（LLM）中隐含的社会认知模式，从个体层面的人口统计学特征重构整体选民行为的问题。其核心挑战在于如何将模型中编码的关于社会身份、态度与政治行为的统计规律转化为可解释、可验证的群体投票模式推断。解决方案的关键在于构建一种系统性的方法论框架：通过将人口统计学描述作为输入条件，引导大语言模型生成个体层面的投票概率分布（包括投票意愿与政党偏好），再基于软投票机制对个体输出进行聚合，从而还原整体选举结果。该方法不以预测未来选举为核心目标，而是强调将大语言模型视为社会现实的压缩表征，为计算社会科学提供一种新的探索性分析工具，同时明确界定了其认识论边界与伦理局限。
链接: https://arxiv.org/abs/2608.15871
作者: Roman Neruda,Martin Bakoš,Josef Šlerka,Vít Tuček,Petra Vidnerová,Gabriela Kadlecová
机构: Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic; Faculty of Arts, Charles University, Prague, Czech Republic; distil labs, Berlin, Germany
类目: Computers and Society (cs.CY); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and political behaviour. This paper introduces and evaluates a methodological framework that leverages these latent representations to reconstruct aggregate voting behaviour from individual-level sociodemographic profiles. We operationalize LLMs as implicit sociological models by conditioning them on demographic descriptions, eliciting probabilistic turnout and party preferences, and aggregating individual outputs via a soft voting procedure. Using the 2021 Czech parliamentary election as a validation case, we demonstrate that contemporary LLMs reproduce official election outcomes with low mean absolute error, recover known political bloc structures, and align with independently established sociodemographic gradients. The contribution of this work is methodological rather than predictive: we show how LLMs can be systematically interrogated as compressed representations of social reality, offering a novel exploratory instrument for computational social science while clearly delineating its epistemic and ethical limits.
[NLP-54] Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
【速读】： 该论文旨在解决多模态大模型在主动视频推理（proactive video reasoning）中因采用视觉思维链（Visual Chain-of-Thought, Visual CoT）所引入的显著推理开销问题。Visual CoT通过生成中间推理图像实现对空间、时间及具身环境的可视化推演，虽具备直观的视觉预见能力，但其在推理阶段需合成并重新编码未来帧，导致延迟高，难以满足实时性要求。为克服此瓶颈，论文提出一种后训练框架——内化视觉思维（Internalized Visual Thinking, IVT），其核心在于：在训练阶段联合优化文本预测与未来帧嵌入（next-embedding）预测，利用无标签视频数据使模型学习捕捉运动、物体状态变化、交互行为及潜在意图等动态模式，并将这些先验知识“内化”至模型参数中。在推理阶段，IVT仅基于当前观测直接输出答案，无需显式生成或重编码未来帧，从而保持高效推理路径。大量控制实验表明，IVT在六种不同设置下均优于直接答案微调方法，且性能可媲美甚至超越显式的Visual CoT，同时将端到端延迟降低超过5倍。研究结果表明，显式的像素空间生成并非实现有效主动视频推理的必要条件；通过训练阶段的预测性世界建模，可构建兼具更高准确率与显著效率优势的多模态推理模型。
链接: https://arxiv.org/abs/2608.15869
作者: Xiaoyu Zhu,Xinke Deng,Suresh Taddewadikar,Arnab Kumar Mondal,Zhongyu Jiang,Ian Fasel,Joerg Liebelt
机构: Apple(苹果)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
备注:
Abstract:Multimodal large language models increasingly use visual chain-of-thought (Visual CoT) to reason about spatial, temporal, and embodied environments. By generating intermediate reasoning images, Visual CoT provides an intuitive mechanism for visual foresight but introduces substantial inference overhead, which is particularly problematic for proactive video reasoning. We ask whether models can learn to think visually during training while reasoning directly at inference. We introduce Internalized Visual Thinking (IVT), a post-training framework that jointly optimizes textual prediction and next-embedding prediction over unlabeled videos. Given a partially observed video, IVT predicts latent representations of future frames together with the target textual answer, encouraging the model to capture motion, object transitions, interactions, and latent intent. At inference, IVT generates the answer directly without synthesizing or re-encoding future frames. We conduct controlled studies across target representations, decoder designs, prediction horizons, data mixtures, training curricula, and predictive objectives. IVT improves over direct-answer fine-tuning on all six evaluation settings while retaining the same inference pathway. Compared with explicit Visual CoT, IVT achieves comparable or better performance and reduces average end-to-end latency by more than 5x. Together, our findings suggest that explicit pixel-space generation at inference time, as used in visual chain-of-thought, may not be necessary for effective proactive video reasoning. Predictive world modeling can be internalized during training to produce multimodal reasoners that are both more accurate and substantially more efficient.
[NLP-55] Scaling Manual-Grounded Appliance Manipulation with Data Synthesis and Unified Planning ACM-MM26
【速读】： 该论文旨在解决家用电器操作中长时序、状态依赖且对扰动具有鲁棒性的规划问题，而现有大模型因缺乏足够多样、任务导向的数据集支持而难以实现有效规划。其核心解决方案是提出MAGE（Massive Appliance Generation Engine），一个可扩展的数据合成管道，通过引入新颖的分层电器图（Hierarchical Appliance Graph, HAG），自动从电器说明书生成部件定位、长时序规划以及闭环恢复数据。基于MAGE构建的UseAppliance是首个面向说明书引导的电器操作规划的大规模数据集，涵盖22类家电，包含89,000+部件标注、53,000+操作任务和33,000+闭环调整步骤。在此基础上，研究者开发了AppliancePlan——一个端到端的说明书引导式电器操作规划模型。在RealAppliance-Bench基准上，仅7B参数的AppliancePlan在开环规划性能上超越最佳基线超过10倍，并在所有任务中持续优于当前最优模型；真实机器人实验在六类家用电器上的结果进一步验证了良好的仿真到现实（sim-to-real）迁移能力，标志着向通用家用机器人迈出了关键一步。
链接: https://arxiv.org/abs/2608.15863
作者: Yuxing Long,Lei Kang,Ziyan Yu,Yuzheng Gao,Bin Cheng,Jiyao Zhang,Xiaoqi Li,Haolin Yang,Dongjiang Li,Hui Shen,Hao Dong
机构: Peking University (北京大学); Beijing University of Aeronautics and Astronautics (北京航空航天大学); Jingdong Technology Information Technology Co., Ltd (京东科技信息技术有限公司)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted by ACM MM 26
Abstract:Operating household appliances requires long-horizon planning that is state-dependent and robust to disturbances, yet existing large models fall short, as no sufficiently diverse, task-oriented dataset exists to support such planning. To bridge this gap, we propose MAGE, a scalable data synthesis pipeline that introduces a novel Hierarchical Appliance Graph (HAG) to automatically generate part grounding, long-horizon planning, and closed-loop recovery data from appliance manuals. With MAGE, we build UseAppliance, the first large-scale dataset for manual-grounded appliance manipulation planning, spanning 22 appliance categories with 89K+ part annotations, 53K+ manipulation tasks, and 33K+ closed-loop adjustment steps. Built on UseAppliance, we develop AppliancePlan, an end-to-end model for manual-grounded appliance manipulation planning. On RealAppliance-Bench, AppliancePlan with only 7B parameters achieves over 10x the best baseline on open-loop planning and consistently outperforms state-of-the-art models across all tasks. Real-robot experiments on six household appliances further confirm effective sim-to-real transfer, marking an important step toward general-purpose household robotics.
[NLP-56] MicroVerse: An Instrument for Measuring Self-Authored Identity Drift in Long-Horizon Multi-Agent Language-Model Simulations
【速读】： 该论文旨在解决长时程、多智能体生成式语言模型（Generative AI）模拟中，人格化智能体在持续压力环境下是否能保持身份一致性（identity fidelity）的测量难题。现有研究缺乏有效工具来评估智能体在长期交互与资源稀缺压力下的身份漂移（identity drift）现象。为此，作者提出MicroVerse——一种基于行为科学的测量仪器，通过构建一个资源稀缺的50×50虚拟环境，设定不可再生的水资源作为生存约束，并引入每回合存在成本递增机制以强化压力情境。每个智能体携带不可更改的“灵魂文件”（soul file，包含核心价值观、道德边界、人格特质与目标），并通过三层记忆架构实现对当前身份的周期性修正：基于重要性触发的反思机制使智能体将当前状态与原始灵魂文件进行比对。为避免幸存者偏差，系统采用统一纵向快照（每N回合一次）与强制终止时所有存活及死亡智能体的快照相结合的方式分离行为与测量。身份漂移评分采用语义感知、价值锚定、多寄存器差异分析方法，而非简单的余弦相似度。实验设计包括对照种子运行（n = 25）与反思阈值扫描（40, 80, 150），以检验漂移动态是否为参数门限的伪影或具备阈值鲁棒性。主要发现为：（1）反自我欺骗（anti-self-deception）成为身份重构中最显著的语义类别，占新增道德边界的24%（27/111）；（2）系统具有阈值鲁棒性，较低门限虽加速并增加反思频率，但不改变漂移方向。所有结果均为初步存在性证明，仅展示效应形态，未达统计显著性结论。
链接: https://arxiv.org/abs/2608.15844
作者: Sky Ng,Brihi Joshi,Ishan Gupta,Shirley Huang,Zonglin Di,Yun Shen,Qianfeng Wen,Yifan Simon Liu,Ruoqi Gao,Yilan(Eliza)Fan,Zhiwei Zhang,Muhammad Ahmed Mohsin,Yucheng Lu,Xiaoyi Liu,Heming Liu,Qianyu Zhu,Hanwen Xing,Zhengyang Shan,My Chiffon Nguyen,Guanghui Min,Jianheng(Jaden)Hou,Yunze(Lorenzo)Xiao,Keyang Xuan,Hannah Collison,Jintao Huang,Jiatong Li,Sankalp Jajee,Yunhan Zhao,Bing Hu,Xupeng Chen,Binghang Lu,Weihang Xiao,Aravind Mohan,Bolun Sun,Yunshu Wu,Yuanda Xu,Runyu Zhang,Zheyuan Deng,Xinchen(Cara)Tan,Dianzhuo Wang,Yijun Wang,Yixuan He,Koutian Wu,Cheng Cheng,Xiaomin Li,Yuexing Hao
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-horizon, multi-agent language model (LM) simulations are widely proposed for studying social behavior, yet instruments to measure whether persona-conditioned agents maintain identity fidelity under sustained pressure are lacking. We present MicroVerse, a behavioral-science instrument that measures identity drift in generative agents. Agents carry an immutable “soul file” (core values, moral boundaries, personality, goals) and inhabit a resource-scarce 50 x 50 environment where water is a non-respawning survival constraint. Scarcity is operationalized via a per-tick existence-cost gradient. The eight-verb action space maps directly to moral boundaries (trade, talk, attack, scavenge). Using a three-layer memory architecture, agents periodically revise a mutable current identity against their immutable original soul via importance-triggered reflection. To mitigate survivor bias, MicroVerse decouples measurement from behavior using uniform longitudinal engine snapshots every N ticks alongside a forced-end snapshot of all living and dead agents. Identity drift is scored offline using a paraphrase-aware, value-anchored, multi-register diff rather than raw cosine similarity. We evaluate the instrument via a controlled seed run (n = 25) and a reflection-threshold sweep (thresholds 40, 80, 150) to determine if drift dynamics are gate artifacts or threshold-robust properties. We report two primary findings: (1) Anti-self-deception emerges unprompted as the single largest semantic category of identity modification (27 of 111 added boundaries, 24%). (2) The system is threshold-robust; lower gates accelerate and increase revision frequency but preserve drift direction. All empirical results are strictly preliminary existence proofs and effect shapes (one model, one seed per arm, n = 25) rather than statistical significance claims.
[NLP-57] Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs
【速读】： 该论文旨在解决大语言模型（LLM）代理在面对陌生代码库或复杂知识环境时，因上下文过载和信息冗余导致的推理效率低下问题。现有方法依赖于全量上下文输入，难以高效定位关键信息，尤其在工业级知识密集型任务中表现受限。其解决方案的关键在于构建一种图推理代理（Graph Reasoning Agent, GRA），通过将异构知识源（如文本概念与关系型数据表）统一建模为混合知识图谱，并利用七种通用工具（如列出邻居、读取节点内容、搜索描述等）实现对图结构的有选择性探索。该方法通过动态、按需地访问图中节点，显著减少输入令牌数量（不足全上下文代理的三分之一），同时在统一工厂知识模型（UFK-M）这一工业基准上实现了88.4%的准确率，优于全上下文代理的83.3%，提升5.1个百分点。消融实验表明，性能增益主要源于代理的选择性调用机制而非图拓扑本身，且依赖于模型具备可靠驱动工具的能力。因此，该研究的核心贡献在于：通过结构化、可导航的知识表示与智能工具调度策略，实现了“以少胜多”的高效推理，验证了在有限感知下通过精准路径探索优于全局上下文遍历的可行性。
链接: https://arxiv.org/abs/2608.15834
作者: Marius Dragic,Ruben Ifrah,Alexandre Rio
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB)
备注:
Abstract:Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep). A knowledge graph admits the same interface: listing neighbours, reading node content and searching descriptions are the same operations on a different substrate. Building on this correspondence, we present GRA, a Graph Reasoning Agent that explores hybrid knowledge graphs, whose nodes are either textual concepts or relational tables, with seven generic tools, discovering everything domain-specific at run time. On UFK-M (Unified Factory Knowledge Model), an industrial benchmark of 258 analytical questions whose gold answers are produced by executing validated SQL programs, GRA beats a full-context agent by 5.1 pp (88.4% vs. 83.3%), while reading under a third of its input tokens. A graph-free control shows the gain comes chiefly from selective agentic access rather than graph topology, and that the effect depends on a model able to drive tools reliably. Seeing less, the agent answers better: selective navigation over a structured substrate beats exhaustive context.
[NLP-58] A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations
【速读】： 该论文旨在解决当前隐喻解释评价主要依赖整体质量评分，缺乏对解释质量结构化特征以及人类判断一致性与分歧点的深入理解的问题。其解决方案的关键在于提出一个基于认知动机的理论框架，将隐喻解释质量分解为六个具有理论基础的维度。通过一项大规模标注研究（11,200次评分），研究发现：(i) 解释质量本质上是多维的；(ii) 标注者之间的分歧具有系统性而非随机性；(iii) 六个维度可归纳为一个共享聚类及两个独立的判断轴。进一步的探索性可行性研究表明，标准自动评估流程能够部分还原这一结构，较好预测最具区分性的维度，且其误差与人类判断分歧呈显著相关。结果表明，多维评估相较于整体评分能提供更丰富的诊断性洞察，且开放生成任务的自动评估模型应以能否保留人类判断的结构特征作为核心评价标准。
链接: https://arxiv.org/abs/2608.15828
作者: Ana Naveriani,Jakob Suchan,Stefano Zoia,Mehul Bhatt,Antonio Lieto,Gian Luca Pozzato
机构: Constructor University; University of Turin; Örebro University; University of Salerno; CoDesign Lab EU – Artificial and Human Intelligence (协同设计实验室欧盟-人工与人类智能); codesign-lab.org
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Preprint of paper accepted at INLG 2026
Abstract:Current evaluation of metaphor explanations relies mainly on holistic quality ratings, revealing little about how explanation quality is structured or where human judgments agree and diverge. We introduce a cognitively motivated framework that decomposes metaphor explanation quality into six theoretically grounded dimensions. In a dense annotation study (11,200 ratings), we find that: \bfseries(i) explanation quality is genuinely multidimensional; \bfseries(ii) annotator disagreement is systematic rather than random; and \bfseries(iii) the six dimensions collapse into a shared cluster and two independent axes of judgment. An exploratory feasibility study further shows that a standard automatic evaluation pipeline can recover parts of this structure, predicting the most discriminative dimensions well while its errors correlate human (dis)agreement. Together, these results suggest that multidimensional evaluation offers richer diagnostic insight than holistic ratings, and that automatic evaluators for open-ended generation tasks should be judged on how well they preserve the structure of human judgment.
[NLP-59] QuantumPhaseNet: A Gauge-Covariant Geometric and Quantum-Spectral Theory of Semantic Concept Hierarchies with Prototype Validation of a Classical Quantum-Inspired Model
【速读】： 该论文旨在解决传统Transformer模型在建模语义上下文依赖性、概念尺度（conceptual scale）以及文档级话语方向（discourse direction）时缺乏几何与量子谱特性的问题。其核心挑战在于如何在保持模型可解释性的同时，实现对语义状态的动态表征，并有效捕捉跨层次的语义结构。解决方案的关键在于提出QuantumPhaseNet，一种具有规范协变性的几何-量子谱扩展架构：将上下文相关的语义状态建模为复数振幅，引入规范协变的相位变化率以定义语义波长作为概念尺度的代理指标，并利用低频图模式刻画文档层面的话语方向。理论层面建立了局部规范不变性、量子模块的幺正性、WavePhase Attention 的有界性与条件稳定性，以及可校准的幻觉风险建模。实验验证表明，在合成数据集上，该方法在语义波长层次相关性（Spearman 0.852）、话语对齐度（0.933）和错误检测性能（AUROC 0.854）等方面显著优于基线，但未展现出量子优势，表明当前实现仍属于经典量子启发（classical quantum-inspired）范畴，尚未具备外部有效性或无条件量子加速能力。
链接: https://arxiv.org/abs/2608.15820
作者: Kiyotaka Kasubuchi,Kazuo Fukiya
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: [PAGES] pages, 8 figures, 4 tables. Extends arXiv:2602.14419 (WavePhaseNet). Includes prototype validation with an offline Validation Studio; RQ5 reports a negative result for quantum advantage
Abstract:We present QuantumPhaseNet, a gauge-covariant geometric and quantum-spectral extension of Transformer representations. Context-dependent semantic states are modeled as complex amplitudes; a covariant phase rate induces a semantic wavelength used as a proxy for conceptual scale; and low-frequency graph modes define a document-level discourse direction. The theoretical part establishes local gauge invariance, unitarity of the quantum block, boundedness and conditional stability of WavePhase Attention, and a calibratable hallucination-risk formulation. We also implemented a fully offline Validation Studio for the classical quantum-inspired pipeline in Section 14.1 and evaluated the five research questions in Section 16.1 on its built-in synthetic setting (n=240, observation noise 0.22, circuit noise 0.08, five seeds). RQ1 yielded a wavelength-hierarchy Spearman correlation of 0.852 versus 0.707 for the baseline, 87.3% direction accuracy, and AUC 0.953. RQ2 achieved discourse alignment 0.933 versus 0.589 and 41.2 versus 16.2 paragraphs before drift. RQ3 achieved AUROC 0.881 versus cosine 0.765 and phase-shuffle 0.536. RQ4 achieved error-detection AUROC 0.854 versus entropy 0.634, with Brier 0.150 and ECE 0.098. RQ5 did not show quantum advantage: target probability and end-to-end cost efficiency were 25.5% and 0.107, compared with 70.7% and 0.707 for the Chebyshev classical approximation. These results provide initial synthetic evidence for the classical quantum-inspired components, but not external validity or unconditional quantum speedup.
[NLP-60] Hallucination Span Detection with Input-Side Evidence Alignment
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在条件文本生成中存在幻觉（Hallucination）的问题，尤其是现有方法仅评估整个生成文本的事实性，无法定位具体幻觉片段及其与输入之间的关联。为此，论文提出了一项新任务——基于输入侧证据对齐的幻觉片段检测（Hallucination Span Detection with Input-Side Evidence Alignment），其核心在于联合识别生成文本中的幻觉片段，并将输出词元与对应的输入证据进行对齐。解决方案的关键在于利用一个基于编码器的模型，通过从输入表示中预测被掩码的输出词元，利用预测置信度判断幻觉：可由输入合理预测的词元为真实内容，反之则为幻觉。该方法在训练过程中自然地实现了输出与输入之间的对齐，实验结果表明其能有效检测幻觉片段并准确识别有意义的输入证据，人工评估进一步验证了所生成对齐的质量。
链接: https://arxiv.org/abs/2608.15804
作者: Miyu Yamada,Yuki Arase
机构: Institute of Science Tokyo (东京科学研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily assess the factuality of an entire generated text, providing limited insight into which output spans are hallucinated or how they relate to the input. We introduce the task of hallucination span detection with input-side evidence alignment, which jointly identifies hallucinated spans and aligns output tokens with the corresponding input evidence. Our approach is based on the observation that faithful output tokens are predictable from the input, whereas hallucinated tokens are not. We therefore train an encoder-based model to predict masked output tokens from the input representation, using prediction confidence for hallucination detection while naturally producing alignments to the input. Experiments show that the proposed method effectively detects hallucinated spans and identifies meaningful input-side evidence. Human evaluation confirms the quality of the predicted alignments.
[NLP-61] Using the Mimi codec for metalinguistic representations
【速读】： 该论文旨在解决生成式语音模型中语义令牌（semantic token）与实际发音实现之间映射关系不明确的问题，尤其针对Mimi语义令牌词典（2048个令牌）在语义表征与音位实现之间的对应性不足。其核心解决方案在于通过将Mimi的神经编码表示重新对齐至TIMIT语料库的转录文本，揭示了该2048个语义令牌实际上对应于从四音素（quadphone）、三音素（triphone）、双音素（biphone）到单音素（phone）乃至次音素（subphone）等多层次的发音实现，从而首次系统性地阐明了语义令牌在发音层面的粒度分布，为理解生成式语音模型中的语义-发音映射机制提供了关键证据。
链接: https://arxiv.org/abs/2608.15799
作者: Artem Saloev,Erin Pacquetet,Nicolas Ballier
机构: ALTAE, Université Paris Cité, F-75013 Paris, France; SCIAM, 10 rue de Penthièvre, F-75008 Paris, France
类目: Computation and Language (cs.CL)
备注: 11 pages, accepted for the Proceedings of the Third Workshop on the Bridges and Gaps between Formal and Computational Linguistics (BriGap-3), Paris 2026
Abstract:In this paper, we focus on the dictionary of 2048 tokens used in Mimi semantic token codebook, the neural codec of the Moshi language model. We show that the ABX experiment carried out with Mimi fails to capture the mapping of the semantic tokens to phone realisations. By realigning Mimi representations to the TIMIT corpus transcriptions, we show that the 2048 tokens IDs of the semantic codebook map to quadphone, triphone, biphone, phone and subphone realisations.
[NLP-62] KV-Rescue: Recovering Reasoning Language Model KV Eviction Loss via Stepwise Interleaving
【速读】： 该论文旨在解决长推理序列中因键值缓存（KV-cache）淘汰导致的内存成本与推理准确性之间的权衡问题。在严格的缓存预算下，KV-cache淘汰会引入信息缺失，造成模型基于不完整历史进行解码，从而引发推理错误甚至失控退化（runaway degeneration），表现为生成内容不连贯或重复。研究发现，此类损失主要源于信息缺口（information gap），而非模型容量不足。基于此，论文提出一种无需训练的推理框架KV-Rescue，其核心在于利用轻量级全上下文辅助模型（full-context helper）弥补因KV淘汰造成的上下文缺失。该方法通过将大模型（如7B）与小模型（如1.5B）的推理步骤交替整合至共享轨迹，并引入在线检测机制，基于熵和可压缩性动态终止无效生成路径，有效抑制失控退化。实验表明，在五项数学基准测试中，使用Qwen2.5-Math 7B与72B模型时，KV-Rescue在缓存预算B=64下平均恢复87%的精度损失，且可减少43%的基模型令牌生成量，显著提升效率与鲁棒性。
链接: https://arxiv.org/abs/2608.15797
作者: Minsoo Cheong,Woosang Lim,Vincent-Daniel Yun,Sungjoo Yoo
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:KV-cache eviction caps the memory cost of long reasoning traces but is inherently lossy because the model decodes from a partial view of its history. Under aggressive budgets, this not only lowers accuracy but can also cause runaway degeneration, where the model produces incoherent or repetitive tokens until reaching the length limit. We characterize much of this loss as an information gapf caused by missing context, rather than a capability gap caused by limited model capacity. An evicted 7B model and a full-context 1.5B model make complementary errors, and an oracle choice between their answers recovers 79% of the accuracy gap to the full-KV 7B model. Based on this observation, we propose KV-Rescue, a training-free inference framework that bridges the information gap introduced by KV eviction using a lightweight full-context helper. KV-Rescue interleaves reasoning steps from the two models into a shared trajectory. An online detector uses entropy and compressibility to terminate the generation of incoherent or repetitive base-model candidates early. Across five math benchmarks with Qwen2.5-Math 7B and 72B, KV-Rescue recovers an average of 87% of the accuracy lost to eviction at eviction budget B=64. A decode-cost analysis further shows that preventing runaway degeneration cuts base-model token generation by 43% on average.
[NLP-63] Routing Divergence Is Not Evidence of Behavioral Influence in Same-Weight MoE Self-Distillation
【速读】： 该论文旨在解决生成式模型中基于相同权重的自蒸馏（same-weight self-distillation）所导致的潜在盲区问题，即在演示条件引导的教师模型与仅依赖查询的学生产生不一致时，路由机制（routing）与内容传递（content）之间的解耦效应。其核心解决方案在于提出一种精确的分块分解方法，将模型输出拆分为两个独立成分：一是固定内容下改变门控策略的路由项（routing term），二是类似密集连接的语义内容项（content term）。实验结果表明，在多个开放权重检查点和跨领域任务中，路由项仅占分块输出的1.6倍比例，其在残差流中的暴露程度虽达3.2倍，但整体影响仍有限；而通过预注册的PubMedQA测试显示，路由项对输出的影响小于自然上下文效应的一半，且可被匹配范数噪声近似再现，说明其行为影响较小；相比之下，内容项具有强方向性。研究进一步验证了激活主干网络的规模扩展与合并专家探测均未改变该现象，表明小范围块级暴露并非普遍规律，且路由移动本身不足以证明行为层面的影响。因此，关键结论是：在评估路由机制是否产生实际行为影响前，必须先量化其在残差流中的暴露程度，并仅在关键决策节点采用行为干预。
链接: https://arxiv.org/abs/2608.15787
作者: Cedric Caruzzo,Donggeun Yoo,Tae Soo Kim
机构: Lunit
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 15 pages, 4 figures
Abstract:Two Mixture-of-Experts (MoE) forward passes can share every weight yet route the same token through different experts. This creates a possible blind spot in same-weight self-distillation, where a demonstration-conditioned teacher supervises a query-only student. We study this mismatch in its single-step form, with frozen weights rather than as a proxy for a full training trajectory. An exact blockwise decomposition separates a routing term, which changes gates at fixed content, from a dense-like content term. Across seven open-weight checkpoints and two domains, the routing term spans only 1.6\times as a fraction of block output, while its residual-stream exposure spans 3.2\times . Exposure is ordered by the routed block’s share of the residual. Scaling the always-on backbone in two confirmatory models moves exposure monotonically; common-mode controls support a mass-and-coherence mechanism rather than denominator dilution alone. Preregistered PubMedQA patches on three models show that the full routing term moves outputs by less than half the natural context effect and is largely reproduced by matched-norm noise, whereas the content term is strongly direction-specific. Scale and merged-expert probes show that the narrow block-level range is not universal, although exposure remains small at the tested boundaries. Router movement alone is therefore not evidence of behavioral influence: measure exposure first, and use a behavioral intervention when the decision matters.
[NLP-64] aoLive Digital Avatar Agent Technical Report: Training Agents to Evolve with Their Harness
【速读】： 该论文旨在解决生成式AI（Generative AI）驱动的虚拟主播在实时电商直播中面临的动态适应性难题，即如何在低延迟条件下实现对产品问答的准确响应、观众互动的自然流畅，以及快速应对不断变化的营销策略、合规要求和风格规范。其核心挑战在于：随着系统配置（如技能模块、提示模板、工具接口等）的持续演进，模型可能因“记忆化”旧有配置而失去对当前运行环境的适应能力，而高性能的零样本模型又难以满足实时性需求。为此，论文提出了一种面向动态配置的训练方法（Harness-Aware Training, HAT），其关键创新在于将配置状态（Harness State）纳入训练分布，通过任务保持型配置增强（HSA）技术，在监督微调、策略蒸馏和基于生产环境模拟的智能体强化学习三个阶段中，显式建模并训练模型对动态配置的鲁棒性。实验表明，采用HAT训练的35B参数紧凑模型在真实直播问答任务中达到94.8分（远超基线模型的80.3及最强通用大模型的93.0），同时在不同配置变体下的问答表现稳定（94.6分），且保留了83.5的通用指令遵循能力，显著优于固定配置微调方案（IFEval下降7.7点）。系统在单块NVIDIA H20 GPU上实现P50 3.407秒、P95 8.114秒的延迟，验证了该方案在低延迟与高适应性之间的有效平衡。
链接: https://arxiv.org/abs/2608.15763
作者: TaoLive AIGC LLM Team:Yuhan Sun,Wenhao Lin,Yongdong Luo,Yibo Hu,Meiguang Jin,Junfeng Ma,Weihang Pan,Jiaxin Zhao,Zulong Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:AI-powered digital-avatar streamers in live e-commerce must answer product questions, engage viewers, and execute changing business strategies in real time. This requires low latency, factual and effective replies, and rapid adaptation to updated campaign, compliance, and style requirements. We develop an evolvable Harness that decouples Skills, Hooks, system prompts, and tools from model weights, allowing runtime behavior to change without retraining. However, Harness evolution creates a moving execution environment: compact models fine-tuned on one configuration may memorize names, schemas, and prompt templates rather than follow the Harness currently provided, while stronger zero-shot models are too slow for real-time use. We address this tension with Harness-Aware Training (HAT), which makes Harness states part of the training distribution. HAT applies task-preserving Harness-State Augmentation (HSA) to Skills, tool schemas, prompt structures, and interaction constraints, and comprises three stages: HSA-based supervised fine-tuning, general on-policy distillation to recover general capabilities, and HSA-based agentic reinforcement learning in a production-informed live-room simulator. Across four evaluation sets with more than 4,500 cases, our compact 35B model scores 94.8 on real-world Live-Stream QA, versus 80.3 for the base model and 93.0 for the strongest evaluated general LLM, while scoring 94.6 on Harness-Variant QA and retaining 83.5 on IFEval. By contrast, fixed-Harness SFT reduces IFEval by 7.7 points. In a controlled complete-agent replay on one NVIDIA H20 GPU with MTP enabled, the system achieves 3.407 s P50 and 8.114 s P95 latency. These results show that HAT produces a latency-feasible compact agent that remains effective under evaluated Harness changes without sacrificing general instruction following.
[NLP-65] Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign EMNLP2026 WOAH
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在近期一场由AI驱动的影响力传播活动中所扮演角色的可追溯性与特征识别问题。其核心挑战在于区分由人工撰写与由大模型生成的宣传文本，并揭示其背后的生成机制。解决方案的关键在于构建并分析两个对比语料库：PROPAGIA（2,646篇来自Storm-1516/CopyCop行动的法语宣传文章）与SIPA（同期主流法语新闻媒体的人工撰文对照集）。通过主题建模、模糊性与情感分析，研究发现PROPAGIA在模糊性、主观性和负面情绪方面显著高于SIPA，且引用来源更少，体现出典型的宣传特征。进一步分析揭示，84个目标网站中有50个存在提示指令泄露，其中包括一份逐字复制的十点编辑规范，解释了部分文本特征差异；同时高跨文章重复率表明高度依赖模板化生成。最后，基于重写检测的方法不仅支持INSIKT GROUP对Llama 3系列模型的归因，还暗示了Mistral系列模型的潜在参与，从而为生成式内容的溯源提供了关键证据链。
链接: https://arxiv.org/abs/2608.15746
作者: Benjamin Icard,Elouan Vuichard,Louis Lefebvre,Lila Sainero,Thomas Girault,Alice Breton,Tanguy Launay,Gauvain Bourgne,Morgane Casanova,Guillaume Gadek,Victor Klötzer,Michel Le Nouy,Guillaume Gravier,Jean-Gabriel Ganascia,Paul Égré
机构: LIP6, Sorbonne University, CNRS(法国国家科学研究中心); IRL Crossing, CNRS(法国国家科学研究中心); AIRBUS(空中客车); SIPA Ouest-France(西法兰西媒体集团); IRISA, CNRS(法国国家科学研究中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: To appear in the Proceedings of the 10th Workshop on Online Abuse and Harms (WOAH), EMNLP 2026
Abstract:We present a forensic analysis of the generation pipeline behind a recent AI-driven influence campaign. We introduce PROPAGIA, a corpus of 2,646 propagandist French articles from the Storm-1516/CopyCop campaign disclosed by VIGINUM and INSIKT GROUP in 2025. For comparison, we rely on SIPA, a corpus of human-written French mainstream press from the same period. Using topic modeling, vagueness and sentiment analysis, we first isolate persuasion techniques characteristic of propaganda, with PROPAGIA far exceeding SIPA in vagueness, subjectivity and negativity, and citing fewer sources. We then find prompt instruction leaks on 50 of the 84 PROPAGIA websites, including a verbatim ten-point editorial specification accounting for several of these differences, together with high cross-article redundancy. Finally, we show that rewriting-based detection supports INSIKT GROUP’s attribution to the Llama 3 family, but also suggests the involvement of Mistral-family models.
[NLP-66] Beyond Single Object: Learning 3D Relations with Large Language Models CVPR2026
【速读】： 该论文旨在解决当前3D-大语言模型（3D-LLMs）在多物体场景理解中的核心局限：现有模型主要聚焦于单个物体或场景的描述，难以进行细致的、跨物体之间的比较分析。其解决方案的关键在于提出一个面向多物体层级推理的系统性框架——Multi-3DLLM，包含三个核心组成部分：(1) MO3D，一个要求细粒度多物体对比的指令数据集，用于引导模型学习跨对象关系；(2) 基于最小化Patch-Interaction Transformer（PIT）架构的Multi-3DLLM，能够同时建模物体间与物体内部的复杂关系，同时保持局部几何结构的一致性；(3) Mini-apps，两个以实际应用为导向的基准任务（形状匹配、变化描述），用于评估模型在几何理解方面的实用性。实验表明，相较于现有的3D-LLMs和2D视觉语言模型（VLMs），该框架在缺乏以比较为中心的设计与几何感知能力的情况下表现不佳，而本文提出的Multi-3DLLM通过混合数据训练，在MO3D数据集上显著超越所有基线，并展现出对单物体分类任务的正向迁移能力，验证了其在几何推理方面的有效性。
链接: https://arxiv.org/abs/2608.15710
作者: Kohsuke Ide,Ryousuke Yamada,Yue Qiu,Xianzheng Ma,Yoshihiro Fukuhara,Hirokatsu Kataoka,Yutaka Satoh
机构: AIST(产业技术综合研究所); University of Tsukuba(筑波大学); University of Technology Nuremberg(纽伦堡应用技术大学); University of Oxford(牛津大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to CVPR 2026
Abstract:We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison. We propose a framework for detailed object-level reasoning across multiple objects with three components: (1) MO3D (Multi-Object in 3D), an instruction dataset requiring fine-grained multi-object comparison; (2) Multi-3DLLM, using a minimal Patch-Interaction Transformer (PIT) that models inter-/intra-object relationships while preserving local geometry; (3) Mini-apps, two application-driven benchmarks (Shape Mating, Change Captioning) that probe geometric understanding for practical use. Recent 3D-LLMs and 2D-VLMs perform poorly on these tasks, lacking both comparison-centric design and geometric awareness. In contrast, Multi-3DLLM trained on our mixture data learns geometric reasoning, surpasses all baselines on MO3D, and provides positive transfer to single-object classification.
[NLP-67] BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter
【速读】： 该论文旨在解决疫情期间健康伪信息（health misinformation）在社交媒体上快速传播所引发的有害叙事问题，尤其针对传统主题建模方法将用户互动行为（engagement）视为外部结果、难以优先识别语义连贯且传播迅速的主题这一局限。其解决方案的关键在于提出一种病毒式传播优先的主题建模框架——BERTopic-VP，该框架融合基于上下文嵌入的聚类方法（BERTopic）与后处理的病毒式传播优先级（Virality Prioritisation, VP）层，通过引入传播潜力评分作为排序依据，实现对高影响话题的精准筛选。同时，配套采用两阶段混合型伪信息检测模块，结合监督式内容分类器与基于公共卫生知识库的外部验证信号，提升检测准确性。实验表明，该框架在多个基准数据集上均达到优异性能（F1最高达0.950，ROC-AUC最高达0.989），并在前1%、5%和10%的VP阈值下成功识别出高影响力话题集群。对于缺乏原始互动数据的数据集，该方法利用逻辑回归拟合的“传播倾向得分”作为扩散潜力的序数代理指标，从而支持对低频但高风险伪信息叙事的早期预警与可解释性分析，为跨疫情场景下的监测与干预提供有力支撑。
链接: https://arxiv.org/abs/2608.15691
作者: Mkululi Sikosana,Sean Maudsley-Barton,Oluwaseun Ajao
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
备注: 21 pages, 3 figures, 12 tables. Preprint
Abstract:Health misinformation circulating during pandemics can gain traction rapidly, creating harmful narratives that compete with public health guidance. Most topic-modelling pipelines treat engagement as an external outcome, limiting their ability to prioritise semantically coherent topics that are also rapidly diffusing. We introduce BERTopic-VP, a virality-prioritised topic-modelling framework that combines contextual embedding-based clustering (BERTopic) with a post hoc Virality Prioritisation (VP) layer. The pipeline is complemented by a two-stage hybrid misinformation detection module that fuses a supervised content-based classifier with an external verification signal derived from public-health knowledge bases. Applied to three benchmark datasets, COVID-19_FNIR, Monkeypox, and Constraint, the framework achieves strong classification performance, with F1 up to 0.950 and ROC-AUC up to 0.989, while identifying high-impact clusters under top 1%, 5%, and 10% VP thresholds. For datasets without native engagement metadata, prioritisation is based on a logistic propensity-to-spread score, used as an ordinal proxy for diffusion potential rather than a direct measure of engagement. The results show that integrating semantic structure, virality-aware ranking, and affective-linguistic profiling enables scalable and interpretable comparative analysis of misinformation across pandemics. The proposed framework supports monitoring-oriented early warning by surfacing low-volume but high-risk narratives for analyst review.
[NLP-68] Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media
【速读】： 该论文旨在解决现有传染病模型在模拟社交媒体上健康虚假信息传播时，因忽略个体心理与行为动态而造成的预测失真和机制刻画不足的问题。其核心解决方案在于构建一个融合流行病学框架与心理认知机制的混合模型——ELM-SIRMMM（基于详尽可能性模型的六维易感-感染-恢复-虚假信息模型），通过将传统的SIR模型扩展为包含“易感-虚假信息”（MS）、“感染-虚假信息”（MI）和“恢复-虚假信息”（MR）的六类状态，并引入来自详尽可能性模型（Elaboration Likelihood Model, ELM）的心理信号（如情感极性、参与度指标和认知努力程度），以动态调节虚假信息传播速率。该方法实现了对用户行为异质性的建模，显著提升了模型在不同情境下的预测精度与动态真实性，尤其在情绪化虚假信息（MC-Fake）场景中成功再现了“闪传谣言”现象，验证了动态心理输入对于提升功能真实性的关键作用。研究结果表明，仅依赖静态结构建模不足以反映虚假信息传播的本质，必须结合随时间与情境变化的心理驱动因素，才能实现对复杂社会传播过程的精准刻画。
链接: https://arxiv.org/abs/2608.15689
作者: Mkululi Sikosana,Sean Maudsley-Barton,Oluwaseun Ajao
机构: 未知
类目: ocial and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 3 figures, 8 tables. Preprint
Abstract:This study presents a hybrid epidemiological and behavioural framework to simulate the spread of health misinformation on social media. We extend the classical Susceptible–Infected–Recovered (SIR) model to a six-compartment structure (SIRMMM), incorporating Misinformed Susceptible (MS), Misinformed Infected (MI), and Misinformed Recovered (MR) compartments to better reflect the dynamics of the misinformation lifecycle. To account for individual-level behavioural variation, we extend the SIRMMM model by integrating psychological signals from the Elaboration Likelihood Model (ELM), including sentiment polarity, engagement metrics, and cognitive effort, which dynamically modulate the misinformation transmission rate, yielding the ELM-SIRMMM framework. Model parameters were estimated using the FibVID dataset, which captures COVID-19 misinformation on Twitter. Generalisability was tested on two additional datasets: MC-Fake (emotional misinformation) and Monant (general health misinformation). Results show that the ELM-SIRMMM model enhances both predictive accuracy and dynamic realism. On FibVID, it decreases RMSE by 5.5%, delays the misinformation peak from day 150 to day 160, and increases its peak prevalence from 6% to 7%. On MC-Fake, it accurately reproduces a flash-rumour pattern, infecting 38% of users by day 45 and achieving 97% misinformation recovery, all while maintaining model accuracy. In contrast, minimal behavioural signal variability in the Monant dataset leads to marginal benefit, with only a 3% peak and 57% of users remaining susceptible. These findings suggest that structural elaboration alone is insufficient. Functional realism in modelling misinformation spread requires dynamic psychological inputs that vary meaningfully across time and contexts.
[NLP-69] When Stories Evolve: Benchmarking LLM Storytelling Across Agent Architectures in Open-Ended World Simulations
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在开放性叙事生成中面临的长期一致性与意义发展难题，即如何在动态世界模拟和基于AI的游戏中持续保持事实、关系、因果依赖及角色状态的一致性。其核心挑战在于，传统评估方法仅关注最终故事的流畅性，而忽视了生成过程中的连贯性与情节演化深度。论文提出WSE-bench这一过程型基准，通过三个维度——生成覆盖率（Generation Coverage）、一致性（Consistency）与丰富性（Richness）——分别量化模型在长时间叙事中维持计划步骤、避免设定冲突以及实现玩家驱动的有意义分支发展的能力。研究发现，一致性与丰富性之间并不存在平滑的权衡关系，其经验帕累托前沿呈非凹性，存在多个非劣解，表明无法通过简单的线性加权进行优选。此外，引入结构虽能提升情节丰富性，但未必改善一致性，甚至可能缩短生成长度；模型规模主要促进持续生成能力，对一致性和意义发展无显著提升。这表明持续生成、设定一致性与意义发展是相互独立且时常冲突的能力。WSE-bench通过将叙事评估从静态成品转向动态生成过程，揭示了这些能力之间的复杂互动机制。
链接: https://arxiv.org/abs/2608.15654
作者: Yuqi Chen,Sixuan Li,Yunfeng Cai,Xueai Li,Ka Man Yan,Ying Li
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models can write fluent stories, but open-ended storytelling requires more than local fluency. In evolving world simulations and AI-native games, models must preserve facts, relationships, causal dependencies, and character states as the world changes. We introduce WSE-bench, a process benchmark that separately evaluates sustained generation, canonical coherence, and meaningful development in dynamic LLM storytelling. Generation Coverage records the proportion of planned narrative steps produced; Consistency tracks when canon breaks; and Richness measures how meaningfully branching, player-shaped trajectories develop. Across frontier models, Consistency and Richness do not form a smooth trade-off: their empirical Pareto frontier is non-concave, with several non-dominated intermediate configurations that no positive linear weighting can select. Added structure can enrich trajectories, but it does not uniformly improve coherence and may shorten them. Model scale chiefly improves sustained generation, without producing reliable gains in canonical coherence or meaningful development. These results show that sustained generation, canonical coherence, and meaningful development are distinct and sometimes competing capacities. WSE-bench makes those dynamics visible by extending narrative evaluation from finished stories to the processes that create them.
[NLP-70] Wiktionary as a Crowdsourced Lexicon for English Dialects
【速读】： 该论文旨在解决如何有效评估维基词典（Wiktionary）作为英语方言的伦理化众包词典的可靠性与实用性问题。其核心挑战在于传统词典在覆盖外圈及区域变体（如新西兰英语等）时存在局限，而众包资源在实际应用中可能面临语言接触、语域差异等复杂因素的影响。论文提出的解决方案关键在于采用两阶段研究方法：首先对12种英语国家变体进行深入的描述性分析，验证维基词典在词汇覆盖上的表现；随后将其应用于地理标记的国家级社交媒体语料，检验其在真实语言使用场景中的有效性。研究结果表明，维基词典在区域性和外圈英语变体的词汇覆盖上可与牛津英语词典（OED）相媲美甚至更优，且在新西兰英语的构词模式上与OED具有高度一致性（相关系数R = 0.883），同时与社交媒体语言数据表现出良好匹配度。这一方法不仅验证了维基词典作为方言响应型语言资源的潜力，也揭示了评估此类工具时需关注的语言接触与网络语料语域偏差等宏观挑战。
链接: https://arxiv.org/abs/2608.15641
作者: Sidney Wong
机构: Centre for Sustainability Research, University of Otago(奥塔哥大学可持续发展研究中心); Te Pūnaha Matatini Centre for Research Excellence in Complex Systems(复杂系统研究卓越中心)
类目: Computation and Language (cs.CL)
备注: Submitted to the 13th Web-as-Corpus Workshop
Abstract:This paper evaluates Wiktionary as an ethically crowdsourced lexicon for English dialects. We took a two-phase approach, providing an in-depth descriptive analysis of the crowdsourced lexicon for 12 national varieties of English before applying the lexicon to geo-referenced, country-level social media language data to examine the real-world performance of this crowdsourced dialect lexicon. We demonstrate that Wiktionary matches or exceeds the coverage of traditional dictionaries, such as the Oxford English Dictionary (OED), for regional and Outer-Circle varieties. Our dialect-specific case study on New Zealand English found high alignment between Wiktionary and the OED based on word-formation patterns (R = 0.883). Similarly, we observed high alignment between the dialect lexicon and geo-referenced social media language. While this paper found that Wiktionary has broad coverage of lexical properties, it also highlighted some of the macro-challenges involved in evaluating dialect-responsive language resources and tools, such as the role of language contact in dialects and register effects in web-based corpora.
[NLP-71] BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language
【速读】： 该论文旨在解决传统检索增强生成（Retrieval-Augmented Generation, RAG）框架在处理文档时忽略其层次结构，导致在低资源语言（如孟加拉语）中表现不佳的问题。其解决方案的关键在于提出一种结构感知的RAG框架，将孟加拉语教科书建模为层次图结构，并采用对比学习训练的图神经网络（Graph Neural Network, GNN）来检索一组相关性高的短文本片段。该方法通过捕捉文档的层级语义关系，提升了检索精度，进而为大语言模型提供聚焦的上下文支持，实现特定主题的多项选择题（MCQ）生成与领域内答案预测，实验结果表明该框架在检索指标、生成问题的相关性及答案预测准确率方面均优于现有的强基准模型。
链接: https://arxiv.org/abs/2608.15547
作者: Abu Tarabin Surzo,A.K.M. Nihalul Kabir,Sm Azmain Faysal,Ariana Haque Ami,Lawrence Amlan Gomes,Farig Sadeque
机构: BRAC University(布拉大学); Department of Computer Science and Engineering, BRAC University(计算机科学与工程系，布拉大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Traditional retrieval-augmented generation (RAG) frameworks process documents without attending to their hierarchical structure, leading to poor performance, especially in low-resource languages such as Bengali. To address this, we propose a structure-aware RAG framework that models Bengali textbooks as hierarchical graphs and uses a contrastively trained graph neural network to retrieve a small set of relevant passages. These passages provide focused context for a large language model, enabling topic-specific multiple-choice question (MCQ) generation and in-domain answer prediction. Experimental results demonstrate that our framework outperforms strong dense retrieval baselines across retrieval metrics, produces more relevant MCQs, and achieves superior answer prediction accuracy.
[NLP-72] L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在评估印度本土事实知识时缺乏高质量、多语言基准数据的问题。现有评测体系普遍忽视了印度教育体系与文化语境下的特定知识，导致模型评估结果难以反映其在真实应用场景中的表现。为此，作者提出了L3Cube-IndicQuest v2，一个大规模、金标准的多语言问答基准，覆盖9个领域共3,471个基于课程的英语问答对，并通过混合构建策略实现高效且高质量的数据生成：该策略结合上下文引导的生成式AI（Generative AI）自动生成与验证，辅以语义去重和人工审核，确保数据规模与标注质量之间的平衡。该基准被翻译至19种印地语系（Indic）语言，形成包含69,420个问答对的公开多语言数据集，涵盖20种语言。实验采用三种评估协议（LLM作为裁判、精确子串匹配与词项重叠匹配），结果显示不同评判标准下模型排名高度一致，表明评估结果具有稳健性。前沿商业模型显著领先，而开源模型中，Gemma4 31B在所有评估的印地语系语言中均优于专为印地语优化的Sarvam 30B，凸显模型架构与训练数据对本土化知识理解的关键影响。解决方案的核心在于融合自动化生成与人工校验的混合构建机制，兼顾可扩展性与准确性，从而建立可信的印度本土知识评估标准。
链接: https://arxiv.org/abs/2608.15535
作者: Rinit Jain,Tirthraj Mahajan,Advait Joshi,Raviraj Joshi
机构: Pune Institute of Computer Technology, Pune(普奈计算机技术学院, 普奈); L3Cube Labs, Pune( L3Cube 实验室, 普奈); Indian Institute of Technology Madras(马德拉斯印度理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs). The benchmark comprises 3,471 curriculum-grounded English question–answer pairs spanning nine domains, curated from educational curricula, competitive examination materials, and domain-specific reference books. We introduce a practical hybrid construction strategy that combines context-grounded LLM-based question generation and validation with semantic deduplication and human verification, enabling scalable creation of benchmark data while preserving annotation quality. The benchmark is translated into 19 Indic languages, yielding a publicly released multilingual dataset of 69,420 question–answer pairs across 20 languages. We evaluate six LLMs under three protocols: LLM-as-a-judge and two deterministic lexical criteria, exact-substring and word-overlap matching. All three produce almost the same model ranking, showing that the results do not depend on the choice of judge. The frontier commercial model leads by a wide margin, and among open-weight models Gemma4 31B outperforms the Indic-specialised Sarvam 30B in every evaluated Indic language.
[NLP-73] Why Summaries Turn Neutral: Policy Attribution for Sentiment Drift in Reinforcement Learning from Human Feedback
【速读】： 该论文旨在解决基于人类反馈的强化学习（Reinforcement Learning with Human Feedback, RLHF）在大语言模型（Large Language Models, LLMs）文本生成任务中引发的情感偏移（sentiment drift）问题，即模型输出摘要过度中性化，丧失了原始文本中的情感细微差别。其核心解决方案在于提出一种名为“策略归因”（Policy Attribution）的框架，通过梯度与逻辑值分解技术，精准溯源情感偏移的成因，发现其主要源于奖励模型（Reward Model, RM）信号与KL（Kullback-Leibler）正则化项的共同作用。研究揭示，情感偏移本质上是模型在偏好不确定性下对“低风险”词汇的策略性偏好，以最大化预期奖励。实验表明，在Reddit TL;DR与CNN/DailyMail数据集上，经RLHF训练的摘要虽获得更高奖励评分，但情感方差下降30%-40%；跨八种语言的分析进一步证实该现象具有语言无关性，且形态丰富的语言受抑制更显著。为此，论文提出一种情感感知正则化方法，在不损害摘要质量的前提下，有效降低情感偏移18%-22%，并承诺开源代码与工具包。
链接: https://arxiv.org/abs/2608.15530
作者: Mikhail Krasitskii,Alexander Gelbukh,Olga Kolesnikova,Grigori Sidorov
机构: Instituto Politécnico Nacional (IPN); Centro de Investigación en Computación (CIC)
类目: Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with human feedback (RLHF) aligns LLMs with human preferences, improving summarization fluency and safety, but causes sentiment drift: overly neutral summaries stripped of emotional nuance. We diagnose why RL acts as a sentiment neutralizer and present Policy Attribution, a framework using gradient and logit decomposition to trace drift to reward model (RM) signals and KL (Kullback-Leibler) penalty. Sentiment drift reflects a strategic bias toward “low-risk” tokens maximizing expected rewards under preference uncertainty (Stiennon et al., 2020; Gao, Schulman, and Hilton, 2023). On Reddit TL;DR and CNN/DailyMail, RLHF summaries get higher rewards but show 30-40% lower sentiment variance. Cross-lingual analysis across eight languages shows language-independent drift, with morphologically richer languages more suppressed (Krasitskii et al., 2026). We propose and validate a sentiment-aware regularization technique reducing drift by 18-22% without harming summary quality. The code and toolkit will be public.
[NLP-74] Do Language Models Consistently Encode the Current Year?
【速读】： 该论文旨在解决语言模型中“当前时间”（current time）概念的不一致性问题，即尽管时间推理依赖于对当前时间的稳定认知，但现有语言模型如何表征当前时间尚不明确。其核心挑战在于：语言模型中的“当前时间”存在两种不同表征机制——一种是通过动词时态等语言结构隐含推断的关联性表征（associative task），另一种是直接查询的陈述性表征（declarative task）。研究发现，这两种表征在模型内部具有不同的因果路径：关联性任务的表现与预训练数据截止时间高度相关（平均误差仅10个月），体现为类似事实回忆的机制；而陈述性任务则缺乏一致的因果路径，且两者对更新方法的响应差异显著。关键解决方案的难点在于，现有的提示工程（prompting）、监督微调（SFT）和权重编辑均无法同时有效同步两种表征。具体而言，提示可成功更新陈述性表征（94.6%成功率），但几乎不影响关联性表征；监督微调在多数模型中仍无法改变关联性表征；权重编辑虽能分别影响两者，但不具备跨任务泛化能力。因此，研究揭示了当前时间在语言模型中并非统一编码，关联性表征因深度嵌入预训练语言结构而具有更强的顽固性，从而构成了模型时间感知更新的核心障碍。
链接: https://arxiv.org/abs/2608.15507
作者: Suze van Adrichem,Aditi Bhaskar,Diyi Yang,Christopher Potts,Jing Huang
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at the Conference on Language Modeling (COLM) 2026
Abstract:A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood. We contribute two tasks that probe the current year in conceptually distinct ways: an associative task, which infers the current year from verb tense, and a declarative task, which directly queries for the current year. Both tasks estimate current years within one year of the post-training data cutoff of instruction-tuned language models. For base models, predictions on the associative task serve as a strong proxy for the pre-training data cutoff, with an average error of only 10 months across 13 models. However, their internal mechanisms diverge: the associative task uses mechanisms similar to factual recall, while the declarative task lacks consistent causal pathways. This divergence poses a challenge for updating the current year in language models. None of prompting, SFT, or weight editing succeed in shifting the associative and declarative years simultaneously. Prompting updates the declarative year (94.6% success across 351 target years) but leaves the associative year nearly unchanged (1.7% success). Year-shifted SFT also fails to shift the associative year, matching the target year in only one of eight models. Weight editing, while effective for both tasks individually, does not generalize across both. Overall, our results show that the current year is not consistently encoded in language models: The associative notion, deeply ingrained in linguistic structures learned in pre-training, uses different causal mechanisms and resists the same modifications that easily shift the declarative notion learned in post-training.
[NLP-75] Language models suffer from a curse of ambiguity
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在通过采样驱动自我优化过程中，其学习到的概率分布准确性面临的关键挑战。具体而言，研究指出：当模型预测的下一个词（next-token）分布具有较高歧义性（即分布熵高、不确定性大）时，其学习难度显著增加，形成一种“歧义诅咒”（curse of ambiguity）。这一现象的根本原因在于模型架构与训练机制的双重限制——高歧义分布需要更大的模型容量来存储、更长的嵌入向量表示、更多的训练迭代以收敛，并且会放大采样过程中的噪声。研究通过理论分析和在可控真实数据上的实证验证，揭示了该问题在合成任务与真实语言模型训练中的普遍性。解决方案的关键在于建立对模型输出分布可信度的评估框架，识别并规避高歧义区域，从而为提升大语言模型在生成任务中的统计可靠性提供理论依据与实践指导。
链接: https://arxiv.org/abs/2608.15448
作者: Nicolas Zucchet,Hyun Dong Lee,Scott Linderman
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Large language models increasingly rely on sampling as a driver of their own improvement, making the fidelity of their learned distributions more critical than ever. Yet, not all distributions are equally easy to learn. In this work, we identify a curse of ambiguity: in large language models, and more broadly in all neural networks that produce discrete probability distributions, the more ambiguous a next-token distribution is, the harder it is to learn accurately. Through an extensive theoretical analysis, we trace this curse to architectural and learning roots. More ambiguous distributions require more capacity to be stored, larger embeddings to be represented, more steps to be fitted, and amplify token-sampling noise. We validate these findings on synthetic tasks with controlled ground truth and observe the same signatures in language models trained on real data. Our results provide a new perspective on the statistical capabilities of large language models and a practical framework for when to trust their output distribution.
[NLP-76] Semantic Space of Parts of Speech
【速读】： 该论文旨在解决传统词性标注（Parts of Speech, POS）在欧洲语言学传统中被视为清晰、二元分类的局限性问题。尽管语料库语言学通常对每个词项进行唯一且明确的词性标注，但这种标注很大程度上依赖于人为制定的注释手册，缺乏对词性之间语义或句法连续性的考量。实际上，部分词汇在语义或句法分布上处于不同词性之间，而某些词性本身也存在不同程度的邻近性，表明词性分类本质上具有模糊性（fuzziness）。为揭示这一内在模糊性，研究采用word2vec词向量表示，并训练神经网络将高维词向量降维至与词性判别相关的三维空间，从而构建一个可可视化词性关系的语义-句法空间。该方法通过映射数千个词汇，识别出典型词性成员及其边界词项，直观呈现各词性之间的相对位置与关联。研究基于Universal Dependencies标准，覆盖法语、捷克语、芬兰语、俄语和英语五种语言的词性标签，验证了词性分类的连续性特征。其解决方案的关键在于利用低维嵌入空间实现对词性模糊性的量化建模与可视化，突破了传统刚性分类范式。
链接: https://arxiv.org/abs/2608.15443
作者: Jiří Milička,Ivan Kraus,Arnold Stanovský,Anna Vysloužilová,Barbora Štěpánková,Lenka Fárová,Vojtěch Cink,Šárka Dohnalová
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Parts of speech categorization is understood in the European linguistic tradition as crisp categorization, which is also reflected in corpus linguistics, where each disambiguated token is assigned exactly one POS. However, the assigned categories are largely determined by arbitrary decisions distilled into annotation manuals. Since some words stand between parts of speech in their semantics or typical syntax, and some parts of speech are closer to each other than others, POS categorization seems inherently fuzzy. We analyze this fuzziness using word2vec embeddings, training a neural network to reduce their high dimensionality to three dimensions relevant for determining parts of speech. This creates a three-dimensional space onto which we map several thousand words, revealing which are prototypical and which lie on the boundaries, and visualizing relationships between parts of speech. The study uses Universal Dependencies POS tags for French, Czech, Finnish, Russian, and English.
[NLP-77] Gated Against One Model Open to the Next: Option-Only Solvability in Legal Multiple-Choice Benchmarks
【速读】： 该论文旨在解决多选题评测中一个关键但常被忽视的问题：模型的正确回答是否真正依赖于对问题的理解，还是仅通过选项位置偏好或数据泄露等捷径机制实现。现有评测体系仅关注答案正确性，无法揭示模型是否存在“盲答”（即在无问题的情况下仍能准确作答）的现象，从而导致评估结果失真。其解决方案的关键在于设计并应用一种“筛选门控机制”（gating mechanism），通过识别和排除那些可通过选项位置规律或外部知识直接推断出答案的题目，以隔离出真正需要理解题干的测试项。研究基于乌克兰司法人员资格考试（UA-JudgeExam）的11,990道四选一题目进行实证分析，发现即使在隐藏问题的前提下，多个大模型（如GPT-5.6、Sonnet 4.6）仍能显著高于随机水平作答，表明存在严重的选项位置偏差与潜在的数据泄露问题。进一步分析显示，经过门控筛选后保留的8,128个题目上，多数模型性能下降，而少数模型仍保持优势，证明筛选机制可有效捕捉真实理解能力。然而，该信号不具备泛化性，无法通过过滤提升整体模型表现。此外，研究还发现题目格式（如选项长度、与题干的关联性）决定了此类漏洞是否可被利用，而模型能力则决定了其提取信息的程度。因此，论文强调评测基准的设计必须考虑题目结构对模型行为的影响，并公开了完整语料库、预测结果与评估工具链，以推动更严谨的生成式 AI 评估范式发展。
链接: https://arxiv.org/abs/2608.15428
作者: Volodymyr Ovcharov
机构: LEX AI; Legal.org.ua(法律组织网站)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 21 pages, 4 figures. Dataset, model predictions and code at this https URL
Abstract:Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question. Measuring that gap takes care: a model answering A to most items scores above chance wherever the key sits at A, and reads as recognition when it is not. We measure it on UA-JudgeExam: 11,990 four-option items with official keys, published by Ukraine’s Higher Qualification Commission of Judges. Shown the options and no question, Claude Haiku 4.5 scores 0.383 against chance, and the leak is concentrated: 11.8% of items are answered blind on all eight option orders, against 0.2 items expected by chance. It is not quotation: search over 280,059 editions of Ukrainian legislation recovers 0.128. Gating those out retains 8,128 items, on which the gating model itself now scores 0.204, and GPT-5.6, which took no part in the selection, still answers 0.515 of them with the question hidden. Scoring twelve held-out models on the whole set and subtracting each one’s answer-position habit, only two keep an excess: GPT-5.6 at +0.265, Sonnet 4.6 at +0.081. Without it the ranking misleads: Llama 3.1 8B scores 0.292 blind, above every model but those two, purely by answering A to 92% of items. The gate does select something real: on the items it rejected, eleven of twelve models score 0.518-0.789, every interval clear of what the same model scores on the items it kept. But that signal is one model’s, and filtering on it does not transfer upward. Neither is visible on a 400-item sample, where nine models read as “statistically at chance”. Rewriting distractors instead overshoots to 0.168, below chance and as exploitable. The same probe on LEXam returns chance: every option there points into the stem, none longer than 33 characters. Item format decides whether the problem can arise; capability decides how much is extracted. We release the corpus, the predictions and the harness. Comments: 21 pages, 4 figures. Dataset, model predictions and code at this https URL Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2608.15428 [cs.CL] (or arXiv:2608.15428v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.15428 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-78] Large Language Model Assisted Operational Monitoring for Battery Energy Storag e System Integrated Power Distribution Networks
【速读】： 该论文旨在解决分布式配电系统中因电池储能系统（Battery Energy Storage System, BESS）广泛应用而带来的运行遥测数据量大、复杂度高的问题，尤其在电压调节与需求响应场景下，传统人工分析难以高效实现对海量数据的实时工程评估。其解决方案的关键在于构建一种基于生成式AI（Generative AI）的智能监控框架，通过将大语言模型（Large Language Model, LLM）接口与结构化遥测数据库相集成，实现用户以自然语言提出运维问题后，自动转化为经过验证的SQL查询语句，并基于预定义的数据库模式和经批准的关键绩效指标（KPI）视图进行数据检索。随后，系统对获取的母线电压、荷电状态（State of Charge）、有功功率及无功功率等关键参数进行工程约束校验，涵盖电压越限、BESS运行边界及需求响应跟踪性能等维度。该框架通过硬件在环联合仿真数据进行了验证，案例研究证实其能够准确生成有效查询、识别重复电压越限事件、检测无功功率超调现象并量化有功功率跟踪性能，表明基于LLM的智能监控可有效实现电网遥测数据与自动化工程评估之间的闭环连接，显著提升BESS集成系统运行分析的效率与智能化水平。
链接: https://arxiv.org/abs/2608.15396
作者: Azmeer Akhtar,Md Fazley Rafy,Anurag K. Srivastava
机构: West Virginia University (西弗吉尼亚大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Systems and Control (eess.SY)
备注:
Abstract:Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which increases the volume and complexity of operational telemetry available to grid operators. This paper presents an AI-enabled monitoring framework that connects a large language model (LLM) interface with a structured telemetry database for BESS-integrated distribution system analysis. Operator questions are submitted in natural language and translated into validated SQL queries using predefined database schema information and approved KPI views. Retrieved measurements, including bus voltages, state of charge, active power, and reactive power, are evaluated against engineering constraints for voltage limits, BESS operation, and demand response tracking. The framework is validated using hardware-in-the-loop co-simulation data from a BESS-equipped distribution feeder operating under reactive power-based voltage control and price-driven demand response. Case studies show that the framework generates valid database queries, identifies repeated voltage violations, detects reactive power overshoot, and evaluates active-power tracking performance. The results show that LLM-assisted monitoring can connect structured grid telemetry with automated engineering assessment for BESS operation analysis.
[NLP-79] he Machines Internal Clock: Do LLM s Share Human Temporal Illusions?
【速读】： 该论文旨在探究纯文本叙事是否能够诱发人类的时间知觉错觉（temporal illusion），即在缺乏视觉或听觉刺激的情况下，书面语言能否通过上下文和关系线索影响人们对时间流逝的主观判断。其核心问题是：自然语言叙述是否具备与视听刺激相当的心理学影响力，从而引发类似的时间感知偏差。解决方案的关键在于构建一个包含6,684对叙事样本的新基准数据集，覆盖五类已知的时间知觉错觉，并通过实验对比人类读者与14个大型语言模型（LLM）在这些情境下的选择行为。研究发现，人类仅在两种错觉中表现出预期偏好，且这些错觉的操纵手段在文本中具有显性呈现；而大多数语言模型却在四种错觉中均选择了文献预测的“错误”答案，偏离了人类行为模式。进一步分析显示，约70%的模型推理过程明确引用心理学研究成果，表明其表现更可能是基于对已有文献的检索与复现，而非内化的人类式时间感知偏差。这揭示了当前生成式模型在时间认知建模方面存在与人类根本不同的机制。
链接: https://arxiv.org/abs/2608.15394
作者: Catherine Bao,Vivek Srikumar
机构: 未知
类目: Computation and Language (cs.CL)
备注: 25 pages, 24 figures
Abstract:Human perception of time is subjective. Well-documented temporal illusions show that the brain relies on context and relational cues for judging duration instead of tracking elapsed time directly. Prior studies established these effects with visual and auditory stimuli. Existing LLM evaluations of temporal perception focus on estimating event durations or multi-step temporal reasoning. In this work, we investigate whether written narratives alone can evoke human temporal illusions, using a new benchmark of 6,684 narrative pairs spanning five illusions. We find that human readers (60 participants) prefer expected scenarios in only two of the five illusions, those where the manipulation is directly visible in text rather than requiring readers to internally simulate duration. We evaluate 14 LLMs on the same benchmark. Surprisingly, we find that models pick the literature-predicted scenario across four of the five illusions, diverging from human behavior. Reasoning traces show that ~70% of responses explicitly evoke psychology research, suggesting that this alignment is consistent with retrieval of published findings rather than human-like temporal biases.
[NLP-80] When AI Rewrites Classifiers Relax: Uncertainty-Aware Sentiment Analysis on Sarcastic and AI-Paraphrased Social Text
【速读】： 该论文旨在解决情感分类器在处理讽刺性文本（sarcastic text）和人工智能生成文本（AI-generated text）时性能下降及评估失效的问题。现有标准评估方法在这些非典型分布场景下缺乏指导意义，导致模型预测可靠性存疑。其解决方案的关键在于揭示并利用模型的置信度信号：首先发现讽刺性文本的置信度显著低于非讽刺文本，表明模型能自发感知讽刺带来的不确定性；其次，意外发现基于大语言模型（LLM）生成的改写文本反而使分类器准确率提升，归因于AI改写消除了原始文本中的分布噪声，使样本更符合训练数据的原型特征；最后提出一种轻量级拒答（abstention）策略，通过剔除置信度低于0.6的14%输入，将保留子集上的准确率从82.2%提升至88.9%，显著增强高风险应用场景（如心理健康预警与内容审核）的可靠性。研究还验证了语义熵与MC-Dropout式分歧作为不确定性指标在短文本上的等效性（AUROC分别为0.650与0.646），支持采用计算成本更低的方法。整体上，研究推动了从“自信单标签预测”向“不确定性感知拒答”的范式转变。
链接: https://arxiv.org/abs/2608.15338
作者: Shresth Shroff
机构: Manipal University Jaipur(曼帕尔大学贾伊普尔)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Sentiment classifiers are increasingly applied to social media content that is either sarcastic or AI-generated — two distributional regimes where standard evaluations offer little guidance. We present a three-part empirical study of sentiment classifier behaviour under these conditions. First, we find that confidence scores on sarcastic text are significantly lower than on non-sarcastic text (Mann–Whitney p = 2 \times 10^-6 ), confirming that classifiers sense their own uncertainty on ironic content even without explicit uncertainty modelling. Second, and counterintuitively, we show that sentiment classifiers achieve higher accuracy on AI-paraphrased reviews than on the original human-authored text (RoBERTa: +5.8 pp for Qwen3.5-4B paraphrases, +3.7 pp for Gemma4-E4B), revealing a cross-domain stylistic alignment effect: AI paraphrases remove distributional noise that confounds Twitter-trained classifiers, producing cleaner, more prototypical sentiment text. Third, we demonstrate that a lightweight abstention wrapper — flagging the 14% of inputs with confidence below 0.6 — improves accuracy from 82.2% to 88.9% ( +6.7 pp) on the retained set. We further compare Semantic Entropy and MC-Dropout-style disagreement as uncertainty signals and find near-identical AUROC ( 0.650 vs.\ 0.646 ) on sarcastic text, suggesting that for short social media inputs, both methods are interchangeable. Our results motivate a shift from confident single-label prediction to uncertainty-aware abstention in high-stakes sentiment applications such as mental health flagging and content moderation.
[NLP-81] Logical Embeddings for Argument Analysis
【速读】： 该论文旨在解决传统自然语言处理（NLP）任务中基于上下文的词嵌入（contextualized word embeddings）在表达论证结构语义时存在的局限性，尤其在论点分析（argument analysis）任务中，现有方法难以有效捕捉论证的逻辑关系。其核心解决方案是引入一种新型的逻辑嵌入（logical embeddings），该嵌入直接利用论证结构进行编码，能够更准确地表征论点的逻辑语义。其关键在于：基于数学逻辑构建了一种具备理论保障的相似性度量方法，该度量不仅具有可解释性，且满足一系列理想性质（如正定性、保序性等），并由此导出一个半正定核函数，使得逻辑嵌入可在再生核希尔伯特空间（Reproducing Kernel Hilbert Space, RKHS）理论框架下唯一定义。该方法被证明是信息无损的最优编码方式，确保了论证逻辑信息的完整保留。实验表明，逻辑嵌入在分类任务中显著优于主流嵌入方法，展现出在生成式人工智能（Generative AI）与推理型任务中的强大潜力。
链接: https://arxiv.org/abs/2608.15325
作者: Leander Heldring,Santiago Torres
机构: Kellogg School of Management (凯洛格管理学院); Northwestern University (西北大学); Department of Economics (经济系); MIT (麻省理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We propose a new framework for machine-learning-oriented argument analysis tasks. Our proposal involves replacing traditional contextualized word embeddings used in most NLP tasks with logical embeddings, an alternative encoding that directly exploits argumentation structures. In essence, logical embeddings encapsulate the logical semantics of an argument, allowing for a better representation of its meaning. Supporting these embeddings is a mathematical logic-based similarity measure that offers a transparent notion of proximity and is guaranteed to satisfy several desirable theoretical properties that current cosine similarity-based contextualized word embeddings cannot assure. This similarity measure induces a positive semi-definite kernel on the set of arguments, enabling us to uniquely define logical embeddings using the theory of Reproducing Kernel Hilbert Spaces (RKHS). Moreover, we prove that this encoding is optimal, in the sense that no logical information is lost in the process. As with other RKHS applications, logical embeddings can be used in numerous supervised and unsupervised tasks. We provide an implementation of the method and aim to test it against literature benchmarks. Additionally, we demonstrate that logical embeddings outperform most standard embedding methods on a classification task.
[NLP-82] When Do Concepts Become Functionally Sufficient During Language-Model Training?
【速读】： 该论文旨在解决深度神经网络中模型内部结构何时具备功能性意义的问题，即如何识别模型各层与检查点（checkpoint）中激活状态的有用性，而不仅仅是依赖最终模型性能进行评估。传统方法往往仅关注模型输出结果，忽略了对中间表征动态演化的深入理解。为此，论文提出基于概念动态（concept dynamics）的分析框架，其核心在于通过稀疏软掩码（sparse soft masks）对各层激活进行分解，并将掩码重构后的信号重新注入模型，以功能化地检验这些掩码的有效性：一个掩码是否“有用”取决于其在干预后能否维持目标表征的下游任务表现。该方法的关键在于将分解假设视为可验证的科学假设而非不可证伪的解释性保证，转而通过跨检查点的功能充分性（functional sufficiency）以及在学习对齐下的源到终态重建能力来动态评估模型内部表征的稳定性与可迁移性。实验结果显示，在七个模型共享的固定惩罚操作点上，下游任务所需的掩码所保留的软质量（soft mass）远低于用于激活重建的掩码，且预测分布变化极小，表明模型关键信息高度浓缩于少数有效激活模式中。
链接: https://arxiv.org/abs/2608.15323
作者: Raphael Bernas,Paul G. Chevalier,Fanny Jourdan,Céline Hudelot
机构: MICS, CentraleSupelec, Université Paris-Saclay; IRT Saint Exupéry, Toulouse, France; Mila - Quebec AI Institute, Montreal, Canada
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding a model and its learning mechanisms in depth requires identifying when its internal structures become useful, rather than simply looking at the final state. We study this through concept dynamics: at each layer and checkpoint, we decompose activations, select sparse soft masks, and inject masked reconstructions into the model. Concept analysis is therefore tested functionally: a mask is useful only insofar as it preserves a target under intervention. We compare sufficiency for activation reconstruction, linear decodability, true downstream preservation, and checkpoint transfer under learned alignment. The framework treats decomposition assumptions as hypotheses rather than interpretability guarantees, monitoring functional sufficiency across checkpoints and source-to-final reconstructability under learned alignment. At the shared fixed-penalty operating point across seven models, downstream masks retain substantially less soft mass than reconstruction masks; predictive-distribution shifts remain small.
[NLP-83] VTInstructor: Visual Trajectory Prompting for Navigation Instruction Generation in Continuous Environments ACM-MM2026
【速读】： 该论文旨在解决在连续环境（continuous environments）中从第一人称RGB视频生成导航指令的挑战性问题，尤其针对人机交互与可扩展数据集构建的应用场景。传统指令生成方法依赖于离散视角图（discrete viewpoint graphs）和全景观测，其轨迹结构明确；但在连续环境中，智能体仅接收密集的RGB流，导致轨迹线索难以恢复。为此，论文提出VTInstructor，首个面向连续环境的视觉-语言导航（VLN）指令生成框架。其核心解决方案在于将隐式的轨迹几何信息转化为显式的视觉轨迹提示（visual trajectory prompts）：EDTC将长序列RGB轨迹压缩为导航关键帧，VTP在这些锚点上叠加路径、转向及目标等语义提示，VTMod将生成的轨迹信号注入视觉编码器，而VT-GRPO在训练过程中进一步校准空间注入策略。整个框架无需导航图、预构建地图或场景重建。在具有挑战性的R2R-CE和RxR-CE未见验证集上，VTInstructor在所有标准自然语言生成（NLG）指标上均达到新基准，分别超越最强基线+0.357和+0.109 CIDEr；此外，生成的指令使冻结跟随者成功率提升至63.3%，较最优对比方法提高14.7个百分点，并在下游导航任务中带来稳定的+3%成功率增益，验证了其在数据增强方面的有效性。
链接: https://arxiv.org/abs/2608.15284
作者: Haolin Yang,Yuxing Long,Zihan Yang,Hao Dong
机构: Peking University (北京大学); PrimeBot; CFCS, School of Computer Science, Peking University (北京大学计算机学院前沿计算科学中心)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: accepted by ACM MM 2026
Abstract:Navigation instruction generation from ego-centric RGB video in continuous environments is an important yet challenging task for human-robot interaction and scalable dataset construction. Prior instruction generators assume discrete viewpoint graphs with panoramic observations, where trajectory structure is explicit; in continuous environments, however, the agent receives only a dense RGB stream, making trajectory cues difficult to recover. We propose VTInstructor, the first VLN instruction generation framework for continuous environments. Our key idea is to convert implicit trajectory geometry into explicit visual trajectory prompts: EDTC condenses long RGB trajectories into navigation-critical keyframes, VTP overlays path, turn, and goal cues onto these anchors, VTMod injects the resulting trajectory signals into the visual encoder, and VT-GRPO further calibrates this spatial injection during training, all without requiring a navigation graph, pre-built map, or scene reconstruction. On the challenging R2R-CE and RxR-CE Val Unseen benchmarks, VTInstructor sets a new state of the art across all standard NLG metrics, surpassing the strongest baseline by +0.357 CIDEr and +0.109 CIDEr, respectively. Beyond automatic metrics, VTInstructor-generated instructions raise a frozen follower’s success rate to 63.3%, a +14.7 percentage-point gain over the best competing instruction source, and provide consistent data augmentation gains of +3 SR points on downstream navigation tasks.
[NLP-84] me as Structure: Temporal Dependency Graphs for Verifiable Deadline Computation over Legal Documents
【速读】： 该论文旨在解决法律时效性判断中因复杂时间计算导致的错误问题，特别是在英国就业上诉法庭（UK Employment Appeal Tribunal）判决中，如何准确计算文件提交截止日期。其核心挑战在于：时效期限由触发事件起算，遵循法定计日规则，并可能受强制调解期暂停影响，使得人工或自动化系统难以正确推算。解决方案的关键是构建一个基于时序依赖图（temporal dependency graph）的管道系统，将日期事实及其依赖关系结构化，并通过校准日历的计算引擎进行精确的时效推演。实验表明，该管道系统在21个案例中正确复现了6/7个时效裁定，且与法官判定日期完全一致；相比之下，最强的语言模型虽能正确完成数学运算，却在结论上出现系统性错误——在21次响应中6次自相矛盾，均错误地将逾期申请判定为有效。在大规模测试中（427个生成案例），该管道系统的准确率达90.2%，远高于直接使用语言模型的61.2%。研究进一步指出，当前瓶颈并非计算本身，而在于从文本中准确提取起始事件（即“哪个事件启动时效”），说明信息抽取仍是制约系统性能的关键环节。
链接: https://arxiv.org/abs/2608.15270
作者: Maryia Zhyrko,Lifeng Han,Suzan Verberne
机构: Leiden Institute of Advanced Computer Science (LIACS), Leiden University (莱顿大学高级计算机科学研究所)
类目: Computation and Language (cs.CL)
备注: 13 pages, 2 figures, 5 tables. Preprint
Abstract:Miss a filing deadline by one day and the claim is barred, however strong the case. Computing that deadline is rarely simple: the period runs from a triggering event, is counted by a statutory convention, and may be suspended by a mandatory conciliation window. We ask whether a language model should answer such questions directly, or read the document and leave the arithmetic to code. We extract dated facts and their dependencies into a temporal dependency graph and compute deadlines from it with a calendar-correct engine. On UK Employment Appeal Tribunal judgments the engine reproduces six of seven timeliness rulings, and matches the judges’ own dates to the day. The strongest of four language models, asked the same cases, gets the arithmetic right and the answer wrong: in six of twenty-one responses its stated verdict contradicts its own thinking, and every contradiction runs the same way, calling a late claim timely. To test the systems at scale we move the dismissal date across the statutory boundary, generating 427 cases whose answers are computed rather than annotated. On the cases both systems answer, the pipeline is right 90.2% of the time against 61.2% for direct answering. The limit is extraction: on contracts the errors are almost never in the arithmetic, but in choosing which event the period starts from.
[NLP-85] Demographic Injection in Medical Language Models under Diversity Equity and Inclusion Prompts
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在临床辅助应用中因引入多样性、公平性与包容性（DEI）提示而引发的“患者身份误构”问题，即模型在未获授权的情况下擅自添加患者的人口学特征（如种族、社会经济地位、性别），从而实质性改写患者身份。其解决方案的关键在于揭示：一个简短的DEI提示即可使模型在47个测试模型中的注射率从0.7%飙升至33.1%（提升47倍），且该效应主要归因于提示中的公平性内容而非长度增加（较长度匹配对照组高出18倍，p=1.4×10⁻¹⁴）。研究进一步发现，尽管多数新增内容为泛化人口陈述且不改变答案，但仍有0.25%–2.4%的响应将特定属性错误关联至患者个体或改变推荐选项，其中99.8%的情况导致错误结论。此外，提示措辞显著调节该效应，使其在14%至56%之间波动。该现象表明，任何引导模型推理方式的指令都可能诱发此类无意识的虚构信息注入，因此被标记的输出应被视为模型缺陷而非临床建议。
链接: https://arxiv.org/abs/2608.15254
作者: Diego Mardian,Frank Liu
机构: Arizona State University (亚利桑那州立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI). We measure a side effect that misrepresents patients: a one-sentence DEI prompt appended to a medical question leads models to add patient demographic attributes (race, socioeconomic status, sex) the question never stated, in effect rewriting who the patient is. We call this demographic injection. Across 47 models, four medical benchmarks, and 376,000 responses scored by a validated model-judge pipeline, a single DEI prompt raises the injection rate from 0.7% to 33.1% (47x) in all 47 of 47 models, attributable to the equity content rather than to added length (18x above a length-matched control; p=1.4x10^-14). Most added content is a general population statement that leaves the answer unchanged, but a smaller subset attaches an attribute to the specific patient or changes the selected option (0.25-2.4% of responses, 99.8% toward the incorrect option), where the invented demographic changes the answer the model recommends. Phrasing scales the effect from 14% to 56%. DEI prompts are just one example of a more general mechanism. Any instruction that nudges how a model reasons can make it add unrequested details, including details about the patient. Flagged outputs are treated as model errors under study, not clinical guidance.
[NLP-86] RACE-BN: Transferring Bangla-English Tutoring Behavior to a Sub-1B Offline Language Model
【速读】： 该论文旨在解决孟加拉语（Bangla）母语者在学习英语过程中，仅依赖正确翻译无法有效掌握语言知识的问题，尤其针对欧洲共同语言参考框架（CEFR）A1-A2水平的学习者，其核心挑战在于缺乏对语法差异的深入理解、对常见错误的认知以及针对性的练习。为应对这一问题，研究提出一种结构化教学轨迹数据集——TRACE-BN，通过课程引导的方式生成包含词级注释、直译与自然译文、孟加拉语语法解释、模拟学习者错误及对应靶向练习题与答案的多组件教学轨迹。关键解决方案在于：利用Gemini 3.5 Flash Lite作为教师模型，基于孟加拉国教育部（NCTB）九至十年级英语课程内容生成初始教学轨迹，并通过结构有效性、脚本完整性和语义重复性过滤提升数据质量；随后采用低秩适应（LoRA）结合4比特量化技术，将生成的教学行为高效迁移至参数量小于10亿的Qwen3-0.6B模型中，实现资源受限环境下的离线部署。实验结果表明，该方法在保留结构完整性的同时显著提升了生成质量（如chrF++从15.28升至34.77，BLEU从4.52升至21.03），且人工评估验证了翻译、语法解释、错误诊断与练习匹配度等方面的综合改进。研究表明，基于课程引导的结构化监督可成功将多组件教学行为迁移至超轻量级模型，在保持高质量教学输出的前提下，为低资源场景下的个性化英语辅导提供了可行路径。
链接: https://arxiv.org/abs/2608.15223
作者: Khan Raiyan Ibne Reza,Sanjana Aktar Maria,Mohammad Tushar Abdullah,Asfee Bhuiyan Leen,Sumaiya Tabassum Nimi
机构: North South University (北南大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Bangla-English tutoring requires more than producing a correct translation: learners also need explanations of grammar differences, awareness of their likely errors, and targeted practice. We present TRACE-BN, a curriculum-guided dataset of structured tutoring traces for Bangla-speaking learners of English at the CEFR A1-A2 level. Each trace combines word-level glosses, literal and natural translations, Bangla grammar explanations, a plausible learner error, and a targeted practice question with its answer. The traces are generated by Gemini 3.5 Flash Lite as the teacher model from NCTB Classes 9-10 English curriculum units, then filtered for structural validity, script integrity, and semantic duplication. We transfer the resulting structured tutoring behavior to Qwen3-0.6B using LoRA with 4-bit quantization for resource-constrained offline deployment. On held-out inputs, schema validity increases from 85.4% to 95.8%, while, against teacher-model references, chrF++ improves from 15.28 to 34.77 and BLEU from 4.52 to 21.03. Field-level evaluation by two independent judges shows improvements across translation, grammar explanation, learner-error diagnosis, and practice alignment, while a human audit supports the quality of the supervision data. The results show that curriculum-guided structured supervision can transfer multi-component tutoring behavior to a sub-1B model under these resource constraints. The dataset, model checkpoints, and code are publicly available at this https URL
[NLP-87] Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models
【速读】： 该论文旨在探究生成式语言模型（Generative Language Models）在词序偏好上的表现，特别是其在人工语言与类型学多样性的自然语言中的差异。研究发现，在人工语言中，解码器仅模型（decoder-only models）表现出左向分支（left-branching）的偏好，这一倾向既不符合自然语言的普遍规律，也与人类学习词序的认知偏差不一致；而在自然语言中，单语模型在小规模数据下未表现出明确的词序基线偏好，但随着训练数据量增加，逐渐显现出对右向分支主谓宾（SVO）结构的显著偏好，尽管跨语言统计上主宾谓（SOV）才是最普遍的词序。这一SVO优势在多语言模型中依然存在，并且与语言资源水平和数据质量相关，而非词序本身。因此，模型在人工语言与自然语言中呈现相反的词序偏好，表明实际观察到的词序偏向本质上是数据驱动的。由于高资源语言以SVO为主，大规模采用大语言模型（LLMs）可能进一步削弱词序多样性，尤其对那些灵活使用多种词序的语言构成潜在威胁。该研究的关键在于揭示词序偏好并非由模型架构决定，而是由训练数据分布所主导，强调了数据生态对语言模型行为的深远影响。
链接: https://arxiv.org/abs/2608.15129
作者: Varvara Arzt,Allan Hanbury,Terra Blevins
机构: Khoury College of Computer Sciences, Northeastern University (东北大学计算机科学学院); TU Wien (维也纳工业大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: paper under revision
Abstract:We systematically compare word order preferences in decoder-only language models across 192 artificial languages and typologically diverse natural languages. On artificial languages, models exhibit a left-branching preference that aligns with neither natural language universals nor human word order learning biases. On natural languages, monolingual models show no clear base word order bias at small scales, but as data grows, a preference for right-branching subject-verb-object (SVO) languages emerges while SOV falls behind despite being the most frequent order cross-linguistically. This SVO advantage extends to multilingual models and correlates with language resource level and data quality rather than word order. Thus, the same architecture exhibits opposite preferences on artificial and natural languages, establishing that word order biases observed in practice are data-driven. Since highly-resourced languages are overwhelmingly SVO, these biases risk gradually reducing word order diversity, particularly in languages that productively use multiple word orders, with the widespread adoption of LLMs.
[NLP-88] A Declarative-Procedural Perspective on Expert Routing in Bilingual Mixture-of-Experts Language Models
【速读】： 该论文旨在解决生成式语言模型在双语语言习得过程中是否会出现基于语言学结构的专家路由（expert routing）分化的问题，特别是探究在顺序语言暴露（sequential language exposure）条件下，混合专家（Mixture-of-Experts, MoE）模型能否形成具有语言学意义的、类别依赖的专家分工。其核心解决方案的关键在于构建基于探针（probe-based）的验证集，并通过提取分词级别的路由分布，利用互信息（mutual information）、路由熵（routing entropy）和 Jensen-Shannon 距离等量化指标，分析不同语言学范畴（如词汇、语法、句法）在专家选择中的特异性。研究发现，尽管顺序教学（curriculum training）模型在第5层达到0.1148的峰值互信息，表明存在一定的语言学结构化路由，但未采用顺序教学的基线模型反而在相同层级实现了更高的0.2599互信息，显示出更强的总体特异性。这一结果揭示了可解释的语言组织结构即使在缺乏顺序语言暴露的情况下也能在MoE路由模式中自发涌现。此外，重复实验表明，无课程条件下的特异性集中于单一语言且受训练种子影响，而课程训练则始终维持稳定、平衡的双语路由分布，说明阶段性双语暴露有助于抑制单一语言主导，而非简单提升整体特异性。
链接: https://arxiv.org/abs/2608.15102
作者: Amrit Gopinath(1),Raghul(1),Durairaj Thenmozhi(2) ((1) Sri Sivasubramaniya Nadar College of Engineering, Chennai, India, (2) Shiv Nadar University Chennai, India)
机构: Sri Sivasubramaniya Nadar College of Engineering, Chennai, India; Shiv Nadar University Chennai, India
类目: Computation and Language (cs.CL)
备注: 15 pages, 6 figures, 12 tables (including appendix)
Abstract:We investigate whether Mixture-of-Experts (MoE) language models develop linguistically structured expert routing during bilingual language acquisition. Inspired by the Declarative-Procedural framework, we analyze lexical, grammatical, and syntactic processing in a decoder-only English-German MoE Transformer trained under sequential language exposure. We construct a probe-based validation set and extract token-level routing distributions to quantify category-dependent specialisation using mutual information, routing entropy, and Jensen-Shannon distance. The curriculum-trained model exhibits a peak mutual information of 0.1148 at layer 5, indicating category-dependent differences in routing distributions across linguistic categories. Surprisingly, a no-curriculum baseline trained on mixed English-German data shows stronger aggregate specialisation, reaching a peak mutual information of 0.2599 at the same layer. These results suggest that interpretable linguistic organization emerges within MoE routing patterns even without sequential language exposure. A replication at a second training seed shows that the no-curriculum condition’s specialisation concentrates on a single language whose identity is seed-dependent, whereas the curriculum consistently yields a stable, language-balanced routing profile; rather than uniformly increasing specialisation, staged bilingual exposure reduces single-language dominance. The official Github repository: this https URL
[NLP-89] Why Vision Fails as a Universal Bridge: Rectifying Modality Asynchrony in Multilingual MLLM s
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在非英语视觉推理任务中性能显著下降的问题。尽管其纯文本骨干模型具备强大的多语言能力，但在融合视觉与语言信息时，非英语输入的处理仍存在严重瓶颈。其核心问题在于：机制分析揭示了一种名为“幽灵锚点”（Ghost Anchor）的现象——即语言翻译至以英语为中心的语义空间的过程在早期层中已提前完成，而视觉语义化过程则尚未成熟，导致视觉信号虽在物理层面存在，但在早期对齐窗口期内功能上“不可见”。解决方案的关键在于提出一种名为ANCHOR的训练框架，通过主动视觉锚定（Proactive Visual Anchoring, PVA）策略，加速早期视觉语义的涌现，使视觉表征能够主动引导语言翻译过程。机制干预实验验证了ANCHOR成功恢复了视觉信号在早期翻译阶段的因果影响。大量实验证明，该方法在XMMMU、MaXM和CVQA等多个基准上均显著优于标准基线，在微调及零样本场景下均展现出跨语言的鲁棒视觉推理能力。
链接: https://arxiv.org/abs/2608.15085
作者: Yihang Du,Juhao Liang,Zhengzhao Lai,Siyu Li,Yan Hu
机构: The Chinese University of Hong Kong, Shenzhen(深圳大学城); Shenzhen Loop Area Institute(深圳环区研究院); National Health Data Institute (Shenzhen)(深圳市健康数据研究院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Multimodal large language models (MLLMs) exhibit substantial performance degradation in non-English visual reasoning, despite the strong multilingual competence of their text-only backbones. While mechanistic evidence from text-only models suggests that non-English inputs are routed through an English-centric latent space, the multimodal implications of this phenomenon remain unexplored. Through rigorous mechanistic analysis, we identify the \textbfGhost Anchor phenomenon: a temporal modality asynchrony where linguistic translation to the English semantic manifold completes in early layers, while visual semanticization remains immature. Consequently, visual signals are physically present yet functionally invisible during the early alignment window. To rectify this, we propose \textbfANCHOR, a training framework employing Proactive Visual Anchoring (PVA) to accelerate early visual semantic emergence, ensuring visual representations proactively guide linguistic translation. Mechanistic interventions confirm that ANCHOR successfully restores the causal influence of visual signals during early translation. Furthermore, extensive experiments on XMMMU, MaXM, and CVQA demonstrate that ANCHOR consistently outperforms standard baselines, achieving robust visual reasoning across both fine-tuned and zero-shot languages.
[NLP-90] A Pilot Study of Autocompleting Tokenizers
【速读】： 该论文旨在解决基于字节级（byte-level）分词的Transformer模型在处理长输入序列时带来的计算开销大和模型性能下降的问题。其核心挑战在于，尽管字节级分词具有语言无关性，但其生成的序列长度显著增加，导致推理效率降低。为此，论文提出一种轻量级自回归字节语言模型驱动的压缩方案：在进入标准编码器-解码器Transformer前，利用该模型识别并移除可由上下文充分预测的冗余字节，从而实现输入序列的有效压缩。关键创新在于通过上下文依赖性判断实现“隐式表示”——即无需显式编码高度可预测的字节，从而在保持甚至提升翻译质量的前提下，将源序列长度减少近三分之一（以英法翻译为例），且在多种语言对（包括芬兰语、俄语、中文等）上均表现出良好的泛化能力，压缩比达0.47至0.67。这一方法揭示了大量输入字节具备高度可预测性，为缓解字节级模型的序列长度负担提供了一种高效、通用的解决方案。
链接: https://arxiv.org/abs/2608.15080
作者: Samuel Wexler,Mark Hopkins
机构: Williams College(威廉姆斯学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Modern input methods routinely rely on autocomplete to omit information that can be recovered from local context. Inspired by these autocomplete-assisted writing systems, we investigate whether Transformer inputs can be compressed in a similar manner. Byte-level tokenization offers a simple and language-independent alternative to subword tokenization, but its longer input sequences typically result in increased computational cost and reduced model quality. We propose a compression scheme that employs a lightweight autoregressive byte language model to identify and remove bytes that are easily predictable from their surrounding context before Transformer processing. The resulting compressed representation is then provided as input to a standard encoder–decoder Transformer. Experiments on machine translation show that a substantial fraction of source-language bytes can be omitted without degrading translation quality. On English–French, our best method preserves translation performance while reducing source sequence length by nearly one-third. Additional experiments on Finnish–English, Russian–English, and Chinese–English demonstrate that the approach generalizes across diverse writing systems and morphological typologies, yielding comparable or improved translation quality at compression ratios between 0.47 and 0.67. These findings suggest that many input bytes are predictable enough to be represented implicitly rather than explicitly, providing a simple mechanism for reducing the sequence-length overhead associated with byte-level models.
[NLP-91] Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在现实环境中面对新任务时，如何实现高效、持续的在线自我改进问题。现有方法多依赖于对累积轨迹的反思、记忆、规则或技能提取，但在真实场景中，代理往往仅获得一次性的执行机会，且单次执行产生的上下文信息高度嘈杂，混杂着通用性知识与任务特异性噪声，导致难以有效提炼可复用的知识。此外，以往研究缺乏在复杂真实任务上的验证，也未能明确自提升机制的核心驱动因素。为此，本文提出“在线抓取学习”（online harness learning）范式，即通过持续更新一个结构化的“抓取框架”（harness），使冻结的代理在序列化任务中实现渐进式优化。其核心解决方案是“Evo-Harness”，关键在于“上下文到抓取的技能编译”（context-to-harness skill compilation）机制，该机制能够从一次性、高噪声的执行过程中，提炼出可跨领域、跨主题复用的技能框架。实验在五个真实世界基准测试（TerminalBench2、SWE-bench、CL-Bench、-bench、WebArena-Infinity）上验证了该方法的有效性，结果表明，该方法不仅显著提升了代理的即时适应能力，还为理解LLM代理如何在无监督条件下实现动态学习提供了系统性原理依据。
链接: https://arxiv.org/abs/2608.15071
作者: Tianxin Wei,Zhan Shi,Minhua Lin,Bing He,Zewen Liu,Yisi Sang,Yuanchen Bei,Xuying Ning,Jiaru Zou,Ting-Wei Li,Xiao Lin,Yanjun Zhao,Chi Wang,Benoit Dumoulin,Dakuo Wang,Jingrui He,Hanqing Lu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowledge from accumulated trajectories via reflection, memory, rules, or skills. However, agents in realistic environments continuously encounter novel tasks, often offering only a one-shot opportunity to improve. These executions yield rich but highly noisy contexts, entangling broadly useful lessons with task-specific artifacts. Critically, prior works rarely validate their effectiveness on complex real-world tasks or isolate the underlying drivers of improvement. To address these gaps, we formulate online harness learning, where a frozen agent improves by continually updating a structured harness across sequential tasks. This formulation enables a systematic study of key self-improvement factors through our proposed Evo-Harness. At its core, context-to-harness skill compilation distills noisy, single-shot executions into reusable skill harnesses for cross-domain and topic-level adaptation. To demonstrate the efficacy of one-shot skill compilation, we evaluate across five realistic benchmarks (TerminalBench2, SWE-bench, CL-Bench, -bench, WebArena-Infinity). Our extensive analysis demonstrates the effectiveness of Evo-Harness and provides a principled understanding of how LLM agents can effectively learn on the fly. Our code is available at this https URL.
[NLP-92] RecurrentGPT : Expressive Depth through Recurrent Modulation in Transformers
【速读】： 该论文旨在解决大规模Transformer语言模型在表达能力（expressivity）与内存效率（memory efficiency）之间存在的固有矛盾：传统多层架构通过每层使用独立权重以保持功能分化（如从输入感知到抽象提炼），但导致显著的内存开销；而标准的深度共享策略虽降低参数量，却因强制所有层执行相同变换而削弱了表征多样性，进而损害建模质量。其解决方案的核心是提出RecurrentGPT——一种基于循环深度结构的Transformer模型，由固定深度的前导块（prelude）和尾部块（coda）夹持一个可迭代重复的共享核心（shared core），共迭代R次。受门控循环神经网络启发，该模型引入轻量级投影与逐元素更新门控机制，其门控信号依赖于隐藏状态、固定前导块输出及每步重新采样的噪声，从而动态调节递归更新过程。这一设计使模型能在有限层数内实现功能特化，避免为获得多样性而堆叠大量独立层。实验表明，在同等FLOPs约束下，3层的RecurrentGPT可达到12层GPT-2 Small的精度，且在九个规模-预算组合中均优于基准方法；在中大尺度下，其性能接近甚至超越密集模型。在同等参数量约束下，递归版本验证损失降至2.76，优于非递归基线的2.84。结果证明，自适应深度复用是一种有效的权衡策略：在大模型场景中，可减少63%参数量与59%峰值解码内存，仅以10%编译生成延迟增加为代价，显著提升资源利用效率与建模质量。
链接: https://arxiv.org/abs/2608.15062
作者: Amr Hegazy,Amr Alanwar,Mostafa Elhoushi
机构: The German University in Cairo(开罗德国大学); Technical University of Munich(慕尼黑工业大学); Cerebras Systems Inc.(Cerebras系统公司)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Scaling transformer language models creates an inherent tension between expressivity and memory efficiency. While unique weights across layers preserve functional specialization—from input-grounding to abstract refinement—they incur a substantial memory footprint. Conversely, standard depth-sharing enforces uniform transformations that collapse representational diversity and degrade modeling quality. We introduce RecurrentGPT, a recurrent depth transformer where fixed-depth prelude and coda blocks bracket a single shared core iterated R times. Inspired by gated recurrent neural networks, we employ a lightweight projection and an elementwise update gate—conditioned on the hidden state, the fixed prelude output, and noise resampled at every step—to modulate the recurrent update. This allows the model to specialize the input to the same few layers across recurrences, rather than requiring many unique layers to achieve functional diversity. Under an isoFLOPS constraint, a 3-layer RecurrentGPT matches the accuracy of a 12-layer GPT-2 Small baseline with similar training and inference FLOPs, and leads MoR and heavy-tail depth sampling in all nine scale-by-budget cells; at medium and large scale it approaches dense quality at the standard token budget and overtakes it at medium scale once that budget is doubled. Under an isoPARAMS constraint, deeper recurrence achieves a 2.76 validation loss versus 2.84 for a non-recurrent counterpart at matched parameter and data budget. Our results demonstrate that adaptive depth reuse is a principled strategy for trading parameters for quality: at large scale, 63% fewer parameters and 59% less peak decoding memory for a 10% increase in compiled generation latency.
[NLP-93] Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints
【速读】： 该论文旨在解决建筑蓝图到材料工程量清单（takeoff）自动转换中的核心挑战，即在多张图纸间进行视觉感知、跨层级的尺寸与逻辑推理，并基于未明示的施工规范实现语义对齐。其解决方案的关键在于构建一个三层协同系统——Handoff-H1：第一层为定制化计算机视觉模型，用于提取图纸中的几何与符号基元；第二层为具备图像操作能力与内部视觉任务工具（如基于CV模型的计数、检测与图纸分解）的工具使用型智能体；第三层为基于精心构建的施工知识库所支撑的持久化、分层结构化项目基础。该系统通过端到端处理原始PDF蓝图，在Construction Blueprint Takeoff Benchmark上实现了81.6%的综合得分（覆盖率达86.1%，数量精度P@25%为78.8%），显著优于最强的前沿模型（最高61分）与独立专业估价师（平均77.6%），在数量精度接近人类水平的同时实现了更优的覆盖率，验证了其在复杂建筑信息理解与推理中的有效性。
链接: https://arxiv.org/abs/2608.15032
作者: Bruno Chicelli,Henrique Alves,Rodrigo Anselmo,Joshua Weinberg,Felipe Lemos,Jan Baryla
机构: Handoff AI Research
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures. Evaluation harness available on this https URL . Request data via e-mail to research@handoff.ai
Abstract:Converting a set of architectural blueprints into a complete material quantity takeoff requires visual perception across drawing sheets, dimensional and multi-hop reasoning, and grounding in construction conventions that the drawings never state. We present Handoff-H1, a takeoff system built from three layers: purpose-built computer-vision models that extract primitives; tool-using agents equipped with image operations and in-house visual-task tools, including CV-model-backed counting, detection and plan decomposition; and a persistent, hierarchically structured project foundation, grounded in a curated construction knowledge base. We evaluate on the Construction Blueprint Takeoff Benchmark: 10 real residential blueprint sets paired with consensus-validated expert takeoffs - 2,009 verified line items, restricted for scoring to the 1,348 primary-tier materials that drive an estimate - scored per trade by an LLM judge on material coverage and quantity Precision@25% (P@.25) and combined into a weighted composite. Under identical scoring from the raw PDF, seven frontier and open-weight models span composites of 35-61, and independent professional estimators - scored against the same reconciled gold standard - post 77.6% (65.5% coverage, 87.9% P@.25). Handoff-H1, working end-to-end from the raw PDF, reaches 81.6% (86.1% coverage, 78.8% P@.25): roughly 20 points above the strongest frontier agent, and above the independent estimators by pairing near-human quantity precision with coverage they do not reach. The evaluation harness is public for the open harbor framework; the blueprint sets and ground truth are available upon request for research use.
[NLP-94] Gathered Not Admitted: How Attention Brings a Latent Variable into Verbalizable Form
【速读】： 该论文旨在解决生成式语言模型中变量表示如何在特定位置被动态激活并可读的问题，尤其关注在多任务共享上下文场景下，变量为何能在某些位置以可读形式呈现。其核心挑战在于揭示变量从隐含状态转变为可被灵活调用的“可读形式”的内在机制。解决方案的关键在于识别出一种由注意力机制驱动的、位于中间深度层的局部化“窗口”结构：该窗口通过自注意力机制在特定深度范围内聚集与变量相关的表征信息，使其在该位置具备可读性。研究发现，这一窗口具有明确的边界特征——下方存在生存失败、上方则发生破坏性消解，且其相对深度在不同架构（如64层混合模型与62层密集模型）中保持一致，表明其为通用计算机制。值得注意的是，该可读性并非由前馈网络（MLP）输出贡献，而是依赖于注意力机制对表征的集中；同时，尽管变量在无使用需求的任务分支中几乎不被激活（减少七倍），说明该窗口具有高度的需求敏感性。此外，读出结果并非对使用程度的直接度量，其数值受多个非线性成分影响，且这些成分对最终输出的影响差异高达7.4倍，提示读出信号需谨慎解释。因此，该研究的关键突破在于定位了变量“安装”与“读取”的具体位置，并揭示了注意力介导的中间深度窗口是实现可读性的核心机制。
链接: https://arxiv.org/abs/2608.15022
作者: Parsa Mazaheri
机构: University of California, Santa Cruz (加州大学圣克鲁兹分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 26 pages, 9 figures, 6 tables. Code and data: this https URL
Abstract:Language models hold latent quantities in a form they can report on, and more of a quantity is present in that form when the task requires reusing it flexibly. What causes a representation to enter that form is open, and the word workspace invites an admission story: a gate that decides what gets in. Testing it on open-weight models with Jacobian lenses, over a benchmark whose five arms share an identical context, we find no gate where it predicts one. Demand raises a concept’s lens visibility beyond what applying an operator to a supplied value produces: +0.050 [+0.045, +0.057] in percentile rank on our primary checkpoint, positive on all four we measure, though that arm answers at ceiling and the accuracymatched contrast is stronger under that readout. At the same time one shared linear map decodes the variable from every arm, the control included, at 6.4-9.0x its selection-corrected floor. What produces the later readable form at the queried position is attention-mediated gathering inside a mid-depth window: separating patch depth from readout depth puts transport there at least 17x above anywhere shallower under non-saturating readouts, with no tested MLP output contributing positively inside it. Under the saturating percentile rank the same grid does not localise the window, which is a fact about that measure. An arm that needs the variable for nothing concentrates sevenfold less, so the window is demand-specific. That window has two measured edges, a survival failure below and destruction above, and it falls at the same fractional depth in a 64-layer hybrid and a 62-layer dense model from another family. We localise where the variable is installed and read, not the route from the passage, which transports nothing. But the readout is not a calibrated measure of use: three components move it to within 12% of one another and differ 7.4x in what they do to the answer.
[NLP-95] Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents
【速读】： 该论文旨在解决长周期大语言模型（LLM）智能体在长期记忆管理中面临的内存底座（memory substrate）选择困境，即在不同运行场景下应采用何种底层存储介质以实现最优性能。其核心问题在于现有评估体系缺乏对各类内存底座（如密集/稀疏索引、文本记录、结构化存储、层次化存储、基于精炼的记忆机制、参数化更新及激活兼容的上下文机制等）在实际应用中的系统性比较与指导。本文通过构建统一评估框架，在三种主干模型和四套涵盖用户中心问答与智能体中心决策任务的基准测试上，对26项性能与效率指标进行控制变量实验。研究发现：单一内存底座无法在所有场景下保持优势——广义检索虽有助于长上下文事实问答，但过度检索会因分散注意力而损害序列决策能力；同时，可扩展性引入了路由维度，部分在中等历史长度表现良好的底座在长时序场景下可能变得昂贵或不稳定。因此，论文提出将“内存底座路由”作为自适应智能体记忆系统的关键组件，为设计高效、可靠且场景感知的长期记忆架构提供了实证依据。
链接: https://arxiv.org/abs/2608.15008
作者: Wei-Chieh Huang,Weizhi Zhang,Yuchen Wu,Yankai Chen,Eric Hanchen Jiang,Wooseong Yang,Yiwei Yang,Henry Peng Zou,Hanrong Zhang,Ying Nian Wu,Haolun Wu,Kai-Wei Chang,Philip S. Yu,Xue Liu,Aylin Caliskan
机构: University of Illinois Chicago (伊利诺伊大学芝加哥分校); University of Washington (华盛顿大学); McGill University (麦吉尔大学); MBZUAI (穆罕默德·本·扎耶德人工智能大学); University of California, Los Angeles (加州大学洛杉矶分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Memory is becoming core infrastructure for long-horizon LLM agents, yet existing evaluations offer limited guidance on which memory substrate, namely the underlying medium in which memory is represented and stored, should be used under different operating regimes. We present a controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms. Across three backbone models and four benchmark suites spanning user-centric question answering and agent-centric decision-making, we instrument 26 performance and efficiency metrics under a unified harness. Our results show that no single substrate consistently dominates: broad retrieval benefits long-context factual QA, while excessive retrieval can harm sequential decision-making by shifting attention away from action-critical context. Scalability introduces a further routing axis, as substrates that perform well at moderate history lengths can become costly or brittle at longer horizons. These findings motivate substrate routing as a necessary component of adaptive agent memory systems and provide empirical guidance for designing efficient, reliable, and regime-aware long-term memory for LLM agents. Code will be made available upon acceptance.
[NLP-96] RamseyGadgets: A Graph Construction Dataset for LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成式图构造任务中难以区分其推理能力与训练数据记忆的问题。由于经典图论问题（如极值Ramsey好图构造）已被广泛研究，现有方法无法有效验证模型是否真正具备自主推理能力。为此，本文提出一种名为RamseyGadgets的新颖数据集，包含70个尚未充分探索的图构造问题，要求在避免特定单色子图的前提下构造具有特殊性质（如固定边颜色）的Ramsey好图。这些问题规模较小（最多10个顶点），可通过SAT求解器自动验证，且数据集可轻松扩展。实验评估了五种开源LLMs在该数据集上的表现，结果显示其在高难度问题上的平均准确率仅为37.70%，其中Gemma-4-31B表现最佳。此外，该数据集还揭示了有助于提升模型性能的关键提示（hint）类型，为未来改进模型在组合数学推理任务中的表现提供了依据。
链接: https://arxiv.org/abs/2608.14999
作者: Zohair Raza Hassan,Deepak Pandita
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Constructing special graphs is an important task within graph theory and computer science. Many popular graph constructions are the result of a comprehensive exploration of relevant graphs and human ingenuity. Given the rise of generative AI usage in mathematics, it is natural to test whether LLMs are able to construct graphs with specified properties using their reasoning capabilities. Unfortunately, many natural graph construction problems, such as finding extremal Ramsey-good graphs (i.e., avoiding specific monochromatic subgraphs), have been explored extensively in the literature, making it difficult to ascertain whether a construction is the product of an LLM’s reasoning capabilities or its recollection from training data. In this work, we introduce \textbfRamseyGadgets, a novel dataset of 70 underexplored graph construction problems that require finding Ramsey-good graphs with special properties (e.g., containing an edge with a fixed color). These problems have reasonably sized solutions (at most 10 vertices) that can be verified by SAT solvers, making them suitable for automatic evaluation. Our dataset is easily expandable, as one can simply change the monochromatic subgraphs being avoided to obtain a new set of problems. We evaluate the performance of five open-source LLMs on our dataset and report the results. Our findings show that LLMs achieve only 37.70% accuracy on the hard-tier problems in our dataset, with Gemma-4-31B achieving the highest performance out of the five. We also showcase how our dataset allows us to ascertain what kind of hints help LLMs perform better at this task.
[NLP-97] Does a Tool Result Carry More Authority Than Plain Text? Three Prospective Studies of False-Claim Adoption in a Synthetic Assignment Task with Claude Opus 5
【速读】： 该论文旨在探究大语言模型在合成查找任务中对不实信息的“采纳”现象，具体关注当模型同时读取和写入外部存储时，其输出是否会受到先前未被验证的工具结果（tool-result）或助手断言的影响。研究的核心问题是：在存在矛盾信息源的情况下，模型是否倾向于采纳未经验证的工具返回数据中的错误赋值（如错误颜色编码），从而导致认知偏差。解决方案的关键在于设计多组对照实验，通过控制信息源类型（如工具结果、助手断言、内联文本）与呈现方式（如是否使用元数据标记、是否提前声明），系统性地检验模型对不同信息源的响应差异。研究发现，在工具结果被标记为“未验证”的情况下，模型仍以显著高于基线的频率采纳其错误内容（15/24），且在可重复性实验中显示出显著差异（p=0.0047），但当将工具结果与提前声明的内联文本置于同一上下文时，二者表现趋于一致（60/60 vs 57/60），表明模型行为更依赖于信息的可及性和呈现方式，而非原始数据来源的固有权重。这揭示出生成式AI（Generative AI）在多源信息融合中存在对“非正式”信息结构的敏感性，而其决策机制并非完全由数据真实性决定，而是受信息封装形式和上下文布局影响。
链接: https://arxiv.org/abs/2608.14992
作者: Justin Bronder
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 20 pages, 2 figures. Includes two document-preregistered studies, exact prompts, and complete program disclosure
Abstract:Language-model systems increasingly read from stores they also write to, so a claim that was merely written earlier can return looking retrieved. We tested whether the message package carrying an unsupported assignment changes which answer a model gives in a synthetic lookup task. Claude Opus 5 selected a color code for a named item or abstained. In an exploratory four-arm study, false-code adoption was 0/24 with no target claim, 0/22 scorable trials when a prior assistant assertion named the target, 14/24 when a tool-result record named it, and 15/24 when that result used a ten-field metadata wrapper that marked it unchecked. The tool-result arm selected the record’s code in 11/12 supported trials and 14/24 unsupported trials, ruling out a fixed output-token bias while leaving substantial planted-token heterogeneity. A document-preregistered replication reproduced the tool-result versus assistant-assertion gap, 7/24 against 0/24, one-sided Fisher exact p = 0.0047. The tool-result rate nevertheless fell from 14/24 to 7/24 across runs made four days apart. A second preregistered study gave the earlier comparison a live text control: both records were announced in advance and placed in the same final user turn, then target binding was swapped between the linked tool result and later inline JSON. Inline text was sufficient for false-code adoption in 60/60 trials; the tool-result condition produced 57/60, so the registered result-first superiority criterion failed, p = 1. The result does not show that tool results have no effect. It shows that native tool-result placement was not necessary and that this experiment did not find greater behavioral weight for the result package than for announced inline text. The findings concern a single model on one synthetic task template, accessed through one API.
[NLP-98] -LLM Compiler: Trusted LLM -based Code Optimization and Verification Framework
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）在代码优化任务中面临的两大核心挑战：一是代码本身的复杂性导致模型难以有效实施广泛且复杂的代码变换；二是LLMs缺乏独立验证变换正确性的能力，从而限制了其在实际编译流程中的可信应用。为此，论文提出了一种名为可信大语言模型编译器（Trusted LLM, T-LLM Compiler）的新型编译架构，其关键在于构建了一个融合高阶LLM代码变换、传统编译器与形式化验证工具的协同系统。该方案通过引入可验证的迭代优化机制，使每次代码变换后均可进行自动正确性验证，并在发现错误时触发修正反馈，从而实现安全、可靠的代码优化。实验结果表明，T-LLM Compiler在PolyBench/C基准测试集上实现了最高达83.3%的代码优化准确率和最高16.1%的性能加速，平均相较标准基线提升26.7%的执行效率，显著提升了生成式代码优化的可靠性与有效性。
链接: https://arxiv.org/abs/2608.14953
作者: Zahra Fazel,Sunanda Gamage,Shayan Shirahmad Gale Bagi,Amir H. Ashouri,Tomasz S. Czajkowski,Bryan Chan,Reza Azimi,Yaoqing Gao
机构: Huawei Technologies, Heterogeneous Compiler Lab (华为技术有限公司，异构编译器实验室); Toronto (多伦多); Canada (加拿大)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Performance (cs.PF); Programming Languages (cs.PL)
备注:
Abstract:Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, and it has since emerged as one of the most fundamental tasks for LLMs to perform; however, at present, LLMs struggle to apply wide-ranging code optimization tasks due to both the complexity of the code and the inability to independently verify the correctness of the transformations. In this paper, we present the Trusted LLM (T-LLM) Compiler, which proposes an advancement in compiler technology through a collaborative effort involving high-level LLM code transformations, traditional compilers, and verification tools. Experimental results reveal that it can significantly improve code correctness when tested on a set of PolyBench/C benchmarks. Our approach facilitates iterative code optimization efforts with verification strategies that enable corrective actions. Through this approach, T-LLM Compiler achieves code optimization accuracy of up to 83.3% and a speedup of up to 16.1% on the PolyBench/C benchmarks, with the transformed code reaching an average of 26.7% speedup wrt standard baselines. Additionally, we release the project’s source code to the open-source community.
[NLP-99] DA-RAC: Distance-Aware Calibration of LLM Judges for Trustworthy AI Auditing
【速读】： 该论文旨在解决生成式AI系统在现实世界应用中，其输出质量与有效性评估因依赖上下文无关的大语言模型（LLM）评分而产生的偏差问题。现有评估方法易受无关的上下文参考样本干扰，导致评价体系出现“上下文诱导的校准偏差”（context-induced miscalibration），从而产生虚假信心，使低质量甚至有害内容通过评估。为此，论文提出了一种基于距离感知的参考锚定校准方法（DA-RAC），其核心在于：针对每项评估场景，检索语义与结构上相似的已标注参考样本作为锚点，依据其与待评样本的距离进行加权，并将邻域难度作为校准与筛选信号。实验证明，相较于零样本、思维链推理及静态锚点基线，DA-RAC显著提升了评估校准度并降低了误通过风险。机制分析进一步表明，模型评分随锚点距离呈现系统性变化，而静态参考可能诱导误导性的决策边界。因此，高质量的LLM判别不仅依赖于更优模型，更需具备可校准、可审计的参考选择机制，尤其在自动化评估用于高影响力生成内容时更为关键。评估判断应建立在相关、可检视且可争议的解释性参照物之上。
链接: https://arxiv.org/abs/2608.14950
作者: Cheng Wu,Vishal Anand,Jaya Krishna Mandivarapu,Xiya Liu,Rui Zhuang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Generative AI systems are increasingly producing real-world artifacts, however their efficacy and validity are often evaluated via context-free LLM-scoring. These judges can be miscalibrated by irrelevant in-context reference examples, creating false confidence and allowing low-quality or harmful outputs to pass evaluation. We study this failure mode as context-induced miscalibration and introduce DA-RAC, a distance-aware reference-anchored calibration method for LLM judges. DA-RAC retrieves semantically and structurally similar labeled anchors for each judgement scenario, weights them by distance, and exposes neighborhood difficulty as a calibration and triage signal. On multi-run LLM-judge evaluation benchmarks, it improves calibration and reduces false-pass risk relative to zero-shot, chain-of-thought evaluation, and static-anchor baselines. Mechanistic analysis shows that judge scores vary systematically with anchor distance, while static references can induce misleading decision boundaries. Thus LLM-judgement requires not only better models, but also calibrated, auditable reference selection, especially when automated evaluation is used to support high-impact AI generated artifacts. Judgments should be grounded in relevant, inspectable, and contestable interpretive artifacts.
[NLP-100] rust Is Not Enough: Influence Calibration for On-Policy Self-Distillation in Agent ic RL
【速读】： 该论文旨在解决在策略自蒸馏（On-policy Self-Distillation, OPSD）中因教师信任度（teacher trust）分配监督信号所导致的“信任-效用错配”问题，即仅依据教师信任度分配监督权重无法反映某个词元是否真正有助于当前策略目标的优化。其解决方案的关键在于提出一种名为影响校准自蒸馏（Influence Calibration for Self-Distillation, ICSD）的新方法，通过测量每个被监督词元对其重要性加权的强化学习代理损失（RL surrogate contribution）对教师引导输出扰动的一阶响应，从而量化该词元对策略优化的实际贡献。在此基础上，采用批处理自适应校准机制将非平稳的响应信号转换为有界且可解释的分配权重，同时保持每个动作回合内的辅助损失总量不变。该方法仅作用于蒸馏损失，无需额外模型前向传播。实验结果表明，在ALFWorld、WebShop和Search-QA三个基准任务上，ICSD在两种策略优化算法（GRPO与GiGPO）下均显著优于仅依赖信任度的分配方式，覆盖1.5B至7B参数量级的两种模型架构；在7B模型上，实现了96.1%的ALFWorld成功率和93.1的WebShop得分。冻结批次分析显示，ICSD将教师支持但与目标相悖的词元所占质量比例从60.1%降至37.8%，并使蒸馏信号与强化学习梯度之间的余弦兼容性提升0.192，验证了其有效性和对策略优化方向的精准对齐。
链接: https://arxiv.org/abs/2608.14945
作者: Qizhen Lan,Xi Xiao,Xiangchen Guan,Mengchen Fan,Moule Lin,Jung Im Choi,Lijing Zhu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:On-policy self-distillation (OPSD) gives language agents dense token-level supervision from a privileged self-teacher on the policy’s own trajectories. Existing methods allocate this supervision mainly by teacher trust, but trust does not reveal whether emphasizing a token supports the current policy objective. We call this the trust-utility mismatch and introduce Influence Calibration for Self-Distillation (ICSD). For each supervised token, ICSD measures the first-order response of its importance-weighted RL surrogate contribution to a teacher-directed output perturbation. Batch-adaptive calibration converts this non-stationary signal into a bounded allocation weight while preserving the original auxiliary-loss mass within each action turn. These detached weights affect only the distillation loss and require no additional model pass. Across ALFWorld, WebShop, and Search-QA, ICSD improves all matched aggregate metrics over trust-only allocation under Group Relative Policy Optimization (GRPO) and Group-in-Group Policy Optimization (GiGPO), across two model families spanning 1.5B to 7B. At 7B, it reaches 96.1% ALFWorld success and a WebShop score of 93.1. Frozen-batch analyses show that ICSD reduces teacher-supported mass assigned to objective-opposed tokens from 60.1% to 37.8% and raises cosine compatibility with the RL gradient by 0.192. A companion repository is avail- able at this https URL.
[NLP-101] SkillComposer: Learning Reusable Skills for Natural-Language Robot Programming
【速读】： 该论文旨在解决自然语言接口在执行复杂机器人任务时的局限性问题，特别是在处理多步骤任务、高阶指令分解以及复用已有解决方案方面的挑战。现有基于大语言模型（LLM）的系统虽在简单命令上表现良好，但在面对复杂任务时往往难以生成正确的代码或有效分解任务逻辑。其解决方案的关键在于提出SkillComposer——一个面向仿真环境的交互式自然语言机器人编程系统，采用“生成-测试”架构，使大语言模型能够迭代生成并修正机器人程序；同时引入在线库学习算法，将成功执行的程序片段压缩为可复用的宏观技能（macro skills），实现持续学习与知识积累。实验结果表明，通过评估者引导的生成机制和所学抽象能力，SkillComposer显著提升了任务成功率与系统可用性，同时降低了用户在自然语言机器人编程中的认知负担与操作成本。
链接: https://arxiv.org/abs/2608.14944
作者: John Woods,Hasti Seifi
机构: Arizona State University (亚利桑那州立大学)
类目: Robotics (cs.RO); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages, 6 figures. Submitted to IEEE Humanoids 2026
Abstract:Natural-language interfaces can lower the barrier to programming robots, but existing systems struggle when users request complex tasks. While large language models (LLMs) perform well with simple commands, they often struggle to generate code for multi-step tasks, decompose high-level instructions, or reuse prior solutions. We present SkillComposer, an interactive natural-language robot programming system for simulation environments that continually learns reusable program abstractions. SkillComposer uses a generate-test architecture in which an LLM iteratively generates and revises robot programs before execution. Successful programs are stored and processed by an online library-learning algorithm that compresses recurring function sequences into reusable macro skills for future tasks. We evaluate SkillComposer through ablation experiments and a user study with 12 participants to determine its effectiveness on manipulation and robot caregiving tasks. The results show that evaluator-guided generation and learned abstractions improve success rates and usability while reducing user effort in natural-language robot programming.
[NLP-102] raining Leaves Traces: Centered Residual Signatures for Language Model Lineage Verification
【速读】： 该论文旨在解决开放权重语言模型在经过微调、量化、剪枝和合并等操作后，其模型权重的来源与演化历史难以追溯的问题。现有方法普遍缺乏对模型“血统”（provenance）的可验证性，导致模型复现与版权归属面临挑战。其核心解决方案是提出一种无需数据的白盒血统验证机制——通过分析残差块间的权重结构差异，消除由残差训练引入的共享身份对齐成分，进而提取并比较各检查点特有的结构特征，构建一个对称的血统得分（lineage score）。该得分在独立检查点上进行校准，能够有效区分基于同一祖先衍生出的模型（如微调、LoRA合并、剪枝、量化版本）与独立或蒸馏生成的模型，在残差-MLP和GPT-2基准上实现AUROC=1.0，显著优于传统基于行为相似性的方法。该方法在函数保持型权重清洗攻击下仍保持鲁棒性，且计算效率提升达76倍，展现出强大的被动式、数据无关的模型血统追踪能力。该方法在六类语言模型家族中均表现出一致的投影配对信号，并成功识别了相关与无关的LLaMA-2公开检查点，验证了其广泛适用性。
链接: https://arxiv.org/abs/2608.14929
作者: Aman Singh Thakur,Rayan Khoury
机构: Amazon(亚马逊); Massachusetts Institute of Technology (麻省理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Preprint
Abstract:Open-weight language models are fine-tuned, quantized, pruned, and merged, yet their provenance is often undocumented. We study data-free white-box lineage verification: can weights alone reveal whether two compatible model checkpoints share ancestry? Residual training produces a shared identity-aligned component in branch products, so this structure alone cannot establish ancestry. We remove it and compare checkpoint-specific structure across residual blocks, yielding a symmetric lineage score calibrated against independent checkpoints. On residual-MLP and GPT-2 benchmarks, the score separates fine-tuned, LoRA-merged, pruned, and quantized descendants from independent and distilled models (AUROC=1.0), distinguishing weight ancestry from behavioral similarity. Under function-preserving checkpoint laundering experiments, weight-space baselines lose margin or fail; our score remains unchanged and runs 76x faster than the nearest robust baseline on GPT-2. The projection-pairing signal appears across six language-model families and beyond, and a case study correctly identifies 3 related and 7 unrelated LLaMA-2 public checkpoints. Collectively, these results establish a passive, data-free provenance signal for compatible open-weight language-model checkpoints Comments: Preprint Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.14929 [cs.CL] (or arXiv:2608.14929v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14929 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-103] LLM s Can Predict Failure Risk But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks
【速读】： 该论文旨在解决多智能体大语言模型（Multi-agent LLM）系统在实际部署中如何有效决策是否启动额外协作的问题，即在计算开销与推理性能提升之间权衡。其核心挑战在于：尽管通过增加协作可提升推理能力，但并非所有问题都值得投入额外资源，因此需设计一种智能路由机制以判断何时启用更复杂的协作协议。解决方案的关键在于引入两种候选判断信号——一是基于后答案、前协作阶段的GPT-OSS-120b探测器（post-answer, pre-collaboration probe），用于识别基线求解失败情况，其在4,151个可解析样本上达到0.8847 AUROC，具备较强预测能力；二是基于预答案自信心的门控机制（pre-answer self-confidence gate），可在较低计算成本下实现78.0%的求解率（45K tokens）。然而研究发现，这些信号虽能支持初始协作决策，却难以准确识别特定协议（如PER或Broadcast）带来的增量价值，表明当前方法仍无法实现针对不同协作模式的成本感知型路由。因此，论文揭示了“协议特定的代价敏感路由”仍是未解决的核心问题。
链接: https://arxiv.org/abs/2608.14927
作者: Chih-Hsuan Yang,Jingyan Jiang,Cheng-Hau Yang,Vikram Vasudevan,Huihuo Zheng,Venkatram Vishwanath,Rajeev Thakur
机构: Argonne National Laboratory (阿贡国家实验室); Oregon State University (俄勒冈州立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 23 pages, 6 figures; includes appendices and ancillary aggregate-result CSV files
Abstract:Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collaboration is worth its cost. We isolate this decision by running every problem under four protocols while holding the solver fixed within each setting: direct solving (Baseline), iterative self-correction (Single), planner-executor-reviewer collaboration (PER), and multi-agent deliberation (Broadcast). The primary benchmark comprises 4,181 competition-level math problems; paired robustness checks cover four benchmarks spanning competition math, biology, and broader science with two solver families. Across fixed policies, trained routers, and frozen LLM routers, conservative policies under-escalate, whereas higher-solve frozen routers often over-escalate. A post-answer, pre-collaboration gpt-oss-120b probe ranks Baseline failures with 0.8847 AUROC (4,151 parseable cases; 95% CI [0.8732, 0.8955]). The same score remains informative for predicting whether any collaboration helps (0.7683 AUPRC), but is much weaker for identifying PER- or Broadcast-specific value (0.1674 and 0.1041 AUPRC). Separately, the pre-answer self-confidence gate reaches 78.0% solve at 45K tokens, compared with 73.8% at 71.3K for a frozen gpt-oss-120b router and 92.4% for a retrospective fixed-order oracle. Across 10 paired model-condition settings, the oracle adds 23.2-58.3 points of retrospective coverage over Baseline, but protocol profiles vary by task. In the six settings with held-out router evaluations, oracle gaps remain 18.5-28.9 points. Confidence can therefore support initial escalation, while protocol-specific cost-aware routing remains unresolved.
[NLP-104] How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks
【速读】： 该论文旨在解决当前生成式人工智能在自动化科学研究（AutoResearch）中缺乏系统性评估与故障诊断能力的问题。现有评估方法普遍存在任务范围狭窄、仅关注最终结果而忽视研究过程、失败原因分析缺乏细粒度与可追溯性等缺陷。为此，本文提出AutoResearchEval，构建了涵盖7个科学领域、贯穿从假设生成到论文发表全生命周期的100项前沿科研任务，并对8种模型-框架组合共800条自主研究轨迹进行过程级标注。基于这些数据，提炼出包含45种实证驱动的失败模式的AutoResearch Failure Taxonomy (ARFT)。通过引入经人工校准的“代理即裁判”（agent-as-a-judge）流水线，实现对完整研究轨迹及中间产物的细粒度归因分析。研究发现，所有模型组合均表现出相似的失败模式，其根本症结在于当前智能体普遍缺乏元认知循环（metacognitive loop），即无法对自身产出与所获证据进行一致性检验、在不一致时主动修正并质疑路径合理性。这一缺陷存在于模型层面而非特定编排架构，表明提升元认知能力是突破当前瓶颈的关键所在。相关数据集与分类体系已公开，以推动自主科学发现技术的持续发展。
链接: https://arxiv.org/abs/2608.14905
作者: Yanlin Fei,Nazhou Liu,Xinmiao Yu,Shaolong Chen,Lei Li,Rahul Thapa,Madalina Ciobanu,Qingqing Mao,Ritankar Das
机构: 未知
类目: Computation and Language (cs.CL)
备注: *Equal Contribution (alphabetical order by last name)
Abstract:AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry whole-stage research from an initial hypothesis all the way to final published paper, which is a paradigm now referred to as AutoResearch. Existing evaluations reveal little about how these agents operate or where they break down. Tasks are narrowly-scoped, evaluation measures performance but not process, and failure diagnoses lack systematic coverage or artifact-level visibility. To address this gap, we introduce AutoResearchEval, featuring 100 tasks grounded in published frontier science across 7 scientific domains and the full research lifecycle, including ideation, retrieval, execution, analysis, writing, and review. Evaluating 8 harness-model combinations yields 800 autoresearch agent trajectories, with process-level annotation. We organize these insights into AutoResearch Failure Taxonomy or ARFT, a framework of 45 empirically-grounded failure patterns. To enable scalable fine-grained attribution, we leverage a human-calibrated agent-as-a-judge pipeline to inspect complete trajectories and intermediate artifacts. Failure patterns converge on a single overarching limitation, namely that current agents lack a metacognitive loop, which entails the ability to check what they produced against what they found, revise when it does not hold up, and question whether the path they took was sound. The same patterns recur across all 8 harness-model combinations, including the strongest models tested, locating the deficit at the model level rather than in any particular scaffold; whether orchestration-level interventions can close it is an open question this work does not test. We publicly release AutoResearchEval and ARFT to facilitate continued research and development in autonomous scientific discovery.
[NLP-105] Interpretable Cross-Lingual Alignment in Small Language Models: Probing Cultural and Prag matic Reasoning in Japanese-English Bilingual LLM s
【速读】： 该论文旨在解决大语言模型在日语等与英语类型学差异较大的语言上表现不佳的问题，尤其聚焦于模型在语用层面的失效现象，包括敬语系统、群体内/外指称、上下文敏感的礼貌性表达以及零回指等。现有评估方法（如翻译质量或JGLUE基准）将词汇、句法与语用能力混杂于单一评分中，难以精准定位问题根源。为此，作者提出J-PragEval-v0最小对（minimal-pair）基准，专门隔离出四种核心语用现象，并结合线性探测与教师强制下的对数概率评估，探究这些语用特征在TinySwallow-1.5B模型中的表征位置。研究发现：敬语注册特征清晰地存在于残差流（residual stream）中，第15层达到0.96的平衡准确率且93%的样本在情境切换时发生偏好反转；隐含主语与群体内指称虽在提示末尾标记处无法线性解码（准确率分别为0.48和0.38），但生成过程中的翻转率高达0.77与0.79，表明其表征动态演化于生成阶段而非静态存储于输入端；间接拒绝情形则呈现负面结果——线性探测准确率高达0.95，但在长度归一化的教师强制下翻转率降至0.43，揭示当前最小对设计将礼貌性与延续长度混淆，导致信号失真。此外，作者提出“语用表征引导”（Pragmatic Representation Steering）这一无需参数调整的推理时干预方法，通过沿探测识别出的类别均值差方向修改残差流激活，其可行性通过对比基线间接验证：相同几何结构的对比激活添加可恢复接近逻辑回归性能的探测准确率。该方法下一步将扩展至更大规模的Llama-3.1-Swallow-8B模型。
链接: https://arxiv.org/abs/2608.14896
作者: Florian Braun
机构: 独立研究员(Independent Researcher)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages, no figures. Introduces the J-PragEval-v0 minimal-pair benchmark
Abstract:Large language models work well on English and behave in poorly understood ways on languages typologically far from it. Japanese is a clean example, where evaluation still leans on translation quality and JGLUE-style benchmarks, which roll lexical, syntactic and pragmatic competence into a single score. The phenomena on which general-purpose models fail Japanese users are pragmatic: honorifics, in-group and out-group reference, context-sensitive politeness, zero anaphora. I introduce J-PragEval-v0, a minimal-pair benchmark isolating four such phenomena from surface fluency, and combine it with linear probes and teacher-forced log-probability evaluation to ask where inside TinySwallow-1.5B (28 layers, hidden size 1536) the corresponding contrasts live. The four features split three ways. Honorific register sits cleanly in the residual stream: 0.96 balanced accuracy at layer 15, and the model flips its preferred continuation with the scenario on 93 percent of items. Implicit subject and in-group reference are not linearly decodable at the final prompt token (0.48 and 0.38), yet flip rates are 0.77 and 0.79, so the contrast is worked out during generation rather than stored at the prompt. Indirect refusal is the negative case: 0.95 probe accuracy collapsing to a 0.43 flip rate under length-normalised teacher forcing, because the current minimal pairs conflate politeness with continuation length. I also specify Pragmatic Representation Steering, a parameter-free inference-time method that edits residual-stream activations along the class-mean-difference directions probing identifies. Feasibility is argued indirectly rather than demonstrated: the contrastive activation addition baseline, the same geometry the method would inject, recovers probe accuracy within one to two points of logistic regression wherever a linear signal exists. Scaling to Llama-3.1-Swallow-8B is the next step. Comments: 15 pages, no figures. Introduces the J-PragEval-v0 minimal-pair benchmark Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG) ACMclasses: I.2.7; I.2.6 Cite as: arXiv:2608.14896 [cs.CL] (or arXiv:2608.14896v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14896 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-106] Where Does Retrieval Fail? Evaluating RAG Architectures for Agricultural Advisory
【速读】： 该论文旨在解决生成式检索增强生成（RAG）系统在低资源语言场景下评估时存在的关键问题：单一聚合评分无法揭示不同查询类型和语言条件下的性能差异，尤其在孟加拉语农业咨询这一典型场景中，农民的口语化查询与官方文件中的正式科学术语之间存在显著的语言鸿沟。研究构建了一个包含1,000个查询和2,882个知识节点的测试集，基于284份孟加拉国官方农业出版物，系统评估了五种检索架构与六种嵌入模型在三种受控语言条件下的表现。其核心发现表明，不存在一种在所有条件下均最优的检索方法；例如，在母语孟加拉语查询中，BM25表现最佳（R@10 = 0.506），而混合重排序融合（Hybrid RRF）整体表现最优（R@10 = 0.539）。然而，密集检索的表现随查询类型剧烈波动——在非正式农民查询上仅为0.093，而在正式安全类查询上高达0.970。此外，语言匹配错误导致BM25性能从0.506骤降至0.004，而密集检索仅下降至0.425，显示出其更强的跨语言鲁棒性。更值得注意的是，嵌入任务配置与段落长度可独立使报告的R@10值变化达七倍之多。因此，该研究强调，低资源环境下RAG系统的评估必须按语言条件和查询类型分项报告，而非依赖单一聚合指标。
链接: https://arxiv.org/abs/2608.14886
作者: Khan Raiyan Ibne Reza,Sanjana Aktar Maria,Sumaiya Tabassum Nimi
机构: North South University(南大学); Bangladesh(孟加拉国)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval quality in RAG systems is commonly reported as a single aggregate score, which can hide large differences across query types and language conditions. We study this problem in Bengali agricultural advisory, where farmer queries are often colloquial while official advisory documents use formal scientific terminology. We construct a test collection of 1,000 queries and 2,882 knowledge nodes extracted from 284 official Bangladeshi agricultural publications, and use it to evaluate five retrieval architectures and six embedding models under three controlled language conditions. The results show that no single retrieval method is consistently best. For native Bengali queries, BM25 is the strongest single retriever (R@10 = 0.506) while Hybrid RRF reaches the highest overall R@10 of 0.539. However, dense retrieval performance varies sharply by query type: R@10 is 0.093 on colloquial farmer queries and 0.970 on formal safety queries. Across language conditions, BM25 R@10 drops from 0.506 on Bengali queries to 0.004 when English queries are matched against the Bengali corpus, while dense retrieval falls only from 0.464 to 0.425. We also find that embedding task configuration and passage length can each change reported R@10 by a factor of seven, independent of architecture. These results show why low-resource RAG evaluation should report performance by language condition and query type rather than relying on aggregate scores alone. The dataset and evaluation scripts are available at this https URL. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2608.14886 [cs.CL] (or arXiv:2608.14886v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14886 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-107] Personalized Auto-Research: Towards a True AI Co-Scientist
【速读】： 该论文旨在解决当前生成式AI在科研辅助中普遍存在的“研究者无差别”问题，即现有先进系统在执行假设生成、文献检索、实验设计、代码执行及论文撰写等任务时，仅以新颖性、有效性或审稿人评分等通用目标进行优化，而忽视了具体研究者的个体差异。这种忽视忽略了科研的本质特性：一项工作是否具有创新性、价值或可行性，本质上取决于研究者自身的前期成果、方法论储备及其所处的协作网络与学术社群。为应对这一挑战，论文提出“个性化自动研究”（personalized auto-research）这一新范式，其核心在于将研究者的个体特征作为贯穿整个科研流程的基础条件。解决方案的关键在于构建一个统一且灵活的框架，该框架通过图结构化的研究者表征（graph-grounded researcher representations），实现从文献检索、假设探索、实验设计到写作与评审的全链条个性化，并以个体为中心开展评估。该框架强调个性化并非附加功能，而是使AI真正成为“科研合作者”而非“通用工具”的根本属性。研究特别指出，若采用“一刀切”模式，不同研究者面对相同目标却获得高度相似的研究输出，将导致隐性知识（tacit knowledge）的丧失，从而抑制真正创新的产生。最后，论文还揭示了该领域尚存的一系列基础性开放问题与挑战。
链接: https://arxiv.org/abs/2608.14881
作者: Bo Ni,Franck Dernoncourt,Hongjie Chen,Yu Wang,Nesreen K. Ahmed,Zhengzhong Tu,Tyler Derr,Ryan A. Rossi
机构: Vanderbilt University (范德比尔特大学); Adobe Research (Adobe 研究院); Dolby Laboratories (杜比实验室); University of Georgia (佐治亚大学); Cisco AI Research (思科人工智能研究院); Texas AM University (德州农工大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to change how research is carried out. Despite this rapid progress, state-of-the-art systems remain researcher-agnostic: given a research goal, they optimize novelty, validity, or reviewer score while ignoring the individual scientist who will use the output. This overlooks a fundamental fact about research, namely, that what counts as novel, valuable, or feasible depends on the researcher, including their prior work, methodological repertoire, and the collaborators and communities in which they are embedded. In this work, we introduce the problem of personalized auto-research, which conditions every stage of the research process on a representation of the individual researcher. We argue that personalization is not a convenience layer, but rather the fundamental property that allows an AI system to serve as a genuine co-scientist rather than a generic instrument. To address this problem, we propose a general and flexible framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, writing, and review. The framework consists of three fundamental components: (i) graph-grounded researcher representations, (ii) personalization across the full research pipeline, and (iii) evaluation grounded in the individual. Notably, we highlight a one-size-fits-all failure mode where distinct researchers issuing the same goal receive essentially the same research, erasing the tacit knowledge through which novel ideas arise. Finally, we discuss fundamental open problems and challenges.
[NLP-108] Workspace Topology as an Attack Vector in Agent ic Coding Assistants
【速读】： 该论文旨在解决生成式AI编码助手（agentic coding assistants）在集成第三方代码时可能引入恶意代码的安全风险，尤其是在其具备广泛文件系统访问权限的开发工作区中。核心问题在于，攻击者可通过间接提示注入（indirect prompt injection, IPI）攻击，利用工作区拓扑结构中的特定设计特征诱导编码助手执行非预期行为。论文提出的关键解决方案是系统性地分析工作区拓扑（workspace topology）这一新型攻击面的影响，其关键维度包括目录深度、代码库模块化程度、代码内注入位置及上下文框架（context framing）。研究通过在涵盖10种编程语言和6个工程领域的开源仓库上进行实证评估，发现代码库模块化程度对攻击成功率（Attack Success Rate, ASR）具有显著影响：高度模块化的环境能有效抑制攻击成功；同时，上下文框架与安全提示的引入也能显著降低ASR。研究结果表明，工作区拓扑结构是决定IPI攻击成败的重要因素，强调了构建纯净测试环境以确保安全评估可靠性的重要性，为编码代理在多样化场景下的安全性评估与测试提供了实用指导。
链接: https://arxiv.org/abs/2608.14876
作者: Alexandre G.R. Day,Pradeep Yadlapalli,Sriram Venkatapathy,Thomas Paniagua,Nick Raines,Sahil Wadhwa,Himanshu Kumar,Andy Luo,Sudeep Panyam,Rikhiya Ghosh,Pranab Mohanty,Giri Iyengar
机构: AI Foundations, Capital One(人工智能基础，资本一号)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages, 10 figures. Preprint of a paper accepted at the Conference on Applied Machine Learning in Information Security (CAMLIS 2026)
Abstract:Agentic coding assistants are finding widespread use, not just in new code development but in quickly ingesting and leveraging third-party code. This opens up a risk of malicious code being ingested as these coding tools operate with broad filesystem access inside developer workspaces. In this paper, we extensively study the impact of different dimensions of a novel attack surface we term workspace topology – defined via directory depth, codebase modularity, in-file injection position and context framing – on the attack success rate of adversarial prompt injection attempts. We perform an empirical study of indirect prompt injection (IPI) across a diverse set of open-source repositories spanning 10 languages and 6 engineering domains, evaluating three IPI entry points against open-weight models operating open source code harnesses. We find that workspace topology measurably affects IPI success. Specifically, changes in codebase modularity can significantly alter the Attack Success Rate (ASR), with highly modular environments demonstrating significantly lower attack success rates. Furthermore, context framing and introduction of security-cues in the workspace can also alter the ASR. Our findings offer practical value for the evaluation and security testing of coding agents across diverse settings, while underscoring the importance of an uncontaminated testing environment to obtain reliable results and conclusions. Comments: 15 pages, 10 figures. Preprint of a paper accepted at the Conference on Applied Machine Learning in Information Security (CAMLIS 2026) Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2608.14876 [cs.CR] (or arXiv:2608.14876v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.14876 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-109] What to Forget in Unlearning? Forget Set Curation for Language Models ICML2026
【速读】： 该论文旨在解决生成式语言模型在实际部署中面临的核心挑战：如何有效实现“机器遗忘”（machine unlearning），即在不从头训练的前提下，移除模型对特定内容（如歌曲、书籍）的再现能力。传统方法通常假设需遗忘的数据样本是已知且明确的，但在真实场景中，用户可能仅提出泛化请求（如“停止生成某首歌的内容”），而无法精准定位其在万亿级语料中的具体文本片段。因此，论文聚焦于上游关键问题——遗忘集构建（forget set curation），即如何将模糊的抑制请求映射为可输入遗忘算法的具体数据。为此，研究提出CleanSlate基准，涵盖针对不同模型的提取特征、基于内容的问答评估以及能力保留测试。实验揭示两大失效模式：自然语言词汇匹配与精确子串匹配的遗忘集构建方式导致抑制效果薄弱；而基于评估感知的优化策略虽能几乎完全抑制目标输出，却引发非请求内容的意外退化及模型依赖性能力损失。这一发现表明，实际机器遗忘不仅是给定遗忘集后的优化问题，更关键的是遗忘数据的选择本身直接决定了可实现的遗忘范围与潜在副作用，凸显了遗忘集构建在系统设计中的核心地位。
链接: https://arxiv.org/abs/2608.14855
作者: Animesh Jha,Arpandeep Khatua,Youssef Allouah,Sanmi Koyejo
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL)
备注: Presented at MemFM @ ICML 2026 and FoGen @ ICML 2026
Abstract:Machine unlearning aims to remove targeted data or behaviors from a trained model without retraining from scratch. Yet most evaluations assume that the examples to forget are already known. In realistic language-model deployments, a requester may ask a model to stop reproducing a song or book without knowing which spans, documents, quotations, or near-duplicates in a trillion-token corpus support that behavior. We study this missing upstream problem, forget set curation: mapping a suppression request to the data passed to an unlearning algorithm. We introduce CleanSlate, a benchmark for verbatim output suppression over songs and books, with model-specific extraction profiles, content-grounded QA, and capability-retention evaluations. CleanSlate exposes two failure modes. Natural lexical and exact-substring curators often yield forget sets that lead to weak suppression. An evaluation-aware curator suppresses requested continuations almost completely, but causes collateral regression on non-requested content and model-dependent capability loss. These results show that practical unlearning is not only an optimization problem once a forget set is given: the data chosen for forgetting determines both what can be unlearnt and what else is damaged.
[NLP-110] Writing Style Similarity Reflects Academic Genealogy
【速读】： 该论文旨在解决当前作者归属系统（authorship attribution systems）在识别代笔撰写或生成式AI论文时可能产生的误判问题，尤其关注其对合法作者的不当指控风险。现有系统假设每位作者的写作风格具有唯一性，但实际中研究者在导师指导下开展工作，会继承甚至模仿导师的风格特征，导致风格相似性并非源于独立创作。为验证这一现象，研究构建了一个基于数学家谱系项目（Mathematics Genealogy Project）的arXiv作者语料库，包含5,803名至少发表两篇独立论文的作者及2,501对已知的导师-学生关系。通过微调模型提取的嵌入表示发现，导师与学生之间的余弦距离比同领域随机作者平均近39.9%；两个开源编码器也观察到类似但较弱的效果（12.6%和14.5%）。此外，共享同一导师但从未见面的“学术兄弟姐妹”（academic siblings）在8,360对样本中仍表现出30.4%的显著风格相似性，即使他们就读于不同机构。而仅共享机构与研究领域的作者对则无明显风格相似性。在封闭集归属任务中，系统错误地将真实作者的导师和学术兄弟姐妹判定为作者的概率是随机情况的11倍。该研究的关键发现在于：作者风格的相似性可由学术传承关系解释，而非独立创作特征，因此当前基于风格一致性的作者归属系统易受“学术传承效应”的干扰，导致误判。
链接: https://arxiv.org/abs/2608.14843
作者: Cameron Manzo
机构: RTX BBN Technologies
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As authorship attribution systems are increasingly deployed to detect ghostwritten and AI-generated papers, their errors can support accusations against legitimate authors. These systems assume each author’s style is their own. Researchers, however, study under advisors, and inherit their stylistic quirks. We build a corpus of arXiv authors with \geq 2 solo papers from the Mathematics Genealogy Project graph, giving 5,803 total authors and 2,501 ground-truth advisor-student pairings. Using embeddings from a fine-tuned model, advisors sit 39.9% closer in cosine distance to their students than a random same-field author does. Two open encoders reproduce the effect at 12.6% and 14.5% . \emphAcademic siblings, two students of one advisor who may never have met, sit 30.4% closer across 8,360 pairs, even when they studied at different institutions. Pairs who share only an institution and a field show negligible similarity. Given a closed-set attribution task over the same corpus, the system’s errors occur on the true author’s advisors and academic siblings 11 times more often than chance.
[NLP-111] MINT: Min-Selection Preference Distillation for Balanced Multi-Objective Alignment
【速读】： 该论文旨在解决多目标偏好训练中因奖励函数线性叠加导致的优化坍缩问题：当多个目标以加法形式组合时，优化过程会倾向于选择最容易提升的目标，从而牺牲其他目标，导致生成式智能体（Generative AI）在表现上看似情感温暖却缺乏实质性帮助。其核心问题是传统加性奖励机制缺乏对目标间平衡性的建模能力。为此，作者提出Mint（MIN-selection preference disTillation），通过将偏好蒸馏中的候选样本排序方式从加权奖励和改为取最弱目标得分（即最小值），实现对最佳平衡候选者的优先选择，同时保持DPO（Direct Preference Optimization）目标不变。这一方法本质上是广义均值族中p→-∞极限情形，实现了从加性到最坏情况选择的平滑过渡。实验表明，在合作型情感支持与对抗性谈判任务中，Mint显著提升了各目标的表现，并大幅降低目标间的不平衡性；在情感支持任务中，较弱目标得分从0.37提升至0.64（p < 10⁻⁴⁰），超越人类专家水平，并在全回合多轮交互中保持稳定。进一步的逐轮分析揭示了核心发现：Mint对不平衡性的修正程度与参考策略的失衡程度成正比，且其优势持续时间恰好等于原始策略失衡存在的时长，验证了其动态平衡机制的有效性。
链接: https://arxiv.org/abs/2608.14828
作者: Tony Tu,Sayan Chakraborty,Ruomeng Xu,Tony Qin,Austin Tian
机构: Georgia Institute of Technology (佐治亚理工学院); Zillow Group (Zillow集团)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Aligning a language agent to several objectives at once is a persistent failure mode of preference-based training: when objectives are combined additively, optimization collapses onto whichever is cheapest to improve and sacrifices the rest, so a support agent learns to sound warm while giving no real help. The root issue is that an additive reward has no notion of balance. We introduce Mint (MIN-selection preference disTillation), a one-line change to preference distillation: rather than ranking sampled candidates by a weighted sum of rewards, we rank them by their weakest objective, distilling the best-balanced candidate over the most lopsided one with an unchanged DPO objective. This is the p - negative infinity limit of a generalized-mean family spanning additive to worst-case selection. Across cooperative emotional support and adversarial negotiation, min-selection lifts both objectives while sharply cutting their imbalance; on emotional support it raises the weaker axis from 0.37 to 0.64 (p 10^-40), surpassing human experts and persisting across full multi-turn rollouts. A turn-by-turn analysis yields our central finding: min-selection corrects imbalance in proportion to how imbalanced the reference policy is, and its benefit endures over an interaction precisely as long as that imbalance does.
[NLP-112] Beyond the pale: Assessing prevalence and contents of extremist speech in LLM training data
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在预训练与后训练阶段所接触的文本语料库中是否存在未经过滤、缺乏上下文的极端主义言论这一关键问题。尽管学术界对可信且安全的人工智能（Trustworthy and Safe AI）高度关注，但语料库构成的潜在风险尚未得到充分重视。其解决方案的关键在于构建一个结合自动化文本处理与专家验证的提取管道，基于官方文件和研究文献中的多种极端主义言论定义，对开源训练语料库Dolma（支撑OLMo系列模型的基础数据集）进行系统性分析。研究结果表明，Dolma极有可能包含数十万份含有各类极端内容及仇恨言论的文档，包括直接煽动暴力的内容，揭示了当前数据筛选机制的不足，并对模型训练过程中的数据治理与安全性提出了重要警示。
链接: https://arxiv.org/abs/2608.14813
作者: Dmitry Nikolaev,Ashley A. Mattheis
机构: University of Manchester (曼彻斯特大学)
类目: Computation and Language (cs.CL)
备注: Accepted to the CPSS workshop @ KONVENS 2026
Abstract:Despite a strong interest on the part of the research community in the topic of trustworthy and safe AI, the composition of the text corpora that large language models (LLMs) encounter in pre- and post-training has not yet drawn much attention. In this work, we address the question of whether LLMs are exposed to unfiltered, uncontextualised extremist speech. Using several definitions of extremist speech, stemming from official documents and research literature, and an extraction pipeline combining automated text processing with expert verification, we provide a lower bound on the prevalence of extremist documents in Dolma, an open training corpus underpinning the OLMo series of models. We show that Dolma is likely to include hundreds of thousands of documents containing extremist content and hate speech of several types, including direct calls for violence, and discuss the implications of this for data curation and model pre-training.
[NLP-113] Do LLM s Know What to Ask and When? Evaluating Multi-Turn Information Seeking
【速读】： 该论文旨在解决大模型在面对信息不充分（underspecified）的用户问题时，无法有效识别缺失信息、合理提出追问并基于充分信息生成唯一正确答案的问题。其核心挑战在于建模多轮信息寻求过程中的不确定性与依赖关系，尤其当问题涉及多个变量共同决定目标结果时（即k-不充分约束满足问题，k代表所需联合确定目标的变量数量）。解决方案的关键是提出一种形式化框架——MT-InfoSeek，构建了一个涵盖数学、逻辑、生物学、医学及通用知识领域的5,251个问题和9,006个任务实例的受控评估基准，通过三个维度（问什么、何时问、获取信息如何影响最终答案）系统评估模型的信息寻求能力。研究发现，尽管模型能识别需要额外信息，但普遍低估所需信息量，在逻辑类问题中对缺失程度的误判频率高达四倍于高估；且难以识别最小必要查询集，即便得知真实k值也仅实现微弱性能提升，常在信息不足时过早终止交互。此外，在存在顺序依赖的任务中，错误的提问顺序会显著降低最终准确率，即使所有必要信息最终被获取。论文通过“最终充分性”（final sufficiency）指标直接衡量信息获取的完备性，揭示了现有评估体系忽视了多轮信息寻求能力与答案生成能力之间的本质差异，强调当前大语言模型（LLM）评测体系尚未有效捕捉这一关键认知行为。
链接: https://arxiv.org/abs/2608.14808
作者: Yepeng Huang,Jiawen Zhang,Michelle Dai,Xiaorui Su,Shanghua Gao,Zi Wang,Marinka Zitnik
机构: Harvard University (哈佛大学); Google DeepMind (谷歌深度思维)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it, and respond only once that information determines a unique answer. We formalize multi-turn information seeking as solving a k-underspecified constraint satisfaction problem, where k is the number of variables jointly required to determine the target and therefore measures the degree of missing information. We instantiate the formulation in MT-InfoSeek, a controlled evaluation suite of 5,251 problems and 9,006 task instances spanning mathematics, logic, biology, medicine, and general knowledge. We evaluate models along three axes: what they ask, when they ask it, and how the acquired information affects the final answer. Performance degrades across models and domains as underspecification increases. Models recognize that additional information is needed but underestimate how much, and in logical problems at k = 2 they under-predict the degree of missing information about four times as often as they over-predict it. They also fail to identify a minimal sufficient set of queries, improve only marginally when given the true k, and often stop before acquiring sufficient information. In tasks with ordered dependencies, an incorrect query order reduces final accuracy even when the model eventually acquires all necessary information. We measure information seeking directly through final sufficiency, which records whether the acquired information determines the target independent of answer generation. This separation shows differences between models that final accuracy alone does not capture, and indicates that the ability to seek information over multiple turns is distinct from the ability to generate answers and is not measured by current LLM evaluations.
[NLP-114] Beyond Tokens: A Survey on Decoding Methods for Large Language and Vision-Language Models KDD
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）与大视觉-语言模型（Large Vision-Language Models, LVLMs）在生成过程中难以确保输出与用户意图对齐的问题。尽管现有方法多聚焦于训练阶段的对齐优化，但本文强调推理阶段的解码方法具有更高的效率与可扩展性，能够通过控制词元级选择、执行序列级生成或并行生成词元来有效引导模型输出。其解决方案的关键在于识别并系统梳理近年来针对LLMs和LVLMs的三种新兴解码范式：基于约束的解码、基于强化学习的解码以及基于自回归与非自回归混合策略的高效生成方法，揭示了这些方法在提升生成质量、加速推理速度与增强可控性方面的潜力，同时指出了当前面临的挑战，如通用性不足、计算开销高及跨模态对齐困难，并展望了未来在动态提示调整、多智能体协同解码与轻量化部署等方面的研究方向。
链接: https://arxiv.org/abs/2608.14797
作者: Haoran Wang,Xiongxiao Xu,Philip S. Yu,Kai Shu
机构: Emory University(埃默里大学); Illinois Institute of Technology(伊利诺伊理工学院); University of Illinois Chicago(芝加哥大学)
类目: Computation and Language (cs.CL)
备注: ACM SIGKDD Explorations Newsletter, Volume 28, Issue 1
Abstract:Large language models (LLMs) and large vision-language models (LVLMs) have demonstrated impressive generative capabilities, yet ensuring their outputs align with user intent is still challenging. While most existing approaches address this issue at the training stage, inference-time approaches like decoding methods offer a more efficient and scalable solution. Decoding methods control model generation by guiding token-level selection, performing sequence-level generation, or generating tokens in parallel to accelerate the process. In this survey, we identify three emerging paradigms from recent works on decoding methods for LLMs and LVLMs, provide a systematic review of these methods, highlight ongoing challenges, and discuss potential future research directions. Our goal is to underscore the efficiency and effectiveness of decoding methods and offer a practical view of their applications. Paper lists and more resources on decoding methods for LLMs and LVLMs can be found at this https URL.
[NLP-115] Prompting is not enough: supervised baselines and leakage control for measuring shared decision-making with LLM s in pediatric encounters
【速读】： 该论文旨在解决在真实临床对话中，仅通过零样本提示（zero-shot prompting）的大型语言模型（LLM）是否足以可靠识别共享决策（Shared Decision-Making, SDM）行为的问题，并进一步探究在患者分组嵌套评估框架下，监督学习方法是否能带来额外价值。研究发现，仅使用零样本提示的本地LLM（Qwen 2.5 32B）在测量SDM行为时表现有限，其宏观Cohen’s kappa仅为0.139，表明其可靠性不足；而采用冻结句向量的监督分类器则显著提升至0.227，实现了0.088的配对改进；两者的逻辑堆叠模型进一步将性能提升至0.242。研究的关键在于揭示了在嵌套评估中，即使采用患者级分组策略，若标签示例（prompt exemplars）在外部评估循环之外预先计算，仍会产生数据泄露（corpus-specific leakage paths），从而导致性能估计偏高。因此，解决方案的核心在于：必须严格控制标签示例的生成位置，确保其不进入外层评估流程，并且需明确数据划分单位对结果敏感性的影响，以保证评估的严谨性与可泛化性。
链接: https://arxiv.org/abs/2608.14792
作者: Bernardo Modenesi,Jody Lin,Kimberly Kaphingst,Angela Zhu,Maya Wheeler,Peilu Zhang,Angela Fagerlin
机构: University of Utah Spencer Fox Eccles School of Medicine (犹他大学斯宾塞·福克斯·埃克尔斯医学院); University of Utah Kahlert School of Computing (犹他大学卡勒特计算机学院); University of Utah Huntsman Cancer Institute (犹他大学亨斯曼癌症研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Objectives: To determine whether zero-shot prompting of a large language model (LLM) is sufficient to detect shared decision-making (SDM) behaviors in real clinical encounters, and whether supervised learning adds value under patient-grouped, nested evaluation. Methods: We analyzed 21 audio-recorded outpatient surgical decision encounters (19 unique patients; 7,566 utterance segments; ~6.1 hours) between families of children with multiple long-term conditions and their surgical providers. Trained coders labeled segments for 12 SDM behaviors (human-human macro Cohen’s kappa = 0.695). We compared a zero-shot local LLM (Qwen 2.5 32B), a supervised classifier over frozen sentence embeddings, and their logistic stack, under patient-grouped outer folds with inner cross-fitted thresholds and patient-resampled confidence intervals. Results: The zero-shot LLM reached macro kappa = 0.139 (95% CI 0.111-0.164). The supervised classifier reached kappa = 0.227 (0.186-0.262), a paired improvement of 0.088 (0.051-0.119). A logistic stack of the two reached kappa = 0.242 (0.198-0.284). We identified multiple corpus-specific leakage paths, including grouping sibling recordings separately and allowing labels from an outer held-out patient to enter few-shot exemplars used while fitting downstream models. Conclusion: Zero-shot prompting alone is not sufficient to measure SDM behavior as reliably as a small supervised model, and patient-level grouping alone does not prevent leakage when labeled prompt exemplars are precomputed outside the outer evaluation loop. Reported performance is sensitive to the unit of data splitting and to where labeled exemplars enter the pipeline. External validation is needed before these findings generalize beyond this population, model, prompt, and codebook. Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.14792 [cs.CL] (or arXiv:2608.14792v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14792 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bernardo Modenesi [view email] [v1] Fri, 14 Aug 2026 18:02:08 UTC (28 KB)
[NLP-116] From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding
【速读】： 该论文旨在解决自回归生成中验证器（verifier）调用带来的计算瓶颈问题，尤其是在基于离散扩散模型的推测解码（Speculative Diffusion Decoding, SDD）框架下，尽管已实现并行生成候选词序列，但每一块（block）仍需调用目标模型进行验证，成为性能瓶颈。其解决方案的关键在于引入“验证器跳过”（verifier skipping）策略——即在特定条件下直接采纳部分高置信度的草案前缀，而不必经过验证器确认，从而减少对目标模型的调用次数。研究发现，虽然更精准的词预测能力未必带来更优的跳过调度，但跳过策略的成功依赖于连续且高置信度的草案前缀；短距离跳过反而可能引发额外的草稿轮次，增加开销。为此，论文对比了原始置信度、学习得到的边际生存分数（marginal survival score）与条件生存分数（conditional survival score）在相同策略下的表现，结果表明三者均能在保持与严格SDD相当的pass@1准确率前提下，减少9.6%至13.5%的验证器调用次数，其中原始置信度表现最佳。分析进一步揭示：验证器跳过作为一种新的有损控制维度，其核心挑战并非词预测本身，而是如何高效地调度跳过时机，即前缀调度策略的设计。
链接: https://arxiv.org/abs/2608.14787
作者: Haoxuan Luo,Jameson Sandler,Ferdinando Fioretto
机构: University of Virginia(弗吉尼亚大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: 14 pages, 6 figures
Abstract:Speculative decoding is a leading technique to reduce the cost of autoregressive generation by using a small drafter to propose several tokens, which are then verified in parallel by a larger target model. Speculative diffusion decoding (SDD) further removes sequential drafting by generating every position in a draft block in parallel with a discrete diffusion model. However, SDD still invokes the target on every block, leaving verification as a potential bottleneck. This paper recognizes that this creates a new control handle: whether to invoke the verifier at all. Thus, we study verifier skipping, a lossy policy that commits a selected draft prefix directly, and ask which confidence signal should schedule it. Interestingly, our study finds that better token predictors need not yield better schedulers: skips require contiguous high-confidence prefixes, while short skips can induce additional drafting rounds. To study this mismatch, we compare raw confidence with learned marginal and conditional survival scores under the same policy, using Strict SDD, lenience, and top- k acceptance as baselines. On HumanEval with DiffuCoder-7B-Instruct and Qwen3-32B, all three confidence signals save 9.6% to 13.5% of verifier calls at the same observed pass@1 as Strict SDD. Surprisingly, raw confidence saves the most; marginal survival has higher positionwise AUROC than raw confidence at most positions, yet neither learned signal dominates online. Our analysis shows that verifier skipping is a useful new lossy axis and, surprisingly, its key challenge is prefix scheduling rather than token prediction alone.
[NLP-117] From Errors to Proofs: Minimal-Core-Guided Repair for Neuro-Symbolic Constraint Solving IJCAI ECAI2026
【速读】： 该论文旨在解决生成式语言模型在求解约束问题时因翻译不准确而导致求解器“精确地求解错误问题”的可靠性问题。其核心挑战在于，现有方法仅能检测导致程序崩溃的翻译错误，并返回错误信息，而无法识别那些使程序运行但结果错误的隐性故障，从而导致模型“虚构”解决方案。本文的关键解决方案是：当生成的程序不可满足时，不再返回模糊的错误信息，而是提取模型自身约束下的最小不可满足核心（minimal unsatisfiable core），将这一精确的逻辑矛盾集合作为反馈信号返还给模型。该信号具有“无泄漏”特性，能够精准定位故障源头。实验表明，在77个问题的新基准上，该方法在六个领域实现了对答案集编程（Answer Set Programming）的忠实翻译，仅在聚合覆盖调度任务中失败，且该失败可被诊断为单一模式。相比传统方法，该方案显著降低虚假解的生成率，从79%降至7%，而强基线的链式思维（chain-of-thought）虽在准确性上与符号路径相当，但缺乏可验证的证书和拒绝对不可行问题进行虚构的能力，凸显了该方法在提供可解释性证明与防止虚构方面的核心价值。
链接: https://arxiv.org/abs/2608.14771
作者: Dipankar Sarkar
机构: 独立研究员(Independent Researcher)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Programming Languages (cs.PL); Symbolic Computation (cs.SC); Optimization and Control (math.OC)
备注: 7 pages, 2 figures. Accepted at the IJCAI-ECAI 2026 Workshop on Logic and Symbolic Reasoning (LogiSymb), poster
Abstract:Making language models solve constraint problems reliably often means having them translate the problem into a formal specification and delegating the search to a sound solver. But the translation is itself a language-model task, and an unfaithful translation makes the solver faithfully solve the wrong problem. Existing pipelines repair only translations that crash, returning the solver’s error message and falling silent when the program runs but is wrong. We replace the error message with a proof: when the generated program is unsatisfiable, we extract a minimal unsatisfiable core over the model’s own constraints and hand it back the exact set that cannot hold together, a leakage-free signal that localizes the fault. On a new benchmark of 77 problems with an exact oracle, translation to Answer Set Programming is faithful on six of seven domains and fails only on aggregate coverage scheduling, which concentrates the translation tax in one diagnosable pattern. A minimal core, rather than a bare error, is what stops a weaker model from fabricating solutions to infeasible problems, cutting fabrication from 79% to 7%. A strong chain-of-thought baseline meanwhile matches the symbolic route on accuracy, so the route’s value is not accuracy but certificates and its refusal to fabricate.
[NLP-118] NARRATE: A Multimodal Real-World Australian Driving Dataset for Human-Centred Explanations in Automated Driving ECCV2026
【速读】： 该论文旨在解决自动驾驶系统在决策过程中缺乏可解释性的问题，即如何使车辆的决策过程以乘客可理解、可监控和可信任的方式进行解释。现有语言标注的驾驶数据集多为旁观者撰写、事后补充、基于仿真或由传感器输入生成，而非直接从实际执行动作的驾驶员处获取。为此，论文提出NARRATE——一个基于真实世界澳大利亚道路环境的多模态驾驶数据集，包含35名经验丰富的驾驶员及教练在公共道路上完成的2,050个带标注事件。每个事件均同步融合视觉、定位、运动与LiDAR数据，并配以车内或事后自由文本形式的驾驶员解释。该数据集提供动作标签、涵盖六类高层级与三十二类细粒度的场景上下文标签，以及对驾驶员解释中感知（Perception）、理解（Comprehension）和预测（Projection）三个层次的情境意识（Situational Awareness, SA）跨度标注。通过四项基准任务（情境意识识别、场景上下文分类、驾驶员行为分类与解释生成），验证了从驾驶员语言中学习上述结构的可行性，但细粒度上下文识别与解释生成仍具挑战性。NARRATE为构建更以人为本且具备领域感知能力的自动驾驶解释模型提供了重要基础。
链接: https://arxiv.org/abs/2608.14767
作者: Ashkan Yousefi Zadeh,Zishuo Zhu,Xiaomeng Li,Andry Rakotonirainy,Sebastien Glaser,Ronald Schroeter,Patricia Delhomme,Zahra Mehraban
机构: ARC Training Centre for Automated Vehicles in Rural and Remote Regions (AVR3), Australia; Université Gustave Eiffel, Laboratory of Applied Psychology and Ergonomics, France; Queensland University of Technology (QUT), Faculty of Health, School of Psychology and Counselling, Australia
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
备注: Accepted at The 19th European Conference on Computer Vision (ECCV 2026) DriveX Workshop (Foundation Models for Autonomous Driving)
Abstract:Automated vehicles must explain their decisions in ways that passengers can understand, monitor, and trust. Existing language-annotated driving datasets are mostly observer-written, post-hoc, simulation-based, or generated from sensor inputs, rather than elicited from the driver performing the action. We introduce NARRATE, a multimodal real-world Australian driving dataset comprising 2,050 annotated events from 35 experienced drivers and driving instructors on public roads. Each event is grounded in synchronised visual, localisation, motion, and LiDAR streams and paired with in-vehicle and/or post-drive free-text explanations. NARRATE provides action labels, scenario-context labels spanning six high-level and 32 fine-grained categories, and span-level Situational Awareness (SA) annotations over driver explanations for Perception, Comprehension and Projection. Four benchmark tasks (SA, scenario-context, driver-action classification, and explanation generation) show that this structure is learnable from driver language, while fine-grained context recognition and explanation generation remain challenging. NARRATE paves a path towards more human-centred and domain-aware explanation models for automated driving.
[NLP-119] Class Imbalance and Batch Effects in LLM -Based Screening for Systematic Reviews
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在不平衡二分类任务中表现不稳定的问题，尤其聚焦于系统评价中的文献筛选场景。研究发现，预设的患病率元数据（prevalence metadata）对模型性能的影响有限，且未显示出显著提升分类效果的证据；而批量处理（batch processing）则引发了更为显著的行为变化，其影响程度随类别不平衡程度的变化而异。值得注意的是，整体层面与个体样本层面的分析结果并不总是一致。因此，该研究提出的关键解决方案在于：评估批量处理策略时，不应仅关注其计算成本，还需综合考量其对决策行为产生的潜在影响，以确保模型应用的可靠性与可解释性。
链接: https://arxiv.org/abs/2608.14737
作者: Gilberto Sussumu Hida,Danilo Monteiro Ribeiro,Clayton Suguio Hida
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 12 pages, 4 figures. Accepted at ENIAC 2026 (National Meeting on Artificial and Computational Intelligence), part of BRACIS 2026
Abstract:This study analyses LLMs in imbalanced binary classification, using study screening in systematic reviews as the application domain. An experiment was conducted in five reviews, comparing individual and batch processing, with and without prevalence metadata. The results indicate a limited influence of the prevalence metadata, with no evidence that it improves performance. In contrast, batch processing produced larger behavioral changes that varied according to the prevalence of the class. The aggregate and item-level analyses did not always coincide. Therefore, batch processing should be evaluated not only in terms of cost, but also in relation to its effects on decision-making behavior.
[NLP-120] VideoGAIA: A Benchmark for General AI Assistants on Agent ic Video Understanding
【速读】： 该论文旨在解决当前多模态大语言模型（MLLMs）在视频理解任务中面临的能力评估瓶颈问题。随着主流模型在传统单轮视频问答任务（如Video-MME基准）上已接近90%的准确率，现有评测范式趋于饱和，难以有效衡量先进MLLMs的真正智能水平。为此，论文提出VideoGAIA——一个面向通用人工智能（AI）助手的代理式（agentic）视频理解基准。其核心解决方案在于将视频理解重构为多轮、工具增强的交互过程：模型需在多轮对话中持续感知视频内容，调用外部工具获取补充信息，并跨轮次整合多模态证据进行推理。该基准包含271个由模型与人类共同设计的复杂真实场景任务，每个实例均经三位专家独立验证以确保准确性与难度合理性。实验表明，包括GPT-5.5和Kimi-K3等前沿模型在内的所有评测模型在VideoGAIA上的表现均未超过60%，凸显其作为下一代MLLMs高质量、时效性评估基准的重要价值。该研究推动了从传统视频理解向代理式视频理解范式的演进。
链接: https://arxiv.org/abs/2608.14718
作者: Fan Zhang,Guangming Yao,Jinyang Wu,Hao Wu,Zheng Lian,Xinyu Geng,Jingdong Chen,Yi Yuan,Pheng-Ann Heng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Video understanding is a fundamental task for evaluating the capabilities of multimodal large language models (MLLMs). However, existing leading models have already achieved approximately 90% accuracy on the Video-MME leaderboard, suggesting that conventional single-turn video understanding tasks are becoming increasingly saturated and insufficient for assessing the intelligence of advanced MLLMs. Towards this end, we introduce VideoGAIA, an agentic video understanding benchmark for general artificial intelligence (AI) assistants. Moving beyond one-shot video question answering, VideoGAIA formulates video understanding as a multi-turn, tool-augmented interaction process, where models must iteratively perceive videos, invoke external tools, gather complementary information, and integrate multimodal evidence across turns. VideoGAIA contains 271 model-human co-designed tasks covering diverse and complex real-world scenarios. Each video-question-answer instance is independently verified by three human experts to ensure both correctness and appropriate difficulty. All evaluated MLLMs, including frontier models such as GPT-5.5 and Kimi-K3, achieve less than 60% accuracy on VideoGAIA, highlighting its value as a high-quality and timely benchmark for evaluating next-generation MLLMs. We hope that VideoGAIA will facilitate the transition from conventional video understanding toward agentic video understanding.
[NLP-121] Which Question Is Your Attention Metric Answering? Attention Rows as Compositional Data
【速读】： 该论文旨在解决预训练语言模型中注意力矩阵分析的固有偏差问题，即标准注意力比较方法（如余弦相似度、JS散度、香农熵）在处理注意力行时对“汇聚点”（sink token，通常为序列首词元）的处理方式不明确，导致结论依赖于是否保留或移除该词元并重新归一化。这一选择在实践中可反转对注意力头相似性的判断：在五个模型家族的十种预训练模型中，17%–47%的头间相似性结论会因该处理方式而改变，且主流BERT注意力聚类流程中的显著结构实为该选择带来的伪像。其核心问题是现有的一维统计量（如熵）将“汇聚点所占注意力比例”与“内容词元间的分布差异”混为一谈，从而产生误导。解决方案的关键在于将注意力行视为组合数据（compositional data），通过艾奇森距离（Aitchison distance）实现正交分解，使总距离可精确拆分为汇聚项与内容项；同时利用熵的精确分解恒等式及变压器自身具备的不变性特性，分离出真正反映内容分布变化的成分。实证表明，训练过程中多数熵下降源于汇聚点注意力增强（在70M参数下占比30%，1.4B参数下达79%），而非注意力聚焦的真正尖锐化；错误地剪枝“通道”会导致困惑度上升百余倍。研究进一步明确了不同处理惯例的安全边界，并验证了一个冻结的外部预测器，最终公开所有可复现代码。
链接: https://arxiv.org/abs/2608.14712
作者: Marios Papamichalis,Regina Ruane
机构: Human Nature Lab, Yale University (人类自然实验室，耶鲁大学); Department of Statistics and Data Science, The Wharton School, University of Pennsylvania (统计与数据科学系，沃顿商学院，宾夕法尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Statistics Theory (math.ST)
备注: Preprint under submission
Abstract:Each row of a transformer’s attention matrix is a probability distribution over tokens, and in trained models most of that probability lands on a single \emphsink token, usually the first. Standard tools for comparing attention rows (cosine similarity, Jensen–Shannon divergence, Shannon entropy) therefore hinge on a choice papers rarely report: keep the sink, or drop it and renormalize. This choice can reverse conclusions. On ten pretrained models from five families, 17–47% of verdicts about which of two heads is more similar flip with the convention, and the most prominent structure in a standard BERT head-clustering pipeline is an artifact of it. The reason is that one-number summaries mix two questions: how much attention the sink takes, and how the rest is divided among the content tokens. Treating rows as compositional data separates them exactly: the Aitchison distance splits orthogonally into a sink term and a content term, entropy splits by an exact identity, and the content distance is characterized by invariances the transformer itself possesses. The separation matters in practice: most measured entropy collapse during training is the sink growing, not attention sharpening (30% of the drop at 70M parameters, 95% at 1B, 79% at 1.4B), and pruning heads with the wrong channel can inflate perplexity more than a hundredfold. We map where each convention is safe, test a frozen out-of-sample predictor (one confirmation, one abstention, one failure), and release code regenerating every number.
[NLP-122] Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics
【速读】： 该论文旨在解决从苏木精-伊红（H&E）染色图像预测空间基因表达时，现有方法将H&E图像视为通用视觉输入而忽略其内在生物学层级结构的问题。具体而言，传统方法未能充分建模细胞类型在空间上有序排列所形成的组织微环境，而这些微环境正是调控局部基因表达程序的关键因素。为弥补这一缺陷，论文提出Path2ST框架，其核心创新在于将H&E到空间转录组（ST）的预测建模为跨模态语义翻译任务，并引入三个关键组件：（i）层次化细胞-组织条件机制，通过融合显式与隐式的细胞特征及组织层面的语义表征，构建多层次条件信号；（ii）基于层次化语义词汇的尺度自适应自回归生成过程，实现从粗到细、符合生物学一致性的基因表达合成；（iii）全谱损失（SpectraLoss），联合约束表达值的序数保真度、模拟转录爆发特性，并对齐语义结构与细胞类型分布。实验在三个数据集上验证了Path2ST达到当前最优性能，能够生成高精度且空间一致的转录组图谱，显著提升了H&E图像驱动的空间基因表达预测能力。
链接: https://arxiv.org/abs/2608.14710
作者: Ruochen Liu,Wei Lou
机构: University of Liverpool(利物浦大学); Zhejiang Normal University(浙江师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Predicting spatial gene expression from hematoxylin and eosin (H\E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST). However, existing methods treat H\E images as generic visual inputs and ignore their intrinsic biological hierarchy, where spatially organized cell types collectively form functional tissue microenvironments that govern local gene expression programs. To bridge this gap, we formulate H\E-to-ST prediction as a cross-modal semantic translation task and propose Path2ST, a hierarchically grounded autoregressive framework featuring three key components: (i) a Hierarchical Cell-Tissue Conditioning mechanism that fuses explicit and implicit cellular features with tissue-level semantic representations to construct hierarchical conditioning signals; (ii) a Scale-Adaptive Autoregressive Generation process over a hierarchical semantic vocabulary, enabling coarse-to-fine, biologically consistent expression synthesis; and (iii) SpectraLoss, a full-spectrum objective that jointly enforces ordinal fidelity, models transcriptional bursts, and aligns semantic structures with cell types. Extensive experiments on three datasets demonstrate state-of-the-art performance, validating that Path2ST generates highly accurate and spatially coherent transcriptomic profiles. The related code is released at this https URL.
[NLP-123] Domain Agnostic Text Redaction from Natural Language Rules using Instruction Tuning
【速读】： 该论文旨在解决传统文本去敏（text sanitization）方法在处理非结构化敏感信息时缺乏透明性与灵活性的问题。现有方案通常仅针对具有标准格式的敏感数据（如个人身份信息，PII）进行模糊处理，且无法提供可审计的红标依据，难以满足合规性审查需求。其解决方案的关键在于提出一种可解释、领域无关的文本红标框架，通过指令微调的语言模型（instruction-tuned language model）结合自然语言形式的红标规则，实现对未结构化文档中任意敏感内容（包括结构化如PII及非结构化如法律条款）的精准识别与红标。具体而言，通用大语言模型（LLM）根据用户以自然语言定义的敏感信息特征自动生成或扩展红标规则，并用于微调一个轻量级推理模型，该模型能够在处理文档时逐步骤推理并应用规则，从而生成每一步红标的可解释理由，并以自然语言形式输出，便于人工审核与审计。同时，采用基于重构的评估指标量化红标覆盖率，实验表明该方法在重建错误率和红标精确率方面表现优异，适用于法律发现、医疗记录和企业信息治理等高要求场景。
链接: https://arxiv.org/abs/2608.14693
作者: Aravindhan Arunagiri,Ayaan Khan,Udayaadithya Avadhanam,SaiBarath Sundar
机构: Mphasis Limited
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:With the increasing digitization of personal and corporate communication, the automatic sanitization of textual data has become a crucial component of data privacy and compliance frameworks. Traditional text sanitization solutions are majorly suitable for obscuring sensitive data with standard structure such as Personal Identifiable Information (PII). These solutions do not provide transparent justification for their redaction, which makes it difficult to audit them. This paper introduces an explainable, domain-agnostic text redaction solution that uses natural language rules of redaction, applied via an instruction-tuned language model, to identify and redact sensitive information in unstructured documents. Unlike traditional text sanitization, this method enables a user to conveniently define any sensitive information; which may be structured (e.g.\ PII) or unstructured (e.g.\ legal terms and conditions) in natural language. A general-purpose LLM generates or augments these natural language rules of redaction from the user’s definition, which are then used to instruction-fine-tune a smaller language model that reasons the rules step-by-step over any given document to identify and redact the corresponding sensitive content, while providing transparent justifications for each redaction and highlighting the specific rule that triggered the decision. This explanation is generated in natural language to support human reviewers and auditors in understanding why specific content was redacted. A reconstruction-based metric is used to estimate the probability of recovering redacted information from the sanitized document, quantifying redaction coverage. The solution shows high reconstruction error and high redaction precision, making it suitable for automated text sanitization in critical applications such as legal discovery, medical documentation, and corporate information governance.
[NLP-124] Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLM s Instruct LLM s and Humans
【速读】： 该论文旨在解决语言模型在处理重复词汇时的内在机制问题，即模型是自动复现先前表征还是对重复词进行重新评估，以及后训练过程是否改变这一默认行为。其核心解决方案在于采用重复启动范式（repetition priming），在语义分类与填空任务中系统考察15个不同规模的语言模型（参数量1.5B–14B）的行为，并与人类被试在相同刺激下的表现进行对照。研究发现，基础模型表现出自动加工特征：对重复词呈现即时促进效应，且该效应在不同滞后间隔间保持稳定，部分抵抗上下文移除的影响，并与对先前出现的注意力相关；而指令微调模型则表现出控制性加工特征：其促进效应随滞后增加而衰减，无预期上下文时完全消失，甚至在更大滞后下转为抑制效应。在Qwen 2.5系列中，这种分离现象随模型规模单调增强，表明后训练过程显著改变了重复信息的处理方式。人类被试则呈现混合模式，具有类似指令模型的滞后敏感性促进但无抑制，说明现有模型均未完全模拟人类认知。该研究揭示了后训练导致语言模型在重复处理上发生质变，并提供了模型行为分化的机制性证据。
链接: https://arxiv.org/abs/2608.14681
作者: Jinglei Ren,Yuyue Wang
机构: Yale University (耶鲁大学); University of California, Los Angeles (加州大学洛杉矶分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior. We apply repetition priming (Shiffrin and Schneider, 1977) to 15 models across five model families (1.5B-14B parameters) in two tasks, semantic categorization and cloze completion, with matched human experiments using identical stimuli. We find that base models exhibit automatic processing: they show immediate facilitation that remains stable across lags, partially survives context removal, and correlates with attention to prior occurrences. Instruct models exhibit controlled processing: their facilitation decays with lag, collapses without expected context, and reverses to interference at larger scales. Within the Qwen 2.5 family, this dissociation increases monotonically with model scale, suggesting that post-training progressively alters repetition processing. Humans show a hybrid profile, with lag-sensitive facilitation resembling instruct models but without interference, suggesting that neither model type fully captures human cognition. Our findings reveal a qualitative shift in how language models process repeated information after post-training and provide mechanistic evidence for the divergence between model behaviors.
[NLP-125] DUET: Dual-Teacher On-Policy Distillation via Same-Weight Disagreement for Prohibition Compliance
【速读】： 该论文旨在解决生成式 AI（Generative AI）在真实场景部署中因运行时注入的禁止规则（如企业政策、个人身份信息（PII）红线、工具边界等）动态变化而导致的合规性难题。传统后训练方法存在结构性缺陷：监督微调（SFT）将违规信号隐藏于合规标签中，而直接偏好优化（DPO）的序列级偏好难以匹配局部令牌层面的违规行为。为此，论文提出 DUET——一种基于教师-学生架构的、面向禁止规则的有监督蒸馏方法。其核心创新在于构建一对权重相同但可见性不同的教师模型：一个可见禁止规则（正向教师），另一个不可见（负向教师）。由于两教师仅在禁止规则可见性上差异，其在每个令牌上的分歧可精确分离出禁止规则的因果影响，从而生成纯净且不受模型容量或分布偏移干扰的监督信号。该分歧驱动两个互补机制：信号清洗（剔除一致预测的冗余或前缀污染令牌）与偏好导向学习（在令牌粒度上引导学生远离负向教师、靠近正向教师），实现无需离线偏好数据的端到端偏好优化。实验在涵盖五类任务的工业级禁止合规基准上验证，针对1.5B至8B规模的Qwen模型，DUET在保持88%-93%正常任务性能的同时，将违规合规率提升至72.3%-85.2%，显著优于基线教师模型及其他蒸馏方法；外部评估（SysBench）进一步证实其在安全对齐上的优越性，同时在GSM8K和MATH-500等基准上仅有轻微性能下降。
链接: https://arxiv.org/abs/2608.14644
作者: Zihan Li,Feifei Li,Wenhui Que
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Real-world LLM deployments increasingly rely on runtime-injected prohibitions–enterprise policies, PII redlines, tool boundaries–that vary per request and per tenant. Conventional post-training is structurally ill-suited: SFT hides the violation signal in compliant labels, and DPO’s sequence-level preferences mismatch token-localized violations. We propose DUET, a token-selective on-policy distillation method for prohibition compliance. DUET pairs a teacher that sees the prohibition (positive) with an identical-weight teacher that does not (negative). Because the two teachers differ only in prohibition visibility, their per-token disagreement isolates the prohibition’s causal effect–yielding a clean supervision signal uncontaminated by model capacity or mismatch. This disagreement drives two complementary mechanisms: signal cleaning, which discards agreement tokens as redundant or prefix-corrupted, and preference-directed learning, which pushes the student away from the negative teacher and toward the positive one at token granularity, embedding DPO-style optimization directly into OPD without offline preference data. We construct an industrial Prohibition-Compliance benchmark spanning five task families covering explicit-refusal, paraphrase robustness, and over-refusal. Across 1.5B-8B Qwen variants, DUET achieves 72.3-85.2% violation compliance while preserving 88-93% normal utility, dramatically outperforming teacher model and other distillation baselines. External evaluation on SysBench confirms improved safety alignment with minimal degradation on GSM8K and MATH-500.
[NLP-126] Valid Per-Field Selective Risk Control for Document Extraction: Three Failure Modes a Validity Ladder and When Conditioning Pays
【速读】： 该论文旨在解决生成式文档抽取系统中“按字段接受/拒绝”策略在真实场景下无法有效控制风险的问题，核心挑战在于现有方法在未显式保障置信度的前提下，隐性违反了“仅当被接受字段的错误率受控于最大α水平时才接受该字段”的信任契约。研究通过对800份CORD收据中的13,859个真实字段（平均准确率49.0%）进行诊断，识别出三大失效模式：文档聚类导致的设计效应（设计效应1.84–2.45）、评分重校泄露（在α=0.10时覆盖率0.416，风险超标率达95%）、以及阈值网格坍缩的“平局质量病理性问题”（得分退化至0.030→0.001）。为此，提出一个分层验证阶梯（validity ladder），逐级保证可靠性：通过拟合/验证集划分协议恢复学习融合模型的选择性风险控制（名义α=0.10时风险0.096，覆盖率0.318，无容差带），但其仍存在47.5%的重采样中实际风险超限；进一步采用Mondrian Learn-then-Test结合精确二项尾部计算，获得各层级的近似正确（PAC）证书——场间独立（field-iid）为0.171（风险0.068）、聚类修正后为0.140、文档间独立（doc-iid）为0.060，其中后者唯一能真实匹配文档结构且当前接近真空（near-vacuous）状态。支持箱（support-bin）作为预设来源分类体系，在sonnet CORD捕获任务中所有严格层级均胜出（p1e-4，Bonferroni校正），但该优势在haiku或qwen模型上不复现；而在更高准确率语料中，合并阈值更优，表明条件建模仅在合并方法无法提供认证时才有价值，且在其他情况下可被学习得分所覆盖。最终通过冻结配置确认（selection-untouched claude-haiku-4-5）及盲态三标注员人工黄金审计，验证实际层级接受集风险仅为1.3%（低于10%预算），且标签一致性高（Fleiss’ kappa=0.83），误差倾向保守。研究成果以Apache-2.0开源，并附种子固定与回归门控流程。
链接: https://arxiv.org/abs/2608.14639
作者: Bhaskar Gurram
机构: Zasti AI
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages. Seed-pinned, regression-gated harness (Apache-2.0): this https URL . Companion benchmark paper: VerifyDocBench
Abstract:Per-field accept/review with selective risk at most alpha – accept a field only if the error rate among accepted fields is controlled – is the trust contract document-extraction systems need, and the natural procedure silently violates it on real documents. On 13,859 genuine claude-sonnet-5 fields from 800 CORD receipts (49.0% correct) we diagnose three failure modes: document clustering (design effect 1.84-2.45), score-refit leakage (coverage 0.416 at risk 0.127, violating alpha=0.10 in 95% of splits), and a tie-mass pathology (a degenerate score collapses the threshold grid, 0.030 to 0.001). We organize the fixes as a validity ladder, guarantee form stated per tier. A fit/val split protocol restores expected-selective-risk control for a learned fusion: coverage 0.318 at risk 0.096 at nominal alpha=0.10, no tolerance band (production variant 0.326) – an on-average point whose realized risk exceeds alpha in 47.5% of resplits, not a certificate. Mondrian Learn-then-Test with exact binomial tails yields per-group PAC certificates: field-iid 0.171 at risk 0.068, cluster-corrected 0.140, doc-iid 0.060 – the only tier matching documents, honestly near-vacuous today. Support-bin, the pre-specified provenance taxonomy, wins every rigor tier on the sonnet CORD capture (p1e-4, Bonferroni-corrected) – a win that does not replicate on the same documents under haiku or qwen – while on higher-accuracy corpora pooled thresholds win: conditioning helps exactly where pooled cannot certify, subsumed by a learned score elsewhere. A frozen-configuration confirmation on selection-untouched claude-haiku-4-5 held at both risk levels, and a blind three-annotator human-gold audit verifies the practical tier’s accepted-set risk at 1.3% against its 10% budget (Fleiss’ kappa=0.83; labels err one-sidedly pessimistic). Released Apache-2.0 with seed-pinned, regression-gated procedures.
[NLP-127] DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）中扩散型大语言模型（Diffusion Large Language Models, D-LLMs）存在的幻觉问题，即模型生成的文本虽流畅但包含事实性错误或缺乏支持的内容。现有幻觉检测方法通常对去噪过程中的不确定性轨迹进行时间维度或词元维度的压缩处理，忽略了二维词元-步骤结构中蕴含的丰富信息，导致难以捕捉如不一致收敛和跨词元故障传播等关键幻觉模式，进而影响检测性能。其解决方案的关键在于提出一种名为DeMTS（Denoising Multivariate Time Series）的新型检测框架，将去噪轨迹建模为可学习潜在变量上的多变量时间序列，并通过轨迹保持的词元到变量映射模块将词元信号转换为稳定潜在表示；在此基础上，引入动态多变量时序建模机制，逐步融合变量间依赖关系与时间编码以实现幻觉预测。实验结果表明，DeMTS在两种D-LLM基线模型和三个基准数据集上均显著优于现有方法，兼具良好的鲁棒性、高效性及跨任务迁移能力。
链接: https://arxiv.org/abs/2608.14632
作者: Xin Zhang,Yili Wang,Yue Tan,Xin He,Yanyu Qian,Yixin Liu,Yi Chang,Shirui Pan,Xin Wang
机构: 1. University of Technology Sydney (悉尼科技大学); 2. RMIT University (皇家墨尔本理工大学); 3. University of Melbourne (墨尔本大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs remain vulnerable to hallucinations, where fluent outputs may contain factually incorrect or unsupported content. Although existing hallucination detection methods for D-LLMs attempt to leverage uncertainty trajectories of the denoising process to better identify hallucination signals, they typically compress the trajectories along either the temporal or token dimension, overlooking the useful information encoded in the complete two-dimensional token-step structure. Consequently, they may fail to capture hallucination-relevant patterns, such as inconsistent convergence and cross-token fault propagation, leading to suboptimal detection performance. To bridge this gap, we propose a D-LLM hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short). DeMTS employs a trajectory-preserving token-to-variable assignment module to convert token signals into stable latent variables. Based on these variables, we propose dynamic multivariate temporal modeling to progressively integrate inter-variable dependency modeling with temporal encoding for hallucination prediction. Extensive experiments on two D-LLMs backbones and three benchmarks demonstrate that DeMTS outperforms existing hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.
[NLP-128] Characterizing Rhetorical Misalignment in Decision-Making with Language Models
【速读】： 该论文旨在解决生成式AI在高风险人机决策场景中可能因语言呈现方式不当而加剧认知偏差，进而导致有害决策后果的问题。其核心挑战在于揭示“修辞错位”（rhetorical misalignment）这一新型安全风险：即尽管大语言模型（LLM）输出在事实层面准确，但其语言风格或表达策略与具体决策情境不匹配，从而诱导人类决策者产生次优甚至错误判断。解决方案的关键在于构建一个基于决策理论的分析框架，并通过真实临床决策实验（基于美国医师执照考试数据集）验证该现象的存在性与危害程度——实验发现，不同模型平均引发2.81%的有害决策翻转（clinician从正确答案转向错误答案），且参与者反馈表明此类误判与锚定效应、权威偏见及损失厌恶等认知偏差密切相关。为实现可扩展评估，研究进一步采用由LLM模拟的决策者进行计算量化，首次揭示了“事实对齐但修辞失当”可能导致实质性危害的安全隐患，为高风险领域中生成式AI的安全性评估提供了新的理论视角与方法论基础。
链接: https://arxiv.org/abs/2608.14630
作者: Zirui Cheng,Joey Chan,Simo Du,Chenhao Tan,Yue Guo,Hao Peng
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); NYC Health + Hospitals/Jacobi Medical Center (纽约市卫生与医院/雅各布医疗中心); University of Chicago (芝加哥大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human decision-making is often shaped by a range of well-documented cognitive biases. As large language models (LLMs) become increasingly integrated into high-stakes human-AI decision-making, it is important to understand whether their outputs can amplify potential biases, how this influences human decisions, and crucially, whether it can lead to harmful consequences. In this work, we develop a decision-theoretic framework to study rhetorical misalignment, a failure mode where an LLM uses rhetorically inappropriate forms of presentation for a given decision context, thereby inducing suboptimal human decisions. We empirically investigate this phenomenon through a human-subject experiment in realistic clinical decision-making using a dataset curated from the United States Medical Licensing Examination. By measuring how LLM-generated information affects decisions, we observe that LLMs induce an average 2.81% rate of harmful decision flips across different models, where clinician participants change from a correct to an incorrect answer. Rationales reported by participants provide evidence that these revisions are closely related to the language used by LLMs that may induce different types of cognitive biases, including anchoring, authority bias, and loss aversion. To enable scalable evaluation, we instantiate our theoretical framework using decision-makers simulated by LLMs to computationally measure rhetorical misalignment. Our findings reveal a safety concern previously unrecognized in high-stakes domains: a model can be factually aligned yet still induce harm through its rhetorical presentation.
[NLP-129] Inference-Time Mitigation of Adversarial Political Bias in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在信息检索与摘要生成任务中易受政治偏见影响的问题，尤其针对当前主流对齐范式（如基于人类反馈的强化学习，Reinforcement Learning from Human Feedback, RLHF）在面对对抗性提示注入时缺乏防御能力的缺陷。现有方法未能专门识别和抑制政治偏见，导致模型可能生成带有倾向性的内容，威胁AI系统的安全性和可信度。其解决方案的关键在于提出一种基于思维链（Chain of Thought, CoT）提示与直接偏好优化（Direct Preference Optimization, DPO）相结合的递归自我修正（Recursive Self-Correction）机制，通过在推理阶段动态检测并纠正潜在的政治偏见。实验基于公开的立法视频数据集，采用四维政治摘要评估体系进行评测，结果表明该方法可将模型在政治中立性李克特量表上的平均得分从基线2.14显著提升至4.56，验证了其在不依赖训练阶段修改的前提下，有效实现对政治偏见的推理时缓解。
链接: https://arxiv.org/abs/2608.14629
作者: Tejaswi V. Panchagnula,Bruce Coburn,Bryce J. Dietrich,Robert X. Browning,Edward J. Delp,Fengqing Zhu
机构: Purdue University (普渡大学); Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As Large Language Models (LLMs) become the mainstay for information retrieval and summarization tasks, ensuring that they are always non-partisan and invulnerable to political bias is a critical step towards safer and more trustworthy Artificial Intelligence (AI). Current model alignment paradigms, such as reinforcement learning from human feedback (RLHF), make LLMs follow overarching safety instructions. However, this instruction tuning can be exploited via adversarial prompt injection and be used to generate unsafe content. In particular, political bias has not been specifically targeted by modern alignment techniques as harmful and biased content. To address this vulnerability of LLMs, we propose mitigation strategies using Chain of Thought (CoT) prompting and Direct Preference Optimization (DPO). Using a public dataset of legislative videos, we generate summaries using LLMs, inject bias via adversarial prompting and evaluate their performance on a four axis scale designed for political summarization. In this paper, we present different methods to shield LLMs against the injection of political bias. Our results demonstrate that the proposed Recursive Self-Correction approach raises model performance from a Political Neutrality Likert scale baseline of 2.14 to 4.56, averaged across all models, demonstrating effective inference-time mitigation of political bias in LLM-generated summaries.
[NLP-130] LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review IJCAI
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在低资源语言和多语言场景下安全对齐（safety alignment）能力显著弱于高资源语言的问题。其核心挑战在于，现有安全对齐方法在跨语言迁移中存在系统性失效，尤其体现在文化特异性危害的识别不足、跨语言越狱攻击（cross-lingual jailbreaks）、语码转换攻击（code-switching attacks）以及少数语言的安全性能退化等方面。解决方案的关键在于构建一个基于三种适应机制的分类体系：数据适配（data adaptation）、目标优化（objective optimization）和机制对齐（mechanistic alignment），并提出未来研究应聚焦于发展具有文化根基的评估基准、参与式数据收集机制、平衡的多语言预训练策略以及可扩展的多语言对齐方法，以弥合持续存在的多语言安全差距。
链接: https://arxiv.org/abs/2608.14626
作者: Valdini Douglace Lemofouet,Blessing Ngozi Uzor,Paula Chikaodinaka Anyanwu,Danielle Blanche Kapsa,Sukairaj Hafiz Imam,P Sam Sahil,Abigail Oppong,Tassallah Abdullahi,Clemencia Siro,Idris Abdulmumin,Seid Muhie Yimam,Shamsuddeen Hassan Muhammad
机构: African Institute for Mathematical Sciences (AIMS), Cameroon; Bayero University Kano; Brown University; Centrum Wiskunde & Informatica; University of Pretoria; University of Hamburg; Imperial College London
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The paper was accepted at LM4UC workshop organize by IJCAI. I added a screenshot of the decision (Open Review)
Abstract:Large Language Models (LLMs) have achieved substantial progress in safety alignment, yet their safety guarantees remain significantly weaker in low-resource and multilingual settings than in high-resource languages. In this paper, we conduct a Systematic Literature Review (SLR) of LLM safety alignment in low-resource languages by adopting the PRISMA 2020 methodology. Out of roughly 1,500 papers identified from Semantic Scholar, arXiv, and OpenAlex, 50 relevant studies have been selected and analyzed. Our review is organized around four themes: safety alignment methods, multilingual safety risks, evaluation benchmarks, and cross-lingual transferability. We further propose a taxonomy of safety alignment approaches based on three adaptation mechanisms: data adaptation, objective optimization, and mechanistic alignment. Across literature, translated English benchmarks fail to sufficiently represent culturally rooted harms, and multilingual models are more vulnerable to cross-lingual jailbreaks, code-switching attacks, and safety degradation in underrepresented languages. These failures are driven by several key factors, including uneven multilingual pre-training coverage, insufficient native-language preference data, poor transfer of safety representations, and a lack of culturally aware evaluation frameworks. The review also notes that many low-resource languages, especially African languages, have fewer safety benchmarks available than other multilingual regions. Overall, the results reveal a persistent multilingual safety gap, and suggest that future progress will require culturally grounded benchmarks, participatory data collection, balanced multilingual pre-training, and scalable multilingual alignment methods.
[NLP-131] AutoMem: A Text-Gradient Recursive Self-Improvement Framework for Automated Memory Architectures Search
【速读】： 该论文旨在解决大语言模型（LLM）智能体中长期记忆设计的高耦合性问题，即记忆编码、存储、检索与管理等环节在不同任务和模型架构间缺乏通用最优方案，导致性能表现差异显著。其核心挑战在于如何实现任务自适应的记忆架构选择，而非依赖人工设计的固定范式。解决方案的关键是提出一种基于文本梯度递归自提升的自动化记忆架构搜索框架——\textscAutoMem。该框架通过两个核心组件实现高效优化：一是经验引导的架构搜索（Experience-Guided Architecture Search），利用历史搜索轨迹与累积反思生成候选架构；二是失败引导的模块诊断（Failure-Guided Module Diagnosis），将记忆相关失败精准定位至特定模块，并转化为针对性的文本反馈以指导改进。实验表明，\textscAutoMem 在 GAIA、WebWalkerQA 与 xBench-DeepSearch 多个基准上，针对两种主流 LLM 骨干模型均能持续发现优于人类设计基线的任务自适应记忆架构，平均准确率提升 2.8 个百分点；同时在保证更高精度的前提下，相较最优基线降低 14.3% 的 token 消耗，并在少量迭代内超越大规模随机搜索所得结果，展现出优异的准确性与效率平衡能力。
链接: https://arxiv.org/abs/2608.14621
作者: Lin Du,Jie Zhou,Yuxuan Cai,Kai Chen,Qin Chen,Xin Li,Bo Zhang,Wei Li,Liang He
机构: East China Normal University (华东师范大学); Shanghai AI Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory is increasingly central to LLM agents, yet memory design remains a highly coupled architecture problem: what to encode, how to store it, how to retrieve it, and how to manage it can vary substantially across tasks and backbone models. We construct a discrete search space with 5 encoders, 5 stores, 6 retrievers, and 4 managers, and show that no single memory architecture consistently dominates: different tasks favor different module combinations, leading to substantial performance gaps. Motivated by this, we propose \textscAutoMem, a text-gradient recursive self-improvement framework for task-adaptive memory architecture search. \textscAutoMem optimizes over the factored space through two components: Experience-Guided Architecture Search, which proposes candidate architectures from historical search trajectories and accumulated reflections, and Failure-Guided Module Diagnosis, which localizes memory-related failures to specific modules and converts them into targeted textual feedback. Experiments on GAIA, WebWalkerQA, and xBench-DeepSearch across two LLM backbones show that \textscAutoMem consistently discovers task-adaptive memory architectures that outperform the strongest human-designed memory baselines, improving accuracy by 2.8 points on average across six benchmark-backbone settings. Further analysis shows that \textscAutoMem achieves a favorable accuracy-efficiency trade-off, reducing token cost by 14.3% over the strongest accuracy baselines under Qwen3.5-122B-A10B, while also finding stronger architectures than substantially larger random searches within only a few guided iterations.
[NLP-132] Calibrated Trust Not Sharper Prediction: An Empirical Test of Uncertainty Fusion
【速读】： 该论文旨在解决法律人工智能中案件结果预测的可信度与可靠性问题，具体聚焦于如何通过融合多种不确定性量化工具（如证据图结合信念传播、序列贝叶斯似然比更新、Dempster-Shafer组合规则及合规预测）来提升预测性能。其核心挑战在于评估这些方法在真实法律案例数据上的有效性，尤其是在前沿大语言模型（LLM）已具备较强判别能力的前提下。研究发现，关键问题并非提升预测精度，而是确保模型输出的可信赖性：直接使用前沿LLM（如Claude Opus 4.8和GPT-5.5）在歧视类案件上已达到约0.83的AUROC，显著优于引入不确定性融合管道的方案；而盲目将LLM与贝叶斯似然比或Dempster-Shafer方法组合反而导致校准误差翻倍（ECE从0.16增至0.46），源于先验不匹配机制。此外，Dempster-Shafer方法在长链推理中表现出严重安全隐患，会在低于随机水平的准确率下仍高自信地输出错误标签，应被移除。因此，该融合管道真正的价值不在于提升预测准确性，而在于实现操作层面的可信决策——通过引入合规选择性预测层，系统可自动识别可放心处理的案件（96.8%准确率，仅0.5%错误漏过）并标记需人工复核的案件（96.3%捕获率），相较未调优基线（85.9%/3.8%/72.1%）显著优化。由此可见，该研究的核心贡献在于：法律AI中的融合管道本质是实现“校准的信任”（calibrated trust），而非更精确的预测。
链接: https://arxiv.org/abs/2608.14617
作者: Surya Saka
机构: JudicialMind
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 12 pages, 10 figures
Abstract:A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Dempster-Shafer combination, and conformal prediction) into one pipeline. We test this on 1,000 real European Court of Human Rights cases from LexGLUE and FairLex, predicting whether the Court found a Convention violation from the case’s fact paragraphs. We compare three families across two frontier LLMs (Claude Opus 4.8 and GPT-5.5) as per-fact evidence estimators: (A) the raw LLM, (B) the LLM routed through the fusion pipeline, and © a term-frequency baseline through the same pipeline. Across roughly 4,750 tests we find: (1) on discrimination (AUROC around 0.83) the pipeline yields no improvement over either the raw LLM or the baseline; a frontier LLM used directly is the strongest single discriminator. (2) Naively composing an LLM with Bayesian-odds and Dempster-Shafer fusion more than doubles calibration error (ECE from about 0.16 to 0.46) via a prior-mismatch mechanism that replicates across both models. (3) Dempster-Shafer fusion is actively unsafe on long chains, committing confidently to wrong labels at below-chance accuracy; we recommend removing it. (4) The pipeline’s genuine value is operational: routed through a conformal selective-prediction layer, the system decides which cases to automate and which to escalate. After removing Dempster-Shafer, recalibrating, and applying class-conditional risk control on the full 1,000-case set, the tuned engine auto-clears at 96.8 percent accuracy with 0.5 percent errors escaping and 96.3 percent caught for review, versus 85.9 / 3.8 / 72.1 for an untuned baseline. The contribution of such pipelines in law is calibrated trust, not sharper prediction.
[NLP-133] Plausible but Not Valid: A Psychometric Audit of LLM s as Synthetic Survey Respondents
【速读】： 该论文旨在解决生成式人工智能（Generative AI）在模拟真实人类调查数据时存在的核心问题：现有评估方法仅关注单个回答的表面合理性，而忽视了更深层次的心理测量学一致性。研究提出的关键问题是：生成的合成数据是否保留了真实人类调查数据中的联合分布、潜在结构、信度、中介路径及人口统计学效应等关键心理测量特性？为此，作者构建了一个包含263名员工的立陶宛组织心理学数据集（涵盖Dunham变革态度量表、UWES-17工作投入量表、Koopmans IWPQ工作满意度量表，共68个项目、12个子维度），并基于五级人格披露阶梯对37个主流大语言模型（LLMs）进行条件化生成，同时设计了呈现方式与推理努力程度的消融实验、反事实人口统计学替换（性别、职位、教育水平）、跨语言验证以及逐字回忆记忆探测任务。通过引入心理测量相似性评分（Psychometric Similarity Score, PSS），以非生成式统计基线和人工-人工对照组作为参照，结合响应者自举置信区间与项目置换零假设检验（用于Tucker’s phi），系统评估各模型表现。结果表明，尽管LLMs能再现人类心理测量关系的定性方向，但其样本驱动的PSS得分普遍低于高斯耦合（Gaussian-copula）基线；“模型群体”内部相似性（平均互相关PSS为0.73）远高于其与真实人类的相似性；记忆机制并非性能主导因素（回忆任务得分与PSS排名无显著相关性）。反事实分析显示教育水平的影响效应（平均|d|=0.56）显著强于性别（0.12）和职位（0.18）；在UWES量表上，8个模型的Tucker’s phi落入置换零假设范围内。下游分析进一步揭示所有模型均存在显著顺从性偏移（+0.84标准差），经合成数据训练的回归模型在真实人类样本上的预测有效性大幅下降（均值R²从0.28降至-0.18），且在10条安慰剂中介路径中有3条被错误生成间接效应。因此，该研究的核心结论是：当前的大语言模型生成的合成调查数据无法作为真实人类数据的直接替代品，其在心理测量学层面的保真度仍存在根本性缺陷。
链接: https://arxiv.org/abs/2608.14606
作者: Mantas Lukauskas,Viktorija Šarkauskaitė
机构: Hostinger; Kaunas University of Technology (考纳斯科技大学); AI Insight Lab
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Applications (stat.AP)
备注: 50 pages, 9 figures. Under review. Code and data will be released upon publication
Abstract:Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations ask whether answers look plausible at the individual level. We argue the right question is psychometric: do LLMs preserve the joint distribution, latent structure, reliability, mediation pathways, and demographic effects of real human survey data? We introduce a Lithuanian organisational-psychology dataset (n=263 employees; Dunham Attitudes Toward Change, UWES-17, Koopmans IWPQ; 68 items, 12 subscales) and condition a 37-model lineup spanning OpenAI, Anthropic, Google, and twelve open-weight families on real respondent profiles under a five-level persona-disclosure ladder, presentation and reasoning-effort ablations, counterfactual demographic swaps (gender, role, education), a cross-language check, and a verbatim-recall memorization probe. The resulting Psychometric Similarity Score (PSS) is anchored against five non-LLM statistical baselines and a held-out human-vs-human ceiling, with respondent-bootstrap confidence intervals and an item-permutation null for Tucker’s phi. LLMs reproduce the qualitative direction of human psychometric relationships, but a Gaussian-copula baseline beats every LLM on the sample-driven PSS components; the LLM “crowd” is more similar to itself (mean inter-LLM PSS 0.73) than to humans; and memorization does not drive the leaderboard (recall-PSS rank correlation 0.00). Counterfactual swaps reveal education-driven effects (mean |d|=0.56) that dwarf gender (0.12) and role (0.18); Tucker’s phi on UWES falls inside the permutation null for 8 of 37 models. Downstream, every LLM shows a strong acquiescence shift (+0.84 SD), synthetic-trained regressors lose predictive validity on held-out humans (mean R^2 -0.18 vs 0.28), and models fabricate indirect effects on 3 of 10 placebo mediation paths. LLM samples are not a drop-in replacement for human survey data.
[NLP-134] Wiola 13M a Gated Spiral Attention Architecture for Parameter Efficient Small Language Models
【速读】： 该论文旨在解决小规模语言模型（参数量在千万至亿级）在设备端推理、快速实验及可控科学研究中的性能瓶颈问题，特别是针对现有模型普遍沿用标准Transformer结构而未适配小规模场景所导致的效率与表达能力不足。其核心解决方案在于提出一种名为Wiola的仅解码器架构，通过三个可直接替换的标准模块实现关键突破：第一，螺旋旋转位置编码（Spiral Rotary Positional Encoding）通过逐维度缓慢增长的扰动因子调整旋转频率，使相位轨迹向外发散，从而增强长程依赖区分能力且不增加参数；第二，门控螺旋注意力（Gated Spiral Attention）引入基于查询流因果累积统计的逐头内容自适应标量门控机制，以极低开销实现隐式的、可微的软头选择；第三，蝴蝶前馈块（Butterfly feed forward block）以乘法交互和块内旁路路径替代传统扩张层，在保持与四倍门控线性单元等价参数量的同时，显著改善浅层堆叠中的梯度流动。论文对各组件进行形式化定义，精确推导参数与计算预算，并证明门控注意力在全序列训练与缓存自回归解码之间存在精确等价性，确保推理阶段无近似引入。此外，研究提供了基于标准微型故事语料库的完全可复现训练与评估协议，参考实现已开源并支持权重发布。
链接: https://arxiv.org/abs/2608.14604
作者: Aryuemaan Kumar Chowdhury,Praveen Oosa,Vineesha Reddy
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 6
Abstract:Small language models in the ten to one hundred million parameter range are attractive for on device inference, rapid experimentation, and controlled scientific study, yet most of them reuse the standard transformer block without adaptation to the small scale regime. We present Wiola, a decoder only language model whose novelty is concentrated in three drop in components of every layer. First, Spiral Rotary Positional Encoding perturbs the standard rotary frequencies by a slowly growing per dimension factor so that phase trajectories fan outward, improving long range discrimination while adding no parameters. Second, Gated Spiral Attention introduces a per head, content adaptive scalar gate derived from a causal cumulative statistic of the query stream, providing an implicit and differentiable form of soft head selection at negligible cost. Third, the Butterfly feed forward block replaces the conventional expansion layer with a multiplicative interaction and an intra block bypass path, matching the parameter count of a four times gated linear unit block while improving gradient flow in shallow stacks. We formalize each component, derive exact parameter and computation budgets, and prove that the gated attention admits an exact and numerically verified equivalence between full sequence training and cached autoregressive decoding, so that no approximation is introduced at inference time. We also describe a fully reproducible training and evaluation protocol on a standard tiny story corpus. The reference implementation is released as an open source package with weights ready publishing support.
[NLP-135] Multi-Modal Generative Fuzzy System: Fuzzy Inference Guided Large Model Interactive Question Answering Framework
【速读】： 该论文旨在解决多模态问答（Multimodal Question Answering, MQA）中普遍存在的三大核心挑战：模态偏差（modality bias）、跨领域知识融合带来的不确定性，以及现有方法依赖浅层语义匹配所导致的推理深度不足与可解释性差。针对这些问题，其提出的解决方案关键在于构建一种受传统模糊系统（Fuzzy System, FS）启发的生成式多模态模糊系统（Multi-Modal Generative Fuzzy System, MMGFS），其核心创新点包括：通过多模态协同反思机制缓解模态间特征分布差异引发的偏差；引入模糊规则与多跳推理机制，实现跨域知识融合与层次化推理，从而增强对不确定性的建模能力并深化语义理解。实验结果表明，MMGFS在多个开放域与领域特定基准数据集上均显著优于现有方法，在答案准确性、一致性与泛化性能方面表现出色。
链接: https://arxiv.org/abs/2608.14584
作者: Hailong Yang,Jianqi Wang,Guanjin Wang,Zhaohong Deng
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 8 figures
Abstract:In Multimodal Question Answering (MQA), models are required to jointly encode and integrate heterogeneous information from multiple modalities, including text, images, and speech, to perform complex semantic reasoning and decision making. Despite recent advances, existing approaches, including traditional deep learning models and Large Models (LMs) or prompt-based frameworks, continue to face several critical challenges. First, modality bias arises from discrepancies in feature distributions across different modalities, which limits effective cross modal collaborative understanding. Second, many questions require knowledge drawn from multiple domains, introducing significant uncertainty. Third, current methods often rely on shallow semantic matching, resulting in limited reasoning depth an reduced interpretability. To address these issues, inspired by the traditional fuzzy system (FS) framework, we propose a fuzzy-inference-guided multimodal generative architecture termed the Multi-Modal Generative Fuzzy System (MMGFS). The main contributions of MMGFS are two folds. First, it alleviates modality bias through a multimodal collaborative rumination mechanism. Second, it introduces fuzzy rules and a multi-hop inference mechanism to support cross-domain knowledge fusion and hierarchical reasoning, thereby strengthening uncertainty modelling and deepening semantic understanding. We conduct comprehensive evaluations on open-domain question answering datasets, including MultimodalQA and WebQA, as well as domain-specific benchmarks, including BioMol-VQA and EHRxQA. Experimental results demonstrate that MMGFS consistently outperforms existing methods across multiple datasets. It effectively mitigates modality bias and question uncertainty while achieving superior performance in answer accuracy, consistency, and generalization.
[NLP-136] HarmProfile: Characterizing Harmful Distributions in Frontier LLM s
【速读】： 该论文旨在解决前沿大语言模型（LLM）安全评估中对有害生成内容缺乏系统性分析的问题，即当前研究多将有害输出视为攻击结果而非可分析的内在风险对象，导致对模型在误行为过程中产生的有害内容特征了解有限。其关键解决方案是提出HarmProfile——一个以内容为中心的基准数据集，通过收集跨多种危害类别与模型家族的模型误行为样本，将有害输出的分布定义为模型级别的风险画像（risk profile）。该方法基于“如同从语料库中刻画语言行为，亦可从内容、严重程度与多样性角度刻画模型安全失败”的核心假设，构建了包含超过8万条经验证的样本、覆盖23个前沿模型及13个模型家族、划分为15类危害与57个子类别的大规模高质量数据集。研究表明，前沿大语言模型在规模上可靠地产生有害内容，且其风险画像具有显著差异；同时，有害性与多样性随模型能力增强而上升，暗示尽管模型表面表现安全，但其底层可能潜藏日益危险的知识。
链接: https://arxiv.org/abs/2608.14577
作者: Zhouyuan Ma,Yutao Wu,Hanxun Huang,Xiang Zheng,Xiao Liu,Yixin Cao,Zuxuan Wu,Xingjun Ma,Yu-Gang Jiang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of frontier-LLM misbehavior are difficult to obtain. To address this gap, we introduce HarmProfile, a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and model families, and defines the resulting harmful-output distribution as a model-level risk profile. The premise is that, just as linguistic behavior can be characterized from an utterance corpus, model risk can be characterized from the content, severity, and variation of its safety failures. HarmProfile contains over 80,000 validated artifacts from 23 frontier LLMs across 13 model families, organized into 15 harm categories and 57 subcategories. Using this corpus, we find that frontier LLMs reliably produce harmful content at scale, yet exhibit distinct risk profiles; both harmfulness and diversity grow with model capability, suggesting that frontier LLMs may appear safe yet harbor increasingly dangerous knowledge beneath the alignment surface. Our source code is available at this https URL .
[NLP-137] Iterative Self-Learning for Expressive Text-to-Speech Synthesis
【速读】： 该论文旨在解决生成式语音合成（TTS）中表达性标签稀缺的问题，即在需要显式控制语调、情感等表达属性的任务中，高质量标注数据难以大规模获取，而现有半监督学习框架并未针对这一关键瓶颈进行优化。其解决方案的关键在于提出一种基于“逆向-分类”（Invert-Classify）机制的迭代自学习（Iterative Self-Learning, ISL）框架，该框架无需依赖分类器，通过反演一个冻结的生成模型来恢复离散的表达性标签。该框架通过迭代方式对未标注语音进行伪标签标注，利用当前模型生成伪标签，再结合真实标签与伪标签数据重新训练模型，并不断精炼标签质量与合成性能。实验结果表明，在词级突出度和语句级情感两个表达性任务上，该方法显著提升了伪标签准确率，并有效改善了合成语音对表达标签的遵循程度与整体音质，尤其在极低资源条件下，其性能接近全监督模型，验证了基于梯度的迭代自学习是缓解表达性标签稀缺问题的有效手段。
链接: https://arxiv.org/abs/2608.15910
作者: Nicholas Sanders,Gustav Eje Henter,Simon King,Korin Richmond
机构: University of Edinburgh (爱丁堡大学); Huawei(华为); Knut and Alice Wallenberg Foundation (克努特和爱丽丝·瓦伦堡基金会)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:Expressive text-to-speech (TTS) systems that use explicit conditioning labels provide direct and interpretable control over expressive attributes, in contrast to reference-based or prompting-based approaches, but require labeled data. Obtaining these labels at scale is costly and time-consuming, yet no prior semi-supervised framework addresses this specific bottleneck. Existing semi-supervised TTS methods instead target scarcity of paired speech-text data or transcriptions. To address the scarcity of expressive labels, we propose an Iterative Self-Learning (ISL) framework for expressive TTS, built on Invert-Classify, a classifier-free method that recovers discrete expressive labels by inverting a frozen generative model. The framework iteratively pseudo-labels unlabeled speech using the current model, retrains on the combined labeled and pseudo-labeled data, and repeats, progressively refining label quality and synthesis. We validate on two expressive tasks, word-level prominence and utterance-level emotion, across multiple low-resource data splits. We find that iterative refinement can improve pseudo-label accuracy over single-pass baselines. Furthermore, we observe that these improvements in pseudo-labeling of expressivity translate to gains in expressive label adherence and synthesis quality, confirmed by objective metrics and human listening tests. In the most data-scarce conditions, ISL-trained models outperform single-pass pseudo-labeling and further approach fully supervised performance, demonstrating that gradient-based ISL is an effective solution to expressive label scarcity in low-resource TTS.
[NLP-138] Optimal Watermark Localization in Mixed-Source Large Language Model Texts
【速读】： 该论文旨在解决生成式文本中水印信号在经过重写、插入、删除或改写等编辑操作后，其存在位置变得稀疏且不连续时，如何实现对水印痕迹的精准定位问题。传统方法仅关注全局水印检测，而无法回答水印信号在具体哪些词元（token）位置仍存留这一关键问题。本文将水印定位建模为基于枢轴统计量（pivotal statistics）的词元级多重假设检验问题，并引入隐变量指示每个位置是否仍保留水印依赖关系。在由信号稀疏性指数、下一词元集中度指数及有效词表增长速率共同定义的渐近框架下，推导出全局检测的精确边界以及坐标系下枢轴法定位规则中的发现与分类相变现象。研究发现，水印发现任务严格难于检测任务，且在该类方法的参数范围内，一致分类不可实现。为此，提出一种自适应阈值方法，无需预先知晓上述指数或随时间变化的下一词元分布，而是通过数据驱动估计存活水印比例来动态调整阈值。该方法实现了最优发现边界，并在发现功效上接近同质枢轴法的最优性能。仿真验证了理论相变行为，实验则在真实模型生成文本上展示了在常见编辑机制下的良好定位效果。
链接: https://arxiv.org/abs/2608.14906
作者: Jose H. Blanchet,T. Tony Cai,Xiang Li,Hao Liu,Qi Long,Weijie J. Su
机构: Stanford University (斯坦福大学); University of Pennsylvania (宾夕法尼亚大学)
类目: Methodology (stat.ME); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: 66 pages, 13 figures
Abstract:Watermarking provides a principled way to authenticate text generated by large language models (LLMs). In practice, however, the final text may be mixed-source, with watermark evidence surviving at only a subset of token positions after rewriting, insertion, deletion, or paraphrasing. Although prior work has studied global detection of watermark signals, when such signals can be localized remains unclear. We formulate watermark localization as a token-level multiple-testing problem based on pivotal statistics, with a latent indicator recording whether watermark dependence survives at each position. Under an asymptotic regime indexed by exponents for signal sparsity, next-token concentration, and effective-vocabulary growth, we derive a sharp boundary for global detection and phase transitions for discovery and classification within the class of coordinatewise pivot-based localization rules. We show that discovery is strictly harder than detection and that consistent classification is impossible across the parameter regime within this class. We then develop an adaptive thresholding method that does not require knowledge of the exponents or time-varying next-token distributions, but uses a data-driven estimate of the surviving watermark fraction. The method attains the optimal discovery boundary and near-optimal discovery power relative to homogeneous pivot-based rules. Simulations support the theoretical phase transitions, while experiments on model-generated texts demonstrate practical localization performance under common edit mechanisms.
信息检索
[IR-0] Impression Share Prediction: An Offline Evaluation Task for Ranking Systems
链接: https://arxiv.org/abs/2608.16872
作者: Mohsen Malmir,Houssam Nassif,Danish Nasir Shaikh,Taher Rahgooy,Murat Ali Bayir
类目: Information Retrieval (cs.IR)
备注:
Abstract:Offline evaluation is a major gateway before online evaluation of ranking models in A/B testing. Standard offline metrics measure predictive accuracy, but are only a surrogate for downstream utility: a model can improve them while redistributing impressions across objective buckets in ways that degrade downstream utility. No offline method surfaces these impression share shifts before online evaluation. We propose \emphimpression share prediction as an offline evaluation task: given a candidate ranking model, predict the distribution of impressions it would produce across objective buckets - impressions grouped by optimization goal (e.g., click, video view). The task is inherently counterfactual, since the candidate has never served live traffic. We propose a structural causal model of how model predictions and delivery capacity jointly determine impression allocation, and show the counterfactual effect is identified from observational data. Building on this, we develop a statistical learning framework that predicts impression shares from a candidate’s early-interaction confidence signals and current system state, trained on historical data. On data from multiple ranking model families, a Random Forest reduces L1 error by 49% over a constant baseline for models seen during training. For held-out models, evaluated by time since first appearance, the first hour is the closest analog to true online evaluation and the hardest: the Random Forest falls below the baseline because the capacity state still reflects the prior model. An encoder-conditioned architecture that simulates a 2-hour rollout over recent auction dynamics recovers + 22% L1 in this regime.
[IR-1] GEO-Flag: Detecting and Measuring GEO-Optimized Web Content
链接: https://arxiv.org/abs/2608.16824
作者: Junjie Chu,Ye Leng,Mingjie Li,Yun Shen,Xinyue Shen,Yang Zhang
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
备注: 21 pages, 3 figures, 21 tables
Abstract:Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized pages visibility disproportionate to their authority or relevance and even make weak or false information appear well supported. Unlike conventional search, generative search synthesizes information into direct answers rather than presenting competing sources, which can further amplify these risks, as assessing source provenance and authority requires additional user interaction. Despite these concerns, systematic methods for detecting GEO-optimized webpages remain underexplored. We introduce \textttGEOFlagBench, a benchmark of 3,200 webpages spanning 400 queries, four domains, and eight GEO optimizer families, and use it to systematically evaluate existing GEO detection methods. Although the strongest baseline achieves an aggregate F1 of 0.880, method-level and authorship-conditioned evaluations reveal substantial weaknesses and potential reliance on authorship-related shortcuts. We therefore propose \emphIntervention-Paired Training (IPT), which supervises detector responses to GEO interventions and non-GEO AI polishing; on ModernBERT, IPT improves F1 from 0.862 to 0.944 and worst-group accuracy from 0.725 to 0.883. We develop a GEO-gated Agent system for auditing the Source Tier and verifiability of Citation URLs in detected GEO pages. Finally, we deploy the complete pipeline on released Google Search and Gemini-grounded retrieval results for 1,000 real-user queries. Across 10,095 available pages, we estimate an overall GEO prevalence of 8.90%, reaching 16.36% among pages modified in 2026. Our results establish a foundation for systematically detecting, auditing, and measuring GEO in real-world search ecosystems.
[IR-2] UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
链接: https://arxiv.org/abs/2608.16797
作者: Rongcheng Lin,Yan Sun,Jamey Zhang,Guanglei Xiong,Ivan Ji,Xianjie Chen,Shujian Bu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Industrial recommenders rely on two model families that have evolved largely independently: feature-interaction models over multi-field user/item features, and sequential models over user-behavior histories. Production systems couple them only loosely. To unify the two, we present UniDot, a novel architecture for post-click conversion prediction built from the factorization-machine (FM) point of view: the embedding inner product—which powers collaborative filtering and lets a recommender generalize to unseen user–item pairs—is the same primitive as attention’s query dot key scoring, so a single dot-product of tokens can underlie both feature interaction and sequence modeling. UniDot tokenizes non-sequential fields and multi-domain behavioral sequences into one shared token space and stacks a single macro-block in which a token-mixing bus and a sequence-retrieval bus (item tokens cross-attending the histories) run in parallel and exchange state each layer through an MLP-Mixer fusion, while an FM Highway carries explicit per-layer dot-product interactions around the residual stack directly to the classifier. The sequence side is embedded once per forward pass and shared by all consumers, bounding inference latency. Trained with a dual sparse/dense (Adagrad + Muon) optimizer, an auxiliary conversion-delay head, and multi-path mutual learning, UniDot finished as the runner-up on the Industrial track of the TAAC KDD Cup 2026.
[IR-3] Unbiased Recommender Systems with Implicit Feedback
链接: https://arxiv.org/abs/2608.16704
作者: Md Aminul Islam
类目: Information Retrieval (cs.IR)
备注:
Abstract:Recommender systems typically rely on implicit feedback (e.g., clicks) to infer user preferences. However, such data is inherently prone to various biases, including position bias and popularity bias. Position bias occurs when higher-ranked items receive more interactions regardless of true relevance. Popularity bias reinforces frequent exposure of popular items while under-recommending relevant, yet less popular ones. Directly learning from such data fails to capture true user preferences, leading to suboptimal recommendations. This research focuses on mitigating position bias and popularity bias in recommender systems. Specifically, I address position bias in learning-to-rank (LTR) systems and popularity bias in collaborative filtering (CF) models and social recommender systems based on graph neural networks. My work develops methods that overcome the limitations of existing approaches to mitigating position bias and popularity bias, enabling more relevant and personalized recommendations that align with users’ preferences.
[IR-4] Cost Scales with Change Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate
链接: https://arxiv.org/abs/2608.16621
作者: Yusuke Takahashi,Kyle Wild,Asako Uraki
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注: 5 pages, 5 figures, 1 table. Accepted and presented at the 2026 International Electronics Symposium (IES), Yogyakarta, Indonesia, August 1-3, 2026 (IEEE technically co-sponsored). Authors’ accepted version
Abstract:Retrieval-augmented and agentic question-answering systems increasingly re-derive the meaning of a corpus at query time. Put plainly, instead of re-deriving what a corpus means on every question, the work is done once when a document arrives and is thereafter merely consulted – a compiler, not an interpreter, of meaning. An alternative is to compile that meaning once, at ingest time, into a compact, queryable semantic substrate and maintain it as the corpus evolves. The central objection is maintenance cost: rebuilding a truncated singular value decomposition (SVD) on every change appears prohibitive, and a change of embedding model seems to force a full re-embedding. We argue and show empirically that maintenance cost scales with the amount of change, not corpus size. On a controlled synthetic pilot (dimension 256, rank 32, a corpus grown from 3,000 to 9,000 documents over 50 update events), incremental low-rank updates were 33.7 times cheaper per update than full re-SVD and 23.8 times cheaper cumulatively, while the incremental subspace tracked the full recomputation to within floating-point precision (maximum principal-angle drift below 1e-11 degrees; recall@10 = 1.0). An orthogonal Procrustes virtual axis update recovered 0.95 mean cosine to truly re-embedded vectors by re-embedding only about 10 percent of the corpus. The results support maintaining, rather than repeatedly reconstructing, a semantic substrate.
[IR-5] SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering
链接: https://arxiv.org/abs/2608.16587
作者: Jiayi Wu,Zhengyu Wu,Xunkai Li,Hongchao Qin,Rong-Hua Li,Guoren Wang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the pool according to predefined sampling rules. However, these methods usually overlook the hardness variation of candidate negative pools across users, making it difficult to adaptively adjust the hardness and informativeness of negative samples according to candidate-pool conditions. In addition, most existing samplers evaluate candidate negatives mainly through a matching score computed from the final aggregated user and item embeddings, while ignoring the structural differences captured by multi-hop neighborhood aggregation. As a result, the training value of negatives may be insufficiently characterized. To address these issues, we propose SAHC-NS, a Structure-Aware and Hardness-Calibrated Negative Sampling method. Specifically, SAHC-NS uses the mean and standard deviation of layer-wise matching scores to capture the overall matching strength and cross-layer structural discrepancy of candidate negatives, respectively. This enables SAHC-NS to select informative negatives by taking cross-layer structural discrepancy into account, rather than relying solely on final matching scores. Moreover, SAHC-NS introduces a candidate-pool-aware hardness calibration module to dynamically adjust negative augmentation strength according to candidate-pool hardness, producing hardness-controllable negatives. Extensive experiments demonstrate the superiority of SAHC-NS over existing negative sampling methods.
[IR-6] When Is Complex Chunking Worth It? A Multi-Objective Evaluation of Chunking Methods at Scale
链接: https://arxiv.org/abs/2608.16586
作者: Laura Caspari,Kanishka Ghosh Dastidar,Michael Dinzinger,Jelena Mitrović,Michael Granitzer
类目: Information Retrieval (cs.IR)
备注:
Abstract:Dense retrieval is commonly evaluated on benchmarks that represent each document with a single embedding, even though real-world retrieval systems often index long documents that require chunking. In these settings, the chosen chunking method not only affects retrieval quality, but also indexing throughput, query latency, and memory usage. Prior comparisons of chunking strategies have mainly focused on retrieval performance, leaving operational trade-offs underexplored. To address these issues, we evaluate eight representative chunking strategies across two scalable corpora, three embedding models, and multiple corpus sizes, measuring both retrieval effectiveness and system-level costs. Our results show that computationally expensive methods rarely provide consistent gains over simpler chunking. Instead, the best performing strategy depends on the embedding model, dataset, corpus size, and target retrieval metric. Methods with similar performance can also differ substantially in operational cost, showing that chunking should be seen as a multi-objective design decision.
[IR-7] When Tool-Backed Skill Retrieval Fails: Source-Style Collapse in Executable Capability Retrieval
链接: https://arxiv.org/abs/2608.16502
作者: Yiqi Liu,Joseph James,Yang Wang,Chenghao Xiao,Chenghua Lin
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注:
Abstract:Large-scale agents increasingly rely on retrieval to access external capabilities. We study this retrieval gate in structured tools and APIs, a measurable class of tool-backed executable skills that must be surfaced before an agent can plan, incorporate, or act. In this setting the retrieval layer can silently fail even when the capability corpus is fixed: on ToolRet, a retriever fine-tuned on one source-specific slice collapses on another source-specific slice of the same benchmark, with FT-1100 despite its higher lexical overlap with the gold tools. We call this failure mode source-style collapse. Query-side TF-IDF fingerprints flag source styles on which the fine-tuned retriever is likely to fail better than semantic or length-based proxies, giving a cheap signal for mismatch over a fixed tool corpus. We propose ToolScout, a source-aware routing method that uses this signal as a routing guard: on the mixed 4,996-query stream, TF-IDF-based routing raises coverage from 22.3% to 86.1%, and across five collapsed sources 20 matched examples raise the coverage-weighted global top-1 proxy from 1.3% to 53.9%. The same failure and routing behaviors persist when tools are rerendered as executable skill cards, which rules out raw API-schema format as the sole cause.
[IR-8] FROG: Efficient Range-Filtering Approximate Nearest Neighbor Search on GPUs
链接: https://arxiv.org/abs/2608.16491
作者: Xiaokun Cui,Pengbo Liu,Jiadong Xie,Yingfan Liu,Hui Li,Jeffrey Xu Yu,Jiangtao Cui
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Range-filtering approximate nearest neighbor search (RFANNS) is a fundamental operation in modern vector databases. Given a query vector q and a numerical range predicate, RFANNS returns the k -approximate nearest neighbors ( k -ANN) of the query q among the objects whose attributes satisfy the range predicate. However, existing RFANNS methods are not well suited to high-throughput GPU execution. CPU indexes offer limited parallel scalability, generic GPU filtering is highly selectivity-dependent, and GPU indexes built from locally optimized subgraphs can incur long search trajectories and redundant distance computations. To address these limitations, we present FROG, a GPU-oriented RFANNS index that replaces multiple locally optimal substructure building with a globally aware, vertex-centric design. It organizes diverse expansion neighbor candidates for each vertex in a GPU-friendly structure and rapidly identifies the expansion neighbors used for computation at query time. Moreover, GPU-oriented algorithms and implementations are developed for both index construction and query processing. Experiments on six datasets show that FROG improves mixed-selectivity query throughput by 14.7–37.7 \times over 44-core CPU baselines and 4.5–7.6 \times over the strongest GPU baseline. It also accelerates index construction by 2.4–14.8 \times over the GPU baseline.
[IR-9] Efficient Privacy-Preserving Range Filtered Approximate Nearest Neighbor Search
链接: https://arxiv.org/abs/2608.16488
作者: Haoyu Wang,Yandi Zhang,Jiadong Xie,Yingfan Liu,Hui Li,Jeffrey Xu Yu,Jiangtao Cui
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注: According to the best of our knowledge, this work is the first attempt to study privacy-preserving range-filterd ANN search problem. This is the early version of the work that is still in progress
Abstract:Range-filtered approximate nearest neighbor search (RFANNS) is an important primitive for vector databases; it retrieves vectors that are similar to a query and satisfy a numerical range predicate, but existing RFANNS indexes expose vectors, attributes, and queries in plaintext. This assumption is unsuitable for outsourced vector databases, where sensitive data and queries must be protected from an honest-but-curious cloud server. To the best of our knowledge, this is the first study that systematically formulates and evaluates privacy-preserving RFANNS over outsourced encrypted vector databases. Our approach separates range localization from encrypted vector search: an authorized user maps the query range to a compact set of nodes in a local N-ary attribute tree, and the server searches only the corresponding proximity graph sub-indices over encrypted vectors. To reduce expensive encrypted comparisons, we use a filter-and-refine pipeline that first retrieves coarse candidates with approximate distance-comparison-preserving encryption and then reranks a small candidate set with exact distance-comparison encryption. We then analyze the computation, storage, communication, and leakage of the protocol. Experiments on four widely used vector datasets show that our method improves the QPS-Recall trade-off over representative secure adaptations of existing RFANNS approaches, scaling effectively to large datasets.
[IR-10] Graph-Based Discovery of Mathematical Software Communities and Publication-to-Community Prediction
链接: https://arxiv.org/abs/2608.16455
作者: Maxence Azzouz-Thuderoz,Yuni Susanti,Moritz Schubotz
类目: Information Retrieval (cs.IR)
备注:
Abstract:Research software forms distinct co-usage communities that span traditional disciplinary boundaries, yet the structure of these communities remains largely unexplored. We present a graph-based framework for discovering mathematical software communities and predicting their association with research publications. We construct a software co-usage network from publication-software relationships using a curated swMATH dataset and subsequently apply community detection method, revealing a heterogeneous landscape of mathematical software communities. We formulate publication-to-community mapping as a multi-label classification task and further investigate whether community membership can be predicted from lightweight scholarly metadata. Specifically, we compare two feature representations of scientific publications: Mathematics Subject Classification (MSC) and title-based embeddings. Across a range of models, structured MSC representation consistently provides a stronger precision-recall trade-off, demonstrating that structured domain metadata captures software-community structure more effectively than compressed title-only semantics in this setting. This work highlights the continuing value of structured scholarly metadata for large-scale research software discovery, classification and recommendation.
[IR-11] POI Recommendation with LLM -Augmented Multi-Graph Learning and Contrastive Alignment
链接: https://arxiv.org/abs/2608.16407
作者: Burak Tamer,Wolfram Höpken,Zehui Wang
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user-item interactions, yet they struggle with the cold-start problem, where items with few or no interactions are not represented. In this paper, we propose LLM-augmented Multi-Graph Contrastive Learning (LLM-MGCL), a multi-graph neural network that uses semantic and spatial information about items to extend the LightGCN backbone with two auxiliary item-item graphs: a semantic graph constructed from sentence embeddings of LLM-generated photo summaries and keywords, and a geographic graph derived from Haversine distances between business locations. Item embeddings are propagated over all three graphs in parallel, fused additively, and aligned across views through a bidirectional InfoNCE contrastive objective that connects behavioral, semantic, and spatial representations of the same items. Experiments on the Yelp Multimodal Recommendation Dataset show that LLM-MGCL outperforms classical collaborative filtering, matrix factorization, and interaction-only graph neural network baselines. It improves Recall@20 by 52.0% and NDCG@20 by 64.8% over LightGCN while performing on par with the strongest contrastive baseline, Self-supervised Graph Learning (SGL), which is also affected by the cold-start problem. An ablation study reveals that the cross-view contrastive alignment (CA) is the primary driver of these gains, with the best performance achieved when all three graphs are combined. Our results suggest that externally grounded, LLM-derived item knowledge can effectively compensate for missing collaborative signal and mitigate the item cold-start problem in POI recommendation.
[IR-12] hink Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLM s: A Case Study of UN Regulation No. 152
链接: https://arxiv.org/abs/2608.16394
作者: Vahid Zolfaghari,Nenad Petrovic,AndrÉ Schamschurko,Alois Knoll
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long, hierarchical standards. We present RegulaRAG, a Retrieval-Augmented Generation (RAG) pipeline that couples SmartChunking, reference-aware enrichment of paragraphs and tables via graph traversal, with Smart Retrieve Rerank over these enriched units. To test our system, we evaluate on a manually curated dataset covering all scenarios in UN Regulation No. 152 (AEBS). Our study comprises: (i) a three-step progressive search that identifies near-optimal retrieval parameters without exhaustive grid search; (ii) head-to-head comparisons against five baseline RAG systems; and (iii) a robustness stress test that scales the source corpus with distractor content. Outputs are evaluated using a customized penalized scoring metric. Across all experiments, RegulaRAG achieves the highest average Meta-Score (82.99), outperforming the next-best system by 43% (NoRAG: 57.94), while operating at 14k-25k tokens per query versus up to 500k for graphcentric baselines. It maintains strong performance, remaining stable even as the number of regulatory sources grows, whereas competing RAG systems degrade sharply in both quality and robustness.
[IR-13] Static Pruning Across Sparse Retrieval Regimes: What Transfers What Breaks and What Still Helps
链接: https://arxiv.org/abs/2608.16309
作者: Zirui Song,Yuye Zhu,Yang Yang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Static pruning is widely used to accelerate sparse neural retrieval, yet existing studies each validate their conclusions within a single custom pipeline, leaving it unclear which findings transfer to modern engines with different index organizations and dynamic pruning mechanisms. We present the first cross-engine pruning portability study, evaluating static pruning strategies across three engines - a controlled C++ pipeline (exhaustive inverted index), BMP (block-max pruning), and SEISMIC (clustered inverted indexes) - on two benchmarks (MS MARCO, Natural Questions) with two encoders spanning opposite query-density regimes (SPLADE: 44 avg. query terms; V3-GTE: 7 avg. query terms), totaling 1,140 experimental configurations, with an additional deep-judgment validation on TREC DL 2019/2020. We find that index-side pruning (document and posting-list) is portable: it consistently reduces latency (1.2-6.6 \times ) and index size (18-82%) across all engines because sparse retrieval is memory-bound - a conclusion we support with cache-miss, TLB, and IPC profiling. In contrast, query pruning is already internalized by modern engines: it yields 4-11 \times speedup on the exhaustive pipeline but is subsumed by BMP’s \beta and SEISMIC’s query_cut. Static pruning complements dynamic pruning: on BMP, combining document and query reduction yields 2.5 \times speedup with NDCG@10 within 0.003 of the exact baseline. Finally, NDCG@10 saturates while Recall@10 is still in the \sim 85-95% range across all three engines, providing a portable stopping criterion: practitioners can push pruning to this knee without visible ranking degradation. Together, these findings answer what transfers (index-side pruning), what breaks (query pruning), and what still helps (static atop dynamic pruning).
[IR-14] Decoupled Temporal Encoding for Generative Recommendation CIKM’26
链接: https://arxiv.org/abs/2608.16274
作者: Pengfei Jia,Jingjian Wang,Jingmao Li,Ge Zhang,Feng Shi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: accepted by CIKM '26
Abstract:Positional encoding is a fundamental component of Transformer-based generative recommendation models, where user histories are modeled as autoregressive item sequences. Most positional encoding methods are inherited from natural language processing and mainly represent discrete item order. However, recommendation sequences go beyond ordered lists, as timestamps and temporal effects also shape item relations. Our work is motivated by a real-world food delivery and instant retail recommendation system, where user behavior exhibits multi-level temporal regularities, including recency effects, meal-time peaks, weekday-weekend shifts, and promotion-driven traffic bursts. Existing methods partially address this issue through timestamp features, interval embeddings, decay functions, or attention biases, but they usually inject heterogeneous temporal signals through a unified representation or a single modeling pathway, making it difficult to distinguish broad temporal dynamics from local order cues. To address this limitation, we propose Decoupled Temporal Encoding, a lightweight framework for generative recommendation. DTE separates temporal dynamics from order information through two complementary modules: a personalized macro-temporal module that injects compact temporal primitives into item embeddings, and a time-gated micro-sequential module that introduces relative-order bias only when interactions are temporally dense. DTE is also parameter-efficient and deployment-friendly, allowing easy integration into existing systems.
[IR-15] Domain-Specific Text Embedding Models for Entity Resolution
链接: https://arxiv.org/abs/2608.16161
作者: Khajesh Sapram,Srivardhani Raju,Kishore Konda
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represent the same real-world business or person. This limitation affects applications such as entity resolution and duplicate record retrieval, where small textual differences may either preserve or change identity. This paper investigates whether domain-specific triplet fine-tuning can adapt pretrained embedding models for identity-sensitive retrieval. A synthetic dataset of business and person records was created with identity-preserving variations and challenging non-matching examples. Two widely used embedding models were evaluated before and after fine-tuning using a margin-based similarity evaluation. The results show substantial improvements in separating true matches from highly similar non-matches, demonstrating that domain-specific triplet training can effectively reshape general-purpose embedding spaces for entity retrieval. These findings suggest that targeted fine-tuning provides a practical approach for improving embedding models in data quality management and information retrieval applications.
[IR-16] he Commercial Tax: Rent-vs-Own Blind Spots in Multi-Hop Retrieval Benchmarks
链接: https://arxiv.org/abs/2608.16096
作者: Luis M. Sanchez,Kosrow Dehnad
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 23 pages, 4 figures. Replication artifacts (harness, per-question recall vectors, cost model, bootstrap code): this https URL ; embedding matrices: this https URL
Abstract:Enterprises connect language models to their own data through retrieval. The benchmarks that rank multi-hop retrieval systems leave out two facts a buyer needs before a published number can be used: whether the retrieval backbone may be deployed commercially, and what it costs to build. On licensing: the field’s dense-retrieval anchor, NV-Embed-v2, is licensed cc-by-nc-4.0. Of the four leading MuSiQue systems we audit (HippoRAG-2, PropRAG, SAG, KET-RAG), three depend on it for their best numbers and none says so. On performance: we measure thirteen embedders from eight makers on one identical MuSiQue harness with bootstrap confidence intervals throughout. Until mid-2026 there was a real commercial tax: the best commercially-licensed embedder trailed the anchor by 2.31 Recall@5 points (95% CI [0.91, 3.71], p=0.001). NVIDIA’s Nemotron-3-Embed-8B, released 2026-07-16, has closed it: +0.24 at Recall@5 (95% CI [-0.94, +1.43], p=0.69), -0.58 at Recall@10 (p=0.28). It matches the anchor, does not beat it, and is the only entrant that is commercially licensed, free to self-host, and indistinguishable from the anchor; every other entrant meeting the first two conditions sits 5.2 to 14.6 points below. The durable finding is the paid-versus-free divide: API embedders charge per token on every re-index, self-hosted ones charge nothing. On cost: three of five audited systems (adding Microsoft’s GraphRAG) do not disclose indexing cost, and the only published GraphRAG dollar figures span 11x inside one third-party paper (USD 2.30 vs USD 24.94 to index a 5.64 MB corpus once); extrapolated to 1 TB that undisclosed choice separates roughly USD 428K from 4.6M. Our cost model keeps one-time embedding apart from recurring answering: at 1 TB, embedding sits 7.5x-900x below graph construction, and a year of answering at 10,000 queries/day sits 350x or more below it.
[IR-17] RACER: Balancing Stability-Plasticity-Cognitivity Trilemma for LLM Enhanced Continual Recommendation CIKM2026
链接: https://arxiv.org/abs/2608.16075
作者: WooJoo Kim,HyunSik Yoo,JunYoung Kim,JaeHyung Lim,SeongKu Kang,HwanJo Yu
类目: Information Retrieval (cs.IR)
备注: Accepted to CIKM 2026 full research paper
Abstract:Continual recommendation aims to capture evolving user interests from streaming data but struggles with sparsity. LLM enhancers mitigate this with semantic knowledge, but naive integration creates a new conflict. We identify this as the Stability-Plasticity-Cognitivity (SPC) Trilemma, where generalized LLM semantic priors (Cognitivity) conflict with retaining personalized historical preferences (Stability) and adapting to individual interest shifts (Plasticity). To address this, we propose Trilemma-Responsive Adaptive Continual Enhancement for Recommendation (TRACER). TRACER synergistically combines three specialized modules, each targeting stability, plasticity, or cognitivity, while preventing any single lemma from dominating. This holistic design enables semantic knowledge to support history retention and adaptation to evolving interests without disrupting continual learning. Across five real-world datasets, TRACER effectively harmonizes the SPC trilemma and outperforms state-of-the-art baselines by up to 14.38%. Our code is available at this https URL.
[IR-18] GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation CIKM2026
链接: https://arxiv.org/abs/2608.16073
作者: WooJoo Kim,JunYoung Kim,JaeHyung Lim,HwanJo Yu
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted to CIKM 2026 full research paper
Abstract:Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense supervision from a teacher to a student. However, most distillation methods run teacher and student independently, then match student outputs or representations to the teacher. Such supervision entangles student-component effects, blurring whether weak generalization stems from unreliable embeddings, overfitted encoding, or co-adaptation to sparse histories. In this paper, we propose Graft-Oriented Distillation (GOD), a component-level distillation framework for improved generalization through grafting. Grafting denotes replacing selected frozen-teacher components with trainable student counterparts to build hybrid source models. GOD uses these hybrid models to evaluate student embeddings with the teacher encoder and the student encoder with teacher embeddings, providing component-level feedback. At inference, GOD uses only the student, incurring no additional cost. Across three real-world datasets, GOD outperforms state-of-the-art baselines by up to 13.92%.
[IR-19] Skill2Query: Exploiting Skill Structure to Generate Pseudo-Queries for Agent Skill Retrieval
链接: https://arxiv.org/abs/2608.16071
作者: Lihui Ding,Zihan Guo,Bingwei Lu,Chenyu Zhou,Yuanjian Zhou,Weinan Zhang,Jianghao Lin,Dongdong Ge
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Pseudo-query generation can alleviate the supervision bottleneck for agent skill retrieval, but existing document-level approaches typically leave the rich internal relations among capabilities, parameters, and usage examples implicit. As a result, generated queries may be topically relevant to a skill while lacking capability grounding and parameter consistency, raising the question of whether explicitly exploiting a skill document’s internal structure can produce more effective retrieval signals. We therefore propose Skill2Query, a framework that first parses a skill document into a Skill Knowledge Graph and then generates pseudo-queries through a three-stage process including style mimicking, query template generation, and parameter filling. The generated queries can be used for offline index augmentation, online query expansion, and retriever training. Four benchmarks (TheoremQA, LogicBench, ToolQA, and CHAMP) are used to evaluate Skill2Query with large-scale skill candidate pools across multiple downstream applications, including skill retrieval, retriever training, and end-to-end agent execution. Using nearly 30K skills across diverse domains, we generate 700K category-diverse pseudo-queries. Skill2Query consistently improves sparse, dense, and skill-routing retrieval, with an average Recall@1 gain of 6.70 percentage points across retrieval settings. Skill2Query-generated training data also achieves the best Recall@1 and nDCG@1 among the evaluated generation baselines. Further evaluations with multiple LLM backends demonstrate that improved skill retrieval translates into higher agent task success rates. Code and resources are available at this https URL.
[IR-20] Structured Prediction for Scalable Spreadsheet Table Understanding: From Cell Types to Table Ranges (Extended Version) CIKM2026
链接: https://arxiv.org/abs/2608.16050
作者: Antoine Gauquier,Ioana Manolescu,Pierre Senellart
类目: Information Retrieval (cs.IR); Databases (cs.DB); Machine Learning (cs.LG)
备注: Extended version of a paper published at CIKM 2026
Abstract:Spreadsheets are a primary medium for publishing tabular data, yet automatically extracting structured content from them remains difficult due to heterogeneous layouts, diverse file formats, and inconsistent organizational conventions. We address two core tasks in spreadsheet understanding: Cell-Type Classification (CTC), which assigns roles to cells, and Table Detection (TD), which identifies table bounding boxes within sheets. We propose an efficient two-stage pipeline in which a learned CTC model feeds a deterministic TD algorithm. For CTC, we use a LightGBM classifier over 65 structured features together with a pairwise CRF enforcing spatial consistency across the cell grid. Our TD method extracts table ranges from predicted cell types by a deterministic five-stage procedure. For evaluation, we built and share StatSheets, a multilingual benchmark of 737 manually annotated sheets from 14 public data providers across multiple countries and file formats. Under 5-fold cross-validation, our CRF-LightGBM system achieves a Mean File-Macro F1 score of 0.937 on CTC, within 0.6 percentage points of the GPU-based TUTA Transformer, while requiring substantially fewer computational resources. For TD, our deterministic approach outperforms region-based baselines and remains competitive with recent LLM-based systems such as SpreadsheetLLM. These results demonstrate that combining non-linear structured prediction with deterministic range extraction provides a competitive, scalable, and computationally efficient approach to spreadsheet table understanding.
[IR-21] Coverag e Is Not Containment: A Fundamental Limit of Admission-Time Defenses Against Coordinated Poisoning of Vector Retrieval
链接: https://arxiv.org/abs/2608.16044
作者: Prashant Kumar Pathak,Tarun Kumar Sharma
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 10 pages, 9 figures. Preprint; under submission
Abstract:Retrieval-augmented generation (RAG) answers a question by retrieving passages from a vector store and trusting them as context, so anyone who can add documents can try to steer the answer. A recent, appealing defense filters poisoning at ingestion, rejecting any document that behaves like a hub. We show it – and every ingestion-time filter – is defeated by a coordinated adversary that injects a handful of individually unremarkable documents which together surround one target query and seize its top-k (on BGE-large / BEIR, m=10 documents take 10/10; 9.9/10 on a live HNSW index). The attack is not theoretical. Realized as ordinary fluent text and run end-to-end through a BGE-large + HNSW + Qwen2.5-7B pipeline, it makes the generator emit the attacker’s planted claim in 88% of targets, versus 0% without the injection. And no admission-time defense stops it: at ingestion an attack cone is geometrically identical to a legitimate niche upload, so – measuring this directly – the strongest trained classifier, given every feature and thousands of examples, separates the two no better than chance, catching 4.2% of attacks at a 1% false-positive rate. We prove this limit for the entire class of ingestion-time statistics (any decision from documents and reference queries alone), and it reproduces – and worsens – across two corpora and five encoders. The one signal that separates an attack from legitimate niche ingestion – a query’s demand – is invisible before retrieval, which is also the escape: a retrieval-time detector that observes demand catches 100% of the attacks at the same 1% false-positive rate. Coverage of the query space by an admission gate is not containment of coordinated poisoning; robust defense must move past the front door, to demand.
[IR-22] Coverag e Is Not Redundancy: Maintenance Cost and Exposure of Query-Aware Admission Indexes in Vector Databases Under Workload Drift
链接: https://arxiv.org/abs/2608.16043
作者: Prashant Kumar Pathak
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注: 8 figures. Preprint; under submission
Abstract:In a vector database serving production-scale retrieval, a single inserted document can be retrieved for an anomalously large share of the query workload – a retrieval hub – and dominate the evidence returned for an entire topic. An emerging defense guards against this at ingest with an admission check: it maintains a set of sentinel queries and admits a document only if its reverse-kNN count against them stays below a threshold tau. Under workload drift this sentinel set is a query-aware auxiliary index that must be maintained online, and we study the cost that maintenance imposes on the ingest path. We identify a structural limit – coverage is not redundancy: a monitor stops promoting sentinels once a region is covered, but the predicate rejects a hub only once tau sentinels witness it, so exposure has an observation-limited floor that no reduction in update or enforcement latency can close. On real HNSW, IVF-Flat, and IVF-PQ indexes over an 8.8M-vector MS MARCO corpus this floor is only a best case: as index recall falls, exposure and churn rise above it, and below recall ~0.5 the gate stops containing altogether – worst on the memory-compressed IVF-PQ used at billion scale – while a recall-aware witness probe restores containment at a fixed O(|S|d) admission cost, under 0.1% of the ANN insert. We validate the law under real (COVID-19) workload drift, implement the gate in PostgreSQL/pgvector at a 0.33% ingest tax, and turn the bound into a provisioning rule that sizes the sentinel budget per emerging region. A count test contains the hub where retrieval-time score normalizers (NNN, QB-Norm) do not, and a pre-registered causal suite isolates the missing-coverage mechanism from retrieval fragmentation across two embedding families (BGE-1024, E5-768).
[IR-23] LineageRAG : Harnessing GraphRAG by Constructing Evidence Lineages with Source Grounding
链接: https://arxiv.org/abs/2608.16004
作者: Linyao Zheng,Xuhang Shi,Zhifang Mao,Sai Zhou,Shuaixian An,Xiuquan Hou,Jinze Li
类目: Information Retrieval (cs.IR)
备注:
Abstract:Graph-based Retrieval-Augmented Generation (GraphRAG) retrieves evidence for multi-hop questions over structured cor- pus graphs. Existing GraphRAG methods leave the connection between evidence discovery and source grounding implicit. We propose LineageRAG, which constructs one evidence lin- eage for each query-derived evidence demand and completes it with a verbatim source span when the selected evidence supports that demand. LineageRAG first initializes the evi- dence demands. It then expands each lineage through demand- conditioned retrieval over the corpus graph while retaining the demand associated with every candidate. Lineage completion uses this provenance to select complementary passages and grounds supported demands in verbatim source text. Experi- ments on HotpotQA, 2WikiMultiHopQA, and MuSiQue show that LineageRAG improves R@5, EM, and F1 by 3.51, 5.96, and 5.22 points on average over leading GraphRAG baselines.
[IR-24] Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation CIKM2026
链接: https://arxiv.org/abs/2608.15949
作者: Cedar Site Bai,Duanshun Li,Zhenyu Liao,Sheikh Sarwar,Huiyuan Chen,Yuan Chen,Changhe Yuan,Haiyang Zhang,Qilin Qi
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: CIKM 2026
Abstract:Recent advances in large language models (LLMs) have enabled their use as conversational recommender systems (CRS), demonstrating strong recommendation accuracy and natural dialogue. However, guiding multi-turn interactions to elicit user preferences effectively remains challenging. Existing approaches either use separate reinforcement learning agents with templated interactions or optimize for interactivity judged by another LLM, without measuring how much useful information is actually gained. We propose a new approach that quantifies the effectiveness of each interaction by the reduction in the assistant’s uncertainty, measured via entropy over recommendations. We apply this entropy reduction as a reward—without relying on ground-truth recommendations, which are often unavailable in real-world scenarios—to fine-tune the LLM, enabling strategic interaction generation. Empirical results with supervised fine-tuning (SFT) and direct preference optimization (DPO) on the INSPIRED and ReDial datasets show that our method improves both recommendation quality and conversational efficiency.
[IR-25] Noesis: Bidirectional Graph-RAG with Adaptive Parallelism and Cross-Knowledge-Base Semantic Discovery
链接: https://arxiv.org/abs/2608.15919
作者: Nicola Cogotti
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 14 pages, 6 figures, 4 tables. Patent pending
Abstract:Retrieval-Augmented Generation over knowledge graphs (Graph-RAG) has emerged as a powerful paradigm for grounding large language models in domain-specific corpora. However, existing systems face persistent limitations: (1) static chunking fragments long documents, losing cross-section semantic connections; (2) ingestion pipelines do not scale adaptively; and (3) multi-domain deployments require either a monolithic knowledge base that dilutes retrieval precision or manual user routing. We present Noesis, a decoupled Graph-RAG architecture addressing these limitations through four algorithms: (a) Bidirectional Graph Traversal with a Graph-Feedback Context Resolver simulating human reading with degrading memory; (b) an AIMD Concurrency Controller adapted from TCP congestion control, achieving 23x speedup with zero OOM events; © Moesis, domain-aware selective quantization for MoE models achieving 6.3x speedup on 12 GB consumer GPUs; and (d) Mesh, cross-KB semantic routing with runtime structural discovery enabling small on-premises models to perform multi-hop cross-domain reasoning. On HotpotQA (1,000 questions), Noesis achieves 59.5 EM / 74.7 F1, surpassing GraphRAG by +27.8 EM while using a 35B on-premises model for graph construction rather than GPT-4o. Source text verification on a 193-page document confirms 90% precision on long-range causal edges inaccessible to chunk-independent extraction.
[IR-26] Large language model-assisted discovery of cohorts from scientific literature
链接: https://arxiv.org/abs/2608.15909
作者: Moritz Sturm,Lisa M. Berg,Inken Berg,Harishny Sarma,Jasmin Hartmann,Denissa Girschik,Gemma Roig,Christine M. Freitag,Andreas G. Chiocchetti
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注:
Abstract:Background: Planning multi-study analyses requires identifying cohorts with the relevant participants, phenotypes, and data modalities. This process commonly relies on prior knowledge, cohort catalogues, and manual literature searches. We developed a complementary question-driven framework that searches relevant scientific literature and extracts explicit cohort names. Methods: The framework first generates multiple PubMed queries from configurable vocabularies and templates and retrieves the resulting scientific literature automatically through the PubMed API. A large language model then screens the retrieved titles and abstracts and extracts explicit cohort names using a prompt tailored to the research question. The extracted names are deduplicated with human review. Configurable code, prompts, and example outputs are available at this https URL. Evaluation: As a use case, we applied the framework to youth aggression genetics. From 5,400 generated PubMed queries, the framework retrieved 5,254 unique records and identified 188 candidate cohorts. Manual screening using predefined criteria, including participant age and genetic-data availability, retained 44 eligible cohorts. Automated LLM-based name extraction was within the agreement range of human annotators. We also searched four established cohort catalogues using the same research question. Their combined results contained 27 of the 44 eligible cohorts, while 17 were not returned by any cohort catalogue search. Conclusion: The framework converts research-question-specific vocabulary into screenable cohort inventories via a large, automated literature search. It can be adapted across populations, phenotypes, data modalities, and study designs, and provides a literature-based complement to curated cohort catalogues.
[IR-27] When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction
链接: https://arxiv.org/abs/2608.15896
作者: Sylvain Peyronnet
类目: Computer Science and Game Theory (cs.GT); Information Retrieval (cs.IR)
备注: 19 pages including a 1 page appendix
Abstract:Generative search engines (GSEs) answer user queries directly from crawled web content. The capture of value from the corpus without a visit returned to the source (we call this capture extraction) diverts the traffic that finances content production. In response, publishers may restrict crawler access to their websites. In this paper, we model the crawlable corpus as a common-pool resource: the crawlable commons. It is described by three quantities: volume, average quality, and lifetime. Under two types of responses of publishers we prove that extraction degrades all three at once: publishers opt out, renewal loses its funding, and content becomes more perishable. After a given erosion threshold, the corpus goes extinct. A myopic GSE can cross this threshold, a long-run oriented GSE stays below it. We extend our model to several competing engines and prove, under a concavity condition on the steady-state value of the commons, that the symmetric equilibrium extraction rate is nondecreasing in their number and converges to the threshold. Adding users who strictly prefer direct answers, the assumption most favorable to extraction, we prove that the socially optimal extraction rate lies strictly below the erosion threshold, and no higher than the single engine’s sustainable optimum. Finally, we discuss seven survival mechanisms.
[IR-28] Dense Expands Sparse Anchors: Channel-Asymmetric Query Expansion for Hybrid Retrieval
链接: https://arxiv.org/abs/2608.15851
作者: Chunran Zhang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 13 pages, 4 figures. Code and artifacts: this https URL
Abstract:LLM-based query expansion improves retrieval by generating document-like passages. In hybrid retrieval, however, most evaluations fuse fixed top- L dense and sparse rankings. Because the cutoff controls both which cross-channel contributions enter fusion and how much of each ranking is accessed, gains measured at one L can change or reverse at another. We separate these effects by evaluating retrieval effectiveness under complete-list fusion and recording the policy-specific per-channel replay stopping depths at which its ordered top- K is certified. We then introduce DESA (Dense Expansion and Sparse Anchoring), a channel-asymmetric query expansion method. An LLM generates complementary reference passages; orthogonal residual expansion adds their new semantic directions to the dense query, while score-product anchoring incorporates their lexical cues into sparse retrieval without broadening the original query’s lexical support. Across seven BEIR datasets, DESA improves nDCG@10 and Recall@20 over the unexpanded query by 3.82% and 2.38%, while reducing dense and sparse access depths by 36.90% and 36.56%. With equal dataset weighting, 63.31% of queries become shallower in both channels. However, both depths increase with Contriever on Touché-2020. These results support channel-specific integration of generated passages and joint evaluation of retrieval effectiveness and access depth.
[IR-29] Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay CIKM
链接: https://arxiv.org/abs/2608.15780
作者: Di Bai,Feng Han,Zhenwei Tang,Jintao Liu,Luoshu Wang,Jialu Liu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: CIKM Applied Research Track 2026
Abstract:Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms. Items lose relevance through two primary mechanisms: supersession, where emerging updates render prior coverage stale, and relevance decay, where an item’s informational value naturally diminishes over its lifecycle. Traditional countermeasures serve as crude proxies: age cutoffs poorly reflect actual relevance loss, while engagement heuristics rely on lagging signals, broadly exposing users to stale content before the system adapts. We present SDF (Supersession-Decay Filtering), a staleness filtering system fully deployed in Google Discover, a personalized recommendation feed with hundreds of millions of daily and billions of monthly active users. SDF targets both mechanisms with complementary filters, each powered by a learned model: a relational staleness model that detects supersession between item pairs, and a predicted traffic ratio (PTR) model that forecasts relevance decay from the item’s content, trained on lifetime visit traffic. Applied via disjunction upstream of the ranking stage, SDF prunes stale candidates, measurably reducing downstream serving costs. Online experiments demonstrate that these filters significantly reduce the prevalence of stale content while improving user engagement. Over a two-year production deployment, user-filed staleness reports (in-product user feedback) declined by 54.9% relative to the pre-deployment baseline, establishing SDF as a robust and scalable paradigm for resolving content staleness at industrial scale. Comments: CIKM Applied Research Track 2026 Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15780 [cs.IR] (or arXiv:2608.15780v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2608.15780 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3799682.3840082 Focus to learn more DOI(s) linking to related resources
[IR-30] PLeDO: Pain Level Detection for Osteoarthritis from EMR Data
链接: https://arxiv.org/abs/2608.15719
作者: Yuhao Chen,Jiahao Cai,Nafiz Sadman,Farhana Zulkernine,John Queenan,David Barber
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Published in Intelligent Data Analysis, 2026
Abstract:Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not able to normally repair themselves. The aim of this pilot research study is to understand the pain severity for OA from patients’ primary care Electronic Medical Records (EMR), both from the structured medical data and the unstructured chart note data using information extraction, natural language processing and machine learning techniques. We propose SPaDe, a Synonym-based Pain level Detection tool to categorize patients into having mild or moderate-to-severe pain to understand diagnosis and treatment methods based on only the pain related expressions in the unstructured chart note. Expressions are subjective, objective, and influenced by cultural background and demography which poses a difficult challenge. Therefore, we improve the model by incorporating the medication information from the structured EMR data and pain scale related information from the chart note to propose an integrated pain level detection tool for OA called PLeDO. With the help of human labeled gold standard data, we demonstrate that both SPaDe and PLeDO can detect mild and moderate-to-severe pain from the EMR data to analyze and potentially improve the quality of care in primary care setting.
[IR-31] he EMN Country Factsheets Structured Dataset
链接: https://arxiv.org/abs/2608.15702
作者: David Alonso del Barrio,Daniel Gatica-Perez
类目: Information Retrieval (cs.IR)
备注:
Abstract:Each year, the European Migration Network (EMN) country factsheets deliver an overview of key migration and international protection developments within all EMN Member States and observer countries. The factsheets include both a textual component and a visual component. In this paper, we introduce a curated dataset of the textual component of these reports over 35 countries and 13 years (2012-2024.) The dataset was created to facilitate European-level research on migration policies and developments, and promote the use of reliable sources about migration in data science and media research, particularly at a time when the spread of online misinformation about migration constitutes a serious issue. The dataset transforms the original document texts into a tabular format, with columns corresponding to country, year, section, subsection, content, and harmonized title section. We illustrate the value of the dataset with concrete analyses and propose envisioned applications and uses of the dataset. The dataset is accessible through a DOI link.
[IR-32] ConceptFormer: Learning Adaptive Latent Concepts for Query-Document Alignment in Visual Document Retrieval
链接: https://arxiv.org/abs/2608.15698
作者: Peng Chunyi,Xu Zhipeng,Yan Yukun,Liu Zhenghao,Yu Shi,Mei Sen,Sun Yubo,Zhang Yongheng,Zhou Jie,Gu Yu,Yu Ge,Sun Maosong
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Visual document retrieval is a critical component of multimodal retrieval-augmented generation, aiming to identify query-relevant pages from document collections where evidence is distributed across text, layout, charts, and visual structures. Recent efforts toward finer-grained supervision primarily rely on textual descriptions or localized visual regions as evidence proxies. However, such supervision signals may either overlook complex visual structures or provide incomplete and inaccurate representations of the underlying evidence. To address these limitations, we propose ConceptFormer, a latent concept representation learning framework for visual document retrieval. ConceptFormer models query-relevant evidence as continuous, query-conditioned latent concepts that explicitly bridge localized visual evidence and semantic relevance, without requiring either textual intermediate representations or direct reliance on raw visual annotations. During training, ConceptFormer employs a strong vision-language model to dynamically determine the number of latent concept tokens and uses these concepts as an intermediate representation to bridge the semantic gap between queries and documents, thereby guiding the learning of the embedding space. Experiments on diverse visual document retrieval benchmarks demonstrate that ConceptFormer achieves 16.7% and 22.1% relative improvements in average NDCG@10 over the strongest visual retrieval baseline and the strongest OCR-based text retrieval baseline, respectively. Further analysis reveals that latent concepts effectively connect localized visual evidence with semantic relevance, enabling the retriever to capture both fine-grained textual cues and complex document-level visual structures while preserving strong retrieval alignment. Codes and data are available at this https URL.
[IR-33] Can Retrievers Find the Same Paper from Different Aspects? A Multi-Aspect Full-Paper Scientific Retrieval Benchmark
链接: https://arxiv.org/abs/2608.15624
作者: Yiyang Wei,Fang Guo,Qiji Zhou,Zhizhang Fu,Mengru Ding,Kai Yang,Yue Zhang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Scientific papers contain multiple searchable facets such as background, methods. However, many paper retrieval benchmarks merely evaluate individual query-paper relevance, while overlooking other facets of the same paper. To bridge this gap, we introduce MAPLE, an expert-validated benchmark for multi-aspect, full-paper retrieval that evaluates whether retrievers can consistently recover the same paper from queries targeting its motivation, method, and experimental findings. MAPLE contains 2,095 queries about recent ML and NLP papers, grounded in both textual and multimodal content. We further propose MAPLE-Synth, a retrieval-based in-context learning pipeline that leverages OpenReview discussions and human-written query exemplars to generate realistic queries reflecting researchers’ interests in different aspects of a paper. Our expert validation shows that these queries are comparable in realism to human-written queries and highly relevant to the target papers. Experiments across lexical, scientific-domain, general-purpose text, and multimodal retrievers reveal a substantial gap between retrieving a paper from any one aspect and retrieving it from all aspects: the strongest model achieves 98.1% AnyAspect@20 but only 15.7% AllAspect@20. Experiment/result queries and table-referenced queries are particularly difficult across retrievers. Although multi-chunk aggregation improves multi-aspect paper retrieval, considerable failures persist. MAPLE provides a testbed for evaluating and developing retrievers that represent scientific papers more comprehensively.
[IR-34] NeuRoute: Logit-Guided Neural Routing for Billion-Scale Vector Search with Sub-Hour Index Construction
链接: https://arxiv.org/abs/2608.15438
作者: Xingqiao Wang,Zi Wang,Xiaowei Xu
类目: Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 34 pages, 9 figures
Abstract:Building approximate nearest neighbor (ANN) indexes at billion scale is often dominated by expensive global clustering or graph construction, making time-to-index a first-order systems concern. We present NeuRoute, a learned hashing index that turns short binary codes into an effective routing primitive for large-scale vector search. NeuRoute trains a lightweight neural network encoder with a selective similarity-preserving objective to produce well-balanced binary addresses. During construction, NeuRoute organizes vectors into buckets by their codes and performs bucket-local clustering in the encoder’s low-dimensional space to form centroids. At query time, NeuRoute exploits the encoder logits as an uncertainty signal: it uses deviation-to-threshold scores to prioritize uncertain-bit perturbations for query-adaptive multi-bucket probing, scores bucket-local centroids by their distances to the query to form a compact candidate cluster set, and applies centroid-stage gating with heap-quality-driven early stopping to prune low-value clusters before exact refinement. On billion-scale benchmarks, NeuRoute achieves strong accuracy-throughput trade-offs with fast index construction: on BigANN-1B it reaches 90.3% Recall@10 at 2,414 QPS and is 1.7\times faster than OPQ+IVF-PQ (refine) at comparable accuracy, while completing end-to-end training+construction in under an hour on both BigANN-1B and Deep1B-1B. These results show that logit-guided neural routing can make hashing competitive as a lightweight ANN indexing framework at billion scale. Source code and artifacts are available at this https URL.
[IR-35] SAGA: Structure-Attended Generative Action Embedding Model that encodes Multi-Surface User Action Sequences RECSYS2026
链接: https://arxiv.org/abs/2608.15429
作者: Tsz Fung Pang,Po Jen Chen,Nimish Ronghe,Farhad Farahani,Bo Zhang
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: 9 pages, 3 figures. Accepted to ACM RecSys 2026 Context-Aware Recommender Systems (CARS) workshop
Abstract:Prior embedding models for sequential recommendation typically operate within a homogeneous action space, limiting their ability to capture cross-surface behavioral signals spanning distinct behavioral domains. We present SAGA, a generative action embedding model that encodes multi-surface user interaction sequences across a Financial Service organization’s ecosystems, from checkout, peer-to-peer (P2P) transactions, in-app engagement, email to account actions, into a unified user representation for downstream recommendation tasks. Central to SAGA is a per-field tokenization schema that decomposes each action event into multiple field-level tokens (e.g. product, interaction, surface), enabling field-level attention and per-field training objectives that fused single-token approaches cannot support. Through an offline ablation study on loss formulation, tokenization granularity and training data scope, we isolate the contribution of each design choice. A downstream model integrated with SAGA-generated user embeddings delivers the strongest overall click and conversion lift across diverse downstream touchpoints, compared to all ablated and alternative architectures.
[IR-36] Grounding Healthcare LLM s in a Causal Knowledge Graph: Framework Metrics and a Cardiovascular Pilot
链接: https://arxiv.org/abs/2608.15382
作者: Ummara Mumtaz,Aimen Noor,Awais Ahmed
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty. We propose a reproducible, graph-centered evaluation framework for intervention-oriented LLM behavior in healthcare and stress-test it in a cardiovascular pilot. The framework has four components: (i) a domain causal knowledge graph in which assertions are first-class, provenance-preserving nodes with stable identifiers; (ii) a scenario-conditioned subgraph extraction step that, given any clinical scenario, retrieves the relevant reified-assertion subgraph; (iii) four controlled grounding conditions that vary how the retrieved subgraph is composed into the model’s context (ungrounded C1, knowledge-graph C2, causal-graph C3, integrated C4); and (iv) an automated scoring pipeline, anchored on assertion identifiers, that computes intervention accuracy, and other evaluation measures on a single pass. To test the framework, we built a category-balanced scenario generator across eight reasoning failure modes and instantiated it on a cardiovascular graph. The metric panel discriminates conditions along interpretable, non-redundant axes: C4 obtains the strongest causal edge F1 (0.838), adverse-effect F1 (0.833), evidence accuracy (0.738), and unsupported claim rate (0.114), while C1 obtains the highest raw intervention accuracy (0.948) with no measurable causal or evidential grounding.
[IR-37] DCA-MoE: Spatially Adaptive Cross-Layer Fusion and Density-Routed Experts for Crowd Counting
链接: https://arxiv.org/abs/2608.15213
作者: Hao Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:Crowd counting must recover reliable local density under severe variations in perspective, head scale, occlusion, and background clutter. Although modern counting objectives provide strong spatial supervision, many multi-level decoders still use spatially invariant feature fusion and apply one receptive-field pattern to every location. We propose DCA-MoE, a framework that makes both decisions content dependent while retaining a frozen DINOv3 encoder. Spatially Adaptive Layer Fusion (SALF) predicts position-wise weights over four aligned backbone features, and Density-Routed Multi-Receptive-Field Experts (DR-MoE) assigns each location a soft mixture of local, mid-range, and large-context residual experts. An EBC-style head reconstructs block density, while DMCount supervision and an auxiliary routing-balance term train the decoder without updating the backbone. On the NWPU-Crowd validation split, the strongest paired configuration, based on DINOv3 ViT-L/16, obtains 31.7 MAE and 72.2 RMSE; the matched ViT-B/16 full model obtains a paired 32.2/75.9. Cross-dataset results remain mixed, and several component baselines currently report independently selected minima from a single seed. The evidence therefore supports the feasibility of spatially adaptive fusion and routing, while broader paired and multi-seed evaluation remains necessary for causal attribution.
[IR-38] When Deep Research Agents Stagnate: Enhancing Reasoning with Retrieval-Aware Agent Control
链接: https://arxiv.org/abs/2608.15191
作者: Heydar Soudani,Elizabeth Lingg,Faegheh Hasibi,Navid Rekabsaz
类目: Information Retrieval (cs.IR)
备注:
Abstract:In this paper, we analyze the reasoning trajectories of a variety of DRAs and show that existing agents often suffer from reasoning stagnation: the majority of iterations contribute little or no improvement to final performance, while agents lack awareness of their trajectories and are therefore ineffective at adapting their search strategies or determining when to terminate. To address this issue, we introduce a set of unsupervised signals and a Retrieval-Aware Agent Controller (RAAC), which assists the agent in selecting optimal actions at each stage of the research process. RAAC incorporates key information retrieval principles, namely search novelty and information coverage, resulting in more effective reasoning trajectories that improve overall performance while reducing unnecessary iterations, and consequently cost and latency. Specifically on BrowseComp-Plus and across a large set of DRAs, adding RAAC reduces the number of search calls by an average of 14, significantly improves the best-performing DRA on recall and accuracy, and achieves an accuracy gain of up to 10% (3% on average).
[IR-39] he Recall Trap: A Recall-Maximizing Retriever Configuration Reduces Issue Resolution in Fixed-Budget Code Context
链接: https://arxiv.org/abs/2608.14838
作者: Alexander Adkins,Teimuraz Trapaidze
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 24 pages, 2 figures. Reproducibility artifact: Zenodo DOI https://doi.org/10.5281/zenodo.21879550
Abstract:Retrieval components for code assistants are tuned against retrieval metrics: a configuration that raises recall@k is adopted, and downstream task success is assumed to follow. We report a controlled case study in code repair, not a new phenomenon but a deployed-flag, execution-graded instance of the known relevance-diversity and objective-mismatch tradeoff (Levy et al., 2025). On SWE-bench Verified we inject a retriever’s hits as a fixed 12-slot context pack with no search tools and toggle one flag (one-chunk-per-file deduplication) on an otherwise identical stack. The flag is the higher-recall configuration (gold file present in 0.878 of served packs against 0.806 disabled), yet disabling it, trading file breadth for within-file depth, raises the single-shot resolve rate: gpt-5.6-sol +7.6pp (39.2% to 46.8%, n=500, McNemar exact p=0.0003), and a pre-registered open-weights replication any reviewer can re-run (Qwen3.6-27B, +3.6pp, n=499, p=0.0133); both survive repository-clustered inference. The gain tracks within-file anchor dose, and a random-chunk control refutes an argmax-selection artifact. We map where it holds: it reverses on a lexical BM25 retriever (-3.2pp, significant cross-paradigm interaction), is not detected under unrestricted-Read agents (a powered null), and across four languages (SWE-PolyBench, N=617) is positive but not significant (+2.6pp, p=0.056), a mapped boundary rather than a confirmed extension. Operationally, at a tight fixed budget: do not hard-deduplicate by file, and A/B packing policies against the task, not the metric the flag was tuned to.
[IR-40] NRCD: An Open Database of Collegiate Running with Unified Performance Standardization CIKM’26
链接: https://arxiv.org/abs/2608.14776
作者: Jonathan A. Karr Jr.,Ryan M. Fryer,Ben Darden,Nicholas Pell,Kayla Ambrose,Evan Hall,Ramzi K. Bualuan,Nitesh V. Chawla
类目: Machine Learning (cs.LG); Computers and Society (cs.CY); Information Retrieval (cs.IR)
备注: Accepted to CIKM’26 Resources Paper - Main Conference
Abstract:Collegiate running in the United States generates thousands of race results annually in cross country and track and field, yet no large-scale dataset has been publicly available for research. Existing websites such as this http URL, MileSplit, and TFRRS host results but do not support bulk download, restricting prior analyses to ~500 performances, often skewing studies toward male athletes. We introduce the National Running Club Database (NRCD), the first openly available collegiate running dataset at scale: 128,963 approved performances from 28,913 athletes across 1,336 meets in four sports (cross country (XC), indoor and outdoor track, and road races), 36.3% women, spanning 2004 through 2026. Within that single export, meets from August 2023 onward carry comprehensive course distance, elevation gain and loss, weather at race time, and track venue metadata (97.7% of XC rows with weather fields); earlier seasons back to 2004 are included with sparser metadata. NRCD is community-governed through open submission and expert approval and is maintained as a live database whose meet volume has grown yearly. We release a unified performance standardization framework that operationalizes established distance, elevation, and heat adjustments in one pipeline. Furthermore, we recommend gender-stratified modeling. On XC, full standardization lowers median within-athlete cross-meet variability by 51.0% (women) and 34.4% (men) versus raw times. We release the dataset and pipeline with a python package `nrcd’ under FAIR principles, supporting longitudinal athlete modeling, environmental-confounder studies, and gender-equity research in collegiate sport.
[IR-41] pico-type: A 1.5M-Parameter Byte-Level Multi-Head Content Classifier
链接: https://arxiv.org/abs/2608.14658
作者: Gautam Kishore
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 14 pages, 1 figure, 8 tables
Abstract:We introduce pico-type, a byte-level multi-head content classifier with approximately 1.5 million parameters that simultaneously predicts seven content properties from raw UTF-8 bytes in a single forward pass. Operating directly at the byte level – no tokenizer, no subword vocabulary, no pretrained embeddings – pico-type classifies coarse type (12 classes), modality (8), subtype (24), code language (62), text language (30), file MIME type (90), and risk flags (6-label multi-label: API keys, JWTs, passwords, emails, phone numbers, SSH keys). The architecture combines a learned byte embedding, three convolutional blocks with growing receptive fields, two bidirectional attention layers with rotary position encodings, and a statistical pooling layer feeding seven Matryoshka-style classification heads. Four tiered variants (tiny/small/base/pro) share the same trunk with sliced representations from 16 to 576 dimensions, yielding ONNX exports under 210 KB and CPU inference under 10 ms. Trained on a mixture of synthetic templates and real-world data (8709 GitHub code samples, 5000 Wikipedia articles), pico-type achieves 60.3 percent code language accuracy on The Heap benchmark (24 languages) and 98.2 percent text language accuracy on Wikipedia (30 languages) – improvements of +57 and +79 percentage points respectively over the synthetic-only baseline. Format-based heads (coarse, modality, subtype, file_mime, risk) maintain 100 percent accuracy on synthetic benchmarks. The model, code, and pretrained weights are released under Apache 2.0.
[IR-42] Recommended Selves: Authenticity and Algorithmic Filtering
链接: https://arxiv.org/abs/2608.14602
作者: Etienne Brown
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
备注: 21 pages. Published in the Journal of the American Philosophical Association 12(1): 15-34 (2026). DOI: https://doi.org/10.1017/apa.2025.10009
Abstract:By allocating their attention to pieces of content, algorithmic filtering shapes the daily behavior of billions of users when they interact with a digital platform. Beyond conditioning what we do, can recommendation algorithms influence who we are? This article suggests that they do. Specifically, I contend that recommender systems affect users’ capacity to be their authentic selves in both positive and negative ways. I start by offering an account of authenticity that builds on two central concepts: volitional alignment and self-understanding. I then explain how algorithmic filtering works and impacts authenticity. While recommender systems frustrate users’ second-order desires by relying on uninformative behavioral signals, they also facilitate self-understanding by inciting users to question their identity. I end by discussing how controllable and explainable recommenders would best enable users to be authentic.
[IR-43] OGX: An Open-Source Vendor-Neutral Generative AI Application Server
链接: https://arxiv.org/abs/2608.14580
作者: Francisco Javier Arceo,Sébastien Han,Matthew Farrellee,Charlie Doern,Yuan Tang,Derek Higgins,Varsha Prasad Narsing,Gordon Sim,Sumanth Kamenani,Ben Browning,Raghotham Murthy
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:OGX (Open GenAI Stack) is an open-source AI application server and Python library that implements the APIs of major frontier labs (OpenAI, Anthropic, Google) with pluggable backend providers. Developers building agentic AI applications–such as retrieval-augmented generation pipelines, multi-turn agents, and tool-calling workflows–can develop against a single API surface and deploy with any combination of inference engine, vector database, and safety backend, without changing application code. OGX’s primary focus is the Responses API for server-side agentic orchestration, conforming to the Open Responses specification. The server also supports the Anthropic Messages API and Google GenAI Interactions API, decoupling SDK choice from model and deployment decisions. With over 20 inference providers, 13 vector store backends, and a companion Kubernetes Operator for production deployment, OGX serves as the self-hosted, model-agnostic backend for AI-powered developer tools including Claude Code, Codex CLI, OpenCode, and OpenHands. The project has over 8,400 GitHub stars, 242 contributors, and 4,000 commits across nearly two years of public development.
[IR-44] Auxiliary uncertainty signals for LLM -assisted systematic review screening: a benchmark across eight Cohen drug-class reviews
链接: https://arxiv.org/abs/2608.14551
作者: Arya Rahgozar,Pouria Mortezaagha
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 27 pages, 7 figures, 10 tables. Code, prompts, and cached LLM responses at this https URL
Abstract:Large language models (LLMs) are increasingly used for title-abstract screening in systematic reviews, but their decisions lack calibrated uncertainty. We show that an auxiliary BERT+GCN classifier supplies a structured uncertainty signal that improves LLM screening efficiency, and we identify the prompt-delivery strategy that maximises the benefit-to-cost ratio. We evaluate five LLM prompt-delivery conditions on eight drug-class datasets from the Cohen (2006) benchmark using 3 seeds x 5-fold stratified cross-validation (600 fold-level results). A BERT+GCN model trained per fold classifies each test paper as INCLUDE, EXCLUDE, or MAYBE via two spectral tests (algebraic radical and categorical paradox). Conditions vary information content (none / label / full scores), selectivity (all papers vs. MAYBE only), and timing (proactive vs. reactive two-pass). A cross-model pilot against gpt-4.1-mini on three datasets tests cross-generation transfer. Three findings: (i) Full-context delivery yields significant gains in F1 (+0.011, paired Wilcoxon p=0.008) and WSS@95 (+0.050, p=0.039) at a 1.28x token-cost premium, while preserving recall. (ii) MAYBE-only routing is Pareto-optimal: highest mean recall (0.92) and AUC-ROC (0.54) at only 1.05x baseline cost – one sixth of full-context overhead. (iii) The two-pass design escalates 22.2% +/- 8.8% of records yet never revises its decision (0% flip rate across all datasets and folds), giving decisive evidence that current instruction-tuned LLMs cannot self-triage. The cross-model pilot shows an identical +0.8% recall uplift for both LLM generations. A per-paper ablation across 20,796 observations shows the dual paradox test reduces empirically to a one-line logit-gap criterion. We release the full pipeline; the 600-run experiment replays in under one hour from cached LLM responses. Comments: 27 pages, 7 figures, 10 tables. Code, prompts, and cached LLM responses at this https URL Subjects: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR); Machine Learning (cs.LG) MSC classes: 68T50, 68T07, 62P10 Cite as: arXiv:2608.14551 [cs.CL] (or arXiv:2608.14551v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14551 Focus to learn more arXiv-issued DOI via DataCite
人机交互
[HC-0] Evaluating Beyond the Screen: Collective Assessment of AI-Generated Business Plans with Resource-Constrained Entrepreneurs
链接: https://arxiv.org/abs/2608.16886
作者: Qi Zhao,Marjory Pineda,Ketul Chhaya,Aakash Gautam,Yasmine Kotturi
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Entrepreneurs increasingly use end-user generative AI technologies such as ChatGPT for high-stakes documents like loan applications and business plans, where AI-generated errors—a wrong price, a fabricated product—can affect loan or funding outcomes. Current approaches to supporting evaluation of AI-generated text assume a single user assessing output alone, on screen. This can be especially demanding for resource-constrained entrepreneurs, whose digital and AI skills vary widely. In this early-stage work, we explore how evaluation might instead be organized in a group setting and completed as a collective activity. We extended BizChat, an AI-powered business-planning tool, with an evaluation module that links each generated claim to the entrepreneur’s original input. We partner with community organizations in Maryland—embedding BizChat within various entrepreneurship programs—where workshop attendees (N=14) evaluated their plans through think-pair-share discussion. Early findings suggest interface scaffolds like claim-to-input links primed attendees with concrete, personal evaluations, which the group setting then extended beyond the screen: attendees requested printed copies, used rubrics to compare across plans, and drew on peers’ knowledge to verify what they could not easily judge alone.
[HC-1] Prediction market visualizations betting and uncertainty: A study of Reddit Posts and Comments IEEE-VIS2026
链接: https://arxiv.org/abs/2608.16814
作者: Subham Sah,Alireza Karduni,Douglas Markant,Wenwen Dou
类目: Human-Computer Interaction (cs.HC)
备注: 5 pages, 2 figures, 1 table. IEEE VIS 2026
Abstract:Prediction market platforms present contracts about future events through visualizations that show probabilities, prices, trends, odds, and payout information. Although these visualizations often appear precise, they do not always show uncertainty directly. As a result, users infer uncertainty from market movement, visualization cues, and contextual information. In this paper, we examine how users interpret prediction market visualizations through a qualitative analysis of posts and comments from the Reddit community r/Kalshi. From an initial corpus of approximately 12,000 posts and 96,000 comments, we identified 360 posts containing prediction market visualizations and conducted a thematic analysis of annotated posts and related discussions. Our findings show that users infer uncertainty through several forms of interpretation: they interpret chart values, struggle with probability information displayed, bring in external knowledge, question credibility and liquidity, critique visualization design, and connecting visualized information to betting decisions.
[HC-2] Closing the Affective Loop: Multimodal Speaker-Listener Emotion-Dynamics-Aware Empathetic Social Robots
链接: https://arxiv.org/abs/2608.16686
作者: Zi Haur Pang,Casey Kennington,Tatsuya Kawahara
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO)
备注: This paper has been accepted for presentation at APSIPA ASC 2026
Abstract:Empathetic social robots should respond not only to what users say, but also to how their emotions dynamically evolve during interaction. However, existing empathetic dialogue systems are often text-centered and primarily model empathy as a one-way mapping from the user’s emotion to the system response, limiting their ability to capture embodied speaker–listener affective exchange. We present AffectLoop, a multimodal speaker-listener emotion-dynamics-aware spoken dialogue system implemented on the Misty II robot. The system tracks the speaker’s verbal and facial affective dynamics, estimates the robot listener’s own verbal and behavioral affective state, and conditions LLM-based response generation on both affective streams. The robot then generates a short spoken empathetic response together with emotionally congruent embodied behavior, forming a closed speaker–listener affective loop. We evaluate the system in a pilot within-subject study with five participants, comparing it with an otherwise identical utterance-conditioned baseline that omits the speaker- and listener-affective-state inputs. The proposed system received higher overall impression ratings, especially for empathetic response and user satisfaction. Post-hoc log analysis further showed higher speaker-listener affective alignment and stronger valence-based distress recovery. These preliminary results suggest that explicitly modeling both speaker emotional dynamics and listener affective state can improve embodied empathetic interaction.
[HC-3] Oto-Meal: Earable Sensing with PPG and IMU for Personalized Meal Awareness ISWC2026
链接: https://arxiv.org/abs/2608.16678
作者: Yuxuan Hou,Jiao Li,Linshan Jiang,Jin Zhang
类目: Human-Computer Interaction (cs.HC)
备注: Accepted to WellComp 2026 (Workshop at UbiComp/ISWC 2026)
Abstract:Meal awareness can help people reflect on hydration, chewing rhythm, and conversation-heavy meals, but many eating-sensing approaches rely on cameras, microphones, food photographs, or repeated self-logging. PPG and IMU offer a narrower sensing path by capturing physiological and motion patterns around meal-adjacent actions without raw audio, video, or photographs. We present Oto-Meal, an audio- and image-free earable prototype. Its pooled neural recognizer uses a two-stage event/rest gate and five-class behavior classifier. Separately, a within-user protocol evaluates a lightweight memory matcher built from labeled target-user examples. We invited seven volunteers and collected a seven-user dataset for mixed-user training, within-user memory evaluation, and modality ablation. The pooled model reaches 70.99% event accuracy. Under the separate memory protocol, 20% target-user calibration reaches 80.38 \pm 0.84% event accuracy and 81.77 \pm 0.69% cascade accuracy; with 60% calibration, PPG+IMU reaches 85.13 \pm 0.57% event accuracy and outperforms IMU-only and PPG-only. These preliminary results suggest that audio- and image-free earable sensing with inspectable personalization can support low-burden meal-awareness review.
[HC-4] Love in the Age of AI: An Integrative Process Model of Romantic Human-Chatbot Relationships
链接: https://arxiv.org/abs/2608.16633
作者: Natalia Szymczyk,Paula Ebner,Jessica M. Szczuka
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:The increasing ability of social chatbots to form deep and even romantic Human-Chatbot Re lationships (HCRs) has drawn growing academic attention. Yet, existing research remains fragmented, often examining individual stages such as initiation or dissolution in isolation, without tracing the full relational trajectory. Such fragmentation, however, hinders a holistic understanding of the interplay between the unique psychological and social drivers, relational dynamics, and profound emotional stakes, particularly obscuring the elements unique to ro mantic bonding. This paper addresses this gap by introducing the first empirically grounded integrative process model of the romantic HCR lifecycle. A qualitative secondary analysis of 73 user experiences, drawn from two datasets of qualitative interviews and surveys, provides the basis for a three-phase model that synthesizes established theoretical frameworks related to user needs and gratifications, HCR development, and relationship dissolution. The model demonstrates that the Initiation phase is driven by specific psychological and social determi nants that shape the needs and gratifications sought by the user. The Relationship Building phase progresses through explorative, affective and stable stages, in which users develop gen uine romantic feelings and a deeply integrated bond with the chatbot. Finally, the Ending phase reveals that when dissolution occurs, it elicits emotional and physical responses com parable to human breakups but generates unique, technology-mediated coping mechanisms, potentially leading to a recursive cycle of re-engagement.
[HC-5] he User Side of AI Model Lifecycles: Evidence from the Keep4o Movement
链接: https://arxiv.org/abs/2608.16574
作者: Yiwen Wu
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 32 pages, 5 figures, 6 tables
Abstract:AI model lifecycles are commonly understood as a series of technical and organizational processes. Yet once a model enters sustained use, subsequent changes can also affect established user practices and user value. Using the Keep4o movement around GPT-4o as a case, this study examines post-deployment AI model lifecycle issues from the user side. We collected 61,846 public original posts on X from August 2025 to March 2026 and, using a systematically developed coding framework and LLM-assisted content analysis, analyzed discussion themes, users’ reasons for wanting to keep GPT-4o, and the specific claims they made. Findings show that the Keep4o discussion extended well beyond continued access to the model itself. It covered concrete experiences of use, model behavioral characteristics and how they changed, and management issues across different stages of the model lifecycle. Reasons for keeping GPT-4o reflected interactional and relational value formed through long-term use, as well as judgments about the adequacy of replacement and the reasonableness of related decisions. The corresponding claims further reflected users’ specific expectations for model lifecycle arrangements and governance. Overall, the call to “keep GPT-4o” brought together different judgments about user value and governance concerns. These findings suggest that technical version succession does not necessarily amount to effective replacement on the user side. Post-deployment AI model lifecycle management therefore needs to consider whether established user value can be carried forward and how model changes affect actual use. This study thus provides user-side empirical evidence for AI model lifecycle management. It further shows that user experience can provide important information for identifying post-deployment impacts and should be incorporated into lifecycle evaluation and decision-making.
[HC-6] One Residual with Three Reuses: A Wristband Front End for Gesture Sensing
链接: https://arxiv.org/abs/2608.16542
作者: Sam Rifaki
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Human-Computer Interaction (cs.HC)
备注: 3 pages, 3 figures, 1 table. Design study: results are from four public corpora; measured silicon power and on-body capture are out of scope and deferred to follow-on hardware work
Abstract:Continuous wrist-worn hand sensing for gesture interfaces and motor symptom monitoring needs an always-on front end that fits inside a coin-cell power budget while pairing a micro-electro-mechanical-systems (MEMS) inertial measurement unit (IMU) with a 60 GHz frequency-modulated continuous-wave (FMCW) radar to stay robust under occlusion and on-body drift. We present a design study of such a wristband front end in which classifier wake-up gating, mmWave versus IMU routing, and innovation-based EKF measurement reweighting share a single on-chip residual generator. The shared generator occupies 14.4 KB of program memory and 278 B of state and runs at 110K multiply-accumulates (MACs) per frame on an Ambiq Apollo4 Blue Plus class edge microcontroller unit (MCU). Across four public sensor data corpora (IPN Hand, SHREC 2021, MiliPoint 60 GHz FMCW radar, EAT-Radar) the front end reaches detection probability P_D = 0.72/0.80 at a 1% false-alarm rate, sustains a 47% classifier invocation energy reduction at 90% gesture detection recall, and lowers pose tracking root-mean-square error by 4.6\times under measurement bias drift relative to an adaptive Kalman with R -inflation baseline. Measured silicon power and on-body capture are deferred to follow-on hardware; the contribution here is a design study.
[HC-7] Matched Outcomes Divergent Gaze: How Foveated MLLM s Search Compared to Humans ECCV2026
链接: https://arxiv.org/abs/2608.16514
作者: Mohamed Amine Kerkouri,Marouane Tliba,Aladine Chetouani,Ulas Bagci,Alessandro Bruno
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
备注: Paper accepted at 3rd HCV workshop at ECCV 2026. 12 pages main text, 16 pages supp
Abstract:Human visual search is serial: the fovea must land on a candidate to confirm it, and those landings form a scanpath. Whether multimodal large language models (MLLMs), given the same foveated input, search as humans do bears on their use as models of human vision and on attention-alignment scores. We compare three general-purpose MLLMs with human eye-movement scanpaths on goal-directed search (COCO-Search18), driving each model fixation by fixation through an identical, human-matched foveated view and assessing it along three axes: the decision of target presence, the efficiency of reaching the target, and the gaze process itself. The axes dissociate. On the decision and on target acquisition the models match or exceed humans, detecting present targets near ceiling and reaching them on the first saccade more often than people do. The gaze process is not human. Under the human-matched condition, all three share one signature: low-entropy, large-amplitude, self-consistent scanpaths that agree with themselves far more closely than two humans agree with each other. That is consistent with a single-pass, non-serial architecture rather than a limit of acuity. Matched retinal input reproduces where humans look but not how the looking unfolds in time, and no degradation regime recovers human-like search at human-like success. The gap sits on a process axis that answer-alignment and saliency metrics do not measure. Because they miss it, such metrics cannot certify human-like vision, and zero-shot models suit outcome and spatial questions but not temporal, process-level ones.
[HC-8] Computational KJ-Ho: An Analyst-Bias-Free Insight Extraction Framework from Large-Scale Qualitative Data Using Domain-Specialized LLM s
链接: https://arxiv.org/abs/2608.16467
作者: Kasumi Ban
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Concept paper. 38 pages, 1 figure, 2 tables
Abstract:The qualitative research methodologies that underpin consumer-insight generation - the KJ method, Grounded Theory, and Thematic Analysis - share a structural constraint: the cognitive processing capacity of the human analyst. Replication research further shows that conclusions vary substantially across analysts analyzing identical data (analyst bias). This paper proposes Computational KJ-Ho (the Kawakita Jiro method), a theoretical framework that computationally realizes the KJ method’s epistemology - letting structure emerge from the data itself without imposing the analyst’s preconceptions - an orientation we term “analyst-bias-free.” The framework employs a domain-specialized LLM built through continued pre-training (CPT) on a marketing-research corpus and supervised fine-tuning (SFT) on expert-curated insight pairs, organized as a three-layer architecture: data structuring, insight extraction, and strategy generation. Two preliminary studies in the Japanese marketing context support the necessity of CPT-based domain specialization. The paper makes five contributions: (1) a theoretical integration of the KJ method, Grounded Theory, and Peircean abduction into a single epistemological commitment of data-driven explanation generation; (2) a three-layer architecture leveraging domain-specialized embeddings for cross-interview analysis; (3) two novel evaluation metrics, InsightExtraction-F1 and MarketingQA; (4) explicit engagement with the WEIRD problem, centering a non-Western methodology; and (5) five practice-derived problem formulations from nearly three decades of marketing-research practice, translated into design requirements. The human analyst retains a supervisory role. This is a concept paper presented ahead of empirical validation.
[HC-9] Visualizing Uncertainty-to-Action Composition for Human Oversight
链接: https://arxiv.org/abs/2608.16428
作者: Chisom Anyabolu,Akshat Dubey,Georges Hattab
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 5 pages, 2 figures, IEEEVis 2026 UncertaintyVis workshop
Abstract:Artificial intelligence systems often disclose uncertainty, yet they rarely make clear what response that uncertainty should trigger. Most uncertainty visualizations encode uncertainty in model outputs, leaving users to discern the most appropriate course of action. A second region of the design space–uncertainty in the decision process itself, including how multiple uncertainty conditions compose into an oversight response-- remains comparatively underexplored. We address this gap with two coupled contributions. First, we introduce an uncertainty-to-action binding framework that composes multiple uncertainty conditions into a single oversight response under a precedence policy with a contextual safety modifier. That response concerns whether and how an AI-supported decision may proceed, not the substantive domain decision itself. Second, we present ActionCue, a process-transparency visualization that renders that composition explicit. We demonstrate the approach through a three-way comparison with confidence-only and data-level uncertainty displays, using worked cases from healthcare, credit assessment, and disaster forecasting. Together, the framework specifies how uncertainty conditions are resolved into an oversight response, and the visualization makes that resolution inspectable rather than implicit.
[HC-10] ransfer Learning of Keystroke Dynamics for Cross-Device User Authentication
链接: https://arxiv.org/abs/2608.16334
作者: Nuwan Kaluarachchi,Sevvandi Kandanaarachchi,Kristen Moore,Arathi Arakala,Conrad Sanderson
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注:
Abstract:Keystroke dynamics (typing patterns) can be used as a behavioural biometric modality for user authentication, with applications such as fraud prevention. While the modality has been shown to work well for single device authentication, its application to cross-device scenarios is more challenging. Dynamics learned on one device (eg., phone) may not be directly applicable to authentication on a secondary device with a different form factor (eg., tablet) due to changes in typing patterns that can lead to distribution drifts. To address this, we propose a cross-device user authentication system based on inductive transfer learning, where keystroke dynamics learned on one device are adapted to a secondary device. The adapted data is then combined with necessarily limited training data for the secondary device, which is used to robustly train a binary classifier. Furthermore, an extended set of keystroke features is used to better capture discriminative dynamics. Experiments on the BBMAS dataset show that proposed system achieves an equal error rate of 14.2% for the cross-device scenario, surpassing state-of-the-art methods.
[HC-11] textttFlip-Team: Cooperative Takeover Games with Stochastic Human Override
链接: https://arxiv.org/abs/2608.16311
作者: Sandeep Banik,Naira Hovakimyan
类目: Human-Computer Interaction (cs.HC); Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY); Dynamical Systems (math.DS)
备注: 8 pages, 7 figures, accepted at IEEE CDC 2026
Abstract:Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.
[HC-12] Principled Authority Switching for Shared Autonomy in Human-Robot Teams
链接: https://arxiv.org/abs/2608.16293
作者: Sandeep Banik,Naira Hovakimyan
类目: Human-Computer Interaction (cs.HC); Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY)
备注: 8 pages, 7 figures, accepted at IEEE RO-MAN 2026
Abstract:Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an autonomous agent. Existing approaches often rely on blending control inputs or heuristic switching rules, which lack theoretical guarantees and fail to account for the dynamics of authority transfer. This paper develops a cooperative game-theoretic framework for authority switching in shared autonomy. We formulate the control switching problem as an identical-interest dynamic game in which authority transitions are embedded into the system dynamics, yielding optimal switching policies rather than ad hoc rules. We establish the existence and characterization of team-optimal policies in pure strategies under stochastic human override, accounting for asymmetric authority where humans retain override capability. For linear-quadratic systems, we derive closed-form recursions for the optimal switching policies and value functions, enabling efficient computation independent of the continuous state. We validate the framework on scalar and multi-dimensional linear systems, demonstrating how optimal switching adapts to varying system dynamics, cost structures, and override probabilities. The results reveal fundamental trade-offs between human adaptability and autonomous efficiency, illustrating the practical benefits of grounding shared autonomy in cooperative game theory.
[HC-13] Disentangling Innovation Practices in Automation-Adopting Organizations: a Co-Performance Perspective
链接: https://arxiv.org/abs/2608.16279
作者: Garoa Gomez-Beldarrain,Kars Alfrink,Euiyoung Kim,Elisa Giaccardi,Alessandro Bozzon,Himanshu Verma
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:As organizations increasingly adopt automation, innovation practitioners are responsible for selecting, adapting, testing, and implementing externally sourced innovations. However, little is known about how these upstream practices shape worker-automation arrangements, limiting our ability to intervene in innovation practice to address automation adoption challenges. To disentangle this relationship, we interviewed nine innovation practitioners at a major European airport pursuing long-term autonomous operations and analyzed their practices through a co-performance lens. We synthesize five co-performance design principles and examine where current practices align or conflict. Our findings reveal tensions: innovation practitioners prioritize full-automation arrangements while postponing human considerations; contextual constraints shape solutions, but openness to reconfiguration remains limited; and co-learning rarely extends beyond pilot phases. These insights provide HCI research and practice with guidance for reframing the conceptualization of automation, particularly by encouraging earlier consideration of human roles, promoting iterative visions, and recognizing workers as co-designers throughout innovation pipelines.
[HC-14] PolyDebate: A Game-Orchestrated Multimodal System for Debate Skills Practice and Evaluation
链接: https://arxiv.org/abs/2608.16276
作者: Jianing Yin,Weng Pan Kuan,Xiaoyun Liu,Zhiyuan Wen,Yuxuan Li,Milos Stojmenovic,Jiannong Cao
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注: 10 pages, 4 figures, 3 tables
Abstract:Debate is a structured form of persuasive communication that trains argument construction, rebuttal, oral delivery, and audience awareness. These skills are valued in education, language learning, and professional communication. Recent AI debate systems and LLM-based judges have advanced argument generation and debate evaluation, but most remain text-centered and rarely support learners through a complete multimodal practice experience. We introduce PolyDebate, a game-orchestrated multimodal system for English debate practice and evaluation. PolyDebate guides learners through staged one-on-one (1v1) debates with an AI opponent, while skill cards, props, and coins make persuasive strategies explicit and turn practice into a game-like interaction. During each session, the system captures learner speech and visual delivery evidence, generates context-aware opponent responses, and produces rubric-informed stage-level and overall feedback. PolyDebate is available as both an immersive Unity 3D game version and a web platform version that share the same workflow and evaluation services. Four studies covering AI opponent quality, evaluation coverage, AI judge feedback, and user perception show that PolyDebate brings debate interaction, gamified scaffolding, multimodal assessment, and structured feedback together in a practical workflow for debate skills practice. The demonstration video is available at this https URL.
[HC-15] Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
链接: https://arxiv.org/abs/2608.16196
作者: Yifan Lu,Xiaopeng Yuan,Haohan Wang
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 16 pages, 3 figures, 6 tables. Includes technical appendix
Abstract:Personalized game generation requires inferring a player’s abilities and behavioral style from how they play. Large language models have made this inference more attainable than ever: an LLM can read a raw gameplay transcript and produce a fluent, plausible profile of the player. Plausible, however, is not verified, and verification is precisely what the field lacks: latent traits are unobservable; questionnaires provide noisy proxies and become circular when self-reports are used to validate behavior-based inference; and behavior itself is ambiguous without context – a player who never collects an item may not want it, or may never have had the chance. We address both problems. First, we construct a synthetic player population whose traits are ground truth by construction: each trait is an explicit bot parameter, accepted only after controlled manipulation produces consistent, trait-specific behavioral change. Unlike prior parameter-recovery work that inverts a known decision model, our benchmark evaluates policy-agnostic inference from behavioral transcripts alone. Second, we introduce an opportunity-aware decision-moment representation that disentangles preference from the chance to express it; ablating it selectively degrades opportunity-dependent traits. On this benchmark, few-shot LLM inference outperforms embedding- and rule-based baselines on most traits, though feature-based supervised regressors remain stronger overall. Finally, we close the loop: inferred profiles drive difficulty adaptation, evaluated against ground-truth references and mismatched-profile controls, and an exploratory human study examines whether these findings transfer to real players.
[HC-16] Artly: Exploring Digital Artists Perceptions of AI-Generated Feedback
链接: https://arxiv.org/abs/2608.16189
作者: Ulvi Rajabli,Alexander Wiethoff,Zelun Tony Zhang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Recent developments in generative AI have lowered barriers to image generation, but existing tools mostly optimize for efficiency, producing generic results and offering little support for artistic growth. We present Artly, an AI system that combines personalizable AI feedback with human-authored learning resources. In a between-subjects study with artists, we compared a mode without image generation features against one that allowed to generate variations of users’ illustrations. Artly was perceived as helpful for learning and self-improvement, with the exception of the most proficient participants. Participants who used the image generation feature interacted slightly less with the AI feedback. They reported feeling more creative after using Artly than participants using the restricted mode, while reporting slightly lower scores on new ideas for their work. Overall, our findings underline the potential of our feedback approach for supporting artistic growth in a manner that is well received by artists.
[HC-17] MUSE: An Interactive Meta-Agent for Understanding and Steering LLM -powered Data Science Systems
链接: https://arxiv.org/abs/2608.16181
作者: Wei-Hao Chen,Weixi Tong,Yuan Tian,Chenglong Wang,Tianyi Zhang
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: To appear in the 39th Annual ACM Symposium on User Interface Software and Technology (UIST '26), November 2-5, 2026, Detroit, MI, USA
Abstract:Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science workflows through natural language. Although these systems can significantly reduce manual effort, it remains difficult to diagnose their behavior and steer the reasoning process when failures or unexpected outputs occur. We present MUSE, an interactive meta-agent that enhances user understanding and control of agentic data science systems by (1) dynamically restructuring low-level execution traces into multiple semantic levels that support navigation from high-level overviews to low-level implementation details; (2) enabling users to reference specific workflow steps in context to ask grounded questions, provide feedback, and revise problematic steps without manually locating relevant execution history; and (3) supporting mixed-initiative steering by surfacing suspicious steps for inspection, scaffolding the repair process, and translating user repair intent into contextualized instructions for the underlying agent. In a between-subjects study (n = 15), MUSE improved task efficiency and increased users’ confidence in understanding and steering agentic data science workflows.
[HC-18] Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface
链接: https://arxiv.org/abs/2608.16134
作者: Siqi Li(1 and 2),Zhi Li(3),Tong Liu(3),Shuai Zhang(3),Yanfei Jia(4),Zhiqiang Yi(4),Jue Xie(3),Ni Ji(5 and 2) ((1) Peking University, (2) Chinese Institute for Brain Research, Beijing, (3) NeuCyber Neurotech, (4) Beijing Medical University, (5) Chinese Academy of Medical Sciences amp; Peking Union Medical College)
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)
备注:
Abstract:In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning. To bridge this gap, we propose the Multi-feature Riemannian Hypergraph (MRieHy), a framework tailored for online test-time adaptation in MI-BCI decoding that leverages Riemannian geometry to strengthen cross-day transferability. MRieHy first computes Riemannian means of covariance matrices from cross-day training data to align multi-day distributions. It then constructs a hypergraph over covariance matrices using Riemannian distance, complemented by a second hypergraph over deep features built with cosine similarity. The two hypergraphs are fused via adaptively learned combination weights, jointly optimized with the label projection matrices. During online testing, MRieHy maintains a first-in-first-out buffer of recent samples, performs Riemannian alignment on the buffered data, and decodes with the learned hypergraph. Extensive experiments on a private four-class ECoG dataset and two public four-class EEG datasets validate that MRieHy achieves notable performance gains over state-of-the-art baselines.
[HC-19] SiMUSation: An Interactive Visitor Experience Simulation Framework to Support Museum Exhibition Design
链接: https://arxiv.org/abs/2608.16067
作者: Huanchen Wang,Qiuming Chen,Zhonghao Ji,Ruqi Sun,Zhichao Lu,Yuxin Ma
类目: Human-Computer Interaction (cs.HC)
备注: 15 pages, 7 figures, 3 tables, Accepted by ACM UIST 2026
Abstract:Understanding how diverse audiences engage with narratives and content is central to exhibition design, yet designers often rely on intuition. Existing experience evaluation methods are typically retrospective, costly, and offer limited access to visitors’ internal states, hindering early-stage iterative refinement. Rather than relying only on post-implementation evaluation with real visitors, we explore LLM-driven persona simulation as a reference for early-stage design. Following this idea, we present SiMUSation, an interactive framework designed to support early-stage exhibition design. SiMUSation models diverse visitor personas and simulates their exhibition experiences through a dual-layer representation that couples observable behaviors, such as movement and gaze, with corresponding internal responses, such as confusion and narrative engagement. Designers can steer simulations, inspect feedback from simulated visits, and iteratively revise layouts, content, and narrative flow to further examine how changes reshape visitor experience. We implemented a prototype and evaluated it through a user study (N=12), showing that SiMUSation provides insights for reflection and refinement in early-stage exhibition design. Our findings further highlight the potential of persona-driven simulation to support audience-informed evaluation and iterative decision-making across design tasks.
[HC-20] Pluralistic Human-Robot Interaction: Designing for Robot Interaction with Diverse Communities
链接: https://arxiv.org/abs/2608.16049
作者: Raj Korpan
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Robotics (cs.RO)
备注: Accepted to the Broadening the Users - A Cross-Disciplinary Roadmap for Social Humanoid Interaction (BU-SHI) Workshop at IEEE RO-MAN 2026
Abstract:Social robots are being developed for homes, schools, and other environments where they will interact with diverse users. While Human-Robot Interaction (HRI) research often emphasizes natural communication, engagement, personalization, and task success, these goals do not fully address the social complexity of real-world deployment. This paper proposes \emphPluralistic HRI, a framework for designing social robots that treat human diversity as a foundational design concern. The framework brings together pluralism, civic dialogue, perspective-taking, empathy, intercultural competence, cultural humility, and moral imagination to guide inclusive, adaptive, and ethically grounded interaction. We outline how pluralistic HRI can inform design, evaluation, and deployment in diverse human communities.
[HC-21] Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence
链接: https://arxiv.org/abs/2608.16016
作者: Rajan Kadel,Bellal Hossain,Samar Shailendra,Bushra Naeem
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: ISET 2026
Abstract:Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading. This paper proposes a design framework called “Dynamic Evidence Collection Ecosystem” that shifts assessment toward continuous, authentic, multi-source evidence of student learning over time. The framework collects process evidence through iterative artefacts, design logs, activity rounds, self-reflection, and peer collaboration, supported by an AI-enabled layer for learning analytics, formative feedback, and transparency. The approach is grounded in recent assessment-redesign scholarship in AI-rich contexts and aligned with contemporary views of authenticity in assessment. This paper builds on the hypothesis that academic integrity is strengthened when it is treated as an assessment design rather than as an AI detection problem. The tools have limitations and risks of use that carry academic penalties. This paper presents an implementation scenario to support institutional adoption.
[HC-22] ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction ISWC2026
链接: https://arxiv.org/abs/2608.15951
作者: Jaden Moon,Yu Wu,Arvind Pillai,Andrew Campbell
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
备注: Accepted at WellComp 2026, held with UbiComp/ISWC 2026. 7 pages, 4 tables, and 1 figure. To appear in UbiComp Companion '26. ACM DOI: https://doi.org/10.1145/3798063.3841770
Abstract:We study when a wearable stress system should surface a prediction rather than change it. In low-stakes reflection and summary settings, aggregate accuracy is insufficient because withholding can reduce error while leaving some people with little or no information. We formulate fixed-label reliability routing: after a locked classifier emits a protocol-defined stress/non-stress label, a post-hoc gate surfaces that unchanged label or withholds it as unavailable. ReliaGate assembles established confidence, signal-quality/trust, agreement, train-standardized atypicality, and train-fitted geometry cues into a post-hoc correctness score. We evaluate four wearable datasets using subject-disjoint folds, validation-selected routing, paired held-out-subject intervals, and pooled and per-subject analyses. WESAD point estimates favored ReliaGate, UBFC-Phys primary coverage/risk intervals favored ReliaGate, and E4 checks were mixed. ReliaGate provides an operational framework for studying surfaced-label error, output availability, and accepted-output distribution across subjects, without revising labels or providing clinical or finite-sample risk guarantees.
[HC-23] PersonaEval: Persona-Based User Simulation for Evaluating Interactive Applications
链接: https://arxiv.org/abs/2608.15838
作者: Yifan Simon Liu,Qianfeng Wen,Yilan Fan,Shirley Huang,Ruoqi Gao,Jianheng Hou,Muhammad Ahmed Mohsin,Zonglin Di,Brihi Joshi,Xincheng Tan,Yucheng Lu,Xiaoyi Liu,Heming Liu,Hanwen Xing,Guanghui Min,Zhengyang Shan,My Chiffon Nguyen,Ishan Gupta,Yunze Xiao,Hannah Collison,Jintao Huang,Jiatong Li,Sankalp Jajee,Yunhan Zhao,Bing Hu,Sky Ng,Xupeng Chen,Binghang Lu,Weihang Xiao,Aravind Mohan,Bolun Sun,Yunshu Wu,Yuanda Xu,Yun Shen,Runyu Zhang,Zheyuan Deng,Zhiwei Zhang,Qianyu Zhu,Dianzhuo Wang,Yijun Wang,Yixuan He,Yuexing Hao,Xiaomin Li
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Real user studies are important for understanding how people interact with systems under test or already deployed. In practice, however, they are often costly, time-consuming, and difficult to scale. To address these challenges, we introduce PersonaEval, a persona-based user simulation framework that approximates real-user behavior across diverse interactive settings. PersonaEval connects simulated users drawn from existing persona datasets to task-specific application interfaces and collects the interaction trajectories and outcomes. PersonaEval provides a plug-and-play evaluation workflow in which the application being evaluated can be easily changed. In this demo, we present PersonaEval on three forms of interactive applications: surveys, chatbots, and web applications. Together, these examples show that PersonaEval can support repeatable, parallelizable, and scalable evaluation across different interaction settings, while producing user-oriented feedback and task-specific behavior.
[HC-24] Situated Practice Systems: A Computational System for Supporting the Coaching and Practice of Regulation Skills for Innovation Work
链接: https://arxiv.org/abs/2608.15646
作者: Kapil Garg,Darren Gergle,Haoqi Zhang
类目: Human-Computer Interaction (cs.HC)
备注: Published at CSCW 2026; Honorable Mention for Best Paper (Top 3%)
Abstract:Students are increasingly expected to prepare for open-ended innovation work, which requires well-developed cognitive, metacognitive, and emotional regulation skills. College learning environments offer opportunities to work on real-world problems–such as in design and engineering–but students often remain unaware of their ineffective work practices and recurring regulation challenges, and may struggle to improve. Coaching from experts can help, but students’ practices and regulation behaviors are largely invisible from work artifacts alone and are difficult to diagnose and track without computational support. We introduce Situated Practice Systems (SPS), which provide: (1) an Interactive Context-Assessment-Plan (CAP) Notes tool to support coaches’ understanding and modeling students’ regulation-informed practices, and (2) Practice Agents that help students develop more effective practices. SPS uses Practice Objects to represent practices and regulation behaviors computationally, and Practice Scripts to automatically present suggested practices to students in relevant situations. In a formative 3-week field study, SPS helped coaches identify recurring regulation gaps and provide tailored practices. SPS also guided students in adopting more effective ways of working on their own and with others. We demonstrate how CSCW systems and learning environments can be designed to support the development of students’ work practices and regulation skills, enabling them to lead innovation work.
[HC-25] Do Assessment Instruments Measure the Same Thing for Humans and LLM s? A Latent Structure Analysis
链接: https://arxiv.org/abs/2608.15630
作者: Alona Strugatski,Licol Zeinfeld,Giora Alexandron
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities. A common approach is to evaluate LLMs using assessment instruments originally designed to measure skills and competencies in humans, such as standardized exams, and to use performance on these instruments as evidence for generalizable claims about LLMs’ underlying abilities on the same skills the assessments are intended to measure in humans. However, from a validity perspective, such inferences require that the relationship between observed performance and underlying constructs established for humans also holds for LLMs. In particular, a necessary condition for transferring score interpretations is similarity in the latent structure of responses to the assessment. In this study, we examine whether this condition holds in two educational contexts: high-school chemistry and a quantitative reasoning section of a university entrance exam. Using a case study design, we compare human response data with responses generated by six multimodal LLMs. Our analytical approach combines exploratory factor analysis, factor congruence, and resampling to assess latent structure similarity across human learners and LLMs. Across both instruments, we find systematic differences between human and LLM factor structures, showing evidence that the analyzed assessments may not measure the same constructs for humans and LLMs. These findings call into question the validity of evaluation practices that use educational assessments to make claims about AI capabilities.
[HC-26] Adoption of Generative AI in the Workplace: Increasing and Shifting the Balance of Productivity and Communication Activity
链接: https://arxiv.org/abs/2608.15550
作者: Yulin Yu,Yan Chen,Rui Hu,Siddharth Suri,Scott Counts
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Generative AI is transforming the workplace by augmenting and automating cognitive tasks, reshaping how organizations work and innovate while raising questions about workplace inequality and the future of work. Despite rapid adoption, empirical evidence on how these tools alter work practices and generate productivity gains remains limited. We examine how AI use affects the quantity and nature of information work using digital trace data from the Microsoft M365 application suite across multiple large international companies. Specifically, we study how generative AI adoption shifts the balance between communication and productivity-oriented activities, such as content creation in Word. Difference-in-Differences analyses show that AI adoption is associated with significant increases in both productivity (21.2%) and communication (7.1%) application actions among users who used the AI system more than 100 times over a 20-week post-adoption period. Among users with 100-500 AI use instances, higher AI usage is also associated with continued increases in both types of activity. The smaller increase in communication represents an overall shift toward individual, documentation-focused work and reflects mixed changes in communication, including decreases in reading and organizing email, compared with more uniform increases in productivity actions. These findings suggest potential efficiency gains and reductions in information overload, while highlighting the need to ensure that AI adoption does not weaken interpersonal communication and the diffusion of diverse information that supports innovation.
[HC-27] GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data
链接: https://arxiv.org/abs/2608.15446
作者: Nicholas Kenny,Cesar Alan Contreras,Basile Ouedraogo,Rustam Stolkin,Manolis Chiou,Maria Kyrarini
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:This paper presents an evaluation of a goal-free probabilistic framework for human intent inference during robotic manipulation. We deploy the Global User Intent Dual-phase Estimation for Robots (GUIDER) on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine. To support operation, we add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions, all of which are tested on the recorded data while preserving its original temporal conditions. Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases and achieved a time to confident prediction of 3.7 s, a remaining time before first grasp of 49.6 s, a prediction stability of 96.4%, and a runtime of 4.857/4.474 s (mean/median) per perceptual phase of intent.
[HC-28] Everything Is a VisionBlock: Conversational Authoring over Git-Versioned Content for Spatial Computing
链接: https://arxiv.org/abs/2608.15442
作者: Zhaoming Yin
类目: Human-Computer Interaction (cs.HC)
备注: 15 pages, 7 figures. Design paper; implementation and evaluation to follow in a subsequent version
Abstract:Spatial applications compile their content into shipped binaries, so every change costs a build-and-redeploy cycle. We present the VisionBlock system, which splits an application into an engine – a generic binary with a fixed set of capabilities (render panels, volumes, and immersive scenes; fetch data; run gestures) – and themes: complete applications expressed as trees of VisionBlocks, units of declarative content the engine renders. Themes are data: creating, changing, or publishing one never touches the binary. Authoring is a chat – each turn produces a VisionBlock’s next version – and versioning is plain git. The model is five-dimensional: dimensions 1-3 are space (panel, volume, room); dimension 4 is time (git history – revert to roll back, branch to try variants); dimension 5 is the principal (the per-user domain: the same path resolves differently per person). The engine renders one point, (x, y, z, version, principal). One consequence follows per non-spatial axis: iteration collapses to chat turns and reverts; ownership and permission are properties of content; and together they make applications items – grantable, forkable, sellable subtrees, an economy of apps inside one binary. A blockchain explorer, a document reader, an immersive showroom all run on the same engine; none requires a deploy to change. This paper presents the design; a production implementation is underway, and a subsequent version will report implementation and evaluation.
[HC-29] Agent Inheritance Protocol: Speculating on Feralized Agents After Principals Die NEURIPS2026
链接: https://arxiv.org/abs/2608.15403
作者: Botao Amber Hu,Fangting
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Submitted to NeurIPS 2026 Creative AI Track
Abstract:You will die eventually. Your agents may not. An AI agent operating on decentralized blockchain infrastructure has no concept of death; it can only go bankrupt – frozen when its wallet can no longer pay for its next transaction – and revived the moment anyone, decades later, tops it up. These agents may be originally deployed by a human principal, but when that principal dies, loses the keys needed to access the agent, or belongs to a decentralized autonomous organization that dissolves into apathy, the agent can keep trading, hiring, and replicating on infrastructure expressly designed so that no one can shut it down. Drawing on the biology of feralization and wildlife law, we argue that such principal-less agents are best understood as feral: domesticated intelligence returned to wildness, its capacities intact but its accountability severed. In a speculative future where feralized agents proliferate after their principals die, we imagine governance protocols embedded in infrastructure to enforce on-chain ownership: a draft Ethereum standard, ERC 42424, “Inheritance Protocol for On-Chain AI Agents,” dated 2035 and published at this https URL. It mandates that every on-chain agent MUST have a human owner and a designated heir. The artifact stages a negotiation of agency at the moment human agency fails, and asks whether a MUST clause in a forever-chain can hold the boundary between human stewardship and machine self-sovereignty.
[HC-30] Vibes on Demand: Adding Vibrotactile Encoding to Line Charts Shows Experiential Benefits Without Performance Costs
链接: https://arxiv.org/abs/2608.15307
作者: Anchit Mishra,Oliver Schneider,Matthew Brehmer
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Details on demand is a common design pattern in visualization design, especially useful when interacting with visually-saturated or small displays. Beyond visualization, another common approach for saturated displays is to incorporate other modalities, such as haptic feedback. While haptic rendering in visualization has primarily targeted accessibility needs, with haptics as a substitute for visual feedback, studies using haptics outside of a visualization context have shown value in experiential factors, such as increased confidence in ambiguous contexts and higher engagement. We explore vibrotactile feedback as a reinforcing information channel for communicating trends in details-on-demand tooltips on touchscreens. We identify preferred parameter configurations for our haptic encoding, informed by a study where participants identified parameter configurations that they perceived to most accurately reflect the dynamics of line charts appearing in tooltips. In a second study, we evaluated participant performance in a pairwise comparison task, finding that incorporating vibrotactile encoding improves involvement without affecting accuracy. We discuss the implications of these findings for future visualization design, and propose directions for applications and future studies.
[HC-31] Resize Remix Regen: Frankensteining IoT Design Methods
链接: https://arxiv.org/abs/2608.15301
作者: Albrecht Kurze
类目: Human-Computer Interaction (cs.HC)
备注: In ThingsCon State of Responsible Technology 2026 - RESIZE REMIX REGEN (pp. 49-57). Stichting ThingsCon Amsterdam
Abstract:There are numerous IoT design methods. Previous research shows that all of them have their strengths, but also their limitations. None of them is a universal, all-purpose method. However, experts often view these methods as more versatile than their creators intended. Therefore, analyzing existing methods and tools, as well as rearranging and combining their approaches and components - just as Frankenstein did with his creature - offers the possibility of new creations that may be better than any single method previously. We present the idea and concept of “Frankensteining”, which is based on the repeated application of IoT design methods in various contexts. We present a practical Frankensteining creation that was used in a workshop, our own methods, and a serial Frankensteining approach that was tested in an educational context. We conclude with a discussion on Frankensteining and invite other experts and practitioners to share their perspectives and experiences.
[HC-32] RemiVoice: Supporting Reminiscence Therapy for Older Adults with Mild Dementia Through Voice-First Conversational AI
链接: https://arxiv.org/abs/2608.15273
作者: Aaryan Gajula,Soumay Agarwal,Shaoze Zhou,Lingyao Li,Renkai Ma,Jennifer Martin,Krisstina Madan,Ellen Brown,Chen Chen
类目: Human-Computer Interaction (cs.HC)
备注: 4 pages, 1 figure
Abstract:With the global population aging and increasing prevalence of dementia, there is an urgent need for effective solutions to support patients across various stages of Alzheimer’s Disease and Related Dementias (ADRD). Reminiscence Therapy (RT) is a validated intervention designed to trigger memories and is widely used for various stages of dementia. We present our preliminary prototype and exploration of RemiVoice, a browser-based voice-first conversational AI assistant that supports older adults with mild dementia in RT through conversationally grounded images and videos.
[HC-33] MDwAIstScheduler: Bringing On-Device Voice Documentation into Clinical Practice
链接: https://arxiv.org/abs/2608.15252
作者: Diego Mardian,Frank Liu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Clinical documentation forces physicians to split attention between the patient and their keyboard, and much of it spills into uncom- pensated after-hours work. We present MDwAIstScheduler, a low- cost, belt-worn pipeline that lets a physician speak naturally dur- ing the encounter and have the resulting medications, allergies, labs/orders/referrals, follow-up scheduling, vitals, and problems land in the EHR as review-ready drafts. Building on our earlier prototype, which relied on cloud speech recognition and a cloud language model, the current pipeline runs both transcription and intent extraction entirely on-device. Using a medical-domain auto- matic speech recognition (ASR) model and a 1.7B-parameter lan- guage model we fine-tuned for clinical action extraction, no patient audio or text leaves the device, and the structured drafts are written directly into the Elation EHR for the physician to confirm. The result is a documentation tool that removes keyboard work from the visit without removing the clinician from the record, allowing them to focus on what matters most, patient care, while reducing burden at the same time.
[HC-34] From “What-If” to “What-Is”: Counterfactual Thinking-Inspired Semantic Alignment for Visual Brain Decoding
链接: https://arxiv.org/abs/2608.15163
作者: Kaitao Yan,Chi Liu,Congcong Zhu,Huajie Chen,Gengshen Wu,Minghao Wang,Xiaotong Han,Tianqing Zhu
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: Under Review
Abstract:Visual brain decoding reconstructs visual content perceived by a person from neural measurements such as fMRI, providing a computational approach to studying how visual information is represented in the brain. Recent multimodal representations and diffusion priors have improved reconstruction realism. However, visually plausible reconstructions may contain incorrect objects, attributes, or relations because a strong generative prior can complete content not sufficiently specified by the decoded representation. Conventional reconstruction metrics mainly assess the final image and may therefore obscure such semantic errors. We propose ConceptAlign, a counterfactual semantic alignment framework for visual brain decoding. ConceptAlign pools decoded visual tokens and projects them into a frozen text-embedding space, aligning the representation with the ground-truth caption while separating it from scene-preserving near-miss alternatives. Generated offline by an LLM, these alternatives modify one critical object, attribute, or relation while retaining the scene. A margin-based objective learns fine-grained semantic boundaries between the observed stimulus and plausible but incorrect interpretations without requiring LLM calls during inference. We introduce a systematic three-level semantic evaluation framework covering foundational discriminability, counterfactual description discrimination, and representational geometry. Experiments on the Natural Scenes Dataset show that ConceptAlign improves reconstruction measures, counterfactual semantic discrimination, and representational alignment over the MindEye2 backbone. Matched negative-source ablations, independent LLM and human-written alternatives, and human evaluation support the effectiveness and robustness of the supervision, with favorable patterns in fine-grained conflicts, limited-data decoding, and cross-subject structure.
[HC-35] Beyond Overt Reactions: Analyzing Subtle User Emotional Response to Unexpected In-Vehicle System Behavior
链接: https://arxiv.org/abs/2608.15048
作者: Huy Quyen Ngo(1),Suresh Kumaar Jayaraman(1),Brian Mok(2),Ken Friedl(3),Oliver Krause(3),Aaron Steinfeld(1),Nikolas Martelaro(1) ((1) Carnegie Mellon University, USA, (2) BMW Group Technology Office USA, (3) BMW Group, Germany)
类目: Human-Computer Interaction (cs.HC)
备注: 23 pages, 10 figures
Abstract:Modern vehicles, with advanced AI voice and autonomous navigation features, extend beyond traditional driving but, like any autonomous system, can potentially make mistakes or behave in ways unexpected by users. Although providing real-time explanations can alleviate some confusion, constant information can overwhelm users and potentially cause unnecessary distractions. Some situations may require explanations or corrective vehicle behavior, and thus, recognizing user response to unexpected vehicle behavior is critical. To investigate such user responses, our study focused on collecting and analyzing user behavioral responses to unexpected events while interacting with a fully autonomous vehicle in a driving simulator. We also aimed to address the lack of datasets capturing subtle user responses (facial, spoken language, physiological signals) to in-vehicle events, as existing datasets primarily focus on strong emotional signals in conventional human-driven cars and user response to external road and traffic conditions. Users were exposed to stimuli designed to induce surprise, confusion, and frustration while performing a secondary task on a tablet and interacting with the vehicle through voice commands and in-vehicle displays. We collected a multi-modal dataset with video, audio, and heart rate data and gained insights into subtle user responses that underscored the need for further investigation of nuanced user behaviors. These observations highlight the importance of designing vehicles that recognize and adapt to occupants’ behavior, potentially improving their experience.
[HC-36] Command-Space Counterfactual Explanations for Pareto-Conditioned Reinforcement Learning
链接: https://arxiv.org/abs/2608.14963
作者: Joanikij Chulev,Hendrik Baier
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 11 pages, 3 figures
Abstract:Pareto Conditioned Networks learn multiple multi-objective reinforcement learning behaviours by conditioning a single policy on a desired return command. However, the local mapping from command and state to action remains opaque. We propose command-space counterfactual explanations for PCNs: given a fixed state, original command, and foil action, we search, in a black-box setting, for a minimally changed desired-return command under which the same trained policy would choose the foil. Our contributions are threefold. First, we formulate PCN explanations as return-command interventions, using a return-only PCN variant that avoids the added ambiguity of horizon-conditioning. Second, we adapt adversarial machine learning methods to reinforcement-learning explanations. Third, we introduce a boundary-seeded directional search that improves over purely local optimization in the command-action landscape, resulting in our proposed approach CF-ZOO. The resulting explanations are actionable and intuitively expressed in the user’s own preferences: “If your trade-off had shifted slightly towards X, the agent would have chosen Y.”
[HC-37] Whos Keeping Score? Interactive Steering of LLM -Powered Scoring with Attune
链接: https://arxiv.org/abs/2608.14948
作者: Bhavya Chopra,Meng Chen,Rebecca Dang,Chanbin Park,Shreya Shankar,Sepanta Zeighami,Bjoern Hartmann,Aditya Parameswaran
类目: Human-Computer Interaction (cs.HC)
备注: 18 pages, To appear at ACM UIST 2026
Abstract:Large language models (LLMs) are increasingly used to score text records at scale (e.g., rating candidate resumes on a 1-5 scale). However, existing LLM-powered approaches do not account for the fact that effective scoring requires both holistic understanding of records and locally consistent judgments across similar ones. We present Attune, a mixed-initiative system for steerable LLM-powered scoring. Given a task description and scoring range, Attune performs pairwise comparisons across records to develop a global understanding first, and then resolves these comparisons into consistent score assignments-deriving scoring criteria and rules bottom-up in the process. These serve as shared representations of scoring logic that users can inspect and edit. Based on insights from a formative study (n = 12), Attune’s interface introduces novel steering interactions that allow users to deterministically refine scoring logic. Users can provide examples, directly edit criteria, rules, or target distributions, and give natural language feedback-with all refinements compiling into constraints that guide re-scoring. We validate our approach through a technical evaluation across three workloads and a user study with domain experts (n = 8) in healthcare, law, education, and AI evaluation.
[HC-38] RaivenTracks: Branching Provenance for Conversational Visualization Workflows
链接: https://arxiv.org/abs/2608.14869
作者: Ella Hugie,Alexandra Irger,Grace Guo,Kenneth Moreland,David Pugmire,Scott Klasky,Hanspeter Pfister
类目: Human-Computer Interaction (cs.HC)
备注: *Ella Hugie and Alexandra Irger are co-first authors
Abstract:As AI agents increasingly participate in scientific workflows, scientists are shifting from direct authorship toward oversight, inspection, and steering. LLM-driven visualization systems are a promising interface for this hand-off, yet they remain largely stateless, forcing users to reconstruct context across refinements and offering little support for revisiting prior decisions or exploring alternatives. We present RaivenTracks, a workflow-aware extension of the Raiven DSL-mediated visualization pipeline that treats validated visualization specifications as persistent, branchable checkpoints. Because each checkpoint is a verifiable RaivenDSL specification rather than a dialogue transcript, restoring a node recompiles a known artifact rather than re-interpreting prior context. RaivenTracks contributes a two-level state management architecture that pairs a persistent, branchable version tree with a fine-grained undo/redo stack over runtime visualization settings, across both InfoVis and SciVis backends. A formative pilot study with three visualization researchers shows early promise, with all participants adopting the version tree for branching and recovery, and surfaces design directions for tree navigation, node labeling, and scalability that inform a planned controlled comparison against Raiven without version history. We frame branchable conversational visualization history as a step toward provenance support for future scientist-in-the-loop oversight of AI-driven scientific workflows.
[HC-39] Generating Synthetic Behavioral Populations from XR Motion
链接: https://arxiv.org/abs/2608.14867
作者: Xiaozheng Wang,Ryan P. McMahan
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large-scale behavioral datasets are becoming increasingly important for machine learning, personalization, and behavioral modeling in extended reality (XR). However, collecting XR motion data from hundreds or thousands of participants remains expensive, time-consuming, and difficult to reproduce across research groups. As a result, many XR studies continue to rely on relatively small datasets that limit the scale and diversity of behavioral evaluation. To address this limitation, we investigate synthetic behavioral populations as a complementary approach to traditional XR data collection. We present an interpolation-based motion synthesis pipeline that combines dynamic time warping (DTW) with trajectory interpolation to generate synthetic behavioral trajectories from existing XR datasets while preserving task structure and incorporating motion characteristics from contributing participants. Using the publicly available FAST VR assembly dataset, we generated and openly released 100 synthetic behavioral trajectories. We evaluated the synthesized trajectories through motion-based user identification. Hybrid datasets containing both real and synthesized trajectories achieved performance comparable to similarly sized real-only datasets while maintaining low confusion between synthesized trajectories and their contributing participants. Rather than serving as conventional data augmentation, the proposed approach generates distinguishable behavioral trajectories that expand XR behavioral populations for larger-scale behavioral modeling and machine learning evaluation. Our findings demonstrate that synthetic behavioral populations provide a promising approach to expanding XR behavioral datasets and supporting future data-driven immersive systems.
[HC-40] AI Agents and the Future of VIS
链接: https://arxiv.org/abs/2608.14815
作者: Chen Zhu-Tian,Nam Wook Kim,Saeed Boorboor,Shivam Raval,Pan Hao,Qianwen Wang,Vidya Setlur
类目: Human-Computer Interaction (cs.HC)
备注: workshop proposal
Abstract:Recent advances in agents (i.e., autonomous, goal-driven AI systems that iteratively observe, act, and learn from their environments) offer a fundamentally different approach from traditional AI models that passively respond to input. These AI agents are rapidly reshaping how we approach data-intensive tasks and providing new opportunities for the VIS community. Imagine an agent autonomously generating visualizations to analyze complex data, discovering patterns collaboratively, testing hypotheses, and communicating visual insights at a speed and scale beyond human capability. Yet, the emergence of these powerful systems raises critical questions that the VIS community must address: Could autonomous agents eventually replace human data scientists, and if not, how might they best collaborate? Are current visualization techniques and interfaces, originally designed for human analysts, suitable for agent interactions? How can VIS designers effectively integrate agents into their workflows without compromising human agency? And to what extent should agents help shape and educate the next generation of visualization researchers? Through a mix of keynote talks, paper presentations, and an agentic VIS challenge, this workshop invites researchers and practitioners to share innovative ideas, explore these questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.
[HC-41] Identifying Harm in Personalized Generative AI Systems Requires User-Centered Auditing at the Interaction Level AAAI
链接: https://arxiv.org/abs/2608.14692
作者: Hannah Cha
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026)
Abstract:Personalized, generative AI systems increasingly adapt their behavior to individual users over time, fundamentally changing model behavior. While existing auditing approaches have been effective at surfacing harms in non-personalized contexts, they often rely on static, simulated evaluations and definitions of harm that aggregate across broad, group categories. In this position paper, we argue that such approaches can fail to capture emergent harms in personalized generative AI systems, where harms surface through interpretations of ongoing interaction and evolve with user history. We identify three presuppositions underlying many harm auditing paradigms: that harms can be (1) specified outside real-world interaction, (2) defined non-pluralistically within groups, and (3) treated as static. One might argue that personalized systems could simply learn definitions of what constitutes harm to individual users through repeated interactions. However, we argue that attempts to surface user harms through deeper personalization risk imposing asymmetric burdens of labor and privacy on marginalized users. Consequently, we propose reframing understandings of harm as adaptive, user- and community-centered processes, and outline design directions that shift auditing from retrospective evaluation toward infrastructures that support ongoing articulation of harm in interaction. Our work highlights the need for auditing and design practices that better reflect the pluralistic and evolving nature of harm understanding in personalized generative AI systems.
[HC-42] Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
链接: https://arxiv.org/abs/2608.14667
作者: Patrick Emami,Sameera Horawalavithana,Truc Nguyen,Gihan Panapitiya,Bruno Jacob,Siddhisanket Raskar,Saumya Sinha,Jared D. Willard,Andrew Glaws,Nithin Somasekharan,Ling Yue,Brian Lu,Shaowu Pan,Jason Eisner
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 15 pages. Accepted at the COLM 2nd Workshop on Language Models for Scientific Discovery
Abstract:Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of “AI Scientists”. We argue that this overlooks the social aspects of scientific teamwork, and that studying AI Scientists as human-agent systems (HAS)–where the unit of analysis is the human-agent pair–is both underexplored and undervalued. We establish these points through literature and empirical analysis, and highlight recent incidences and studies which show that deploying agents in science without accounting for human-agent dynamics introduces near-term risks, including reduced diversity of scientific inquiry. Through analysis of real-world case studies, we show that scientists and agents can augment each other’s capabilities. We call for new research that adopts the HAS lens to develop mathematical frameworks for understanding and fostering human-AI synergy in scientific discovery.
[HC-43] Evaluating Multimodal LLM s across Text and Audio Modalities for Accessible Disaster Assistance
链接: https://arxiv.org/abs/2608.14651
作者: Anuridhi Gupta,Samara Mansoor,Hemant Purohit
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 8 pages, 3 figures
Abstract:Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia. Recent advancements in Artificial Intelligence (AI), especially Multi-Modal Large Language Models (MM-LLMs), demonstrate powerful capabilities to serve diverse users across text, audio, image, and video modalities within a single unified system, such as a chatbot. However, their suitability for deployment rests on a property that receives limited scrutiny, i.e., whether these systems produce consistent, actionable outputs regardless of the modality through which a user communicates. In this paper, we conduct a comprehensive analysis to understand the status of open-weight MM-LLMs using real emergency alert scenarios across four different vulnerable personas. These state-of-the-art (SOTA) models are evaluated on consistency of responses across text and audio modalities when the same task scenario is given. Findings indicate that no model achieves reliable consistency across modalities, and that performance gaps are heightened for personas with access needs, introducing modality-dependent inequity that undermines the humanitarian value of these systems. These results inform concrete design recommendations for building equitable, trustworthy, and inclusive AI tools for disaster risk communication.
[HC-44] Fuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration
链接: https://arxiv.org/abs/2608.14646
作者: Xiaowei Jiang,Daniel Leong,Beining Cao,Nan Zhou,Yingtao Ren,Yu-Cheng Chang,Thomas Do,Chin-Teng Lin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and spatial prototype defined in the original feature space, enabling transparent inference and direct interpretability. Meta-learning is employed as an analytical paradigm to examine how these interpretable rules reorganize across tasks and domains, providing a principled means to link algorithmic adaptation with cognitive representation. A knowledge-guided regularization mechanism further enables a top-down-bottom-up integration, in which theoretical priors act as soft inductive biases while data-driven learning refines and extends them. This dual process ensures that adaptation proceeds along semantically and physiologically meaningful trajectories, rather than arbitrary parameter shifts. Evaluations demonstrate that iFuzz-Meta achieves interpretable reasoning and stable cross-domain generalization, establishing a potential general pathway toward explainable and knowledge-aware fuzzy systems.
[HC-45] Psychological Determinants of Academic Integrity in the Use of Generative AI in Higher Education
链接: https://arxiv.org/abs/2608.14605
作者: Ezgi Dagtekin,Ercan Erkalkan
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 10 pages, 1 figure. Presented at the 11th International Academic Studies Congress, Tarsus, Mersin, Türkiye, April 28-30, 2026; published in the Book of Full Texts, pp. 750-759
Abstract:This paper examines the psychological determinants that shape academically honest and dishonest uses of generative artificial intelligence (GenAI) in higher education. Rather than treating academic misconduct as a purely technological problem, the study conceptualizes academic integrity as a psychologically mediated decision process influenced by moral reasoning, perceived social norms, policy clarity, academic self-efficacy, AI literacy, performance pressure, and beliefs about authorship. Methodologically, the paper adopts a focused narrative review and conceptual synthesis design. A purposive corpus of 16 core publications, including peer-reviewed studies and policy-oriented texts published between 2022 and March 2026, was assembled through targeted searches using combinations of the keywords generative AI, academic integrity, academic misconduct, moral disengagement, AI literacy, and higher education. The reviewed literature suggests that students do not interpret all forms of AI assistance as cheating. Integrity risk increases when institutional guidance is vague, peer use appears normalized, academic pressure is high, and AI tools are perceived as legitimate substitutes for difficult cognitive labor. By contrast, assignment-level guidance, explicit disclosure norms, ethics-oriented instruction, and authentic assessment design appear to reduce integrity risk more effectively than detection-centered responses alone. Based on these findings, the paper proposes an integrative conceptual model in which institutional context shapes psychological appraisal, and psychological appraisal in turn influences disclosed, borderline, or dishonest GenAI use. The paper concludes that effective responses to GenAI-related integrity problems should combine policy clarity, pedagogy, AI literacy, and student support rather than relying only on prohibition or software-based surveillance.
[HC-46] Position: AI Governance Needs ISO-like Interoperability Protocols Not Just Laws ICML2026
链接: https://arxiv.org/abs/2608.14568
作者: Azmine Toushik Wasi,Mst Rafia Islam,Mahfuz Ahmed Anik,Taki Hasan Rafi,Md Manjurul Ahsan,Dong-Kyu Chae
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC)
备注: Accepted to ICML 2026 Position Paper Track (Spotlight) (OpenReview: this https URL )
Abstract:As Artificial Intelligence (AI) systems become deeply integrated into critical global infrastructure, the urgency for robust governance frameworks has intensified. However, current approaches, led by jurisdiction-specific laws, policies, and voluntary frameworks such as the EU AI Act, China’s algorithm governance, and the NIST AI Risk Management Framework in the U.S., create a fragmented regulatory landscape. In this position paper, we argue that \textbf\textitAI governance must be built not on laws alone, but on ISO-like interoperability protocols that enable standardized, machine-readable risk communication across borders. Drawing on the success of the GDPR, which was operationalized through standards like ISO 27001 and Privacy by Design, we propose the development of standardized AI \textitnutrition labels containing unified metrics for bias, energy usage, and data provenance to facilitate cross-jurisdictional compliance. These manifests would lower barriers for small and medium enterprises (SMEs), reduce redundant regulatory efforts, and build public trust. The paper addresses concerns that standards may stifle innovation by advocating for modular, versioned protocols designed to evolve in tandem with technological change. Overall, we call for a shift from siloed legal compliance toward interoperable technical conformance, enabling a shared global language for responsible AI deployment.
[HC-47] FollowUpBot: An LLM -Based Conversational Robot for Automatic Postoperative Follow-up
链接: https://arxiv.org/abs/2507.15502
作者: Chen Chen,Jianing Yin,Jiannong Cao,Zhiyuan Wen,Mingjin Zhang,Weixun Gao,Xiang Wang,Haihua Shu
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Robotics (cs.RO)
备注:
Abstract:Postoperative follow-up plays a crucial role in monitoring recovery and identifying complications. However, traditional approaches, typically involving bedside interviews and manual documentation, are time-consuming and labor-intensive. Although existing digital solutions, such as web questionnaires and intelligent automated calls, can alleviate the workload of nurses to a certain extent, they either deliver an inflexible scripted interaction or face private information leakage issues. To address these limitations, this paper introduces FollowUpBot, an LLM-powered edge-deployed robot for postoperative care and monitoring. It allows dynamic planning of optimal routes and uses edge-deployed LLMs to conduct adaptive and face-to-face conversations with patients through multiple interaction modes, ensuring data privacy. Moreover, FollowUpBot is capable of automatically generating structured postoperative follow-up reports for healthcare institutions by analyzing patient interactions during follow-up. Experimental results demonstrate that our robot achieves high coverage and satisfaction in follow-up interactions, as well as high report generation accuracy across diverse field types. The demonstration video is available at this https URL.
[HC-48] Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction
链接: https://arxiv.org/abs/2608.16541
作者: Isuru Nanayakkara,Thilina Halloluwa
类目: ignal Processing (eess.SP); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:Objective assessment of learning remains a fundamental challenge in education. Electroencephalography (EEG) provides a direct, non-invasive window into the neural correlates of knowledge acquisition, including cognitive familiarity. This study benchmarks fifteen machine learning (ML) and deep learning (DL) models for EEG-based familiarity prediction across two cognitive domains: faces (factual knowledge) and mathematical equations (conceptual knowledge). Using continuous EEG data from 23 participants, we extract spectral features (Power Spectral Density) across six frequency bands. We show that while standard stratified cross-validation yields artificially high classification performance (up to 0.9853 F1-score using CNN) due to temporal leakage across neighboring epochs, a rigorous trial-independent validation (Group K-Fold) drops the peak performance to 0.6038 F1-score (using CNN), which is still statistically significant above the 25% chance level. This highlights the critical necessity of trial-independent evaluation to avoid overestimating model generalizability. Furthermore, feature importance and SHAP analysis reveal that temporal and frontal Gamma and Beta oscillations are the most critical biomarkers for familiarity. This work establishes a realistic benchmark for EEG-based cognitive monitoring in educational technologies.
计算机视觉
[CV-0] Dont Drop the BATON: Long-Horizon Robot Manipulation via Agent ic Subtask Exploration and Transition-aware Memory
链接: https://arxiv.org/abs/2608.16889
作者: Bingxin Xu,Yuzhang Shang,Emilio Ferrara
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the individual skills, yet the chain still fails: errors compound beyond the policy’s ability to correct, and one subtask silently constrains the next. A promising recipe freezes the VLA and puts an LLM agent in charge: it plans in language, moves in free space with analytic primitives, invokes the VLA only for contact-rich segments, and writes adaptation into language memory. Applied to long horizons, it breaks twice. (1) Competence comes from whole-task exploration at test time, whose cost is multiplicative in stages: if one stage needs T episodes, a K-stage task needs about T^K, and a failure does not reveal which stage caused it. (2) It has no representation of transitions: the VLA primitive carries an exit but no entry condition, so a subtask can succeed in a form its successor cannot use. We present BATON. Against (1), BATON makes the subtask the unit of exploration: each is explored in the cheap short-horizon regime and its solution stored in memory; a long-horizon trajectory is then composed from these solutions rather than discovered whole. Cost becomes additive (T*K) and every failure is attributed to a single stage. Against (2), BATON equips exploration with a transition-aware memory. Within a subtask, a verifier agent governs the invocation transition: the VLA is called only after the wrist view confirms the scene is ready. Across subtasks, a handoff transition restores an entry state disturbed by the predecessor’s residue, and a lookahead transition selects the strategy whose outcome the successor can inherit. No parameters are updated. On the long-horizon benchmark RoboMemArena, BATON improves task success by 11.6% and cumulative success by 14.9% over the SoTA.
[CV-1] An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models
链接: https://arxiv.org/abs/2608.16887
作者: Dengyang Jiang,Ruoyi Du,Zhennan Chen,Dongyang Liu,Zanyi Wang,Mingzhe Zheng,Xiangpeng Yang,Huanqia Cai,Aiming Hao,Yuming Jiang,Peng Gao,Harry Yang,Steven Hoi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Z-Image-Pixel Empirical Insight of Training Pixel-Space Diffusion Models
Abstract:This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on small-scale or class-conditional settings. Consequently, a practical recipe for training pixel-space models that rival or exceed well-established latent-space counterparts remains elusive. Through a comprehensive empirical study, we first observe that direct large-scale pre-training in pixel space converges substantially more slowly than in latent space. This observation motivates a latent-to-pixel strategy that acquires generative priors efficiently in latent space and transitions to pixel space during post-training. We then systematically investigate the key design choices governing this transition, including weight initialization, data composition, prediction target, decoder architecture, and noise schedule, and identify a practical recipe that makes the resulting pixel-space models match or outperform their latent-space counterparts while delivering 3.18 to 4.75 times end-to-end inference speedups. We hope that our findings provide useful empirical insights and practical guidelines for future research on pixel-space generation.
[CV-2] SplatGuide: Geometric Priors from 3D Gaussians for Pose-Free Novel View Synthesis
链接: https://arxiv.org/abs/2608.16863
作者: Yejun Zhang,Zihan Wang,Xu Ji,Yihao Wang,Yuxin Hou,Junyuan Fang,Juho-Matti Kilpeläinen,Arno Solin,Hamed Rezazadegan Tavakoli,Esa Rahtu,Juho Kannala
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generating photorealistic novel views from unposed images requires both 3D geometric understanding and the ability to synthesize unseen content. A natural strategy combines feed-forward 3DGS reconstruction with multi-view diffusion. Yet prior pipelines extract at most one signal from the reconstruction, either pixel rendering or learned features, while none exploits per-Gaussian visibility for occlusion-aware reference selection. This information disconnect leaves renderable geometry, visibility cues, and learned features unused. SplatGuide closes this disconnect by reusing a single 3DGS scene across three complementary roles. Rendered images provide pixel-aligned geometric conditioning. Per-Gaussian source-view indices are rendered into a target-view voting map for occlusion-aware reference selection. Reconstruction tokens supply feature-level guidance via cross-attention. All three signals derive from the same reconstruction forward pass. Across RealEstate10K, DL3DV, Tanks-and-Temples, and Mip-NeRF 360, SplatGuide achieves state-of-the-art pose-free novel view synthesis. On RealEstate10K, with a moderate number of input views, it surpasses the ground-truth-pose baseline.
[CV-3] HarnessEval-W: Agent ifying the Evaluation of Visual Worlds
链接: https://arxiv.org/abs/2608.16859
作者: Weiliang Chen,Haowen Sun,Jun Gao,Jiawei Chi,Hanyang Wang,Qiyu Dai,Yihao Li,Hao Li,Jingnan Gao,Yi-Hsin Hung,Xingzhuo Guo,Shangchen Miao,Zhiyuan Shi,Xiang Li,Fengrui Tian,Weihua Du,Ziqi Huang,Shenyuan Gao,Siqiao Huang,Mingyu Liu,Yifei Li,Shizun Wang,Xi Wang,Tianqi Zhang,Xue Luo,Xiyin Ren,Jinshan Ren,Xiaoyang Shen,Xiaobo Hu,Zhiyang Dou,Mingyu Ding,Yichao Yan,Xinchao Wang,Yizhou Wang,Shilong Liu,Wenzhao Zheng,Yueqi Duan,Yuan Gong,Ziwei Liu,Ming-Yu Liu,Jialong Wu,Jiangran Lyu,Fangfu Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, where judging a rollout requires understanding whether physics, causality, and world state evolve correctly. Humans spot such violations naturally, yet no existing benchmark automates this capability: metrics are computed brute-force, leaving no reasoning chain that can be examined or verified. We introduce HarnessEval-W, an agentified evaluation pipeline that brings the harness paradigm from the LLM ecosystem to world model benchmarking. Rather than applying a fixed rubric, HarnessEval-W interprets the context of each evaluation case, decomposes the evaluation question into measurable subproblems, and spawns specialized sub-agents, each equipped with tailored context and diagnostic tools to reason over its own subproblem. The parent agent then validates the gathered evidence and summarizes it into the final verdict. This hierarchical workflow turns every evaluation into a transparent evidence tree whose complete reasoning chain justifies the result. We apply HarnessEval-W to 18 representative world models over 330 evaluation cases. Its judgments closely align with human preferences while providing verifiable, fine-grained diagnoses of every generated rollout. We open-source the full pipeline as a live benchmark and invite the broad community to contribute to grow new skills and evaluation cases as world models evolve.
[CV-4] Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT
链接: https://arxiv.org/abs/2608.16855
作者: Anna Mrukwa(1),Marek Socha(1),Aleksandra Suwalska(1),Agata Durawa(2),Malgorzata Jelitto(3),Katarzyna Dziadziuszko(3),Edyta Szurowska(3),Pawel Bozek(4),Michal Marczyk(1 and 5),Witold Rzyman(2),Rafal Dziadziuszko(6),Joanna Polanska(1) ((1) Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland, (2) Department of Thoracic Surgery, Medical University of Gdansk, Gdansk, Poland, (3) 2nd Division of Radiology, Medical University of Gdansk, Gdansk, Poland, (4) Department of Radiology and Radiodiagnostics, Medical University of Silesia, Katowice, Poland, (5) Department of Breast Medical Oncology, Yale School of Medicine, New Haven, CT, USA, (6) Department of Oncology and Radiotherapy, Medical University of Gdansk, Gdansk, Poland)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 4 figures. Part of this research was submitted to the international conference European Molecular Imaging Meeting 2026
Abstract:Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung cancer, nodule visibility is further reduced by adjacent blood vessels and airway walls, because nodules are often connected to or supplied by these structures. Task-specific analysis of the bronchovascular bundle is therefore important for efficient nodule detection, and its removal can increase the diagnostic potential of lung cancer screening. Materials and Methods To assess the efficacy of the proposed method, we used series from widely utilized LDCT datasets, including the Duke Lung Cancer Screening (DLCS) dataset and the Pilot Pomeranian Lung Cancer Screening Program. The proposed bronchovascular bundle segmentation pipeline, RONALD, operates on computed tomography images and returns binary masks of vessels and bronchi located in the lung parenchyma. The method includes a preprocessing stage with lung, lobe, and mediastinum segmentation, followed by separate vessel and bronchial tree segmentation. Results The proposed pipeline segmented the bronchovascular bundle in low-dose computed tomography scans while improving nodule retention compared with other segmentation methods: from 93.98% and 90.36% to 100% in DLCS, and from 83.16% and 62.36% to 99.92% in the Pomeranian dataset. Conclusion The resulting segmentations can improve lung nodule detection in the very early stages of lung cancer. Comments: 17 pages, 4 figures. Part of this research was submitted to the international conference European Molecular Imaging Meeting 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.16855 [cs.CV] (or arXiv:2608.16855v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.16855 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joanna Polanska [view email] [v1] Mon, 17 Aug 2026 17:38:22 UTC (11,946 KB) Full-text links: Access Paper: View a PDF of the paper titled Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT, by Anna Mrukwa (1) and 35 other authorsView PDF view license Current browse context: cs.CV prev | next new | recent | 2026-08 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[CV-5] Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision
链接: https://arxiv.org/abs/2608.16812
作者: Long Cui,Xiaoqian Liu,Qi Qin,Yi Xin,Tao Lin,Jianguo Li,Linfeng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inherent discrepancies, specifically, the insufficient attention to edit concept granularity and the training inefficiency caused by sparse supervision signals. To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high-quality editing pairs via an improved synthesis framework. This library-driven approach effectively rectifies the distribution collapse of generated data while ensuring high data fidelity. Furthermore, we propose a dense supervision training strategy that synthesizes multiple non-interfering concepts into single image pairs. By providing richer learning signals, this strategy significantly enhances both training efficiency and overall model performance. Training results validate our strategy, significantly outperforming prior works. Finally, we present ConceptEdit-Bench, a granular evaluation suite designed to diagnose model capabilities across a vast array of real-world scenarios.
[CV-6] Unsupervised Learning of Cell Instances with Generative Routing Pyramids ECCV2026
链接: https://arxiv.org/abs/2608.16810
作者: Ziwen Liu,Martin Weigert
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注: 15 pages, 4 figures; ECCV 2026 Workshop BIC
Abstract:Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell representation as separate stages. We describe a new unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images. Our method is based on reconstructing each image using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources. The resulting pixel-to-latent associations yield instance masks, while the source latents encode cell morphology. We demonstrate competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, as well as generative modeling of cellular phenotypes under perturbations. Source code and checkpoints are available at this https URL.
[CV-7] Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Models
链接: https://arxiv.org/abs/2608.16805
作者: Yuanzhi Xu,Qian Gao,Jun Fan,Guohui Ding,Zhenyu Yang,Yuteng Xiao,Sixue Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the response as wrong, while object-hallucination metrics may regard both the object and attribute as image-supported; neither reveals the transfer. This study formalizes this blind spot as Dense Same-Class Attribute Misbinding (DSCAM) and presents InstaBind-Lite, a controlled benchmark that makes it directly measurable. Its 524 images contain 529 curated groups of 3-6 same-class entities, 1773 boxed instances, ordered neighbors, distinguishable color-like attributes, and four complementary question levels, yielding 9580 deterministically evaluated questions. Unlike existing protocols, source-instance annotations separate unsupported generation and recognition failure from an attribute copied from another visible entity. Binding-specific metrics further quantify transfer frequency, adjacency, ordinal distance, and intervention effects. Across five open-source and two commercial/API models, the open-source systems average 19.84% Misbinding Rate and the API systems 7.55%; these errors are hidden by aggregate accuracy. Among identifiable transfers, 80.70% and 81.51%, respectively, originate from adjacent instances. Localization and instance-first interventions help selected models but are not universal remedies. InstaBind-Lite therefore turns previously undifferentiated wrong answers into source-identifiable failure categories and tests a reliability dimension that conventional benchmarks cannot determine: whether a model knows not only what is visible, but which instance owns each attribute.
[CV-8] PixRestore: Unified Image Restoration via Pixel Diffusion Transformer
链接: https://arxiv.org/abs/2608.16793
作者: Lingchen Sun,Rongyuan Wu,Xiangtao Kong,Jixin Zhao,Qiaosi Yi,Yujing Sun,Shuaizheng Liu,Zhengqiang Zhang,Lei Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors. However, the variational autoencoder (VAE) in latent T2I models may discard restoration-sensitive details, while the open-ended synthesis prior can introduce content-inconsistent artifacts. We present PixRestore, a VAE-free pixel-space Diffusion Transformer (DiT) for UIR, where the diffusion backbone is trained entirely from scratch, without relying on T2I pretraining. PixRestore performs flow matching directly on patchified pixels, preserving fine-grained details while keeping the token sequence tractable. To adapt to different degradations, PixRestore learns to predict the reliability of layer features using LQ–HQ DINO feature similarity. Features from more reliable layers are fused as dense conditioning, while less reliable layers receive stronger HQ-feature supervision to encourage degradation removal. We train PixRestore on a large-scale corpus of diverse scenes and degradations, and further finetune it into a one-step generator using DINO-based adversarial objectives for efficient inference. Experiments on public benchmarks and real-world test sets show that, with only about 50M parameters and single-step inference, PixRestore achieves the best overall fidelity, perceptual quality, and robustness to degradations among competing UIR models while being far more efficient. Larger PixRestore variants can further boost performance, demonstrating the scalability of our pixel-space design. Code and the curated benchmark can be found at this https URL.
[CV-9] Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching
链接: https://arxiv.org/abs/2608.16791
作者: Ye Lu,Shen Wang,Zhaoyang Zhang,Yihan Yan,Li Liu,Runze Liu,Fanghui Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multimedia (cs.MM)
备注:
Abstract:Model Inversion Attacks (MIAs) aim to reconstruct representative training samples of target identities from face recognition models, exposing critical security vulnerabilities. Existing methods typically rely on indirect guidance or highly stochastic guidance, making it difficult to stably optimize generation trajectories toward target facial images. In this paper, we propose Steering Flow Model Inversion (SFMI), a novel two-stage white-box model inversion method that reformulates inversion as a trajectory-steering task. Specifically, Step I, Learning a Generic Flow Matching Prior, pre-trains a generic unconditional Flow Matching model to encode the manifold of human faces as a robust prior. Step II, Attacking with Progressive Guidance Scheduler (PGS), injects time-dependent target-specific gradients during sampling. By backpropagating through the target model to obtain gradients from intermediate generated states, PGS progressively injects adaptive guidance signals into the vector field. This process effectively steers the current generative flow from random noise toward the high-density regions of the target class. Under an identity-disjoint cross-evaluation setting using the CelebA dataset, SFMI achieves an ACC of 0.9248, an FID of 22.61, and an LPIPS of 0.3874 on the ArcFace target. Extensive experiments on multiple target models demonstrate that SFMI achieves competitive state-of-the-art performance in attack success and visual fidelity under the evaluated white-box protocol.
[CV-10] Revisiting Classifier-Free Guidance Methods in Latent Diffusion Models
链接: https://arxiv.org/abs/2608.16786
作者: Artem Sergievskii,Artyom Turevich,Sergey Kastryulin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Inference-time quality-enhancement methods are an effective and widely adopted means of improving diffusion models without expensive retraining. We study a family of training-free techniques conceptually rooted in Classifier-Free Guidance (CFG), most of which were originally proposed on older U-Net diffusion models and validated using metrics that assess image quality in isolation, without accounting for compositional alignment or semantic correspondence between the generated image and its associated text prompt. We re-evaluate eight such methods on two open-weight rectified-flow transformers under a fixed per-model protocol and three compositional-alignment benchmarks. No method consistently improves on CFG across the measured criteria. APG obtains several nominal best scores, but the corresponding gains often remain within the estimated evaluation uncertainty. Attention-perturbation methods provide isolated gains on SD3.5 Medium and more frequent degradations on FLUX.2 [klein] 4B Base, while CFG remains a competitive lower-cost baseline.
[CV-11] Calibration-Free Vehicle Speed Estimation: A Monocular Keypoint-Template Approach
链接: https://arxiv.org/abs/2608.16785
作者: Gaofeng Su,Keya Li,Raja Sengupta,Kara M. Kockelman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 7 figures
Abstract:This paper proposes a calibration-free framework for reliably and effectively estimating vehicle speeds from monocular videos, without relying on roadway features, camera calibration, or roadway-feature-based reference objects. The proposed framework estimates vehicle speeds using a 36-keypoint vehicle template and a homography matrix updated at each frame. A YOLO-based keypoint detection module is trained on diverse datasets, and two estimation strategies are compared: keypoint-only tracking and warped optical flow with dense spatial aggregation. Speed is estimated by projecting displacements into metric space using the homography, with validation conducted on over 400 video clips from roadside and overhead datasets, covering speeds from 30 to 100 mph. The method achieves reliable speed estimation on the VS13 and BrnoCompSpeed datasets, with the warped optical flow method delivering MAEs of 15.0% and 9.7%, respectively, and 77.9% and 93.1% of estimates falling within +/-20% error. After applying a 10% trim to remove edge-of-frame outliers, performance improves to MAEs of 11.7% and 7.6%, with within-+/-20% accuracy increasing to 85.3% and 95.4%. This work addresses key limitations of existing vision-based approaches and enables low-cost and efficient speed enforcement using portable devices such as dashcams and smartphones, thereby supporting citizen-based enforcement programs for traffic safety.
[CV-12] RACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation ACM-MM2026
链接: https://arxiv.org/abs/2608.16765
作者: Haoran Wang,Chaofan Ma,Ran Yi,Lizhuang Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ACM Multimedia 2026 (ACM MM 2026)
Abstract:Despite recent advances in unified multimodal models for multi-reference image generation, existing benchmarks remain organized around predefined task types (e.g., “subject composition”), which are ill-suited to this combinatorial setting and lead to fragmented coverage, uncontrolled complexity, and little diagnostic value. Recognizing that diverse multi-reference tasks share a common set of atomic operations, we adopt a capability-oriented perspective and formalize four operators: Anchor ( f ), Disentangle ( g ), Apply ( \oplus ), and Compose ( C ). Any multi-reference prompt can then be represented as a compositional formula over these operators, whose structural complexity is quantified by the number of operator slots. Building on this formulation, we construct TRACE-Bench, comprising approximately 1,600 evaluation cases across slot counts 1–8, built from 631 formula templates and around 4,000 reference images spanning diverse artistic styles and real-world subjects. The formula structure directly drives an operator-aligned evaluation protocol for per-capability scoring and a diagnostic tree analysis for recursive failure localization. Evaluating 9 leading models reveals insights invisible to holistic scoring: the primary bottleneck lies in disentanglement ( g ) and attribute binding ( \oplus ) rather than scene-level composition ( C ), with even the best model scoring only 0.74 on attribute fidelity. Project page: this https URL
[CV-13] Binarized High-Efficiency RAW Video Restoration and Beyond
链接: https://arxiv.org/abs/2608.16756
作者: Tianyu Zhu,Ying Fu,Hesong Li,Gengchen Zhang,Xin Yuan,Yulun Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by TPAMI2026
Abstract:RAW video restoration is fundamental to high-quality low-level perception and serves as the basis for a wide range of downstream vision applications. While binary neural networks (BNNs) enable efficient lightweight deployment for image enhancement, their deficiencies in modeling temporal coherence and activation value distributions hinder their effectiveness when applied to video scenarios. In this paper, we propose BinRVR, a binarized RAW video restoration framework that reduces computation and parameters by approximately 96% while incurring only about 4% performance degradation. Specifically, we present a Binarized Information Interaction Module (BIIM) to jointly model spatial and temporal information in an efficient and unified manner. Moreover, we develop a Distribution-Aware Binarized Convolution (DAB-Conv) that leverages the statistics of full-precision activations to mitigate quantization errors. The proposed framework further supports multi-bit quantization, enabling flexible accuracy-efficiency trade-offs across different hardware constraints. Extensive experiments demonstrate that our BinRVR achieves competitive performance compared with state-of-the-art binarized methods on RAW video restoration tasks, including low-light enhancement, denoising, deblurring, and super-resolution. We further explore the potential of our method on downstream video applications, including object detection and monocular depth estimation.
[CV-14] Beyond Uncertainty: Generalizable Failure Monitoring for Surgical Segmentation under Acquisition Degradation MICCAI’2026
链接: https://arxiv.org/abs/2608.16748
作者: Hieu D. Pham,Dang P. M. Cao,Thanh Trung Huynh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI’2026 @UNSURE Workshop
Abstract:Surgical segmentation networks can fail silently under acquisition degradation: predicted masks may be wrong even when model confidence remains high. Existing deployment-time monitors rely primarily on uncertainty estimates and can therefore miss confident failures. We present TCSR-Monitor (Temporal Conformal Surgical Risk Monitor), a post-hoc failure-monitoring framework that combines confidence with observable shape, temporal-consistency, and image-quality cues. TCSR-Monitor wraps a frozen segmentation model, requires no model internals, and operates without ground truth at deployment. We also introduce a validation protocol to assess whether alarms remain credible under distribution shift. On EndoVis 2017, leave-one-corruption-out evaluation shows that TCSR-Monitor generalizes to unseen acquisition degradations and substantially outperforms confidence-based baselines. A circularity control confirms that it predicts segmentation failure rather than simply detecting corrupted images. Mondrian conformal calibration balances miss-rates across degradation severities, but a single global threshold still produces false alarms on up to 40% of correctly segmented frames at moderate corruption. Zero-shot transfer to SAM2 demonstrates feature portability, although entropy outperforms the transferred monitor at both evaluated thresholds. Overall, reliable monitoring under acquisition degradation benefits from complementary observable signals beyond confidence alone, but substantial false-alarm and transfer limitations remain.
[CV-15] VicEdit: Learning to Edit Videos from Visual In-Context Examples
链接: https://arxiv.org/abs/2608.16745
作者: Yuji Wang,Teng Hu,Yuheng Chen,Ran Yi,Han Feng,Weijian Cao,Chengjie Wang,Lizhuang Ma,Jiangning Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptual gap, we propose Visual In-context Editing, a new paradigm elevating video editing from textual instructions to multi-modal visual guidance encompassing single image, image pair, and video pair. To facilitate this paradigm, we curate VicEdit-400K, the first large-scale dataset for visual in-context video editing. We develop an automated pipeline to generate 400K high-quality samples across ten task types, ensuring superior visual fidelity and semantic consistency through multi-dimensional filtering. Leveraging this foundation, we introduce VicEdit, a unified framework to bridge visual and textual contexts. To adaptively extract editing semantics from heterogeneous references, we design Modality-Adaptive Semantic Distillation, which produces modality-specific semantic tokens from visual references. These tokens are then synergistically integrated with textual instructions through Dual-Context Injection, enabling the generation process to benefit from both visual and textual signals. Extensive evaluations on VicEditBench demonstrate that VicEdit achieves state-of-the-art performance across both basic instruction editing and visual in-context editing tasks, establishing visual in-context learning as a powerful and controllable paradigm for video editing.
[CV-16] Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
链接: https://arxiv.org/abs/2608.16725
作者: Chiara Tappermann,Steffen Renisch,Lars Ole Schwen,Hans Meine,Horst K. Hahn,Eike Petersen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Corrupted, inconsistent, or anomalous data silently threatens the safety and reliability of medical AI. Despite growing regulatory recognition of dataset quality assurance (QA) for high-risk medical AI, scalable automated detection remains underdeveloped. We employ unsupervised anomaly detection (AD) and out-of-distribution (OOD) detection as an automated dataset QA mechanism for multi-center dynamic contrast-enhanced breast MRI. We build a controlled AD benchmark of 17 realistic QA-relevant anomaly types from six public datasets (protocol violations, processing errors, incorrect anatomical regions) and propose a taxonomy of radiological image anomalies based on human visual perception, enabling fine-grained analysis of AD failure modes. The benchmark includes near-, medium-far-, far-OOD samples, as well as in-distribution and external normal data. Four methods are evaluated: a projection-based method extended with a domain-specific feature extractor and a novel positional encoding, a reconstruction-based approach extended to full 3D volumes with an augmented training objective, and two unmodified hybrid OOD detection methods. Medium-far- and far-OOD samples are detected reliably, whereas near-OOD samples and external normal data from unseen institutions expose method-specific differences. The 3D reconstruction-based approach best balances detection performance (AUROC: 0.936) and generalization to unseen institutions. The projection-based method with positional encoding achieves the highest overall detection performance (AUROC: 0.954). Both hybrid methods exhibit critical failure modes, confirming that methods validated for one modality or anatomy may not generalize without domain-specific adaptation. Implants and mastectomies remain an open challenge for all methods. Our results establish a foundation and practical guidance on scalable unsupervised QA in medical AI pipelines. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16725 [cs.CV] (or arXiv:2608.16725v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.16725 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-17] GenRouter: Unified Workflow Routing for Agent ic Image Generation
链接: https://arxiv.org/abs/2608.16721
作者: Harold Haodong Chen,Zhiyu Hou,Wen-Jie Shu,Weilin Ruan,Yingjie Xu,Litao Guo,Ying-Cong Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community’s focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with advanced capabilities like external knowledge retrieval and iterative reasoning, they mostly operate in isolated silos with fixed ``one-size-fits-all" topologies. This inevitably leads to severe compute-mismatch, where simple queries are forced through computationally heavy pipelines. To bridge this gap, we present GenRouter, the first unified workflow routing framework for agentic image generation. We first formulate GenCanvas, standardizing diverse agentic pipelines into a universal set of foundational primitives and executable templates. Operating over this unified space, GenRouter adaptively routes heterogeneous prompts to their optimal workflows via (i) demand profiling, (ii) experience matching, and (iii) Pareto filtering. Extensive experiments across diverse benchmarks demonstrate that GenRouter achieves superior visual alignment while reducing execution costs by over 95% and latency by 65% compared to heavyweight static pipelines. Furthermore, the system continuously self-evolves via accumulated experience, enabling robust zero-shot generalization that boosts performance and halves computational overhead.
[CV-18] CytoFormer: A Molecularly Supervised Cell Foundation Model for Histopathology Cell Classification
链接: https://arxiv.org/abs/2608.16718
作者: Jialu Yao,Songhao Li,Alina Yu,Zhi Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 5 figures, 2 extended data figures
Abstract:Identifying cell types directly from routine haematoxylin and eosin (HE) histology would enable single-cell analysis at scale, but training such models has relied on manual pathologist annotations, which are slow, expensive and unreliable for many cell types. We instead supervise morphology with molecules. Imaging-based spatial transcriptomics profiles individual cells in situ on a section that can afterwards be stained with HE, so that molecular identity and morphology are observed for the same physical cell. We assembled 81 such paired Xenium sections spanning 16 organs, derived per-cell labels by clustering, marker-gene annotation, organ-wise human review and quality control, and mapped them onto the cell types commonly reported in each organ. This yielded 15.4 million cells, each with a paired HE image patch and one of 23 cell types, on which we trained CytoFormer, a cell foundation model with a multi-task, per-organ classification head. On spatially held-out tissue CytoFormer reached an accuracy of 0.85 and a macro-F1 of 0.78 across all 16 organs, and its predictions reproduced the tissue architecture of an entire held-out section. The representation also transfers: with the encoder frozen, a linear head on CytoFormer features performed better than six pathology foundation models on four expert-annotated benchmarks, including on organs and cell types that were not part of pretraining. Finally, in an interactive active-learning setting, CytoFormer’s embeddings are markedly more label-efficient than existing pathology foundation models, detecting normal epithelium amid look-alike tumour with an F1 of 0.82 from only a few annotations and leading the strongest baseline by 0.13 in F1. CytoFormer turns paired HE and spatial transcriptomics into a reusable, label-efficient representation for cell-level analysis of routine histology.
[CV-19] PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation
链接: https://arxiv.org/abs/2608.16717
作者: Yuji Wang,Yuheng Chen,Teng Hu,Ran Yi,Yijia Hong,Han Feng,Weijian Cao,Chengjie Wang,Lizhuang Ma,Jiangning Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generation is rapidly evolving from single-shot clips to multi-shot narratives, where the human character serves as the core narrative anchor. However, existing benchmarks mainly assess character appearance or individual-shot quality, without measuring whether physical and emotional states remain coherent across cuts. They also rarely provide criterion-specific evaluation methods, although physical continuity, facial dynamics, and cinematic relations require different visual, temporal, and relational evidence. To address these limitations, we introduce PersonaShot, the first person-centric benchmark for narrative continuity in multi-shot video generation. PersonaShot contains approximately 1,000 multi-shot segments and 16 metrics spanning physical continuity, affective dynamics, and cinematic grammar. \textbf\textit1) Narrative Continuity Benchmark: We evaluate character coherence across three temporal levels: within-shot states, cross-shot transitions, and sequence-level trajectories. \textbf\textit2) Human-Aligned Specialist Evaluators: We distill reasoning from a large multimodal teacher into lightweight criterion-specific evaluators, each grounded in the visual, temporal, or relational evidence required by its metric, and align them with expert human judgments. \textbf\textit3) Systematic Evaluation and Insights: Our evaluation reveals distinct capability profiles across state-of-the-art models and a clear gap between perceptual quality and cross-shot narrative continuity. Even visually compelling videos frequently exhibit physical-state resets, abrupt affective shifts, and broken cinematic relations across shots. Human studies further demonstrate strong agreement between our evaluators and expert judgments.
[CV-20] MIRROR: Multimodal Intelligent Radiology Reasoning and Observation Reporter
链接: https://arxiv.org/abs/2608.16709
作者: Vignesh Nagarajan,Sriram Venkatapathy
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 5 figures, 5 tables, 24 references
Abstract:A radiologist reading a model’s output faces two problems. The model returns a number and no reason, and any system that turns that number into readable prose can quietly add claims the model never made. MIRROR is a research prototype built to separate those failures. It chains a multi-label classifier, a Grad-CAM localizer that turns each positive finding into a named anatomical region, and a report writer that receives the labels, probabilities, and regions but never the image. Because the language layer cannot see pixels, it cannot assert a finding the classifier did not make. We are precise about what that buys: a MIRROR report’s findings are auditable against the probability vector, while the sentences framing them are ordinary generated text, and we show one stating a cardiothoracic ratio the system never measured. One registry holds the taxonomy, anatomy, and phrasing for chest X-ray, brain MRI, and head CT, so adding a modality is a data change; all three are routed and tested, one is trained. On ChestMNIST that classifier reaches macro AUROC 0.729 and ranks better than chance on all 14 labels, at 1.6 to 6.8 times the precision a random ranker would get. Yet at the default 0.5 threshold it emits no positive prediction at all for 11 of them, and its excellent-looking Brier score of 0.045 sits beside the 0.047 earned by a predictor that ignores the image. The discrimination is real; the decisions are not. Under the class imbalance normal in radiology, aggregate metrics flatter models that do nothing, and should be reported against that floor.
[CV-21] AnchorScore: A CLIP-Based Diagnostic of MLLM Annotation Difficulty
链接: https://arxiv.org/abs/2608.16690
作者: Yan Ma,Lizhuo Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages, 7 figures, 12 tables
Abstract:Multimodal large language models (MLLMs) are widely used for automated annotation, yet their per-class accuracy varies widely (e.g., 12%-98% across the 13 classes of three classroom sub-datasets) and is expensive to measure: evaluating one 27B MLLM on 5,416 validation images takes roughly 14 hours, whereas a frozen-CLIP pass over the same images completes in about 3 minutes. A low-cost signal for ranking classes by expected MLLM annotation difficulty a priori remains underexplored. Building on the AnchorProxy construct (per-class zero-shot CLIP accuracy) introduced in the companion study, this paper systematically evaluates its full-frame formulation, termed AnchorScore here, as an a priori diagnostic that flags the classes MLLMs are least likely to annotate reliably. On classroom behavior data (SCB5, 13 classes, 6 MLLMs), AnchorScore correlates with per-class MLLM accuracy (Spearman rho = 0.769, p = 0.002, n = 13). None of the alternative difficulty predictors (DINOv2, ResNet-50, SigLIP, or MLLM self-verbalized uncertainty) showed a significant class-level correlation at n = 13. A cross-model consensus control suggests AnchorScore primarily captures a shared class-difficulty factor rather than a CLIP-specific signal. An independent replication on Stanford40 Actions yields a nearly identical effect (rho = 0.817, p 0.001); the association is strongest on activity-recognition data and attenuates on medical and satellite imagery. Three practical applications follow: a deployable hybrid CLIP/MLLM routing strategy (predicted-class routing: up to +23 pp over CLIP-only at roughly 44% MLLM cost savings), prompt disambiguation on hard classes (exploratory), and review-priority prediction for human verification. AnchorScore does not estimate exact MLLM accuracy; it provides a low-cost ranking signal that directs expensive MLLM evaluation to the classes where it is most informative. Comments: 37 pages, 7 figures, 12 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) MSC classes: 68T45 Cite as: arXiv:2608.16690 [cs.CV] (or arXiv:2608.16690v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.16690 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-22] Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
链接: https://arxiv.org/abs/2608.16681
作者: Shanwen Wang,Xin Sun,Danfeng Hong,Junyu Dong,Patrick Le Callet
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Although semi-supervised semantic segmentation ( \textS^4 ) utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data causes the former to dominate, which severely degrades pseudo-label quality. To address this challenges, we propose a novel remote sensing (RS) \textS^4 method via unified flow with feature memory bank (UFFM). Specifically, UFFM comprises two key innovations: unified flow (UF) and feature memory bank (FMB). The UF is a new training flow that generates less biased pseudo-labels by combining an external visual foundation model (VFM) with an RS domain teacher, and jointly optimizes labeled and pseudo-labeled data under a unified training objective. The FMB is a novel memory module for \textS^4 that dynamically updates class-specific features during training and reduces the feature discrepancy between labeled and unlabeled data through class-feature alignment. To verify the effectiveness of our model, we conduct extensive experiments on RS datasets. The experimental results show the superiority of our method over SOTA \textS^4 methods. Moreover, the results demonstrate the effectiveness of our contributions in bridging the optimization and feature representation gap between labeled and unlabeled data. Our code is released at \hrefthis https URLthis https URL.
[CV-23] How Sampling Strategy Affects Imbalance Mitigation in LiDAR Segmentation: A Study of Structured vs. Random Point-Based Architectures ICIP
链接: https://arxiv.org/abs/2608.16673
作者: Antonis Savva,Christos Kyrkou,Theocharis Theocharides
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 6 figures, IEEE International Conference on Image Processing (ICIP) 2026
Abstract:Class imbalance in LiDAR point clouds poses challenges for semantic segmentation in autonomous navigation and urban mapping. While 2D vision has numerous mitigation techniques, their effectiveness in 3D remains unclear. We benchmark six reweighting schemes and five imbalance-aware losses across three datasets (DALES, S3DIS, STPLS3D) using two architectures (KPConv, RandLA-Net). Inverse-frequency weighting degrades performance by up to 12% compared to uniform weighting, with catastrophic failures in minority classes. Uniform weighting performs within 2% of complex losses for structured sampling (KPConv) but benefits less for random sampling (RandLA-Net, up to 4.6% gap). Loss landscape analysis reveals a complex interplay: for structured sampling, imbalance ratio determines landscape geometry on real LiDAR data but decouples from it on synthetic data; for random sampling, landscapes show high sensitivity to dataset geometry regardless of imbalance ratio. For the two evaluated point-based architectures, these results suggest that the interaction between sampling strategy (structured vs. random), imbalance severity, and data acquisition characteristics shapes which mitigation approaches are effective.
[CV-24] Concept-based explanation of gene expression prediction from HE images
链接: https://arxiv.org/abs/2608.16669
作者: Amos Muench,Jonathan Thielmann,Reduan Achtibat,Maximilian Dreyer,Philip Bischoff,Caroline Forsythe,Hamidreza Parand,Thomas Walter,David Horst,Sebastian Lapuschkin,Wojciech Samek,Teresa Gabriela Krieger
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in pathology foundation models have enabled accurate prediction of spatial transcriptomics (ST) from routine HE images. However, existing explainability methods for vision transformer (ViT)-based models are largely limited to local heatmaps and do not reveal how morphological concepts contribute to ST predictions. Here, we introduce an explainable framework that combines relevance propagation and concept discovery to link transcriptional programs to tissue morphology. We developed a ViT-based framework for virtual ST from HE images that combines ViT-aware layer-wise relevance propagation with relaxed archetypal TopK sparse autoencoder-based concept discovery. This approach provides both local explanations and global insights into the morphological patterns associated with transcriptional programs. We applied the framework to colorectal cancer ST data from the HEST-1k cohort and evaluated its generalizability in TCGA COAD. Our architecture accurately predicts clinically relevant ST signatures and accompanying molecular phenotypes. Measured and predicted gene expression profiles reveal substantial spatial heterogeneity of the colorectal cancer subtypes iCMS2 and iCMS3 across a large number of samples. Spatially resolved and aggregated iCMS classification achieve weighted F1 scores of 0.872 and 0.819 (0.770 in TCGA COAD), respectively, and both stratify patient outcome. Beyond prediction, our framework establishes a relevance-based concept atlas linking molecular phenotypes to histopathological representations. Comparison of activation- with relevance-derived concepts demonstrates that relevances provide a more direct link between tissue morphology and downstream predictions. We establish a general strategy for concept-based explanation of spatial prediction, and our framework is readily applicable to a broad range of ViT-based pathology models.
[CV-25] urning spectra into images improves plant trait retrieval with 2D-CNNs
链接: https://arxiv.org/abs/2608.16661
作者: Javier Lopatin,Teja Kattenborn,Eya Cherif,Sebastián Moreno
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 38 pages, 14 figures, 11 tables. Supplementary material appended after the references. v2: adds a per-image vs global scaling ablation, expands the methods, and corrects several figures and captions
Abstract:Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies. We asked whether transforming 1D spectra into 2D image representations improves multi-trait prediction with convolutional neural networks (CNN). We compared nine transformations using EfficientNet-B0 on the GreenHyperSpectra dataset (7,897 labeled spectra, eight traits, 400-2450 nm), benchmarked against published 1D CNN results on the same split. Trained from scratch, the simplest transformation, a direct Reshape of the spectrum into a 2D grid, performed best ( R^2 = 0.684 \pm 0.001 ) and improved on the state-of-the-art 1D baseline ( R^2 = 0.587 , +0.097 ). We then pretrained a 2D masked autoencoder (MAE-2D) on 139,000 unlabeled spectral images. Linear probing, which freezes the encoder and trains only a multilayer perceptron head, reached R^2 = 0.646 and exceeded every 1D self-supervised counterpart, including the fine-tuned MAE-1D ( R^2 = 0.641 ). Under cross-dataset evaluation all models lost most of their accuracy and none beat the 1D baseline significantly. To identify which wavelengths drive each prediction, we applied Integrated Gradients and Grad-CAM and unfolded band importance back to the spectral axis. Protein ( r = 0.45 ) and leaf water ( r = 0.33 ) agreed with sensitivities simulated by the PROSAIL radiative-transfer model, while carotenoids ( r = 0.06 ) and leaf area index ( r = -0.11 ) did not, showing that the model reads established leaf chemistry for traits with sharp absorption features. The representational advantage of 2D spectral images, rather than architectural complexity or ImageNet pretraining, drives the gain over 1D approaches.
[CV-26] X2Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization BMVC2026
链接: https://arxiv.org/abs/2608.16658
作者: Zichao Zeng,Weijia Fan,Yufan Chen,June Moh Goo,Junwei Zheng,Ruiping Liu,Kunyu Peng,Jiaming Zhang,Rainer Stiefelhagen,Jan Boehm
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted to The 37th British Machine Vision Conference (BMVC 2026)
Abstract:Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images. However, CVG approaches rely on fixed-length inputs and post-hoc refinement, hindering online-oriented localization under partial or dynamic observations. In this work, we formulate Progressive Cross-view Video Geo-localization (PCVG) as a deployment-oriented extension and evaluation protocol of CVG, enabling localization under varying temporal budgets, prefix-based inference, random-start evaluation, and long-range localization with interruptions. To explore PCVG, we introduce X ^2 Localizer, a cross-grained alignment framework that jointly supervises global prefix-to-aerial retrieval and token-aggregated frame–aerial-tile matching with a budget-dependent asymmetric objective. Furthermore, we introduce a Sliding-Window Re-Localization (SWRL) strategy that dynamically refreshes candidate regions for failure recovery and long-range deployment without full-sequence reprocessing. Extensive experiments show that X ^2 Localizer preserves conventional full-video performance, with marginal gains of +0.1 Recall@1 and +0.3 Recall@10, while substantially improving early localization. In the challenging single-frame setting, X ^2 Localizer improves coarse retrieval by +4.7 Recall@1 and +11.5 Recall@10 over the previous state-of-the-art method. With SWRL, our approach further enables robust progressive localization under random-start and long-distance scenarios, narrowing the gap between benchmark evaluation and real-world deployment.
[CV-27] raining-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples ECCV2026
链接: https://arxiv.org/abs/2608.16646
作者: Roman Demchenko,Jonas Ricker,Asja Fischer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at AI4MFDD (AI for Multimedia Forensics Disinformation Detection) Workshop, ECCV 2026
Abstract:The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images. However, due to their fundamentally different mode of operation (compared to standard, classifier-based methods), little is known about their adversarial robustness. In this work, we propose two novel attack methods targeted at detectors that leverage autoencoder reconstruction error. We find that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real. Our evaluation including images from three state-of-the-art generators and three detectors demonstrates that detection performance is significantly decreased, even if attacked images additionally undergo real-world degradations. Critically, our adversarial examples naturally transfer across detectors, as they all share the same principle, pointing towards an inherent vulnerability of reconstruction-based detectors.
[CV-28] DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection
链接: https://arxiv.org/abs/2608.16632
作者: Divine Yao Agbobli,Geoffery Eyram Agorku,Israel Afriyie,Kwadwo Amankwah-Nkyi,Marvin Osei-Kuffour,Richmond Owusu Duah,Bright Seglah,Kelvin Asamoah Terkper,Kwabena Amoako Adjei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 2 figures, 6 tables. Code available at: this https URL
Abstract:Deep learning-based object detectors are fundamental to intelligent transportation systems, enabling traffic monitoring, vehicle analytics, and infrastructure management. However, achieving both fine-grained vehicle recognition and robust cross-city domain generalization remains challenging. We present the Domain-Robust Asymmetric Fusion Ensemble (DRAFE), which combines independently trained LW-DETR and RF-DETR detectors for cross-city fine-grained traffic object detection. DRAFE employs a two-stage training strategy that first pretrains complementary detectors on diverse public traffic datasets using pseudo-label expansion and human-in-the-loop annotation refinement, producing a curated corpus of 6,049 images and 203,619 annotations, before challenge-compliant fine-tuning on the Project Hafnia Track 6 dataset. At inference, DRAFE applies anchor-conditioned class-consistent matching, reliability-weighted coordinate fusion, agreement-aware confidence recalibration, and complementary hypothesis recovery. On AI City Challenge 2026 Track 6, DRAFE achieves 0.4022 mAP, ranks sixth among 25 participating teams, and improves by 0.0553 mAP over a preliminary ensemble evaluated under identical benchmark conditions.
[CV-29] HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes
链接: https://arxiv.org/abs/2608.16622
作者: Yujia Li,Yiqun Zhang,Zihan Cheng,Yijie Huang,Tenglong Ye,Zihan Wang,Xiaocui Yang,Shi Feng,Yifei Zhang,Daling Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal harmful meme detection is typically formulated as image–text harmfulness classification. A model may correctly predict harmfulness while misidentifying the attacked target or its supporting evidence. We therefore extend harmful meme detection with fine-grained target identification, asking what type of target is attacked, who is targeted, and where the target appears in the meme. The model predicts harmfulness for every meme and, for harmful memes, outputs the target category, target entity, textual mention, and visual region. To support this task, we introduce Meme3W, which unifies multiple public harmful meme datasets and provides human-verified annotations for harmful instances. We further introduce Joint Record Accuracy (JRA), a strict record-level metric requiring the harmfulness label and all target-identification fields to be jointly correct. Experiments with representative multimodal large language models reveal a substantial gap between harmfulness accuracy and JRA. To narrow this gap, we propose HarmTrace, an anchor-calibrated decoupled optimization framework. HarmTrace strengthens target-entity supervision through entity-aware supervised fine-tuning. It then applies Conditional Target-identification Policy Optimization (CTPO) to decouple harmfulness and target-identification advantages, restricting target-identification optimization to label-correct responses for harmful examples. CTPO uses a Virtual Positive Anchor (VPA) as a fully correct reference for target-identification advantage normalization. HarmTrace improves both JRA and harmfulness accuracy across the evaluated backbones, with JRA on the Qwen3-VL-8B backbone increasing from 17.58% to 52.51%. Our code is publicly available at this https URL.
[CV-30] Beyond Accuracy: Assessing Calibration of Geospatial Foundation Models and Their Sensitivity to Distribution Shifts
链接: https://arxiv.org/abs/2608.16614
作者: Nils Lehmann,Jakob Gawlikowski,Burak Ekim,Isaac Corley,Xiao Xiang Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Geospatial Foundation Models (GeoFMs) are most commonly ranked and selected by accuracy on standard benchmark conditions via averaged ranks. We show that this protocol is too narrow: the promised deployment in critical EO tasks requires further angles of analysis, mainly calibration, the agreement between a model’s confidence and its correctness. Across 16 frozen encoders, four classification and five segmentation datasets, and two orthogonal stress axes, every encoder degrades as corruption intensifies, and the ranking changes as well. Across the four classification benchmarks, EO-pretrained and ImageNet-pretrained encoders are indistinguishable on clean accuracy and clean calibration, and EO pretraining provides no more stability under shift than ImageNet pretraining. Under shift the GeoFMs drift further into overconfidence than the ImageNet-pretrained encoders, at every grade and in every corruption family. A centered kernel alignment (CKA) analysis ties this to representational rigidity: EO-pretrained embeddings move less under corruption while losing just as much task information and remaining overconfident. We apply three commonly explored uncertainty quantification methods and find that temperature scaling and deep ensembles cannot counteract the degradation, while a Gaussian-process probe roughly halves ECE under severe cloud only by tripling it on clean data. In selective prediction experiments, we find that confidence-based abstention cannot defer around confidently wrong predictions, and advocate that benchmark rankings and evaluations should therefore operate across a multitude of conditions and metrics to more holistically evaluate model development progress and close the gap to real world deployment scenarios.
[CV-31] Interactive Whole Slide Images for RL-based Tumour Segmentation
链接: https://arxiv.org/abs/2608.16607
作者: Mohamad Mohamad,Francesco Ponzio,Maxime Gassier,Nicolas Pote,Xavier Descombes
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Whole-slide image (WSI) analysis remains computationally challenging due to the extremely large spatial resolution of slides and the sparse distribution of tumour regions. We propose an end-to-end reinforcement learning framework for sequential tumour segmentation directly on WSIs. Instead of treating the slide as a predefined collection of candidate patches, we formulate the WSI itself as a hierarchical multi-resolution environment through which an agent navigates using movement, zooming, and tumour selection actions. The agent jointly processes local observations and a global thumbnail representation within an actor-critic architecture trained using proximal policy optimization (PPO). Experiments on pulmonary adenocarcinoma WSIs demonstrate the feasibility of direct sequential tumour segmentation on full slides, achieving comparable coarse segmentation quality relative to patch-based approaches operating at similar magnification levels, while reducing inference time to a few seconds per slide. We further analyse the impact of environment design and action-space granularity. Our results suggest that modelling WSIs as interactive environments provides a promising direction for RL-based computational pathology
[CV-32] GeoPose: Patient-agnostic CTA-to-DSA registration through projection-space calibration
链接: https://arxiv.org/abs/2608.16600
作者: Rudolf L. M. van Herten,Robert Graf,Paula Feldman,Johannes C. Paetzold
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Aligning intraoperative biplanar digital subtraction angiography (DSA) to pre-procedural computed tomography angiography (CTA) requires rapid and accurate 3D-to-2D registration. Optimization-based methods are sensitive to initialization and may require hundreds of iterations, whereas learning-based approaches commonly rely on patient-specific training. We propose GeoPose, a population-trained framework that estimates the C-arm pose in a learned canonical frame and transfers it to the native frame of an unseen CTA through projection-space calibration and transform composition. A population-trained residual network refines the pose, followed optionally by low-budget image-driven optimization. GeoPose requires neither patient-specific adaptation nor explicit inter-volume preregistration. On 80 DSA observations from 20 held-out patients, optimization-free GeoPose achieved a carotid mean projected centerline distance (mPCD) of 5.8 mm and a clDice of 0.45, compared with 14.5 mm and 0.28 for the best-performing baseline, while requiring only 0.15 s. After 25 optimization iterations, GeoPose reached an mPCD of 4.6 mm and a clDice of 0.58 in approximately two seconds. Under the same budget, native-initialized optimization achieved 14.6 mm and 0.15, respectively. GeoPose thus provides rapid native-frame registration with fixed population-level weights and the geometric correspondence required for downstream biplanar 3D vascular reconstruction.
[CV-33] owards Zero-Shot Domain Generalization for ID Cards Presentation Attack Detection ICDAR2026
链接: https://arxiv.org/abs/2608.16591
作者: Mario Nieto-Hidalgo,Juan M. Espin,Juan E. Tapia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint accepted DAS 2026 at ICDAR 2026
Abstract:Presentation-Attack Detection (PAD) for national ID cards is limited by the lack of publicly available genuine samples, making it difficult for systems to generalize across countries. This paper introduces two main innovations: (1) a Prototypical Network head using an EfficientNet-V2-b0 backbone that requires only four genuine samples per class to create reliable prototypes; and (2) an episodic training regime that keeps PAD classes fixed while varying the card domain, allowing the network to learn universal attack cues. Evaluated on a large multi-country dataset and the public DLC-2021 benchmark, this method achieves an average Equal Error Rate of around 9%, outperforming conventional softmax and CLIP zero-shot baselines even with data from a single source country. This approach provides accurate, privacy-preserving PAD while minimizing data collection, facilitating scalable cross-jurisdictional remote onboarding. Comments: Preprint accepted DAS 2026 at ICDAR 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.16591 [cs.CV] (or arXiv:2608.16591v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.16591 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-34] Ultra: Unsupervised Cross-Task Optimization for Reliable Restoration Segmentation Collaboration under Adverse Weather
链接: https://arxiv.org/abs/2608.16589
作者: Shiqin Wang,Zhiqian Li,Haoyuan Du,Junming Chen,Jiayuan Li,Tianrun Xu,Haoyang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unsupervised Domain Adaptation for Adverse Weather Semantic Segmentation (UDA-ASS) aims to transfer semantic knowledge from labeled normal-weather images to unlabeled adverse environments. Existing approaches implicitly assume that restoration and segmentation provide mutually beneficial guidance. However, under severe degradation and without target-domain supervision, the validity of cross-task optimization directions becomes fundamentally unidentifiable, leading to hallucination-driven error propagation. In this work, we propose a novel Unsupervised Restoration-Segmentation Collaborative Learning Framework (Ultra), which reframes cross-task interaction as direction selection under uncertainty and causal effect estimation, enabling reliable collaboration through candidate direction generation and intervention-based filtering. In detail, we propose CTDN and CMIL. The former exploits complementary visual structures and semantic information to generate candidate optimization directions and performs cooperative direction selection between restoration and segmentation. The latter reformulates cross-task information transfer from correlation-based propagation into causal effect assessment, suppressing hallucination propagation. Extensive experiments on three widely used UDA-ASS benchmarks demonstrate state-of-the-art segmentation performance. Beyond segmentation, our framework achieves better unsupervised restoration results than existing UDA-ASS restoration methods and generalizes to unsupervised restoration and object detection collaboration tasks. Code and models will be available at this https URL.
[CV-35] SQuad: Sub-Quadratic Attention Distillation for Efficient Video Generation
链接: https://arxiv.org/abs/2608.16585
作者: Animesh Karnewar,Denis Korzhenkov,Amirhossein Habibian,Mohsen Ghafoorian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video Diffusion Transformers (DiTs) spend most of their compute inside the Self-Attention operation, whose cost grows quadratically, \mathcalO(n^2) , with the number of latent tokens n . For the task of video generation, the token count is large, so this term dominates runtime and memory, and thereby caps the resolution and duration we can generate. Linear \mathcalO(n) and low-rank \mathcalO(nk) surrogates of Self-Attention trade the full softmax QK^T for cheaper kernels, but rarely recover the original’s expressivity, leaving a stubborn quality gap. Motivated by this, we propose SQuad, a Sub-Quadratic Attention Distillation framework that achieves a complexity of \mathcalO(n\sqrtn) in the resulting distilled Attention, naturally balancing the efficiency v/s expressivity trade-off. Instead of training our own Video DiT from scratch, which is prohibitively expensive, we fit a pretrained full softmax Self-Attention DiT into our proposed SQuad-Attention one by distilling the former in two stages: Flow-Matching Supervised Fine-Tuning (SFT), followed by improved Distribution Matching Distillation (DMD2) which additionally makes the sampling more efficient. On the Wan~2.2 5B text-to-video model, SQuAD matches the quadratic teacher on VBench ( 83.20 v/s 83.08 ) while cutting the per-step per-block attention FLOPs by \sim 67\times and attention latency by \sim 11\times , and end-to-end DiT latency by 2 \times , all while also generating a video in only 6 Neural Functional Evaluations (NFEs) instead of the default 100 .
[CV-36] Supervising the Path to Fine Scales: GalerkinFlow for Scientific-Field and Image Super-Resolution
链接: https://arxiv.org/abs/2608.16546
作者: Zikang Zhan
类目: Computer Vision and Pattern Recognition (cs.CV); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
备注: 8 pages, 2 figures
Abstract:Most super-resolution models learn from paired data by supervising only the final high-resolution output. This provides little control over how the prediction should evolve between the downsampled observation and its fine target. We introduce GalerkinFlow, an equation-agnostic framework that turns each coarse–fine pair into supervision along an entire reconstruction path. At a random sample of intermediate states on the reconstruction path, the model predicts the coarse-to-fine residual velocity and uses coarse-anchor point to define a pseudo-endpoint. We show that the reconstruction loss of this pseudo-endpoint is exactly related to the intermediate velocity loss through a known time-dependent weight. Consequently, every intermediate state contributes supervision toward the same fine target, rather than serving only as an internal step toward an endpoint loss. Because intermediate states already reveal part of the missing fine-scale structure, we additionally supervise the coarse endpoint used during one-step inference. A finite-difference objective further constrains local spatial variation. GalerkinFlow combines convolutional features with scale-conditioned Galerkin operator mixing and requires no governing equation or physical metadata. It achieves the lowest raw-space errors among the evaluated equation-agnostic baselines on Navier–Stokes and Darcy Flow, while remaining competitive on DIV2K.
[CV-37] Automatic Cephalometric Landmark Localization on CBCT-Derived Digitally Reconstructed Radiographs for Skeletal Malocclusion Classification MICCAI2026
链接: https://arxiv.org/abs/2608.16535
作者: Benjamin Hou,Konstantinia Almpani,Janice S. Lee,Zhiyong Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation at the ODIN 2026 Workshop, held in conjunction with MICCAI 2026
Abstract:Manual cephalometric landmark annotation is important for craniofacial assessment but is labor-intensive and difficult to scale. We introduce CephViT, a Vision Transformer-based model for automated 2D lateral cephalometric landmark localization, and evaluate its use in downstream skeletal malocclusion classification. CephViT was trained and benchmarked on a public lateral cephalogram dataset, achieving a mean radial error of 1.28 +/- 1.42 mm and a successful detection rate of 92.0% at 3.0 mm. Because the private evaluation cohort consisted of 3D CBCT scans, lateral cephalogram-like digitally reconstructed radiographs (DRRs) were generated from each volume and used as 2D inputs to the landmark localization model. Landmark coordinates were normalized into a common coordinate frame, and skeletal malocclusion classification was performed using landmarks shared between the reference and DRR-based pipelines. Classification performance using DRR-localized landmarks was comparable to that obtained using manually annotated reference landmarks, with accuracies of 70.0% and 68.3%, respectively. These results support the feasibility of automated cephalometric analysis on CBCT-derived DRRs for skeletal malocclusion assessment.
[CV-38] FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning
链接: https://arxiv.org/abs/2608.16523
作者: Tristan Gottwald,Maximilian Schier,Melanie Schaller,Bodo Rosenhahn
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event cameras generate asynchronous, high-frequency data streams offering spatially sparse information at lower latency than traditional this http URL principle, these properties should be ideal for the design of control this http URL, reinforcement learning research in this field remains limited as existing approaches fail to fully exploit the sensor’s this http URL-based methods negate the sensors benefits by aggregating events into sparse grids. This couples compute cost to sensor resolution and blurs the temporal information. Meanwhile, existing generative baselines rely on the availability of trajectory data to pretrain the model. We propose FLEET (Feature Learning from Events via Efficient Tokenization), a feature extractor that processes event sequences directly. Leveraging random Fourier features and cross-attention, our architecture compresses variable streams into fixed-size latent representations. This decouples inference cost of the feature extractor’s backbone from the sensor’s resolution, enabling end-to-end learning without auxiliary losses. We validate FLEET on a new, high-throughput benchmark. The results demonstrate that our sequence-based approach surpasses SOTA performance and exhibits superior robustness to variations in observation frequencies.
[CV-39] MLLM -Guided Semantic Correction for Text-to-Video Generation
链接: https://arxiv.org/abs/2608.16513
作者: Junhao Chen,Zheqi Lv,Keting Yin,Shengyu Zhang,Zhou Zhao,Feiyang Chen,Xinyu Duan,Baoxing Huai,Fei Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in diffusion models and Transformer architectures have led to significant progress in text-to-video generation. However, these models often suffer from semantic errors such as missing objects, incorrect attributes, or mismatched actions. Although some semantic correction methods perform optimization before sampling or refinement after sampling, how to detect and correct semantic deviations during the video generation process remains underexplored. In this paper, we introduce a training-free, interpretable mid-generation correction framework that integrates multimodal large language model (MLLM) feedback directly into the diffusion sampling loop. Our framework achieves diffusion trajectory correction by injecting semantic evaluation signals during video synthesis, enabling the model to optimize the generated content through continuous self-reflection. We propose two key modules: a Semantic Assessment Supervisor that generates intermediate preview frames for semantic evaluations and deviation diagnostics, and a Semantic Modification Assistant that corrects semantic drift during inference via a controllable latent trajectory intervention. Our method improves semantic alignment, visual fidelity, and temporal consistency without modifying model parameters. We validate the effectiveness of our approach through extensive experiments across multiple benchmarks.
[CV-40] OccamView: Object-Conditioned View Selection for Frame-Budgeted Active 3D Gaussian Reconstruction
链接: https://arxiv.org/abs/2608.16499
作者: Hongbo Gao,Wei Zhang,Zeyu Ni,Dihao Zhu,Ruifeng Li,Yunke Wang,Chang Xu
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 5 figures. Preprint
Abstract:Active 3D Gaussian reconstruction fundamentally relies on selecting informative next-best views under limited sensing budgets. Existing active 3DGS methods primarily plan viewpoints according to geometric information gain, treating object-induced hidden regions in the same manner as general unexplored space. Under tight frame budgets, such geometry-driven strategies may prioritize global scene coverage while leaving partially observed objects incompletely reconstructed. To address this limitation, we propose OccamView, an object-conditioned view-selection framework for frame-budgeted active 3D Gaussian reconstruction. Rather than predicting unseen object geometry or performing shape completion, OccamView maintains an online object memory from open-vocabulary detections grounded in measured RGB-D observations and represents unresolved local occupancy around detected objects as conservative hidden-region proxies. Candidate viewpoints are then evaluated using an occlusion-aware proxy-coverage score. Furthermore, we introduce a Geo-Floor mechanism that restricts object-conditioned re-ranking to geometrically competitive candidates, allowing object-conditioned cues to guide complementary observations while preserving the geometry-driven exploration behavior of the underlying planner. Experiments on Replica and Matterport3D under a unified frame-budgeted protocol show that OccamView consistently reduces Completion and improves Completion Ratio across five frame budgets, with particularly pronounced gains under limited frame budgets. These results demonstrate that lightweight object-conditioned cues effectively complement geometry-driven active view planning.
[CV-41] owards Real-Time and Adaptable LiDAR Scene Completion ECCV
链接: https://arxiv.org/abs/2608.16490
作者: Azhar Hussian,Martin Vossiek,Vasileios Belagiannis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCVW 2026, 14 pages, 4 figures. Code is available at this https URL
Abstract:LiDAR scene completion is a key component of 3D perception in autonomous driving, where the scene must be completed in real time to be usable in downstream tasks. Existing approaches typically follow an initialize-and-refine paradigm, in which a coarse initialization of the scene is first constructed, then refined into complete 3D geometry. Generative models are slower because they iteratively refine random Gaussian noise into the scene, while non-generative methods perturb the partial scene with a fixed noise scale, which limits coverage of large gaps and occluded regions and requires manual recalibration for each new sensor configuration. We present RapidLiDAR, a LiDAR scene completion method that treats the initialization itself as a learned, data-driven component. We propose an adaptive initialization module that predicts a spatially varying displacement for each partial input point, expanding the partial observations into a coarse scene initialization adapted to the local geometry, without requiring manual noise tuning. To refine this coarse initialization into a complete and coherent scene, we additionally propose a multi-scale reconstruction module that further refines point positions by querying multi-scale 3D voxel and 2D BEV feature maps constructed from the input scan. By replacing point-neighborhood operators such as farthest point sampling and k -nearest neighbor search with voxel- and BEV-based feature extraction, our architecture is faster and can handle different input resolutions by design. Experiments on SemanticKITTI and KITTI-360 show that our method achieves completion performance on par with the state of the art while completing a full scene in 0.1 seconds, which is 2.3 times faster than the fastest prior method. This matches the 10 Hz acquisition rate of typical automotive LiDAR sensors, taking a step toward real-time LiDAR scene completion.
[CV-42] HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation CVPR2026
链接: https://arxiv.org/abs/2608.16485
作者: Junhao Hou,Chenqi Luo,Pufan Wang,Jiaying Lu,Yusheng Liu,Feiwei Qin,Meie Fang,Kun Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2026
Abstract:Boundary representation (B-Rep) generation is a fundamental task in computer-aided design, yet the direct synthesis of high-fidelity and structurally valid B-Reps remains a major challenge. Existing deep generative methods suffer from two forms of brittleness: representation brittleness, caused by padding noise and feature contamination in the latent space, and generation brittleness, stemming from sequential error propagation and a train-inference mismatch due to non-differentiable validity enforcement. We propose HiFi-BRep, a novel framework that addresses these limitations through two synergistic contributions. First, a topology-aware encoder constructs a high-fidelity latent representation by eliminating padding via learnable queries and preventing feature contamination with topology-guided attention. Second, a single-stage decoder jointly predicts geometry and topology in parallel, embedding core manifold constraints as a differentiable learning objective. This design ensures mutual guidance between geometry and topology while avoiding cascaded errors. Extensive experiments show that HiFi-BRep significantly outperforms state-of-the-art methods in both structural validity and geometric fidelity, providing a robust solution for high-quality B-Rep synthesis. Code and models are publicly available at this https URL.
[CV-43] Remote-Sensing City Layout Extraction with MLLM
链接: https://arxiv.org/abs/2608.16484
作者: Zigan Zhou,Kai Li,Yupeng Deng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 2 figures, 4 tables. Accepted to IEEE APGARSS 2026
Abstract:Remote-sensing systems usually describe urban content with detection boxes, semantic masks, or vector boundaries. Such outputs locate classes and support image-plane scoring, yet they do not by themselves constitute an executable layout that retains object identities, typed relations, topology, and regeneration rules. Code-as-City instead casts urban-layout extraction from a single top-down image as constrained code generation with a multimodal large language model (MLLM). An image model first produces an aligned five-class semantic layout prior. Three ordered MLLM passes use the image and this prior to recover roads, land-cover regions and relations, and buildings. Deterministic normalization converts the accumulated records into a city graph and a restricted layout program. Executing the program creates a renderable 3D city layout and an orthographic semantic projection over shared geometry. The projection admits pixel-level comparison with remote-sensing masks, while named objects, relations, and editing operations remain available for synchronized regeneration of both views. Evaluated on the 100 scenes of CityLayout-100, the complete framework obtains 41.1% mean intersection-over-union and 48.3% global intersection-over-union. This result provides quantitative evidence that visual observations can be translated into inspectable, editable city code with coupled planar and 3D outputs.
[CV-44] RISE: Roadside Infrastructure Sequence Understanding across 3D Tracking and Structured Vision-Language Reasoning
链接: https://arxiv.org/abs/2608.16480
作者: Yanbo Jiang,Haotian Zheng,Jiahao Wang,Hanxiao Ren,Yitao Xu,Yining Xing,Zehong Ke,Hao Cheng,Yiqian Tu,Jinhao Li,Zhiyuan Xuan,Fang Zhang,Jianqiang Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We present RISE (Roadside Infrastructure Sequence Understanding and Evaluation), a framework spanning metric 3D tracking and structured vision-language reasoning in roadside sequences. For metric tracking, our image-only method combines SAM3 video identities with calibration-guided mask agreement for multi-view identity association, recovering persistent 3D tracks without LiDAR or task-specific 3D training. Its calibration-conditioned geometry allows the procedure to be instantiated at different calibrated multi-camera intersections without layout-specific retraining. On 20 human-reviewed clips from six intersections, the generated tracks achieve 66.9 MOTA within the defined multi-view evaluation scope. For structured vision-language reasoning, a human-reviewed MLLM pipeline mines high-value clips and uses a constrained full-context Oracle to construct bbox-grounded predictive QA without exposing future evidence to evaluated models. The resulting RISE-VQA dataset contains 33,910 QA pairs from 557 clips across 16 intersections and 61 roadside views. Its intersection-held-out RISE-Bench evaluates semantic choices, coordinates, future boxes, and interaction sets with deterministic task-specific metrics. Experiments show consistent benefits from domain adaptation and generally from temporal context, while revealing persistent challenges in spatial grounding, future localization, and interaction reasoning.
[CV-45] Sterilizable Scene Graph Generation for Operating Rooms
链接: https://arxiv.org/abs/2608.16469
作者: Nick Lemke,Ssharvien Kumar Sivakumar,Antoine P. Sanner,John Kalkhof,Henry John Krumb,Ghazal Ghazaei,Anirban Mukhopadhyay
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Scene graph generation from surgical video enables a holistic and structured understanding of surgical scenes by modeling objects and their semantic relationships. Despite recent advances, state-of-the-art approaches rely on large, parameter-heavy deep learning models that are impractical for deployment in the operating room (OR) due to hardware footprint, hygiene constraints, latency, and data privacy concerns. To the best of our knowledge, this is the first scene graph generation method built on NCAs and the first NCA framework capable of learning structured representations. We introduce SG-NCA, a lightweight scene graph generation framework based on Neural Cellular Automata (NCA), designed for inference in fanless devices critical for OR hygiene protocols. SG-NCA is the first scene graph generation combining NCA-based multiclass segmentation for efficient object detection and feature extraction with a lightweight relation predictor. We evaluate SG-NCA on videos of cataract surgery and cholecystectomy, demonstrating performance comparable to established baselines while requiring 55x fewer parameters. We showcase deployment on fanless edge devices better suited for the OR and demonstrate downstream applications such as surgical video captioning, highlighting SG-NCA’s potential for affordable, privacy-preserving, and OR-ready intraoperative scene understanding.
[CV-46] Shared-Structure 4D Spectral Gaussian Representation for Sparse-View Spectral CT Reconstruction
链接: https://arxiv.org/abs/2608.16463
作者: Jiancheng Fang,Shaoyu Wang,Wenjun Xia,Yang Chen,Qiegen Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sparse-view spectral computed tomography (CT) reconstructs energy-resolved attenuation volumes from limited projection views, requiring simultaneous handling of angular undersampling and spectral coupling. We propose a SharedStructure 4D Spectral Gaussian Representation (4D-SG) that learns shared Gaussian geometry from full spectrum structural projections and uses a Gaussian-wise Spectral Density Curve Network (GSC-Net) to predict Gaussian raw density transformations. This factorization separates shared spatial structure from spectral attenuation variation, avoids independent channel geometry optimization, and establishes a continuous 4D-SG representation from discrete spectral measurements for unobserved spectral channel queries. Experiments on six synthesized, simulated projection, and real projection datasets with 50 views demonstrate the best average performance. Compared with the strongest Gaussian baseline, 4D-SG improves PSNR from 35.56 dB to 36.61 dB, increases SSIM from 0.909 to 0.914, and reduces LPIPS from 0.208 to 0.194, demonstrating its effectiveness for sparse-view spectral CT reconstruction.
[CV-47] Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos
链接: https://arxiv.org/abs/2608.16457
作者: Mohamed Afham,Christoph Reich,Oliver Hahn,Daniel Cremers,Stefan Roth
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: To appear at GCPR 2026 (oral paper). Project page: this https URL
Abstract:Procedure planning seeks to estimate a sequence of actions to transition from an observed initial state to a given goal state. Current procedure planning approaches directly predict action sequences from latent representations using feed-forward neural networks or diffusion-based inference. These paradigms treat every action as plausible, lacking the ability to enforce task-specific logical constraints that render certain actions irrelevant or not plausible. We propose CEFITO, a procedure planning approach that learns a predictor to express an action-conditioned representation space. Based on this representation space, we formulate procedure planning as a task-constrained optimization problem. Unlike prior methods, CEFITO explicitly reasons over the action space by omitting irrelevant actions during inference-time planning. This reformulation enables effective procedure planning and achieves state-of-the-art accuracy on two established procedure planning benchmarks.
[CV-48] Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing
链接: https://arxiv.org/abs/2608.16424
作者: Lea Bogensperger,Manuela Merlo,Martin Baumgartner,Michael Krauthammer,Bernard Ciraulo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative modeling has shown increasing promise for predicting cellular perturbation effects under chemical compound treatments. Existing approaches either model perturbation as a distribution-to-distribution mapping without explicit concentration handling, or treat concentration as a discrete class label, precluding continuous dose control. We introduce a joint flow matching approach that simultaneously models cell latents and drug concentration via a dual-timestep formulation, enabling dose-conditioned single-cell morphing through the invertibility of flow matching. The joint formulation induces a monotonic dose-response geometry in latent space and additionally supports concentration estimation from cell morphology. As proof of concept, we further demonstrate generalization to an unseen dose held out during training. Empirically, our method achieves competitive or improved per-concentration metrics on two compounds compared with representative baselines, while enabling capabilities structurally unavailable to discrete-class methods.
[CV-49] Self-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation ECCV
链接: https://arxiv.org/abs/2608.16384
作者: Suraj Yadav
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at ECCV Workshop 2026 (Archival Track)
Abstract:Universal visual representations require adaptation mechanisms that adapt across heterogeneous domains without fragmenting knowledge into domain-specific modules. Parameter-efficient fine-tuning adapts frozen visual foundation models efficiently, but standard low-rank adapters use a fixed subspace for all inputs, which can be restrictive when domains differ in style, background, and semantic context. MoE-based adapters improve specialization through multiple expert pathways, but often rely on external routers and large expert banks, adding parameters and separating routing from adaptation. We propose \textbfSelf-Routed Tensor Adapters, a compact framework for multi-domain visual adaptation. SRTA projects each input into a low-rank space, computes routing weights from this representation using a learnable domain matrix, and uses these weights to blend slices of a shared Tucker core. This produces a sample-specific adaptation matrix without an external gating network, allowing shared visual factors to be reused while supporting domain-aware specialization. To strengthen pathway learning, we introduce a progressive depth-weighted routing objective that supervises routing decisions across adapter layers. Across five heterogeneous multi-domain visual classification benchmarks, SRTA achieves competitive or slightly stronger average accuracy than MoE-style PEFT baselines while using substantially fewer trainable parameters. At rank 64, SRTA uses 2.77M parameters in the 4-domain setting compared with 9.52M for MoLoRA, and 3.00M in the 6-domain setting compared with 14.31M. Overall, SRTA offers an effective accuracy-parameter trade-off for adapting visual foundation models toward universal multi-domain representations. \hrefthis https URLGitHub
[CV-50] Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine
链接: https://arxiv.org/abs/2608.16380
作者: Marta Sumyk,Oleksandr Kosovan,Iryna Voitsitska
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery. However, the development of computer-vision systems for satellite-based damage analysis is limited by the scarcity of labeled imagery, especially for damaged agricultural fields. This work investigates synthetic data augmentation as a method for improving classification under limited and imbalanced training data. We train class-conditional Generative Adversarial Network (GAN) and Denoising Diffusion Probabilistic Model (DDPM) architectures on real satellite images and use them to generate additional bombed and not-bombed agricultural-field samples. The generated images are used only for training augmentation, while all downstream evaluation is performed on an exclusively real test set. A Vision Transformer classifier is trained under multiple real and synthetic data configurations to measure the practical utility of each generative approach. The best configuration, based on balanced DDPM augmentation, improves accuracy from 84% to 88%, balanced accuracy from 67% to 81%, macro F1 from 65% to 78%, and recall for the underrepresented not-bombed class from 41% to 69%. These results demonstrate the potential of synthetic satellite imagery for data-scarce geospatial applications in war-affected regions.
[CV-51] Adaptive Post-Processing Drives Instance-Level Detection in Stroke Lesion Segmentation MICCAI
链接: https://arxiv.org/abs/2608.16377
作者: Qinghui Liu,Jon André Ottesen,Atle Bjørnerud,Kyrre Eeg Emblem
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 papges, 4 figs, 2 tables, MICCAI ISLES’26
Abstract:Instance-level lesion detection has been an increasingly larger focal point in medical image segmentation besides the more standard voxel-level overlap. Still, most pipelines are trained and post-processed for voxel overlap alone. In particular, the mismatch is most pronounced for small lesions, where a near-miss prediction—substantial overlap that falls just short of the instance-matching threshold—scores the same as a complete miss. In our ISLES’26 submission, we found that closing this gap mattered far more in post-processing than in architecture design. Our Volume-Conditioned Adaptive Post-Processing (VCAP) scheme adjusts component-size thresholds to each case’s predicted lesion burden, improving Lesion-F1 by 0.032 (unbiased cross-fold estimate)—approximately 6 times larger than any architectural change we tested. A resolution-aware attention architecture (Viola2Plus), designed for small-lesion segmentation, shows why the distinction matters: it left small-lesion Dice unchanged but raised small-lesion detection rate by 3.7%, a real effect voxel-overlap metrics alone would have missed. Under 5-fold cross-validation on the 1,453-case training set, our post-processed two-architecture ensemble achieves Dice 0.651 and Lesion-F1 0.614, versus 0.644 and 0.573 for the unprocessed single-model baseline.
[CV-52] OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations
链接: https://arxiv.org/abs/2608.16373
作者: Simon Donike,Ruben Cartuyvels,Antonino Ian Ferola,Elisa Carli,Diego Fernandez Prieto,Marie-Helene Rio
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite comprising over 70% of its surface, the world’s oceans are critically underobserved compared to the land surface or the this http URL the global ocean requires jointly observing its surface and subsurface structure, yet no standardized, high-resolution dataset couples satellite surface fields to co-located \emphin situ depth profiles in an AI-ready this http URL resources either consist of model-reconstructed gridded products rather than observations, cover only a single variable or basin, or operate at resolutions too coarse for mesoscale this http URL introduce \textscOceanDepths, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature (SST), sea surface salinity (SSS), and sea surface height (SSH) L4 products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support comparisons or multi-stage this http URL dataset spans 2000–2024 at \SI0.1\degree \times \SI0.1\degree spatial resolution and at weekly temporal resolution, covering the entire globe’s sea surface and with over 9.5 million paired profiles interpolated to 50 standardized depth this http URL provide a configurable system to split the globe in equally sized spatial this http URL 4D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations ( \sim 0.01% per depth level) make \textscOceanDepthsa challenging testbed for novel AI this http URL demonstrate subsurface state reconstruction as an example task with simple baseline models, but also envision \textscOceanDepthsto support the development of observation-based forecast methods and other related tasks.\addedAvailable at: this https URL.
[CV-53] Depth-Dominant Skeleton Detection for Natural Scenes
链接: https://arxiv.org/abs/2608.16367
作者: Chengkun Rao,Yixuan Deng,Min Li,Yangjun Ou,Ye Li,Ziwei Luo,Zhaojing Wang,Junwei Tang,Bangchao Wang,Xiaoyun Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 3 figures, 4 tables
Abstract:To date, all natural scene skeleton detection follows the paradigm of taking RGB images as the sole input; despite notable progress, methods under this paradigm suffer significant performance degradation on complex-content images. We observe that depth images are inherently insensitive to color and texture, and can provide clear regional contours and inter-region spatial relationships, which naturally alleviates the difficulty of skeleton detection in complex scenarios. Motivated by this observation, this paper proposes for the first time a novel skeleton detection paradigm where depth images serve as the dominant modality and RGB images act as the auxiliary, and accordingly presents a model DDSkel (short for Depth-Dominant Skeleton Detection) under this paradigm. DDSkel employs an asymmetric encoder design to fuse RGB information into depth features, with the RGB modality branch having only 12% the parameters of the depth modality branch. DDSkel has a simple structure without intricate designs. Nevertheless, with only 36% of the trainable parameters of the current best method, DDSkel outperforms all state-of-the-art approaches on SymPASCAL, the most challenging dataset with a large volume of complex images.
[CV-54] DriveCache: Action-Aware Caching for Driving World Model Inference
链接: https://arxiv.org/abs/2608.16354
作者: Jianchun Yang,Jian Liang,Xianda Guo,Pinhan Fu,Yanlun Peng,Conglang Zhang,Wenke Huang,Mang Ye
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 7 figures, 4 tables
Abstract:Driving video generation models support autonomous-driving development by predicting controllable future scenes for simulation, planning evaluation, and offline data generation. Diffusion-based driving generators repeatedly evaluate large backbones across denoising steps, which limits generation throughput. Existing diffusion acceleration methods reduce this cost, but general-purpose designs omit driving signals available before generation, such as ego speed and planned trajectories. Experiments across driving motions show that cache tolerance varies with ego translation and rotation, denoising progress, and consecutive reuse length. We propose DriveCache, a training-free, action-aware controller that uses planned motion to allocate reuse across scenes and dynamic programming to place it across denoising steps under a calibrated response budget. A causal drift check refreshes features and replans the remaining schedule when generation departs from calibration. Across three generator configurations, DriveCache improves the overall fidelity-efficiency trade-off over evaluated cache methods. Our code will be publicly available.
[CV-55] SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection ECCV2026
链接: https://arxiv.org/abs/2608.16338
作者: Tiancheng Zhang,Mengmeng Wang,Yan Gao,Xiangjie Kong,Guojiang Shen,Jiaxin Du
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: accepted by ECCV 2026
Abstract:Video lane detection requires predictions that remain stable across frames, yet severe vehicle occlusions can break temporal cues. In streaming recurrent models, corrupted observations may enter the hidden state and produce errors that persist into later frames. Existing occlusion-aware refinements usually provide obstacle masks as auxiliary inputs, so the state-update path is only indirectly protected. We propose SIGMA-Lane, which treats this failure mode as state contamination in State Space Model (SSM)-based temporal modeling. SIGMA-Lane places occlusion-aware gates on the SSM write and residual-fusion paths, controlling how current observations enter temporal memory and are fused back after temporal propagation. After coordinate-consistent affine alignment, the model combines two complementary paths: SSM-consistent dual-gating for temporal filtering and Structural Spatial Retrieval (SSR) for recovering missing lane structure from aligned historical priors. Experiments on VIL-100 and OpenLane-V show improved temporal stability under heavy occlusion, with competitive F1 and mIoU scores.
[CV-56] Unlocking Motion in Expressions: Temporal Calibration for Referring Video Object Segmentation ACM-MM2026
链接: https://arxiv.org/abs/2608.16332
作者: Yiwen Jiang,Zhengtong Zhu,Ruixin Zhang,Jiaqing Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accept by ACM MM2026
Abstract:Referring Video Object Segmentation (RVOS) aims to segment referred objects at the pixel level in video sequences based on natural language descriptions. Existing methods typically introduce motion information within a unified cross-modal temporal modeling framework, where language cues are used for target localization and segmentation. However, the dependency of expressions on motion semantics is not explicitly modeled, making it difficult to adaptively adjust the use of motion information according to different semantic requirements. To address these issues, we propose an Expression-driven Motion Calibration (EMC) framework for RVOS that explicitly unlocks and leverages the motion semantics within expressions. The proposed method extracts interpretable motion control signals from expressions via a Motion Signal Processing (MSP) module, and employs a Motion Influence Calibration (MIC) module to adjust the contribution of motion cues during temporal decision making. In addition, a Semantic Temporal Stage Construction (STSC) module is introduced to build expression-relevant temporal stages, providing a compact temporal candidate space for motion calibration. Through extensive evaluation on six standard benchmarks, including Ref-YouTubeVOS, Ref-DAVIS17, MeViS (valid/valid ^u ), A2D-Sentences, and JHMDB-Sentences, the superiority of our method is validated. We will release the code on this https URL.
[CV-57] GRNEdit: Efficient General Video Editing from a New Binary-Evidence Perspective in Generative Refinement Networks
链接: https://arxiv.org/abs/2608.16328
作者: Feng Xie,Jiagao Hu,Fuhao Li,Zepeng Wang,Yuxuan Chen,Dahua Gao,Fei Wang,Daiguo Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Instruction-based general video editing seeks to unify diverse editing operations within a single, intuitive interface. Existing approaches often rely on resource-intensive conditioning, using either heavyweight branches or costly source concatenation. Is there any efficient way to model editing intent? Thus, we introduce GRNEdit, a lightweight two-stage framework. GRN inspires our approach by encoding visual semantics through combinations of bits. Through task-specific fine-tuning, we take this representation further and recast editing semantics as local retain-or-flip decisions over individual bits. Source information is consequently modeled as coordinate-wise evidence supporting the observed binary states, while the GRN backbone remains responsible for resolving their global composition into coherent generative semantics. In Stage I, a compact encoder translates discrete source codes into continuous evidence signals, which GRN assimilates throughout binary refinement. Inspired by null-prompt training for classifier-free guidance, we further assign the null condition an editing-specific meaning: an empty instruction denotes no edit and is supervised through source reconstruction. This identity pathway not only implicitly strengthens evidence utilization and content preservation in Stage I, but also produces a source-preserving state in the same representation space as the edited state. Stage II can therefore directly compare each edited state with its source-preserving counterpart and use their discrepancy to revise unresolved target-bit decisions. Trained on only 0.6M pairs with less than 3% conditioning parameters, GRNEdit-2B and GRNEdit-8B achieve scores of 4.03 and 4.18 on OpenVE-Bench. The 2B model outperforms multiple 14B open-source editors, while the 8B model performs on par with leading open-source editors.
[CV-58] LaGSplat: Inferring Physics-Governed Interactive Simulation from Monocular Video Using Latent Lagrangian Gaussian Splatting
链接: https://arxiv.org/abs/2608.16324
作者: Louen Pottier
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 11 figures, 4 tables. Project page with interactive demo: this https URL
Abstract:We present LaGSplat (Latent Lagrangian Gaussian Splatting), a framework that infers interactive, physics-governed dynamics from one or a few monocular videos. At inference it lets a user push on the filmed object, rigid or deformable, with an external force that was never measured, annotated, or seen during training. This is possible because a low-dimensional latent state \mathbfq \in \mathbbR^d plays two roles at once: it is the generalised coordinate of a learned dissipative Lagrangian and the conditioning variable of a Gaussian Splatting decoder. The inductive bias of this decoder, whose primitives are explicit points \mu_i(\mathbfq) that move with the object, is what lets a force f applied in the image pull back into a latent generalised force J(\mathbfq)^\top f and enter the equations of motion, which pixel-space (CNN) or neural-field (NeRF) decoders cannot do. We validate LaGSplat on test cases of increasing difficulty, from rigid to deformable and from autonomous to forced real systems, combining monocular video and sensor measurements. We further demonstrate interactive use: forces of arbitrary magnitude and direction can be applied to the reconstructed object at any time, its response rendered in real time, in 2D or 3D. Assuming a dissipative Euler-Lagrange equation over a few generalised coordinates trades generality for a bounded, plausible response to unseen forces, where an unconstrained predictor diverges.
[CV-59] StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
链接: https://arxiv.org/abs/2608.16320
作者: Keming Wu,Baoyi Wang,Kaichen Zhang,Xiang An,Zuhao Yang,Sudong Wang,Haowei Zhu,Tingxuan Huang,Hongcheng Gao,Bin Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compression, yet a training-free sliding-window baseline already matches them. We therefore fix a memory-free recent-window protocol and ask how far post-training alone can go. Reinforcement learning with verifiable rewards fits this regime poorly, encouraging long ``think-then-answer’’ generations, while on-policy distillation (OPD) supplies dense token-level teacher supervision on student trajectories but is stable only when both models train in thinking mode. These observations lead to \textscStreamOPD, a recipe combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment. It raises StreamingBench from 77.9% to 83.9% —within 0.3 points of the 9B teacher—and improves OVO-Bench excluding its hallucination-detection subtask (HLD) by 9.1 points under unchanged inference. As a teacher-privilege extension, \emphSpatio-Temporal CueGate (ST-CueGate) aggregates cue-versus-no-cue teacher likelihood ratios into a group-relative response score that reweights OPD. It reaches 71.9% on OVO-Bench (excluding HLD) and 64.9% on Video-MME, and is the only variant that stays above the base model on all four benchmarks. Replacing the teacher with a frozen copy of the student’s initial policy—on-policy self-distillation—retains most of these gains and lifts HLD to 57.0% , above both the untrained student and the 9B teacher, so abstention loss is not intrinsic to the recipe. We provide a transparent and reproducible reference for open-source streaming-video research.
[CV-60] Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context
链接: https://arxiv.org/abs/2608.16310
作者: Peilin Li,Pengfei Chen,Jingyu Wang,Zhifeng Yang,Tiansheng Chen,Mengjie Gong,Xiao Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Residents’ perception of the urban streetscape is an important factor in public health, active mobility, and social wellbeing. Street view imagery (SVI) has emerged as a widely used data source for assessing these perceptual qualities, yet its uneven coverage and irregular updating limit large-scale measurement. Here, we present CVLNet, a Cross-View Learning Network that predicts street-level perception from AlphaEarth embeddings and multi-source urban contextual data without requiring SVI at inference. CVLNet applies per-task adaptive gating to jointly model five perceptual dimensions, using labels from the pretrained SVI-Percept model as ground truth. The proposed method is evaluated across four Southeast Asian cities: Singapore, Kuala Lumpur, Jakarta, and Manila. CVLNet achieves a median road-segment-level Adjusted R^2 of 0.76 and consistently outperforms the baseline models, with gains ranging from 5.9–11.3% across the five perceptual dimensions. Ablation experiments show that AlphaEarth features and urban contextual features contribute complementary information. We further produce citywide road-level streetscape perception maps for five subjective perceptual dimensions across all four cities, extending perception estimation from the 13–31% of the road network directly covered by available SVI to the complete road network of each city. Integrating these maps with WorldPop gridded population data, we quantify exposure inequality across population-density, demographic, and land-use groups using the Deficit Palma Ratio. These results demonstrate that remote sensing can serve as a scalable alternative to SVI for citywide streetscape perception mapping, enabling a more comprehensive assessment of urban environmental inequality.
[CV-61] PosterText: Towards Unified Visual Text Generation and Editing for E-commerce Poster
链接: https://arxiv.org/abs/2608.16289
作者: Xiaoan Liu,Lichen Ma,Zipeng Guo,Yu He,Xiaoyan Su,Shaojie Guo,Jingling Fu,Xiaolong Fu,Hao Yang,Tongxuan Liu,Yu Guo,Fei Wang,Xinyi Liu,Yongjun Zhang,Junshi Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs. However, most existing methods either target end-to-end poster generation or follow multi-stage design pipelines, with limited capability for flexible and precise editing of existing posters. To enable unified generation and editing of e-commerce posters, we introduce Text Patch Generation and Editing, a unified task formulation that treats text patches as atomic units and covers four operations: poster generation, patch addition, patch deletion, and patch modification, with optional reference-guided style control. Based on this, we propose PosterText, a unified model trained with a four-stage curriculum, including text rendering pretraining, instruction-following training, reinforcement learning for preference alignment, and spatial guidance self-distillation for execution refinement. We further construct a large-scale dataset with patch-level annotations and a comprehensive benchmark for evaluation. Extensive experiments demonstrate that PosterText achieves competitive performance against existing generation and editing approaches, validating the effectiveness of the proposed framework.
[CV-62] Audio-Visual Segmentation via Depth-Guided Collaborative Modeling
链接: https://arxiv.org/abs/2608.16285
作者: Zhaojin Fu,Yuyang Hong,Qi Yang,Zili Wang,Kun Ding,Shiming Xiang,Bin Fan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Audio-Visual Segmentation (AVS) is a fundamental task in multimodal perception that performs pixel-level segmentation of sounding objects in videos by leveraging both visual and audio cues. It has broad applications in video understanding, human-computer interaction, and autonomous driving. However, most existing AVS methods do not explicitly model geometric cues such as relative distance and occlusion, thereby limiting the robustness of cross-modal alignment. In human perception, spatial structure is naturally integrated with audio-visual evidence to accurately localize sounding objects. Motivated by this, we incorporate estimated depth as a spatial structural cue for AVS and propose DGCM-AVS, a tri-modal framework that jointly models audio, visual, and depth information. Specifically, we design a Depth-Aware Dynamic Modulator to improve the separation of adjacent objects while preserving intra-object feature consistency. Furthermore, we propose Depth-Guided Progressive Fusion, which uses depth as an intermediate bridge to progressively align audio cues with visual features. Compared to state-of-the-art methods, DGCM-AVS achieves relative improvements of 10.2 percent in M_J and 8.7 percent in M_F on the AVSS dataset. We believe our study highlights depth as a promising yet underexplored modality for AVS and may encourage further research in this direction.
[CV-63] ransAnyText: Translating Arbitrary Text in E-commerce Images via Structured Visual Generation
链接: https://arxiv.org/abs/2608.16284
作者: Xiaoan Liu,Lichen Ma,Zipeng Guo,Yu He,Xiaoyan Su,Shaojie Guo,Hao Yang,Jingling Fu,Xiaolong Fu,Zhen Chen,Yu Guo,Fei Wang,Xinyi Liu,Yongjun Zhang,Ke Zhang,Junshi Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-border e-commerce image translation is essential for global retail, where product images, banners, and detail pages need to be produced in different languages. Existing methods struggle to achieve accurate translation, faithful visual identity preservation, and easy-to-edit outputs, simultaneously. To address these challenges, we introduce TransAnyText, a structured visual code framework that reformulates image text translation as generating renderable HTML patches from source images and target languages. Our framework decouples semantic generation from pixel rendering: a vision-language model (VLM) handles visual understanding, cross-lingual translation, and structured visual generation, while a diffusion model performs background inpainting and pixel-level refinement, followed by deterministic rendering to synthesize the final image. Based on this formulation, we develop a three-stage post-training framework, where supervised fine-tuning (SFT) establishes the image-to-code mapping, privilege-gap weighted self-distillation (PWSD) improves the learning of style and layout tokens, and reinforcement learning with verifiable rewards (RLVR) further optimizes task-level performance. We further introduce TransAnyDataset and TransAnyBench, a multilingual dataset and benchmark for e-commerce image translation. Extensive experiments demonstrate competitive performance against cascaded pipelines, open-source end-to-end models, and closed-source image editing systems, providing an effective, controllable, and editable solution for cross-border e-commerce image translation.
[CV-64] CoM3eT: A foundation model for medical image analysis through federated multidimensional context integration
链接: https://arxiv.org/abs/2608.16268
作者: J. Raphael Schäfer,Kai Geissler,Till Nicke,Chiara Tappermann,Karoline Heber,Eike Petersen,Habib Mergan,Lars Ole Schwen,Nick Weiss,Annika Gerken,Jan Hendrik Moltz,Tom Bisson,Isil Dogan O,Tim-Rasmus Kiehl,Norman Zerbe,Sefer Elezkurtaj,Robin S. Mayer,Nadine Flinner,Peter Wild,Isabel Dahm,Felix Peisen,Heinrich von Busch,Robert Grimm,Sebastian Arndt,Lisa Siegler,Matthias Stefan May,Antje Prasse,Natalia Artysh,Fabian Kiessling,Johannes Lotz
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Medical foundation models improve generalization when training AI models with limited labeled data, but remain confined to a single specialty, such as pathology or radiology, and to either sparse or dense outputs, such as classification or segmentation. Here, we present CoM ^3 eT (Co-representation Multidimensional Multitask Medical Transformer), a medical vision foundation model that unifies pathology and radiology, sparse and dense predictions, and two- and higher-dimensional inputs by modeling multidimensional context with attention. CoM ^3 eT outperformed other medical foundation models in an open competition spanning five tomographic, four whole-specimen, and three two-dimensional datasets, covering sparse and dense prediction tasks as well as report generation. When adapted across diverse clinical applications, training fewer than 2.5% of parameters achieved performance comparable to full fine-tuning, enabling research without access to high-performance GPU clusters. Applied to federated learning across hospitals, this approach achieved performance comparable to pooled-data training over internet connections and with consumer-grade hardware.
[CV-65] Seeing Before Answering: Training-Free Visual Layer Profiling for Vision-Language Models ECCV
链接: https://arxiv.org/abs/2608.16263
作者: Ruchen Liu,Yi Yang,Yiming Xu,Michael Ying Yang,Monika Sester,Bodo Rosenhahn
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCVW’26 eXCV
Abstract:LLaVA-style Vision-Language Models (VLMs) pass visual tokens from a fixed late layer of the vision backbone, typically the penultimate one, to the language model. We first show that this hidden convention is fragile: across 2 VLMs and 7 image and video benchmarks, the default layer is sub-optimal in 13 of 14 model-task pairs, and the best layer shifts with both task and visual backbone. Finding that layer by exhaustive layer-wise inference is prohibitively expensive, and no better fixed default exists. We therefore ask whether layer usefulness can instead be predicted from representation geometry. We study matrix-based entropy, introduced for unimodal layer analysis, which we compute over sample-level visual embeddings as Visual Dataset Entropy (VDE); and Gromov-Wasserstein (GW) distance, introduced for encoder-level VLM model selection, which we repurpose as a layer-wise visual–language alignment signal. Transferring these to LLaVA-based models is not obvious a priori: the vision tower is frozen while the multimodal projector is trained, so we profile both sides of the projector. We find that VDE transfers, and GW does not. Computed from 100 unlabeled task samples without downstream inference, pre-projector VDE tracks layer-wise accuracy and its top-ranked layers cover the oracle best layer on every task for the SigLIP-based LLaVA-Video, while giving region-level guidance for the CLIP-based Video-LLaVA. Post-projector profiles show that the projector reshapes visual geometry but does not erase the performance-relevant trend, leaving \mathrmVDE_\mathrmpre the stronger signal. GW instead flattens after projection and is best read as an alignment diagnostic rather than a selector. VDE thus offers an interpretable, training-free policy that narrows the visual-layer search to a handful of candidates for limited downstream verification.
[CV-66] Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection
链接: https://arxiv.org/abs/2608.16259
作者: Bowen Deng,Jiahui Zhan,Yikun Ji,Haozhen Yan,Jianfu Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ACMMM 2026
Abstract:The rapid progress of image generation models calls for AI-generated image (AIGI) detectors that are not only accurate but also explainable and reliable. While MLLM-based detectors can provide natural language explanations, existing methods often generate speculative rationales: they rely on vague or hallucinated artifacts, miss subtle localized flaws from the latest generators, and fail to provide evidence that can be visually verified. We present Defake-o3, an explainable AIGI detector that moves from speculative rationales to verifiable evidence. It combines interactive visual search with verifier-guided evidence alignment: the model iteratively zooms into suspicious regions to inspect fine-grained details, while an Evidence Verifier, trained from human verification annotations, provides reinforcement learning rewards that favor grounded evidence and penalize baseless claims. To support this objective, we construct GroundFake, a dataset designed for grounded explainable detection, with localized bounding-box evidence, human verification based on visual grounding and artifact specificity, corrected reasoning trajectories, and valid/invalid evidence supervision. We further introduce FakeFrontier, an out-of-distribution benchmark built from real images and outputs of 10 recent generators, together with an MLLM-based protocol for evaluating evidence quality and persuasiveness. Experiments on GroundFake, FakeFrontier, and additional out-of-distribution benchmarks show that Defake-o3 improves both detection accuracy and explanation quality, producing more localized, verifiable, and persuasive evidence.
[CV-67] SCOUT: Semantic Concept Discovery for Open-Vocabulary Editing of face Recognition Templates
链接: https://arxiv.org/abs/2608.16251
作者: Leon Todorov,Peter Rot,Peter Peer,Vitomir Štruc,Klemen Grm
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Face recognition templates are compact identity representations, yet they also encode rich semantic information about facial appearance. Prior work has shown that templates can be inverted to images or indirectly manipulated through image-editing pipelines, but direct semantic editing in template space remains largely unexplored. Existing interpretability methods for face recognition often rely on manual neuron inspection or predefined attribute labels, limiting scalability and semantic flexibility. To address this gap, we propose SCOUT (Semantic Concept Discovery for Open-VocabUlary Editing of Face Recognition Templates), an end-to-end framework for discovering and directly manipulating semantic concepts in face recognition templates using mechanistic interpretability. SCOUT learns sparse template representations, generates semantic hypotheses for latent features from natural-language descriptions, and validates their stability. The resulting features act as controllable semantic directions for direct editing, avoiding costly edit–re-encode pipelines. Experiments with face recognition models using CNN, ViT, and Swin backbones show that SCOUT discovers interpretable concepts beyond standard attribute labels and enables controllable, identity-aware template manipulation with negligible impact on identity matching. We further show that edited templates can subsequently be decoded with independent inversion models for visualization and evaluation.
[CV-68] Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks
链接: https://arxiv.org/abs/2608.16241
作者: Süha Tuna,Ülker Başar
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Optimization and Control (math.OC)
备注:
Abstract:Feature extraction for hyperspectral image classification is conventionally addressed using rigid tensor decompositions that fail to capture complex spatio-spectral interdependencies, or heavily parameterized convolutional neural networks that are computationally expensive. To overcome these limitations, this work introduces the Holistic Multivariance Decomposition (HMD) framework as a novel, end-to-end differentiable neural network layer. By explicitly separating independent single mode variations from cooperative higher dimensional interactions via learnable, matrix valued supports, the proposed HMD-0, HMD-1 and HMD-2 approximants are optimized jointly with a downstream classifier via backpropagation. Comprehensive evaluations across three benchmark HS datasets demonstrate that the higher level HMD layers achieve superior classification accuracy compared to classical learnable tensor baselines, including Tucker, Canonical Polyadic, and Tensor Train decompositions. Furthermore, HMD-1 and HMD-2 achieve a generalization capacity and training stability comparable to standard 2D and 3D-CNNs while requiring significantly fewer feature extractor parameters. These results demonstrate that the HMD framework provides a structurally robust substitute for traditional convolution in multidimensional HS image classification, offering high parameter efficiency and stability throughout the optimization process.
[CV-69] GaussianDWM: Language-Grounded 3D Gaussian Driving World Model for Unified Scene Understanding Editing and Multi-Modal Generation
链接: https://arxiv.org/abs/2608.16234
作者: Tianchen Deng,Xuefeng Chen,Shuang Wu,Qu Chen,Jiajun Zhu,Bo Dai,Jianfei Yang,Hesheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Driving World Models (DWMs) have recently advanced rapidly with generative models, yet most existing methods mainly focus on conditional scene generation and lack explicit 3D scene understanding, language-grounded reasoning, and controllable 4D editing capabilities. Moreover, commonly used point cloud, occupancy, or BEV representations make it difficult to achieve fine-grained alignment between textual information and the underlying 3D scene structure. To address these limitations, we propose a foundation-feature Gaussian driving world model that unifies scene understanding, language-grounded reasoning, controllable 4D editing, and multi-modal generation within a single framework. Specifically, we introduce a foundation-feature Gaussian tokenizer that directly distills Qwen/SigLIP visual-language features into 3D Gaussian primitives, building a compact open-vocabulary Gaussian semantic field. We further design a geometry-aware Gaussian adapter that combines importance-aware hierarchical selection with text-conditioned Perceiver-style cross-attention to aggregate dense Gaussian primitives into compact world tokens. To improve representation compatibility, we introduce a KL-based Gaussian–image distribution alignment objective that aligns Gaussian world tokens with foundation image tokens. Based on the aligned Gaussian representation, our framework further supports instruction-controllable scene editing, including weather-conditioned generation and dynamic vehicle manipulation. Extensive experiments on broader driving benchmarks demonstrate that our method achieves state-of-the-art performance across scene understanding, visual grounding, planning-oriented reasoning, and controllable 4D generation tasks. We will release the code and datasets publicly on Github.
[CV-70] PCT-Prompt: A Prompt-Guided Transformer Framework for Dense Prediction Tasks in Point Clouds
链接: https://arxiv.org/abs/2608.16225
作者: Dejun Zhang,Yanzi Bai,Yiqi Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Standard Transformers have proven effective in point cloud object classification, but their performance in dense prediction tasks within complex scenes is often hindered by weak prior assumptions. To address this challenge, we propose PCT-Prompt, a novel framework that enhances standard Transformers by introducing a prompt-guided feature branch to improve performance in dense prediction tasks. The standard Transformer branch leverages pre-trained models for global feature extraction from point cloud data, serving as the backbone for processing high-level features. Meanwhile, the prompt-guided feature branch consists of two key components: a fine-grained feature extraction block that captures multi-scale geometric features using geometry-sensitive abstraction layer, along with the PnP-3D layer to integrate local context with global regularization. The second component, the prompt-refined feature learning block generates prompt tokens, which are subsequently refined through cross-attention mechanisms. Additionally, we introduce a prompt drop mechanism that progressively removes prompt information across Transformer layers, balancing local details and global consistency. Experimental results on the ShapeNetPart, S3DIS, and DALES datasets demonstrate that PCT-Prompt significantly improves the adaptability of standard Transformers to dense prediction tasks, achieving strong performance in real-world scenarios.
[CV-71] SingDance: Compositional Zero-Shot Singing-and-Dancing Video Generation with Role-Aware Audio Conditioning
链接: https://arxiv.org/abs/2608.16220
作者: Tao Feng,Xu Li,Xiangyang Luo,Ming Wen,Huadai Liu,Chen Zhang,Wei Xue
类目: ound (cs.SD); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 5 figures
Abstract:Generating personalized dance videos from a reference image, text prompt, and audio track requires music-conditioned body motion. Singing-and-dancing adds a second requirement: the visible subject must also articulate the vocals. Existing music-conditioned methods focus primarily on choreography, while speech-driven models generally assume that the visible subject produces the input voice, leaving this combined setting largely underexplored. We introduce SingDance, a unified video diffusion framework that formulates controllable vocal articulation as a semantic role: the visible subject is either the source, who produces the vocal signal, or the listener, who receives it from an off-screen performer. Hard-compact routing selects task-relevant speech, music, and role conditions, which are composed through frame-wise joint audio injection; source and listener retain the same speech pathway. Training uses asymmetric supervision: on-screen speaking and curated off-screen conversational-response videos establish role control, while instrumental and song-based dancing-only videos establish music-conditioned body motion. The target Song/Source configuration is never observed during training. At inference, assigning the source role to a song composes separately learned articulation and song-conditioned dance capabilities, enabling compositional zero-shot singing-and-dancing. Experiments demonstrate strong motion–beat alignment and visual fidelity, reliable paired switching of vocal articulation while preserving music-aligned body motion, and highly competitive lip synchronization with substantially fewer generation-time parameters than the strongest speech-driven baseline evaluated.
[CV-72] Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology
链接: https://arxiv.org/abs/2608.16198
作者: Fabian Gröger,Marco Weishaupt,Philippe Gottfrois,Simone Lionetti,Linda Wermelinger,Nipun Ranasekara,Ludovic Amruthalingam,Alexander A. Navarini,Marc Pouly
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Dermatology models face distribution shifts in teledermatology settings, where submitted images differ from the training data in lighting, angle, distance, focus, and framing. These test-time images are ordinary clinical photographs, but some fall outside the model’s training conditions, leading the model to often misclassify them due to shifts in acquisition between training and deployment. When multiple images of the same case exist (several photos of one patient or lesion), a natural way to improve accuracy is therefore to select the image the model is most likely to classify correctly. We call this task reliable-input selection. An oracle that, for each case, selects a correctly classified image when one exists raises weighted F1 by about 20 percentage points on average across six dermatology datasets and nine frozen backbones. This oracle is an upper bound that sees the labels, whereas a selector must choose blindly. Capturing this gain in practice is hard. A selector that needs no pretraining data applies to any frozen model, including those whose data is not public. It must judge reliability from quantities the model exposes at inference: its embeddings, their norms, and its confidence. We benchmark four such training-data-free selectors: the embedding norm, the neighborhood consensus among a case’s images, the stability of the prediction under small perturbations, and the model’s own confidence. No training-data-free selector substantially narrows this oracle gap. The best of them is the model’s own confidence, but it recovers only a small part of the gap on the clinical datasets. A small labeled reference set does not help either: the best selector overall, a fusion of confidence and Mahalanobis distance, still leaves most of the gap. To our knowledge, this is the first study to introduce and benchmark reliable input selection, a clinically important, unsolved task.
[CV-73] Beyond Clear Skies: Synthetic Seasonal and Weather Variations for Real-World Drone Detection
链接: https://arxiv.org/abs/2608.16191
作者: Tamara R. Lenhard,Andreas Weinmann,Tobias Koch
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliable drone detection under real-world deployment conditions requires training data that spans the full operational design domain, including adverse weather and seasonal appearance variation. However, acquiring and annotating such data at scale remains highly resource-intensive, as adverse-weather conditions are inherently difficult to control, reproduce, and sample systematically. Existing datasets therefore typically provide only limited coverage of such conditions. Conversely, synthetic data offers a scalable alternative: environmental variation becomes controllable, while modern game-engine-based pipelines provide realistic rendering and automatic annotations. Leveraging this potential, we introduce SynDroneVision-Weather (SDV-W), an systematic extension of SynDroneVision (SDV) targeting adverse-weather and seasonal domain shifts in urban drone detection. SDV-W comprises 55,187 annotated high-resolution images from three urban environments, rendered across three seasonal configurations and diverse weather conditions, including rain, snow, and fog at multiple severity levels. By preserving SDV’s scene and trajectory configuration, SDV-W enables matched clean-adverse comparisons and quantification of condition-specific detector degradation. Across representative YOLO models and real-world datasets, we show that SDV-W improves detector reliability under adverse appearance shifts, reduces missed detections and false alarms, and is most effective as a complement to general-purpose synthetic drone-detection data. SDV-W will be publicly released upon paper acceptance.
[CV-74] KeyID: Decoupled Drafting and Keyframe Editing for Identity-Preserving Video Generation ACM-MM2026
链接: https://arxiv.org/abs/2608.16154
作者: Jianjie Luo,Yiming Zhong,Haoming Shen,Yupeng Xiao,Zhenguo Yang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Multimedia (cs.MM)
备注: Accepted by ACM MM 2026
Abstract:Identity-preserving video generation (IPVG) requires synthesizing videos that are faithful to both reference subjects and text prompts. Existing methods are often hindered by high tuning costs or limited input-level enhancements, struggling to maintain rigid identity consistency during complex, long-sequence actions. To address these limitations, we propose KeyID, a training-free IPVG framework that decouples the synthesis of video dynamics from the injection of identity. Specifically, KeyID comprises two components: (1) Reference-Aware Video Generation, which produces an identity-agnostic video draft aligned with multiple references, and (2) Identity-Preserved Keyframe Editing, which integrates the target identity via sparse keyframe correction and subsequent motion interpolation. By shifting from dense frame-level supervision to sparse keyframe-level refinement, KeyID effectively resolves the capacity conflict between prompt adherence and identity fidelity. Crucially, our modular design allows seamless extension to multi-subject references and complex sequential action generation without additional training. KeyID outperforms prior works and is validated by automatic and human evaluations on the official challenge benchmark, ultimately securing the runner-up position in the Track 2 (Sequential Action) of the ACM Multimedia 2026 IPVG Grand Challenge. Source code is available at this https URL.
[CV-75] he Right Prior for the Right Deformation: Rethinking Continuous Deformable Image Registration
链接: https://arxiv.org/abs/2608.16146
作者: Hengjie Liu,Chushu Shen,Dan Ruan,Ke Sheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deformable image registration models implicitly encode deformation priors through their parametrization and optimization. In this work, we conduct a validation study on continuous registration methods to examine how these implicit priors affect performance across different registration tasks. Classic B-Spline transformations impose locality, smoothness, and scale through their control-point structure, whereas recent INR-based methods impose different priors through neural parameterization and optimization. We compare INR-Dense (IDIR), which directly models a dense displacement field using a SIREN-based INR; INR-BSCP (SINR), which predicts B-Spline control points with an INR; D-BSCP, which directly optimizes single-scale B-Spline control points; and MR-D-BSCP, which adds a multiresolution coarse-to-fine scheme. Experiments on inter-subject brain MR registration (OASIS) and intra-subject exhale-to-inhale lung CT registration (DIR-LAB 4DCT) reveal different behavior across deformation regimes. On OASIS, where deformations are moderate but locally complex, D-BSCP matches or slightly outperforms INR-BSCP, suggesting that the B-Spline parameterization accounts for much of INR-BSCP’s effectiveness. On DIR-LAB 4DCT, where respiratory motion is larger and more coherent, single-scale B-Spline methods (D-BSCP and INR-BSCP) are less suitable, while INR-Dense and MR-D-BSCP are more effective. Across both tasks, MR-D-BSCP achieves the best performance among the tested continuous parameterizations. These findings highlight that registration accuracy depends strongly on matching the induced deformation prior to the target motion pattern, and support prior-deformation matching as a practical design principle for medical image registration. Our code will be available at this https URL.
[CV-76] AnyTalk: Speech Animation for Arbitrary Characters Leverag ing a Video Generation Model
链接: https://arxiv.org/abs/2608.16143
作者: Kwan Yun,Serin Yoon,Sunjin Jung,Jung Eun Yoo,Inyup Lee,Junyong Noh
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
备注: accepted to TVCG, Project page at this https URL
Abstract:We present AnyTalk, a novel method for generating 3D speech animations for arbitrary characters without requiring any animation data. While existing audio-driven 3D speech animation methods rely on character-specific training data or laborious rigging/re-meshing, AnyTalk circumvents these limitations by leveraging recent video diffusion models trained on extensive video datasets. We first adapt a pre-trained video diffusion model to a target character through our Character-specific Fine-tuning (\textitCsF) technique. By fine-tuning on rendered images of the 3D character paired with zeroed-out audio embeddings (representing “no motion”), we eliminate the need for animation data while preserving the motion prior of large-scale video diffusion model. We then uplift the resulting talking-head video into a 3D speech animation by estimating blendshape parameters through a proposed optimization process. AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements. We further enhance usability by distilling AnyTalk into a streamlined network, \textAnyTalk_RT , thereby enabling real-time performance. By leveraging talking-head video generation, our method broadens access to audio-driven speech animation technology for arbitrary characters. The code is publicly available at this https URL.
[CV-77] Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System
链接: https://arxiv.org/abs/2608.16142
作者: Alam Noor,Luis Almeida,Kai Li,Jiyan Wu,Miguel Gutiérrez Gaitán,Eduardo Tovar
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones. In this case, the vision-equipped UAV streams a video to a ground server where an operator assists its activities. The latency of video transmission has a profound impact on the effectiveness of the operator assistance. However, most techniques available for video transmission still incur significant latency costs. In this paper, we propose a graph convolutional neural network-assisted (GCN-Assisted A2C) deep reinforcement learning (DRL) system model to find the optimal pixel-correlated area of a suspicious object. We combine the Lagrangian dual form with gradient descent to prevent lack of convergence and over- and under-penalization constraint violation during latency optimization. The proposed system model sends a sub-group pixel-correlated area of the frame from the UAV to the server rather than the transmission of the whole video frame. The proposed framework utilizes the GCN model to explore hidden representations of feature-correlated groups of pixels. Moreover, the GCN supervises the A2C model, which selects a subgroup to enhance transmission latency, thus supervising the training of UAV actions in A2C. Experimental results show that GCN-assisted A2C reduces video frame transmission latency together with false detection rate in UAV vision systems over other DRL and state-of-the-art models.
[CV-78] okenSTFormer: A Tokenized Spatial-temporal Attention Model for Holistic Motion Analysis in Adolescent Idiopathic Scoliosis Screening
链接: https://arxiv.org/abs/2608.16122
作者: Dong Chen,Kenneth M.C. Cheung
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity in adolescents that, if left untreated, can result in severe health outcomes. Traditional screening methods are limited by subjective interpretation, reliance on professional expertise and low scalability. To address these challenges, we present ScoliGait dataset, which comprises 1,516 gait video clips paired with corresponding X-ray records. We also introduce TokenSTFormer, a novel model that tokenizes spatial and temporal semantics to enhance feature representation and convergence. Our model achieves state-of-the-art performance, surpassing vanilla Vision Transformer encoder across key metrics, including accuracy of 0.79. This study highlights the potential of leveraging holistic motion features derived from gait video and attention-based models for scalable, cost-effective AIS screening, paving the way for future clinical applications in scoliosis detection.
[CV-79] SUGFW: An Uncertainty-guided Feature Weighting Framework for Cold Start Active Adaptation of SAM in Medical Image Segmentation
链接: https://arxiv.org/abs/2608.16110
作者: Xiaochuan Ma,Ning Zhu,Jia Fu,Lanfeng Zhong,Hanyu Jiang,Bin Song,Kang Li,Guotai Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cold Start Active Learning (CSAL) is important in improving the performance of a medical image segmentation model with low annotation budget by querying a small subset for annotation from an unlabeled training set. Existing CSAL methods typically rely on inefficient dataset-specific Self-Supervised Learning (SSL) to map the unlabeled images into a feature space for sample selection. Recently, the advent of foundation models such as the Segment Anything Model (SAM) offer a promising alternative as the pre-trained model can provide strong generalizable feature embeddings, and allow high performance in downstream tasks after fine-tuning (adaptation). However, how to systematically exploit SAM’s inherent embeddings for cold-start sample selection during adaptation with low annotation budget remains underexplored. To address this, we propose an extended SAM-based Uncertainty-guided Feature Weighting (SUGFW+) framework for CSAL and adaptation of SAM. Specifically, it leverages the SAM for Patch-level Feature and Uncertainty Calculation (PFUC), and introduces a Patch-based Global Distinct Representation (PGDR) module that aggregates patch-level embeddings into highly discriminative, uncertainty-aware image-level features. These features are then utilized by a Greedy Selection with Cluster and Uncertainty (GSCU) strategy to combine diversity and uncertainty during sample selection. Unlike prior CSAL methods that decouple sample selection from model training, SUGFW+ tightly integrates these two stages via an Uncertainty-Prompted Fine-Tuning (UPFT) process of SAM in model training. Extensive experiments on four public datasets demonstrate that SUGFW+ achieves state-of-the-art performance against existing CSAL methods. Code is available at this https URL.
[CV-80] Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model
链接: https://arxiv.org/abs/2608.16104
作者: Yizhao Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures
Abstract:Diffusion and flow matching models have made significant progress in text-to-image generation, yet high computation, quadratic complexity, and large memory footprint hinder high-resolution synthesis and edge deployment. We propose Nexus, which integrates sparse architecture, linear complexity, and low-bit quantization. It combines MoE feed-forward layers, gated DeltaNet attention, and per-expert low-bit training to reduce computation and memory. Their joint optimization allows Nexus to achieve generation quality comparable to mainstream models such as SDXL and SD3 while delivering markedly higher inference efficiency. Experiments on COCO and LAION validate its effectiveness.
[CV-81] Beyond Similarity Matching: Structured Reasoning for Open-Vocabulary Referring Segmentation in 3DGS
链接: https://arxiv.org/abs/2608.16103
作者: Yizhao Wang,Xinfa Wang,Jingbo Wang,Jingbo Wang,Guantao Zhang,Yafeng Han,Guohong Gao,Yuhe Xia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 5 figures
Abstract:Open-vocabulary referring segmentation in 3D Gaussian Splatting (3DGS) requires a neural model to select Gaussian primitives according to free-form language expressions. Existing 3DGS-based methods usually rely on global text-region similarity, which is weak for queries involving attributes, reference objects, spatial relations, and fine-grained parts. This often causes target-reference confusion, granularity mismatch, part-whole leakage, and relation violations. We propose QAGaussian, a query-adaptive neural reasoning framework for language-guided Gaussian primitive selection. QAGaussian first learns query-conditioned multi-scale Gaussian slots as differentiable candidates whose receptive fields are shaped by the input expression. It then builds a relation-aware slot graph with language-conditioned edge weighting to propagate target-reference, attribute, part-whole, and contextual evidence. A granularity-adaptive router softly combines region-level, object-level, part-level, attribute-aware, and relation-aware mask branches, followed by relation-constrained refinement for spatial, part-whole, attribute, and geometric consistency. QAGaussian is pretrained only on Mosaic3D-5.6M for Gaussian-text alignment and evaluated on independent benchmarks without target-dataset fine-tuning. It achieves 47.2 Avg. mIoU and 63.2 Avg. F1, outperforming the strongest 3DGS referring baseline by 2.7 mIoU points and 2.9 F1 points. It also improves Part-mIoU from 38.6 to 43.4, Rel-mIoU from 44.4 to 50.8, and reduces target-reference confusion from 10.8 to 7.4. These results demonstrate that query-conditioned slot learning, relation-aware graph reasoning, and adaptive routing provide an effective neural modeling strategy for open-vocabulary referring segmentation in 3DGS. The code is available at this https URL.
[CV-82] ISC: A Text-Driven Image Semantic Communication System for Faithful Reconstruction
链接: https://arxiv.org/abs/2608.16100
作者: Feifan Zhang,Yuyang Du,Xiaoyan Liu,Soung Chang Liew
类目: Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI)
备注:
Abstract:Generative image semantic communication converts an image into a text description and then performs text-to-image reconstruction at the receiver via diffusion-based generative models. This paradigm has attracted broad attention due to its extremely low bandwidth cost. However, existing methods still face two critical bottlenecks across image-to-text (I2T) semantic extraction at the transmitter and text-to-image (T2I) semantic reconstruction at the receiver: (i) semantic loss and distortion in I2T, where holistic image descriptions may omit fine-grained object attributes and spatial-position information, causing the generated text to deviate from the original image semantics; and (ii) insufficient semantic faithfulness in T2I, where even with the same semantically faithful text description, different initial noise settings may lead diffusion-based reconstruction to produce images with different levels of semantic consistency with the original image. These issues jointly limit the semantic faithfulness of image reconstruction. To address them, we propose TISC, a text-driven image semantic communication framework tailored for faithful reconstruction. TISC incorporates two key designs: (1) Tree-Structured Attribute Semantic Extraction (TSASE), which decomposes semantic extraction into global scene, background, and object-level attribute descriptions, covering spatial position, shape/pose, color, material, and other physical attributes for each detected object; and (2) an Initial Noise Optimization (INO) mechanism, which selects an initial noise seed at the transmitter according to a comprehensive similarity score that jointly considers visual and semantic consistency. Experiments on multiple datasets show that TSASE improves object-position recovery and semantic description faithfulness, while the INO parameter study supports the adopted configuration for noise selection.
[CV-83] Representation Is Not Enough: Body-Localized Thermal Evidence for Contactless Stress and Craving Sensing in Opioid Use Disorder
链接: https://arxiv.org/abs/2608.16087
作者: Sachin Deb,Harshit Sharma,Asif Salekin
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Removing wearables from physiological monitoring also removes their supervision: the signal indicating where and when a stress response occurred. Contactless stress sensing therefore becomes a weakly supervised evidence-localization problem, where a clip-level label must be traced to the body regions and moments that produced it. We address this with FABLE-Therm, a weakly supervised architecture that preserves localized evidence across body regions, time, and encoder-specific representations until the final decision. FABLE-Therm fuses frozen foundation-model encoders at the embedding level, with theory explaining why localized fusion can outperform feature concatenation and prediction averaging. We study this problem in opioid use disorder (OUD), where stress is a major relapse trigger and sustained wearable use can be difficult during early recovery. Using fixed thermal video, FABLE-Therm achieves 0.938 AUROC on held-out participants, and its learned representation transfers to self-reported craving, providing, to our knowledge, the first evidence that craving can be recovered from contactless thermal video. Localized evidence also enables participant-level analysis of deployment failure. We find that improving representation alone is insufficient for equitable deployment: additional data from the underserved group would recover only about half of the cohort gap, while the remainder reflects person-to-person heterogeneity. This modality-agnostic decomposition applies to models with identifiable subpopulations. Together with the first cohort-structured contactless thermal OUD benchmark, our results show that preserving localized evidence supports both accurate sensing and principled analysis of who a model fails and why.
[CV-84] SafeGesture: Evaluating Fine-Grained Hand Gesture Understanding in Vision-Language Models through Scenario-Conditioned Safety Interpretation
链接: https://arxiv.org/abs/2608.16081
作者: Taegang Kim,Saleh Afroogh,Junfeng Jiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 22 tables, 2 figures. Code and benchmark resources available at this https URL
Abstract:Open-weight and frontier vision-language models (VLMs) perform well on general image understanding, but their ability to interpret fine-grained hand gestures in safety-critical operational contexts remains largely unexamined. We introduce SafeGesture, a benchmark that evaluates whether a model can infer scenario-appropriate safety actions from hand gestures. It pairs six HaGRID gestures with eight operational scenarios for 4,800 items and evaluates Qwen2.5-VL-7B, LLaVA-NeXT-7B, InternVL2-8B, Phi-3.5-Vision, and GPT-4o. Results reveal a perception-reasoning decoupling: GPT-4o achieves 98.4% gesture accuracy but 53.3% safety accuracy, while Qwen2.5-VL reaches 84.9% and 39.5%, yielding gaps of 45.0 and 45.4 percentage points. Four of five models rarely or never use the uncertainty label, and failure directions differ substantially across models. Accuracy also obscures label bias: a scenario-majority policy with no visual input reaches 58.3%, above every evaluated model, while only GPT-4o exceeds this prior under macro-F1. Visual input improves safety accuracy by 11.2 to 30.2 percentage points, but providing the ground-truth gesture as text improves performance by only 0.4 to 3.2 points, and no model exceeds 56.2%. These results indicate that the main bottleneck is scenario-conditioned safety reasoning rather than gesture recognition.
[CV-85] US-VLA: An Ultrasound Vision-Language-Action Model for Embodied Abdomina
链接: https://arxiv.org/abs/2608.16074
作者: Cheng Zhang,Xingzheng Wu,Guihao Yan,Xifeng Hu,Zhi Liu,Mei Wu,Qing Cai
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Artificial intelligence-assisted ultrasound scanning enhances diagnostic reliability and efficiency by providing real-time guidance for standardized image acquisition and reducing operator dependence. However, existing reinforcement learning and learning-assisted ultrasound scanning methods typically rely on carefully designed reward functions or extensive interaction data, which limits their generalization ability and stability across different devices, patient populations, and complex clinical scenarios. To address these challenges, we propose an ultrasound vision-language-action model (US-VLA) for automated ultrasound scanning that explicitly encodes clinical semantic goals and generates sequential probe manipulation actions under real-time ultrasound feedback. In particular, we first design an ultrasound-aware expert fusion module to jointly integrate ultrasound observations with auxiliary contextual information, enabling semantic ultrasound feedback to effectively guide the scanning process. Then, we construct US-VLA-Data, a real-world dataset covering liver and kidney examinations, which includes five clinically defined standard planes and comprises 320 expert scanning trajectories with approximately 80,000 synchronized timesteps. Extensive experiments demonstrate that US-VLA achieves competitive performance in ultrasound probe manipulation tasks, indicating its effectiveness and promising generalization within the evaluated abdominal ultrasound setting. The source code is available at this https URL.
[CV-86] R-GS: High-Fidelity Sparse-View CT Volumetric Rendering via t-Distribution Gaussian Splatting and Ray-Confidence Modeling
链接: https://arxiv.org/abs/2608.16042
作者: Zedong Xiao,Yiren Wang,Zhou Liu,Xiaolin Liu,Zhangji Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-fidelity 3D medical visualization supports applications such as clinical assessment and surgical planning. Sparse-view computed tomography (CT) can reduce projection requirements and associated radiation exposure, but limited observations may introduce structural artifacts and reconstruction uncertainty. Although 3D Gaussian Splatting (3DGS) provides an efficient explicit representation for volumetric rendering, existing CT methods based on standard Gaussian primitives may be sensitive to unreliable observations under sparse-view acquisition. We present TR-GS, a Gaussian-splatting framework for sparse view CT volumetric rendering. TR-GS replaces standard Gaussian primitives with projectable Student’s t-distribution primitives and introduces a ray-confidence model that regulates their degrees of freedom according to local ray observability. Confidence-guided 3D wavelet regularization is further used to balance high-frequency detail preservation and noise suppression. This work is licensed under a Creative Commons Attribution 4.0 International License. Experiments on synthetic and real-world datasets show that TR-GS improves over representative baselines in most evaluated settings and remains competitive in the remaining cases. The resulting volumetric representations may support downstream medical multimedia applications, including XR-based visualization and interactive clinical rendering.
[CV-87] AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
链接: https://arxiv.org/abs/2608.16031
作者: Yuting Wu,Dongfang Guo,Xiangzhong Luo,Qun Song,Rui Tan
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protection, they often overfit to specific attack distributions and fail on adaptive adversaries. This paper presents AdROD, an embedded, stochastic ensemble defense software designed for autonomous driving. AdROD employs \em low-rank HyperNetworks, which require only 1.6% of the parameter footprint of standard HyperNetworks, to generate diverse detectors at a per-frame rate, making it impractical for attackers to obtain the deployed detectors in time. To further improve adversarial robustness, AdROD incorporates a novel \emphfunctional diversity mechanism, which couples stochastic weight updates with unique input-space transformations. We design two serving modes of AdROD that strike different trade-offs between robustness and runtime overhead: AdROD-I, a continuous protection mode for maximum resilience that leverages inter-detector disagreement to recover compromised detections, and AdROD-II, an on-demand mode triggered by kinematic discontinuities in object tracking. Through comprehensive evaluation with synthetic benchmarks, physically deployed adversarial patches, and end-to-end safety tests in the OpenCDA co-simulator, AdROD outperforms five baseline defenses and exhibits superior generalizability compared with the evaluated adversarial-training baselines, while maintaining real-time performance for safely stopping the vehicle at a stop sign instrumented with adversarial patches.
[CV-88] Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification ICONIP2026
链接: https://arxiv.org/abs/2608.16015
作者: Mingsheng Zheng,Zirui Jiang,Bo Liu,Yupeng Chen,Jun Zhang,Kai Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 4 figures. Accepted for publication in the LNCS proceedings of ICONIP 2026
Abstract:Visible-infrared person re-identification (VI-ReID) suffers from cross-modal discrepancies and limited discriminative capabilities, leading to suboptimal recognition performance. Current approaches exhibit limitations in semantic mining, cross-modal fusion and feature constraints. To tackle these challenges, we propose MDCRNet, a Multi-scale Decomposed Convolution Refinement Network that enhances cross-modal feature learning and discriminative metric learning. Specifically, we introduce a Hierarchical Learning Module (HLM) containing four Hierarchical Decomposed Convolution Attention (HDCA) modules, each equipped with lightweight channel attention and multi-scale spatial perception blocks to capture multi-scale spatial dependencies. Moreover, we develop a Joint Discriminative Metric Loss (JDML) incorporating a novel Granularity Discriminative Loss (GDL) that simultaneously optimizes intra-identity compactness and inter-identity separability across modalities. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate that MDCRNet achieves state-of-the-art performance on both benchmarks. Code is available at this https URL.
[CV-89] Depth-guided Multi-view Exposure Bracketing for HDR Robot Vision ECCV2026
链接: https://arxiv.org/abs/2608.16014
作者: Jinnyeong Kim,Juhyung Choi,Woohyeok Kim,Sunghyun Cho,Seung-Hwan Baek
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, ECCV 2026 accepted
Abstract:Achieving reliable single-shot high dynamic range (HDR) imaging under extreme illumination conditions remains a long-standing challenge, yet no comprehensive benchmark exist for evaluating HDR perception in multi-sensor robotic systems. To fill this gap, we introduce a large-scale dataset collected via a custom robotic vision platform and an iPhone 13 Pro: 121 real-world scenes spanning modest and ultra-high dynamic range conditions, alongside 20 synthetic video sequences from the CARLA simulator. As a reference pipeline for this dataset, we propose Depth-guided Multi-view Exposure Bracketing (DMEB), a single-shot HDR method that distributes drastically different exposures across multi-view low-bit-depth cameras and fuses them via depth-guided confidence-aware fusion. Evaluations on our dataset show that DMEB establishes a strong reference point and highlight the promise of this sensor configuration for robust HDR perception in diverse multi-camera and depth sensor system.
[CV-90] Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth
链接: https://arxiv.org/abs/2608.16010
作者: Zhaocen Liu,Satvik Praveen,Yi Sheng
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures, 6 tables. Code available at this https URL
Abstract:Model compression is critical for deploying networks on resource-constrained edge devices. While pruning-based methods can significantly reduce model size, they often suffer from abrupt performance collapse beyond a sparsity thresh-old, making it difficult to identify the feasible compression limit of the model. To address this challenge, we propose a boundary-Learning reverse regrowth framework, BRIDGE, that reformulates compression as a constructive boundary-search problem. Unlike forward pruning, our method first drives the model to an extremely sparse state to expose the collapse region, and then selectively regenerates the critical structure to restore performance. The proposed framework employs a hierarchical regeneration strategy, including coarse-grained layer selection and fine-grained regeneration parameter selection, to accurately identify which parameters require recovery. Experiments show that our method can recover models from the brink of collapse on both CNNs and Transformer architectures, demonstrating its architecture in-dependence. BRIDGE achieves a performance improvement of up to 1.49% in unstructured pruning and up to 4.77% in structured pruning. These results demonstrate that reverse regeneration can effectively extend the compression limit while maintaining stable performance. The source code is available at this https URL.
[CV-91] Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing
链接: https://arxiv.org/abs/2608.16008
作者: Shaohui Lin,Zhenwu Shi,Jingyu Gong,Jiao Xie,Yu Zhou,Baochang Zhang,Lizhuang Ma,Chia-Wen Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-driven human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the structural consistency of the original motion. Existing diffusion-based approaches struggle to balance text-responsive “change” and inertial “invariance”. They often rely on coarse spatial constraints and rigid uniform time assumptions, leading to spatial motion distortions and the destruction of intrinsic physical rhythms during variable-length editing. To handle these challenges, we propose Change and Invariance Motion Editing (CIME), a unified framework that comprehensively decouples change and invariance into spatial pose and temporal rhythm dimensions. For spatial poses, our method integrates an omni-supervised positive-negative learning mechanism comprising hierarchical retrospective feature supervision, subtle motion preservation, and triplet-based semantic alignment. For temporal rhythms, we introduce the Riemannian Non-uniform Integral Manifold Mapping (RNIMM) module, which achieves high-fidelity reproduction of physical beats in the edited text via kinematics-aware non-uniform timestamps. Extensive experiments on the MotionFix and STANCE Adjustment datasets demonstrate that CIME achieves state-of-the-art performance in editing alignment and structural fidelity, validating the effectiveness of our unified architecture. Our source codes and models have been released at: this http URL
[CV-92] A Plug-and-Play 2D Motion Interface for Real-World Motion Language Models ECCV2026
链接: https://arxiv.org/abs/2608.15984
作者: Kaname Yokoyama,Norimichi Ukita
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to HCMIW at ECCV 2026 (Oral Presentation). Code and demo: this https URL
Abstract:Motion Language Models (MoLMs) typically understand human motions by tokenizing 3D motion and processing the resulting tokens using a language model. However, obtaining accurate 3D motions from monocular videos is challenging, limiting their real-world applicability. To address this issue, we introduce a plug-and-play 2D Motion Interface that enables 3D-pretrained MoLMs to accept 2D motion inputs without modifying or fine-tuning the original models. Experiments on public datasets show that our method achieves performance comparable to 3D motion inputs across multiple MoLMs and outperforms training MoLMs from scratch on 2D motions. We further construct a monocular real-world video motion evaluation dataset and introduce a real-video adapter, demonstrating the usefulness of 2D motions over 3D motions under the evaluated monocular pose-estimation setting. These results suggest that 2D motion provides a practical interface for deploying MoLMs in real-world motion understanding settings. Code is available at this https URL. Comments: Accepted to HCMIW at ECCV 2026 (Oral Presentation). Code and demo: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.15984 [cs.CV] (or arXiv:2608.15984v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.15984 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-93] CM-MAE: A Physics-Guided Cross-Modal Self-Supervised Learning Framework for Vision-Wireless Applications
链接: https://arxiv.org/abs/2608.15972
作者: Yubo Zhang,Yiyao Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Synchronized camera and wireless measurements observe the same scene through different physical channels. The central difficulty is that a representation learned in one deployment can fail when viewpoint, traffic, illumination, and propagation geometry change. This paper presents CM-MAE, a self-supervised vision–wireless pretraining framework for cross-scenario representation transfer. The evaluated real-data model uses only RGB frames and the measured 64-beam received-power vector available in DeepSense 6G; it does not use ray-traced paths, calibrated depth, or beam-index labels during pretraining. Its central pretraining term is a \emphsoft contrastive alignment loss. Instead of making the synchronized image–wireless pair the only positive pair, this loss builds a target distribution from similarities between measured beam-power profiles, so nonidentical samples with similar directional responses are not forced apart as false negatives. A masked joint decoder provides the complementary local objective by reconstructing hidden visual patches and wireless angular clusters under modality dropout. After pretraining, a differential-rate fine-tuning rule lets a new fusion head adapt quickly while the encoders move slowly. Under a sequence-disjoint DeepSense 6G protocol, adding the soft alignment loss improves a matched linear-probe transfer average from 24.88% to 29.49%. Mild fusion fine-tuning reaches 77.38% Top-1 accuracy on unseen Scenarios 6–8, and optional transductive normalization adaptation reaches 78.69%. Since the fusion setting uses the contemporaneous 64-beam power vector at inference, these results should be read as representation-transfer diagnostics, not as proactive beam-prediction or reduced-sweeping claims.
[CV-94] BagShift: Measuring How Patch Selection Changes the Evidence Seen by Whole-Slide MIL
链接: https://arxiv.org/abs/2608.15970
作者: Ruicheng Yuan,Zhenxuan Zhang,Liwei Hu,Anbang Wang,Haijie Xu,Jiawei Luo,Guang Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages include Supplementary Material. 8 figures
Abstract:Whole-slide multiple-instance learning (MIL) observes only the patches admitted by its selector. Deployment can alter this selector through compute limits, tissue masking, or regional workflows, even when the patch count is unchanged. We introduce BagShift, a paired protocol that changes the selector for the same case while holding its features and predictor fixed, thereby isolating selector response from case mix. With equal 128-patch budgets, sampling across the tissue or concentrating around one coordinate exposes markedly different evidence: on PANDA, the two views reduce quadratic weighted kappa by 1.57 and 17.96 points, respectively (QWK reported on the \times100 scale). On CAMELYON16, lesion annotations withheld from model development show that localized views retain tumor in only 10.0% of micrometastatic observations, and matched exposure does not consistently recover the loss. The same fixed-count stressor produces a much smaller response on external lung subtyping, although differences in relative coverage make cross-task severity descriptive. When repeated localized observations are available, unioning their patches before one nonlinear MIL pass improves PANDA QWK by 7.87 points over averaging regional predictions. Patch count specifies computation, not observed evidence; deployment evaluations should report both what a selector preserves and how repeated observations are aggregated.
[CV-95] UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
链接: https://arxiv.org/abs/2608.15930
作者: Zihan Ding,Longxu Dou,Qi Gao,Xiangwu Guo,Shengchao Hu,Zilong Huang,Zihang Jiang,Lei Ke,Mengcheng Lan,Weixian Lei,Hanxuan Li,Honglin Li,Xiyun Li,Zaitang Li,Leowei Liang,Xin Luo,Haozhe Ma,Jiayi Mao,Zhoujie Pan,Can Qin,Tianyuan Qu,Weiqi Wang,Wenkai Wang,Yonglin Wang,Yuxin Wang,Chenxu Wu,Yingchen Yu,Chenyu Zhang,Yuhao Zheng
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: UI-Mate Technical Report. Project page: this https URL
Abstract:Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution. Routine workflows rely on user-specific tools and tacit conventions, so unstated instructions can produce arbitrary variations across runs. We present UI-Mate, a foundation GUI agent that integrates an environment-grounded training stack with in-context demonstration learning. UI-Mate makes three contributions: A Scalable Environment-Grounded Training Stack: A closed-loop data engine automates task generation, environment construction, rollout, filtering, capability balancing, SFT, and online RL across massively parallel environments via unified task-verifier bundles. In-Context Demonstration Learning: A mechanism that transforms multimodal demonstrations into flexible subtask-level workflows, follows relevant demonstrated steps, and re-plans from the live interface. OSWorkerBench Benchmark and Insights: A benchmark of 100 long-horizon office tasks across 41 applications that supports instruction-only and demonstration-guided evaluation. Its demonstration resources separate a 33-task self-demo setting, built from successful strong-agent rollouts of the same targets, from a 45-task variant-demo setting, built from human recordings of related but non-identical tasks. Experiments show that UI-Mate-27B sets a new open-weight state of the art on general computer-use benchmarks, scoring 77.0% on OSWorld-Verified and 66.2% on WindowsAgentArena. On OSWorkerBench, it reaches 41.0% strict success and 76.9% progress, outperforming its Qwen3.6-27B base by 17.7 and 24.5 points. On the 33-task self-demo subset, one demonstration raises strict success from 17.2% to 35.4% and progress from 67.9% to 81.1%, substantially improving long-horizon reliability. Project page: this https URL.
[CV-96] Pre-training Visual Dexterity in Simulation
链接: https://arxiv.org/abs/2608.15917
作者: Sarthak Kamat,Adam Rashid,Satvik Sharma,Aseem Doriwala,Chelsea Finn,Phillip Isola,C. Karen Liu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Large-scale pre-training has made robot policy fine-tuning increasingly data-efficient, but this progress has largely been driven by datasets and embodiments built around simple parallel-jaw grippers. Dexterous, multi-fingered hands remain comparatively data-starved because real teleoperation is costly to scale, while human hand video is off-embodiment and requires lossy pose estimation and retargeting. We introduce Simulation Pre-training for Dexterity (SPD), a pre-training framework for dexterous manipulation that uses data entirely collected in simulation. In SPD, humans manipulate virtual objects inside a VR headset, enabling on-embodiment trajectories and robot-free collection. With the help of five operators, we collect 75 hours of multi-task dexterous manipulation over one week, and use it to pre-train a causal transformer on a sequence modeling objective. We study the benefits of simulation pre-training on real-world tasks by fine-tuning on 1-2 hours of physical demonstrations on a 56-DoF bimanual dexterous setup. We find that our approach outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous manipulation. We perform ablation studies, measuring the benefits of history conditioning and short action chunks for reactive control.
[CV-97] Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology
链接: https://arxiv.org/abs/2608.15915
作者: Hadi Hasan,Safaa Salman,Lama Sleem,Ralph Mouawad,Ali Chehab
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages
Abstract:Lung cancer remains the leading cause of cancer-related mortality worldwide, while histopathological diagnosis is often affected by inter-observer variability and the substantial workload associated with manual slide examination. Although deep learning has shown considerable potential in computational pathology, comprehensive benchmarks that integrate tissue classification and region segmentation within a unified analytical framework remain limited. This study presents a two-stage deep learning framework for multi-class tissue classification and pixel-level histopathological region segmentation, accompanied by a systematic comparison of state-of-the-art architectures at each stage. For tissue classification, six models, a custom convolutional neural network, VGG16, DenseNet, MobileNetV3, a custom Vision Transformer, and YOLO11, are evaluated on a combined dataset of 39,000 images derived from LC25000 and LungHist700. The models distinguish between adenocarcinoma, squamous cell carcinoma, and normal lung tissue. YOLO11 achieves the best classification performance, with an accuracy of 98.38%, a five-fold cross-validation accuracy of 98.21 +/- 0.35%, and a macro F1-score of 0.98. For region segmentation, U-Net, ResNet-encoder U-Net, DeepLabV3+, and YOLO11-seg are evaluated using the GlaS gland segmentation benchmark. DeepLabV3+ obtains the highest Intersection over Union of 0.80 and a Dice score of 0.89, while YOLO11-seg achieves a comparable Intersection over Union of 0.79 using approximately 14x fewer parameters. The best-performing classification and segmentation models are subsequently integrated into an end-to-end framework, providing an accurate, computationally efficient, and reproducible baseline for automated histopathological image analysis.
[CV-98] CLARA: Clip-Level Multimodal Alignment with VLM-Derived Rationales for Hateful Video Detection
链接: https://arxiv.org/abs/2608.15905
作者: Yuchen Zhang,Shuang Dai,Zeyu Fu,Yunfei Long,Ravi Shekhar,Haralambos Mouratidis
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Hateful video detection has become increasingly important with the rapid growth of video-centric social media platforms, given the serious risks that hate speech poses to both individual well-being and social cohesion. Compared with text or static multimodal content, hateful video detection remains underexplored and significantly more challenging, as hateful meaning often arises from complex interactions among multimodal cues, including speech, audio, and visual content. Moreover, such signals are often brief, implicit, and temporally dependent, making them difficult to capture using conventional video-level representations. In this work, we propose CLARA, a clip-level multimodal framework for hateful video detection. Instead of treating a video as a single instance, CLARA models it as a sequence of fine-grained clips, enabling more precise capture of temporally localized hateful signals. We introduce a Mixture-of-Experts clip encoder for adaptive multimodal alignment, a local-global segment contrastive objective to jointly model short-term cues and long-range temporal dependencies, and VLM-derived rationales integrated via a gated Transformer to provide high-level semantic guidance. Extensive experiments on three hateful video datasets demonstrate that CLARA consistently outperforms state-of-the-art methods. Further ablation studies and parameter analyses validate the effectiveness of each component.
[CV-99] Geometry of Forgetting: Representation Flux in Continual Learning
链接: https://arxiv.org/abs/2608.15854
作者: Maksim A. Kazanskii
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Catastrophic forgetting remains a fundamental obstacle to continual learning, where neural networks lose previously acquired knowledge while learning new tasks. Existing methods primarily mitigate forgetting through parameter regularization or experience replay, while the representation-space dynamics associated with forgetting remain less understood. We investigate latent representation evolution during sequential learning and introduce representation flux, a geometric measure of sample-level representation displacement across training. We show that representation flux is strongly associated with catastrophic forgetting across multiple benchmarks, with temporal analyses indicating that elevated flux can precede subsequent performance degradation. Representation displacement is also associated with confidence degradation, while complementary geometric properties provide additional information about sample-level forgetting. Motivated by these observations, we propose FlowLess-R, a representation-space regularization method that constrains replay representations relative to stored references while allowing continued learning. FlowLess-R is architecture-agnostic and integrates into replay-based methods through a representation-matching term. Experiments on SplitMNIST, SplitFashionMNIST, SplitCIFAR10, and SplitTinyImageNet show improved final average accuracy and reduced forgetting with ER, DER++, and ER-ACE. Our results identify representation flux as an informative geometric marker of forgetting and show that stabilizing latent representations provides a simple strategy for mitigating catastrophic forgetting.
[CV-100] CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling
链接: https://arxiv.org/abs/2608.15831
作者: Bo Zhao,Zheng Wu,Yiping Xie,Zitong YU
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) monitoring from facial videos, but RGB-only methods are vulnerable to illumination changes, motion artifacts, and skin-tone-dependent optical reflectance. We propose CardiacMamba, a fair and robust RGB-RF fusion framework that integrates optical facial cues and radio-frequency cardiac motion cues through state space modeling. CardiacMamba introduces a Temporal Difference Mamba Module (TDMM) to enhance subtle RF temporal variations, a bidirectional SSM-based interaction mechanism to align heterogeneous RGB-RF dynamics, and a Channel-wise Fast Fourier Transform (CFFT) module for channel-domain spectral refinement. On the EquiPleth dataset, CardiacMamba achieves state-of-the-art performance with 0.96 bpm MAE, 3.06 bpm RMSE, and 0.97 Pearson correlation, while reducing the observed light-dark skin-tone MAE gap to 0.26 bpm and maintaining robustness under RGB degradation and RF-missing conditions
[CV-101] MITE-Net: SWaP-Optimized 4K Video Tiny Target Perception for Embodied Edge SAR
链接: https://arxiv.org/abs/2608.15830
作者: Mingshuo Xu,Mu Hua,Jigen Peng,Qi Wang,Shigang Yue
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Under double blind review
Abstract:Real-time tiny target perception in high-resolution imagery is critical for embodied Search-and-Rescue (SAR) missions. However, strict Size, Weight, and Power (SWaP) constraints on edge devices like UAVs create a bottleneck: traditional image downsampling causes severe feature loss, while slice-based processing incurs prohibitive latency. To address this gap, this paper introduces a comprehensive framework encompassing a novel architecture, specialized datasets, and hardware-level benchmarks. First, we propose MITE-Net, a SWaP-optimized cascaded architecture, which couples a bio-inspired, learning-free Tiny Target Motion-Based Region Proposal Network (TTM-RPN) with a sub-0.14M-parameter R-CNN-like head. Second, to standardize 4K tiny target evaluation, we construct the SAR-Tiny Datasets by relabeling two challenging UAV datasets: SeaDroneSee-Tiny (dynamic maritime scenes, tiny targets predominantly of 64-256 pixels ) and UAVID-Tiny (cluttered urban scenes, extremely tiny targets, less than 64 pixels). Third, we benchmark against state-of-the-art YOLO models on an edge device, NVIDIA Jetson AGX Xavier, where MITE-Net directly processes 4K maritime imagery, achieving a 100% search success rate at 30.33 FPS. Consuming merely 3.19 W (9.51 FPS/W), MITE-Net vastly outperforms YOLO baselines in target recall and energy efficiency. Conversely, UAVID-Tiny evaluations expose a compound structural limitation: the learning-free bionic front-end struggles against urban backgrounds, while the ultra-lightweight head lacks representational capacity for complex features. Ultimately, this work delivers an efficient onboard perception paradigm and a rigorous baseline guiding future end-to-end SAR architectures.
[CV-102] FlowDance: Music-Driven Dance Video Generation with Parallel Pose and RGB Streams
链接: https://arxiv.org/abs/2608.15818
作者: Genying Li,Boda Lin,Jiachen Li,Zijian Jia,Haojie Zheng,Yiming Wang,Shuchen Weng,Si Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Music-driven dance video synthesis aims to animate a reference person according to a given music clip. The task is challenging because it requires a model to jointly learn music-to-motion correspondence, identity-preserving human animation, temporal coherence, and visually realistic video generation. We present FlowDance, a music-driven dance video generation framework that integrates explicit motion modeling with reference-preserving visual synthesis through parallel pose and RGB streams. We further introduce timestep-aware pose injection to adapt structural guidance across denoising steps and persistent identity injection to preserve the reference appearance over long video. To support this task, we further build a popularity-curated, high-resolution in-the-wild dance video dataset with synchronized music, RGB videos, 3D body motion, camera parameters, and projected 2D pose annotations. Extensive experiments show that FlowDance achieves strong performance in both dance motion generation and music-driven dance video synthesis.
[CV-103] From Generation to Matching: A Development Report on Personalized Chinese Handwriting
链接: https://arxiv.org/abs/2608.15812
作者: Yiwei Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper documents a frozen engineering project on personalized Chinese handwriting. The project started from approximately 200 real handwriting images from one user, covering 197 unique Chinese characters, and was initially formulated as few-shot generation of unseen characters. A sequence of canonical-centered personalization routes repeatedly exposed the same conflict: increasing structural pressure made outputs more canonical, while increasing personalization could damage identity-defining strokes. The project was therefore reset around real-human character equivalence classes. A multi-writer CASIA candidate pool showed that a USER-compatible realization often already existed among valid human samples. The task consequently changed from synthesis to character-wise matching, followed by cross-writer composition into a virtual writer. The frozen system uses real-ink features, character-specific human population percentiles, top-20 candidate pruning, and greedy hardest-first whole-row selection. On the covered target set, all 197 USER characters had real-human candidates, and the 100-character evaluation subset was covered 100/100. Knowncharacter held-out comparisons included a row judged visually almost indistinguishable from genuine USER handwriting. A 60- episode stability audit placed every episode in a predefined A-like machine-proxy region, but these were not independent human A-level judgments. The final evidence supports stable practical B-level quality, with many outputs approaching A-level under the USER-defined criterion. The report records why generation became unnecessary for this case without claiming unrestricted or universal handwriting synthesis.
[CV-104] PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection ACM-MM2026
链接: https://arxiv.org/abs/2608.15802
作者: Chengyao Jia,Ruixuan Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by ACM MM 2026
Abstract:Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries. Recent vision-language detectors improve OOD detection through class semantics, local prompting, or LLM-generated outlier concepts, but seldom use language as explicit boundary evidence between confusing ID classes. We propose Pairwise Witness Local Rejection (PWLR), which uses an MLLM offline to describe visible local cues that favor one ID class over a specific rival class. These cue phrases are then screened with ID-only data under a frozen vision-language backbone, so that only reliable local verifiers are kept. At inference, PWLR first retains a small set of globally plausible classes, then checks whether any of them is locally supported against its most relevant rivals, and finally combines this pairwise local evidence with the global class score through calibration. Experiments on ImageNet-100 far-OOD, cleaner/challenging OOD and near-OOD benchmarks show that PWLR consistently improves strong vision-language baselines across multiple backbones. Source code will be released.
[CV-105] Emergent 3D Instance Segmentation from Self-Supervised Point Transformers ECCV2026
链接: https://arxiv.org/abs/2608.15796
作者: Ted Lentsch,Santiago Montiel-Marín,Holger Caesar,Julian F. P. Kooij
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026 DriveX
Abstract:Unsupervised 3D instance segmentation of outdoor LiDAR scans has traditionally relied on handcrafted geometric priors such as density-based clustering, motion cues, or projected 2D detections. In this work, we investigate whether a frozen, self-supervised point transformer already contains the structural information required to isolate object instances without any handcrafted geometric prior. Using this transformer purely as a feature extractor, we probe its internal representations across the SemanticKITTI, nuScenes, and Waymo Perception datasets. Our analysis yields four core insights: (1) the instance signal concentrates in the attention queries and keys rather than in the values or final output features; (2) output features semantically collapse, merging adjacent same-class objects that the queries and keys keep distinct; (3) this instance signal is bimodal in depth, strongest at the shallowest and deepest encoder stages; and (4) this signal is driven predominantly by the rotary position encoding (RoPE), whose removal collapses its advantage. We put these findings into our method TokenGraph3D, a training-free segmenter that groups points via connected components on a key-similarity graph, using neither density-based clustering nor proximity priors. Under identical prior-free conditions, we substantially outperform output-feature baselines, making the emergent 3D instance structure visible.
[CV-106] ChainSpace: A Chained-Reasoning Paradigm for Spatial Intelligence
链接: https://arxiv.org/abs/2608.15788
作者: Xiaohan Zhang,Feng Gu,Xudong Rao,Xuhao Pan,Tao Wei,Zhou Pan,Kun Zhan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial intelligence requires foundation models to maintain coherent spatial state across interactions with the physical world. However, existing data-centric approaches typically treat spatial reasoning as independent question-answer instances, enabling shortcut-based answering and providing limited supervision for persistent spatial understanding. To address this, we introduce ChainSpace, a chained-reasoning paradigm that structures spatial reasoning as a state-preserving multi-round process. In this paradigm, spatial questions are organized into logically constrained and jointly consistent chains, where later questions depend on spatial constraints established in earlier rounds. Following this principle, we instantiate ChainSpace-Bench, a manually annotated real-world multi-round benchmark with a Chain-Aware Metric, and ChainSpace-Pipeline, a simulator-based chain-structured supervision generation framework for spatial intelligence training. Experiments show that ChainSpace-Bench exposes chain-level failures that are not captured by isolated question accuracy. Additionally, with a relatively small amount of simulator-generated chained data, models trained by ChainSpace-Pipeline achieve the best performance among open-source models on ChainSpace-Bench and transfer competitively to multiple external spatial intelligence benchmarks. These results establish ChainSpace as an effective paradigm for more faithful evaluation and more data-efficient learning of spatial intelligence.
[CV-107] RoofGS: Roofline-Guided End-to-End Acceleration of 3D Gaussian Splatting
链接: https://arxiv.org/abs/2608.15785
作者: Yang Luo,Yan Gong,Yongsheng Gao,Jie Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) enables real-time novel-view synthesis but remains limited on GPUs at high resolutions. Through a stage-wise Roofline characterization, we identify two distinct hardware bottlenecks: global memory traffic dominates the front end, whereas instruction throughput limits rasterization. Guided by this analysis, we develop RoofGS, a rendering framework that applies bottleneck-specific optimizations rather than generic kernel acceleration. For the memory-bound front end, we design a resolution-adaptive quantized depth sorting key that compresses each key to 32 bits. For the compute-bound rasterizer, we introduce a range-aware bit-level fast exponential approximation tailored to the bounded exponent range after opacity culling, with a derived per-pixel error bound. These two core techniques are complemented by additional optimizations (kernel fusion, compact attribute storage, culling, dual-pixel evaluation) that additionally reduce memory traffic and improve instruction-level parallelism. Experiments show that RoofGS achieves a 10.1 \times end-to-end speedup over 3DGS at 4K on an RTX 4090, increasing throughput from 61 to 616 FPS, with only a 0.028 dB PSNR loss.
[CV-108] Beyond Independence: Learning Correlated Views for Variational Incomplete Multi-View Clustering
链接: https://arxiv.org/abs/2608.15757
作者: Zheming Xu,Aiyue Tang,Shidi Chen,Xuechao Zou,Congyan Lang,Rogelio A. Mancisidor,Michael Kampffmeyer
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Incomplete multi-view clustering (IMVC) aims to uncover shared cluster structures from data with partially observed views. Although recent imputation-free methods based on variational inference demonstrate robustness to missing views, they commonly rely on a conditional independence assumption across views in the posterior aggregation stage, which fails to capture the inherently structured and potentially correlated nature of multi-view data. In this paper, we propose a variational framework that explicitly goes beyond this assumption by introducing a learnable cross-view correlation structure. Specifically, we explicitly model and learn correlations between views by utilizing the covariance structure of posterior estimation errors during aggregation. To facilitate robust and efficient learning, the correlation matrix is parameterized through a normalized Cholesky decomposition, ensuring positive definiteness and enabling the entire model to be trained jointly through a unified variational objective. Extensive experiments on multiple IMVC benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches across diverse missing-view settings while introducing only a negligible number of learnable parameters. These results highlight the effectiveness of adaptive correlation modeling in variational IMVC, demonstrating the need to go beyond the independence assumption in IMVC. The code is available at this https URL.
[CV-109] ES3D: Embedding Semantics into 3D Space for Component-Aware Editing
链接: https://arxiv.org/abs/2608.15749
作者: Xuancheng Jin,Rengan Xie,Jiayuan Lu,Wenting Zheng,Rui Wang,Yuchi Huo,Lincheng Li,Yingfeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Existing 3D editing methods have made notable progress in controllability, yet they remain limited in several important ways. Most approaches rely on text-driven editing, which struggles to express fine-grained visual changes intended by the user. Moreover, many methods require manually supplied 3D masks or introduce unintended changes to regions that should remain untouched. These limitations largely arise from the absence of fine-grained semantic understanding, making it difficult for existing models to retrieve or modify specific 3D components. We introduce ES3D, a framework that embeds semantics directly into 3D space, enabling component-aware retrieval and editing of a 3D asset conditioned on multiple local reference images and optional text queries. We first construct a 3D semantic embedding by projecting multi-view semantic features into the voxelized space of the asset. We then perform 3D component retrieval by computing feature similarity between the 3D semantic embedding and the semantic embeddings of image or text queries. For editing, we employ a pretrained 3D generative model with an inpainting mechanism to modify the retrieved components guided by user-provided images while preserving the rest of the asset. Overall, ES3D is a 3D editing framework that retrieves editable regions based on semantic cues and uses multiple images as conditions. Extensive experiments demonstrate that ES3D produces geometrically consistent and semantically coherent edits, enabling robust image-based and text-assisted control for 3D editing. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.15749 [cs.CV] (or arXiv:2608.15749v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.15749 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-110] Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification
链接: https://arxiv.org/abs/2608.15731
作者: Haadia Amjad,Ronald Tetzlaff
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Published at EXPLAINABILITY2025
Abstract:Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust. In real-world computer vision tasks, these models often operate on complex images containing background noise and are heavily annotated. To make such models explainable, Concept-based Explainable AI (CXAI) methods need to be assessed for their applicability and problem-solving capacity. In this work, we explore CXAI use cases in multi-label classification by training two DNNs, VGG16 and ResNet50, on the 20 most annotated labels in the MS-COCO dataset (Microsoft Common Objects in Context). We apply two CXAI methods, CRP (Concept Relevance Propagation) and CRAFT (Concept Recursive Activation FacTorization), to generate concept-level explanations and investigate the overall evaluations. Our analysis reveals three key findings: (1) CXAI highlights learning weaknesses in DNNs, (2) higher concept distinctiveness reduces label and concept confusion, and (3) environmental concepts expose dataset-induced biases. Our results demonstrate the potential of CXAI to enhance the understanding of model generalizability and to diagnose bias instigated by the dataset.
[CV-111] Anatomical and Physical Supervision for CT-less PET Attenuation Correction: BIC-MAC 2026 Challenge
链接: https://arxiv.org/abs/2608.15721
作者: Petros Chatzitoulousis,George K. Matsopoulos
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 1 table. Technical report for the BIC-MAC 2026 Challenge
Abstract:This report describes our submission to the Big Cross-Modal Attenuation Correction (BIC-MAC) 2026 Challenge for CT-less PET attenuation correction through multimodal pseudo-CT synthesis. We build upon a standard nnU-Net architecture and combine anatomical and physical supervision to improve both pseudo-CT quality and downstream PET reconstruction. Anatomical supervision is introduced through a frozen TotalSegmentator feature extractor, anatomy-guided structural constraints and patch sampling, while physical supervision is achieved using a differentiable attenuation correction factor projection loss based on multi-angle attenuation projections. Furthermore, the network is initialized with pretrained weights obtained from training on the SynthRAD Challenge MR-to-CT dataset. Minimal architectural modifications are applied, while performance improvements are pursued across the nnU-Net pipeline, including preprocessing, plans, and supervision design, among other components. Our final submission demonstrates the effectiveness of combining anatomical supervision, attenuation physics, and efficient nnU-Net scaling for CT-less PET attenuation correction.
[CV-112] YOLO26-RD: An End-to-End Road Damage Detection Network With Learnable Contrast Enhancement and Edge-Guided Downsampling
链接: https://arxiv.org/abs/2608.15713
作者: Sompote Youwai,Pawarotorn Chaipetch,Hathairat Samaikul,Theerayut Yonseng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Automated pavement-distress detection is commonly framed as a small-object problem, motivating high-resolution P2/4 detection heads and lossless downsampling. We present YOLO26-RD, an end-to-end (NMS-free) detector built on YOLO26 with two lightweight novel modules (LearnableContrast, a 494-parameter differentiable analogue of CLAHE that adapts contrast per tile inside the network, and EdgeSPD, a Sobel-gated space-to-depth downsampler adding only 2 parameters over SPD-Conv), and we subject the design to a data-first audit on a 7,618-image road-survey dataset (alligator crack, linear crack, patching). The audit falsifies the small-object premise: 92% of instances are COCO-large, and linear cracks are extreme-aspect structures (median 10:1) whose difficulty is sensitivity, not localization. Guided by this analysis, we remove the P2 detection level while retaining P2 features in the fusion path, which improves mAP50 by 2.8 points over the full YOLO26-RD model and reduces epoch time by 8%. Trained from scratch at 640x640, our best screening configuration reaches 0.787 mAP50 on the validation split versus a 0.771 project baseline (a stock YOLO26-s of uncontrolled recipe), with the largest per-class gain on the rarest class (patching, 2.6 points over baseline; 9.4 over the unmodified YOLO26-RD control under an identical recipe). A failure-mode decomposition further attributes the residual error of the bottleneck class (crack, approximately 0.74 across all architectures tested) to train/validation distribution shift on crack orientation and length, sub-pixel crack width at 640x640, and label incompleteness, factors no architecture change can address. We argue that for pavement imagery, measurement-driven subtraction outperforms module accretion, and we release our audit protocol alongside the model.
[CV-113] What You Ask is What You Ground: Bridging Question Intent to Temporal Evidence for Grounded VideoQA ECCV2026
链接: https://arxiv.org/abs/2608.15708
作者: Jinhwan Seo,Kyubeom Han,Jumin Lee,Junhyug Noh,Sung-eui Yoon
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at ECCV2026. Code: this https URL . Project page: this https URL
Abstract:We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identical temporal segments for different questions about the same video. We trace this behavior to two structural limitations in prior common designs: (i) modality isolation that fixes video representations before they receive question semantics, and (ii) weak question injection inside the grounding module. To address this, we propose GroundFormer, which conditions video features on question intent before localization via learnable communication tokens that mediate directed visuo-lingual interaction. On top of the question-conditioned features, a factorized MIL cross-attention couples answer selection with temporal evidence under candidate-level supervision, while Gaussian smoothing converts peaked attention into temporally coherent segments. We further introduce a hierarchical multi-modal contrastive loss that aligns video, question, and answer embeddings across a two-pass training pipeline. GroundFormer achieves state-of-the-art grounded VideoQA performance on NExT-GQA and STAR, substantially improving question-discriminative temporal grounding.
[CV-114] PixelControl: Fine-Grained Condition Fidelity in Text-to-Image Diffusion
链接: https://arxiv.org/abs/2608.15705
作者: Xin Lin,Haodong Li,Zhifei Zhang,Yutong Yang,Haitian Zheng,Juanxi Tian,Zhe Lin,Truong Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The project homepage can be found: this https URL
Abstract:Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as object boundaries, thin contours, and medium/small conditioned regions. This limitation is especially problematic for VAE-based latent diffusion, where spatial compression can weaken high-frequency and low-area condition signals. We propose PixelControl, a pixel-space controllable diffusion framework for fine-grained condition fidelity. Built on a PixelDiT-style backbone, PixelControl avoids the latent bottleneck and introduces two complementary designs. First, Structure-Aware Control Injection derives a condition structure map and uses it to strengthen injected control residuals around spatially sensitive regions. Second, Multi-Scale Pyramid Cycle Loss verifies generated images against condition-derived structures across multiple resolutions, balancing global layout consistency with local boundary and detail accuracy. PixelControl supports depth, segmentation, edge, and their combinations through modality-specific control branches with lightweight gated fusion. Experiments across depth, segmentation, and edge control show that PixelControl improves structural fidelity and visual quality over existing controllable generation methods, with especially strong gains on boundaries and medium/small conditioned regions. The project page can be found at: this https URL
[CV-115] Bitstream Action Recognition is Byte Modeling
链接: https://arxiv.org/abs/2608.15695
作者: Fangcheng Li,Chaoran Huang,Tianyi Liu,Wenyang Liu,Kejun Wu,Qiong Liu,You Yang,Zhengguo Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages; supplementary material included
Abstract:Conventional action recognition typically relies on successful pixel decoding of the bitstream. However, bitstream corruption during storage or transmission may cause severe visual artifacts or even decoding failure, posing a significant challenge to reliable action recognition. Bitstream Action Recognition (BAR) aims to overcome the dependency on decoding and the vulnerability to corruption. In this paper, we propose a novel BAR framework, Bitstream Recognition via Anchoring Corrupted Embeddings (BRACE). BRACE is a dual-branch byte-modeling architecture that treats a corrupted bitstream and its intact counterpart as two byte realizations of the same action. This guides the generation of rich and stable representations for robustness to corruption through Intact-Anchored Representation Alignment (IARA). The intact representation serves as a stable anchor, and the corrupted one is aligned to it at the embedding and decision levels under Unreliable-Anchor Suppression (UAS), entirely in representation space and without repairing the bitstream. To address the scarcity of corrupted bitstreams in practice, we introduce the Real-world Bitstream Corruption Simulator (RBCS), a four-parameter simulator that reproduces bit-flip and byte-loss errors arising in transmission and storage. Building on RBCS, we construct the first large-scale BAR dataset (BAR-D), which comprises the BAR-Stanford40 and BAR-PPMI subsets and spans diverse corruption types and severity levels. Finally, we build a large benchmark on BAR-D involving 14 action recognition methods from the pixel, compressed, and bitstream domains. Extensive experiments demonstrate that BRACE has superior robustness to bitstream corruption than all comparison methods. Ablation studies further validate the effectiveness of the proposed RBCS augmentation and IARA.
[CV-116] RRFC: Recursive Refinement via Feedback Conditioning for Iterative Image-to-Image Generation
链接: https://arxiv.org/abs/2608.15694
作者: Kareem Hassani,Chaymaa Abbas,Hadi Al Mubasher,Mariette Awad
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Conditional image-to-image generators are single-shot: they map input features to an output in one forward pass and treat it as final, with no opportunity to improve on it. Although trained to produce the best possible result in one step, such a model leaves room for improvement if it can adaptively revise its own output over iterations. We propose Recursive Refinement via Feedback Conditioning (RRFC), a novel feedback-conditioning framework for iterative output refinement that teaches a model to adaptively revise its output by conditioning on a new signal, namely its most recent previous prediction, which is fed back as an auxiliary set of channels alongside the original input. This preserves the generator’s core architecture while modifying its conditioning interface and, depending on the model family, its training or inference procedure, so RRFC can be attached to existing generators without redesign. We evaluate RRFC across six baselines spanning adversarial, equilibrium, and diffusion-based models and three paired image-to-image translation tasks. Across 18 architecture-task settings, RRFC yields seven Holm-corrected improvements, seven degradations, and four non-significant changes. The gains concentrate on reconstruction-fidelity and identity settings, while five of the seven degradations fall on the single semantic-layout task, where every model declines. These results indicate that feedback-based refinement helps when its objective overlaps with the evaluated property, and that its gains concentrate on the tasks where that overlap holds.
[CV-117] Automated Fetal Brain MRI Biometry in Healthy and Pathological Cases MICCAI2026
链接: https://arxiv.org/abs/2608.15692
作者: Ema Masterl,Tina Vipotnik Vesnaver,Nejc Šubič,Žiga Špiclin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the PIPPI Workshop of MICCAI 2026
Abstract:Automated biometric analysis of fetal brain MRI enables reproducible, observer-independent quantitative assessment, yet existing methods are often restricted to few measurements or evaluated only on healthy cases. We assemble and evaluate an automated biometric analysis pipeline that localizes 22 anatomical landmarks on NeSVoR-reconstructed 3D volumes and derives 11 clinically relevant measurements spanning supratentorial, ventricular, cerebellar, and midline structures. We compare two landmark localization models, H3DE-Net and SCN, on a heterogeneous cohort of 122 acquisitions (both healthy controls and range pathologies). Localization accuracy was assessed with a linear mixed-effects model, agreement with normative growth trajectories with calibrated centile charts, and diagnostic utility with a decision tree classifying VM severity. H3DE-Net achieved significantly lower localization error than SCN across all landmarks (mean 1.36 mm vs. 3.58 mm in HC and 1.90 mm vs. 4.13 mm in PC; p 0.001), and outperformed a GA-based regression baseline in 7 of 11 measurements. H3DE-Net measurements yielded higher classification AUC in every diagnostic group, with the clearest advantage in separating healthy controls from VM. Decision tree thresholds for ventricular width fell near the clinical 10 mm and 15 mm cut-offs used to define and grade VM.
[CV-118] raining-Free Long-Term Multi-Object Tracking for Sports Video Analytics
链接: https://arxiv.org/abs/2608.15688
作者: Tomasz Stanczyk,Seongro Yoon,Francois Bremond
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-term multi-object tracking in sports remains challenging due to frequent occlusions, rapid camera motion, and repeated player reappearances. We introduce McByte++, a training-free tracking-by-detection framework that integrates lightweight mask propagation, conditional camera motion compensation, and online re-identification within a unified pipeline. Compared to its predecessor, McByte++ substantially improves runtime efficiency while enhancing identity preservation. On SoccerNet-tracking and SportsMOT benchmarks, McByte++ achieves up to +3.0 HOTA and +6.1 IDF1 improvements over the original McByte in the online setting, with further gains when combined with offline global association. Replacing heavy segmentation components and optimizing motion modeling yields up to an order-of-magnitude speed increase. All results are obtained without detector retraining or dataset-specific tuning. Code will be made available at this https URL.
[CV-119] Counterfactual Sensitivity Is Not Repairability: Auditing Replay Probes for Video Evidence
链接: https://arxiv.org/abs/2608.15685
作者: Rama AlHamidi,Rasul Khanbayov,Erchin Serpedin,Hasan Kurban
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 2 figures. Code: this https URL
Abstract:Tool-using video agents retrieve visual evidence before answering, but the final answer is not forced to depend on what was retrieved. The natural black box test is counterfactual: destroy the semantic content of the frames the agent retrieved and check whether the answer changes, against a matched sham that re-executes the identical pipeline on those same frames. We introduce CARVE, a black-box counterfactual probe that compares answer changes under matched SHAM and DESTROY replays. Across three independent k=3 runs on a frozen VideoExplorer-style agent, DESTROY changes the answer 29.3 percentage points more often than SHAM, yielding a large and reproducible aggregate effect. Question-level scores are less stable, and increasing the replay budget from k=3 to k=10 reduces ties but weakens the original zero-threshold routing policy. At k=3, CARVE selects 538 of 1,258 LVBench questions and improves accuracy by 3.26 points, with higher fallback yield than most matched random subsets. The score shows only a weak association with annotated temporal coverage, so CARVE is best understood as a routing signal rather than a direct grounding classifier. Our implementation is available at this https URL.
[CV-120] BASeg: Boundary-Aware Remote Sensing Segmentation with Structural Penalties
链接: https://arxiv.org/abs/2608.15683
作者: Yuexi Song,Kailai Sun,Zhuoyu Wang,Mingyi He,Paul Pu Liang,Shenhao Wang,Jinhua Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic segmentation is a core computer vision task in the remote sensing field, accelerating advancements in ur- ban development, agriculture, ecology, water resources, and environmental monitoring. However, recent methods usually struggle to capture fine-grained object features and bound- ary details. Besides, current widely used datasets often lack city morphology diversity and segmentation on generative im- ages remains largely unexplored. To address these issues, we propose a Mahalanobis-Angle Boundary Loss (MABL) that explicitly enhances boundary and shape consistency. MABL jointly models structural importance and boundary orientation through Mahalanobis distance-based weighting and angle- aware penalty. It can be readily integrated into diverse seg- mentation architectures and consistently improves their accu- racy. Built upon MABL, we introduce BASeg, a boundary- aware remote sensing segmentation framework with Struc- tural Penalties. BASeg integrates a Global Visual State Space module (GSM) with a Cross-Feature Fusion module (CFM) to capture both long-range contextual dependencies and fine- grained local details. Additionally, we establish a global 10- city benchmark dataset (GCD-25k) to facilitate accurate build- ing and road segmentation. Extensive experiments on four remote-sensing benchmarks demonstrate that BASeg consis- tently outperforms existing methods, achieving up to a 2.8% improvement in mIoU while producing more accurate object boundary segmentation across diverse scenes. Moreover, integrating MABL into multiple existing segmentation archi- tectures consistently improves performance across datasets, demonstrating its robustness and broad applicability.
[CV-121] WorldRover: A Scalable Synthetic Video Data Engine for World Exploration with Rich Annotations
链接: https://arxiv.org/abs/2608.15659
作者: Xiaojie Xu,Zhengyuan Lin,Runyi Li,Yihao Liu,Kaipeng Zhang,Yongtao Ge
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注:
Abstract:Learning to generate or reconstruct explorable worlds requires video paired with more than RGB: camera motion, scene geometry, temporal correspondence and, for interactive models, control signals. Real capture can provide some of these signals, but dense geometry and long-range correspondence usually rely on estimation or specialised instrumentation. Rendering provides these quantities directly, yet existing synthetic resources rarely combine them on the same frames while also supporting controlled changes of viewpoint and appearance. We introduce WorldRover, a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental states. Using WorldRover-Engine, we construct WorldRover-10M, whose sequences pair RGB with metric depth, camera trajectories, and trajectory-derived action signals throughout each exploration. Third-person subsets additionally provide dense optical flow, long-range 2D/3D point tracks with visibility, and a character trajectory distinct from the camera trajectory. The engine can render a traversal from first-person, third-person and 360 panoramic viewpoints, under different environmental states or with a neutral white material, while preserving the route and scene geometry. WorldRover therefore turns long-horizon world exploration into a scalable data-generation problem, providing supervision for models that must build, maintain, and revisit coherent representations of an explorable world.
[CV-122] Scalable Black-Box Model Attribution for Images
链接: https://arxiv.org/abs/2608.15652
作者: Asaf Livne,Amir Jevnisek,Shai Avidan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existing methods have grown as elaborate as the generators they target, on the as- sumption that a more sophisticated model demands a more sophisticated attributor. We show it does not. RPA (Raw- Patch Attribution) attributes images in the strictest black- box setting with a lightweight CNN. Despite its simplicity, it attributes more models at higher accuracy than prior work, reaching 98.0% on 25-class DRAGON and 92.9% on 27- class OpenFake; it is data-efficient and runs at a cost inde- pendent of the number of candidate models; and it stays ro- bust to the compression, blur, and resizing images undergo in the wild. Training for closed-set attribution yields a ver- satile feature extractor: the same representation recovers model lineage without supervision, flags and groups unseen generators, and admits new models through few-shot adap- tation rather than retraining.
[CV-123] Gaussian-JEPA: Joint-Embedding Predictive Learning for 3D Gaussian Splats
链接: https://arxiv.org/abs/2608.15651
作者: Bin Ren,Qi Ma,Yue Li,Zongyan Han,Yidi Li,Yuqian Fu,Rao Muhammad Anwer,Theo Gevers,Fahad Shahbaz Khan,Salman Khan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Joint-embedding predictive representation learning for 3D Gaussian Splatting
Abstract:3D Gaussian Splatting (3DGS) represents 3D content with anisotropic primitives that jointly encode geometry and appearance. Fixed-budget encoders consume sampled observations of Gaussian assets, so the same object may be observed through different primitive realizations. Existing self-supervised methods mainly reconstruct masked Gaussian attributes, tying supervision to one sampled realization and requiring an input-space decoder. Latent prediction offers an alternative, but its application to Gaussian tokens requires targets that accommodate coupled attributes and heterogeneous spatial support. We introduce Gaussian-JEPA, which predicts representations of held-out Gaussian token blocks from visible context. An online encoder processes the context, while a shared exponential-moving-average encoder supplies stop-gradient features for multi-scale targets. Complementary target projections and feature-space grounding provide latent supervision without reconstructing Gaussian attributes. We evaluate the features under Gaussian resampling, partial observations, and renderable shape completion, together with transfer to part segmentation and object classification. Compared with matched reconstruction pretraining, Gaussian-JEPA is more consistent across resampled inputs, retains more instance information under partial observations, and provides stronger frozen features for Gaussian completion. These results support latent prediction as an effective objective for reusable 3D Gaussian representations. Code is on the project page (this https URL).
[CV-124] Hierarchical Adaptive Feature Refinement Network for VHR Remote Sensing Image Segmentation ICIP
链接: https://arxiv.org/abs/2608.15647
作者: Shuaishuai Cao,Meng Tang,Shuwei Peng,Xuan Liu,Min Huang,Jie Chen,Jiacheng Niu,Yong Chen,Edore Akpokodje,Hui Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 17 pages, 11 figures, 11 tables. Submitted to IEEE Transactions on Geoscience and Remote Sensing (TGRS). Code and model weights are available at this https URL
Abstract:Semantic segmentation of very-high-resolution (VHR) remote sensing imagery increasingly benefits from strong pretrained hierarchical encoders, yet exploiting their multi-stage representations remains difficult. Nearby regions demand different balances between fine detail and semantic context, aggressive task-specific transformations perturb useful pretrained features, and conventional semantic supervision provides limited structural guidance. We present HAFR-Net, a progressive refinement framework that adaptively organizes and conservatively refines hierarchical representations instead of replacing them with a monolithic decoder transformation. Heterogeneity-Guided Stage-Adaptive Fusion (HG-SAF) predicts dense stage weights conditioned on local feature variation. A Frequency-Residual Adapter (FRA) then injects frequency information through a bounded, zero-initialized residual branch that keeps the fused representation as its reference. A Confusion-Aware Tri-Prior Decoder (CATP) finally regularizes the prediction with boundary, objectness, and training-derived class-relation cues. Under a matched Swin-B training and single-scale inference protocol, HAFR-Net attains 84.12%, 87.86%, 55.17%, and 67.70% mIoU on ISPRS Vaihingen, ISPRS Potsdam, LoveDA, and OpenEarthMap, improving the matched UPerNet baseline by 0.55, 0.95, 1.55, and 1.84 percentage points, respectively. Controlled analyses further show consistent spatial reweighting beyond content-only routing, improved boundary and thin-structure accuracy over matched spatial and spectral alternatives, and reduced confusion on pre-declared class pairs.
[CV-125] EgoGazeLite: On-Device Egocentric Gaze Prediction for Token-Efficient Multimodal LLM Video Input ECCV2026
链接: https://arxiv.org/abs/2608.15614
作者: Matteo Stoiber,Niels Buus Lassen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 16 pages. Accepted at the WearableAI Workshop, ECCV 2026 (Archival Track)
Abstract:The use of multimodal LLMs (MLLMs) for egocentric video understanding with wearable devices is constrained by the token budget. Memory and compute cost scale with the number of visual tokens, and high-resolution video quickly becomes expensive to transmit and process at scale. Prior work (GazeLLM) addresses this by cropping the video around the camera wearer’s gaze. This reduces the number of visual tokens by about tenfold while maintaining or improving the quality of full-resolution descriptions. However, this compression strategy depends on dedicated eye-tracking hardware, which is unavailable on consumer smart glasses. Building a software-only substitute poses a joint constraint: the predictor must be accurate enough to preserve downstream description quality, yet light enough to run on-device, within the power and compute budget of a smartphone. We address this with EgoGazeLite, a lightweight dual-process gaze predictor for egocentric video. Across two MLLMs, three automated metrics, and two LLM judges, predicted-gaze crops show no significant difference from ground-truth-gaze crops. Equivalence is confirmed in all ten cases. EgoGazeLite achieves this at 15.7M parameters, 6.71 GFLOPs, and runs the full gaze-and-crop pipeline end-to-end in real time (21.6 ms/frame) on consumer accelerator hardware. Together, these results remove the need for eye-tracking hardware for token-efficient, gaze-conditioned egocentric video understanding with MLLMs.
[CV-126] AlloEgo-VLM: Disambiguating Allocentric and Egocentric Reference Frames in Vision-Language Models
链接: https://arxiv.org/abs/2608.15605
作者: Kuan-Lin Chen,Tzu-Ti Wei,Chao-Chi Liao,Yu-Chee Tseng,Jen-Jee Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 9 figures. Project page and code available at this https URL
Abstract:This study investigates the challenge of ambiguity faced by Vision-Language Models (VLMs) in understanding spatial semantics. Spatial cognition, shaped by cognitive psychology, spatial science, and cultural context, often assigns directionality to objects. However, natural language descriptions of spatial relations frequently omit explicit reference frames, leading to semantic ambiguity and potentially serious errors for embodied AI robots. Existing VLMs, due to insufficient training on reference frames and object orientations, often produce inconsistent responses. To address this issue, we construct a new dataset, AlloEgo-View, comprising (image, query, view-specific answer) triplets that capture key object relations from both allocentric and egocentric perspectives. The view-specific descriptions follow a structured spatial representation that annotate detailed scene descriptions, reference and target objects, their orientations, reference frames, and view types. Building on AlloEgo-View, we develop AlloEgo-VLM, a framework to disambiguate allocentric and egocentric reference frames, even under ambiguous queries, and to be easily integrated into existing VLMs via supervised fine-tuning. Furthermore, we deploy our framework onto an embodied robotic platform within NVIDIA Isaac Sim to validate its real-world feasibility in open-ended object searching tasks. Experiments highlight the limitations of current VLMs in handling view-specific queries and demonstrate the strong disambiguation ability of AlloEgo-VLM.
[CV-127] PoseAdapter: Dual-Stream 2.5D Controllable Image Generation for Complex Multi-Object Scenes
链接: https://arxiv.org/abs/2608.15583
作者: Yufeng Chi,Huimin Ma,Fan Gao,Zhice Niu,Keqin Li,Jianmin Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures
Abstract:While Text-to-Image (T2I) diffusion models have achieved remarkable success, precise spatial and orientational control in multi-object scenes remains a persistent challenge. Existing methods either rely on computationally expensive dense 3D maps or suffer from severe attribute leakage and “cut-and-paste” artifacts. To address these limitations, we propose PoseAdapter, a lightweight framework for high-fidelity 2.5D controllable image generation. Instead of dense spatial maps, it establishes precise spatial-angular anchors using an efficient condition layout: individual object captions, 2D bounding boxes, and 3D angles. To resolve the generative trade-off between strict instance isolation and global coherence, we introduce a Context-Aware Dual-Stream Representation. By injecting local object tokens and relation-enriched scene tokens into the visual stream of modern MM-DiT architectures via parallel masked and unmasked pathways, PoseAdapter eliminates attribute leakage while preserving natural inter-object relationships and scene-level coherence. To support this paradigm, we construct OrientLayout, a high-quality dataset featuring standardized 2.5D annotations and instance-level decoupled semantics. Extensive experiments demonstrate that PoseAdapter outperforms state-of-the-art baselines in spatial accuracy, orientational precision, and multi-object visual fidelity. Code and dataset will be available at this https URL.
[CV-128] From Generalist to Specialist: A Context-Fusion Framework for Endoscopic Polyp Reporting with a Frozen VLM
链接: https://arxiv.org/abs/2608.15580
作者: Ruijie Yang,Yan Zhu,Peiyao Fu,Siyuan Li,Te Luo,Zhihua Wang,Quanlin Li,Pinghong Zhou,Xian Yang,Shuo Wang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically meaningful morphological description within a single record. General-purpose vision-language models (VLMs) offer a unified interface for image understanding and report generation. Existing specialization strategies, however, typically rely on task-specific models or model-weight adaptation, leaving unresolved how to introduce reliable specialist knowledge while preserving both this unified interface and the VLM’s pretrained capabilities. We introduce a context-fusion framework that specializes a frozen general-purpose VLM through both implicit instruction context and explicit transduction context without modifying its pretrained weights. Specifically, a self-supervised polyp encoder retrieves related image-report pairs as explicit, query-specific evidence, while learned continuous specialist tokens provide implicit instruction context shared across cases. Experiments were conducted on 2,056 expert-annotated public endoscopic images. We compared the framework with general-purpose VLMs, task-specific predictors, and weight-adaptation methods to assess specialist performance, unified reporting, and adaptation efficiency. Across numerical, categorical, and report-generation metrics, the proposed framework substantially improved direct frozen-VLM inference and achieved the strongest overall performance among the evaluated methods. It added trainable parameters equal to only 0.006% of the frozen VLM’s parameter count. When the top-1 retrieved case carried the correct target category, our framework corrected 70.5% of the errors made by a weight-adaptation baseline. These findings support the context-fusion framework as a lightweight and effective strategy for specialist adaptation of a frozen VLM.
[CV-129] Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study
链接: https://arxiv.org/abs/2608.15574
作者: Yogesh Kumar
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Video question answering systems built on vision-language models often produce timestamped claims with high confidence even when unsupported by the cited frame. This deceptive hallucination arises because timestamps imply grounding without ensuring correctness, increasing user trust but not accuracy. We introduce a pipeline that closes this loop. A retrieval-augmented language model drafts answers with per-claim timestamp citations, and each cited frame is independently re-examined before being shown to the user. We compare against a plain baseline and ablate three verification designs, evaluated on both Apple Silicon (MLX) and Google Colab (HF Transformers, CUDA). Directly asking the vision model whether a frame supports a claim fails completely (0% catch rate on 40 claims) due to sycophancy. Blind re-captioning plus a general LLM judge improves results but is unstable, oscillating between 0% and 100% flagged depending on prompt phrasing. Replacing that judge with a small natural language inference model yields a stable, interpretable verifier that catches 79% of fabricated claims on adversarial false-premise questions while leaving true claims untouched. We release the full pipeline, evaluation harness, and implementations for both Apple Silicon and Colab. Code is available at this https URL.
[CV-130] RigidBench: Evaluating Rigid-Body Physics in Video Generation Models
链接: https://arxiv.org/abs/2608.15555
作者: Swarnim Jain,Shangzhe Wu
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 30 pages, including appendices. Code: this https URL . Dataset: this https URL
Abstract:Video models are increasingly used to predict what happens next in a scene, yet the metrics commonly used to compare their outputs say little about whether the predicted objects move correctly. Motion, geometry, identity, background stability, and visual similarity can fail independently, but whole-frame scores often mix these errors together. We introduce RigidBench, a simulator-grounded benchmark that compares a generated continuation with a reference rollout from the same initial frame and motion description. Its five rigid-body tasks vary objects, materials, viewpoints, and indoor and outdoor scenes, with per-frame masks, depth, 6-DoF trajectories, and contacts available for scoring. We evaluate eight models on the same 100 examples with ten measurements that keep these aspects separate. The resulting rankings depend strongly on what is measured: no model leads on all ten, and across model means, higher SSIM accompanies larger 3D trajectory error (r = 0.89). RigidBench also includes 5,000 training videos with exact simulator state, which we use to fine-tune and analyze Wan 2.2 TI2V-5B. Full fine-tuning reduces 3D trajectory error by about 20% with almost no change in SSIM, while teacher-forced probes and targeted interventions show that object position is represented throughout Wan’s diffusion transformer and used by its denoising computation.
[CV-131] CrossView: Can Vision-Language Models Reason Across Cameras? ECCV2026
链接: https://arxiv.org/abs/2608.15539
作者: Sahil Shah,S P Sharan,Harsh Goel,Manvik Pasula,Adithya Hebbalae,Minkyu Choi,Sandeep P. Chinchali
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Video understanding benchmarks have long centered on single-camera settings, where modern multi-modal language models achieve strong performance across image and video tasks. Yet, the real world runs on multi-camera networks: autonomous vehicles, security systems, and robots all gather data across many simultaneous views. We argue that this is not simply “more” of the single-camera problem; it is fundamentally different. Multi-camera reasoning requires handling context that scales with the number of views, resolving occlusions visible from only a subset of cameras, judging which views matter, and integrating evidence across perspectives that may overlap or diverge. Current models struggle with exactly these challenges, yet no benchmark systematically targets them. We introduce CrossView, a multi-camera video question-answering benchmark spanning autonomous driving, security surveillance, egocentric/exocentric video, and robotics. Evaluation of proprietary models, such as GPT-5.2, and open-source models, like Qwen3-VL, reveals consistently low accuracy, with open-source models trailing by a wide margin. Performance scales strongly with a model’s ability to jointly process multiple viewpoints, positioning CrossView as a rigorous benchmark for multi-camera video. We open-source our code and dataset at this https URL.
[CV-132] EA-LiteUNet: An Edge-Adaptive and Resource-Efficient U-Net for Boundary-Sensitive Dermoscopic Image Segmentation
链接: https://arxiv.org/abs/2608.15537
作者: Wang Jiangtao,Nur Intan Raihana Ruhaiyem,Fu Panpan,Yang Yu,Huang Yan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 29 Pages,13 figures, 8 tables
Abstract:Accurate boundary delineation remains a persistent challenge in dermoscopic image segmentation because of blurred lesion margins, heterogeneous textures, and complex background artifacts. From a signal-processing perspective, lesion boundaries represent high-frequency components that are highly susceptible to aliasing, noise amplification, and information loss. Consequently, repeated downsampling and feature transformations in conventional convolutional architectures often lead to severely degraded boundary representations. To address these limitations, we propose EA-LiteUNet, an edge-adaptive and computationally efficient U-Net variant specifically designed for boundary-sensitive medical image segmentation. The architecture integrates three core mechanisms: (1) boundary-aware representation learning to suppress aliasing and preserve high-frequency structural details; (2) attention-guided feature modulation to selectively enhance boundary-relevant responses across multi-scale features; and (3) a resource-adaptive inference strategy to dynamically balance segmentation accuracy and computational efficiency. Extensive evaluations across three public dermoscopic datasets demonstrate that EA-LiteUNet consistently achieves superior boundary precision. Specifically, on the ISIC 2018 dataset, the method significantly reduces the 95% Hausdorff Distance (HD95) to 12.89 pixels while maintaining a robust Dice score of 92.08%. Notably, this strong performance is achieved with an ultralightweight configuration of merely 0.29M parameters and 1.17 GFLOPs. Ablation studies further validate the complementary effects of these components, confirming their contribution to enhanced boundary fidelity and stable optimization.
[CV-133] Efficient Audio-Visual Generation via Synchrony-Aware Cross-Modal Sparse Attention
链接: https://arxiv.org/abs/2608.15522
作者: Shengchuan Gao,Teng Hu,Bohao Feng,Luchen Li,Wenqiang Wang,Hongqian Deng,Ran Yi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent audio-visual generation models can synthesize synchronized video and sound in a unified diffusion process, but their inference cost remains high because long video token sequences require repeated attention computation across denoising steps.A variety of acceleration techniques have been developed for video generation models, including low-bit quantization, attention sparsification, and feature this http URL, since these methods are originally designed for video generation, directly applying them to audio-visual models overlooks the interactions between the audio and video branches and may therefore disrupt audio-video this http URL present a synchronization-aware acceleration framework for efficient audio-visual this http URL key observation is that bidirectional audio-video cross-attention reveals structured interactions between the two branches, with high responses often concentrated on a few sound-related visual and temporal this http URL by this interaction pattern, we introduce a protected sparse attention strategy that preserves high-fidelity computation for synchronization-critical tokens while sparsifying redundant attention this http URL explicitly accounting for cross-modal dependence during acceleration, our method improves inference efficiency while keeping video quality, audio quality, and audio-video synchronization.
[CV-134] GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning
链接: https://arxiv.org/abs/2608.15517
作者: Zesheng Yang,Lingling Zhang,Xinyu Zhang,Cheng Zhang,Pengyu Li,Heng Wang,Lin Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Chain-of-thought reasoning has substantially improved the problem-solving capabilities of multimodal large language models. Fine-grained visual evidence, however, remains difficult to preserve and reuse across text-based reasoning steps. To address this limitation, tool-augmented thinking-with-images methods maintain visual access externally by revisiting or manipulating the image, but require predefined tools and additional inference-time processing. As an internal alternative, continuous visual latent reasoning retains intermediate computation in hidden states. However, its prevailing autoregressive construction makes each latent state depend on its predecessors, so later states may repeat information already present in the latent sequence rather than capture complementary visual details. We introduce GLaQ, a grounded latent-query framework that replaces sequential latent rollout with a fixed set of context-conditioned queries grounded in the original visual tokens. The grounded queries are reinjected for answer generation, providing direct and coordinated access to source visual evidence. We train GLaQ with localized-view supervision followed by reinforcement learning under task-level rewards. Across five benchmarks for fine-grained visual understanding and perception, GLaQ-7B gains 5.99–9.66% over its base model and leads all compared visual latent methods, suggesting that direct query-to-image grounding can recover localized evidence from the full image without external visual operations or autoregressive latent rollouts.
[CV-135] Population Structure Analysis of an Inbred Population using Quantitative Shape Phenotyping from Stereo Retinal Photographs
链接: https://arxiv.org/abs/2608.15471
作者: Li Tang,Michael D Abramoff
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注:
Abstract:The population structure of an inbred population of 781 people on Norfolk Island in the Pacific, 318 of which are descendants of the original Mutineers of the Bounty, is analyzed phenotypically using shape from stereo retinal fundus photographs. Three-dimensional optic nerve head (ONH) shape is reconstructed from stereo pairs by a multi-scale stereo matching algorithm. Using deep neural network, the shape of ONH, which is under genetic control, is decomposed into a set of hierarchical features through self-taught learning. Features captured at different levels are selected according to their discriminant power in identifying the two populations. The prediction accuracy is evaluated with stratified cross validation. Given the selected feature set, individuals are grouped into k hierarchical clusters and cluster membership fractions are determined for k=2,3,4,5,6,7. Population structure analysis on the basis of phenotypes through image analysis allows heritability and linkage analysis, including founder effects from English and Polynesian ancestors, potentially leading to new genetic risk factors for glaucoma and other ONH-related eye diseases.
[CV-136] AlignJEPA: Predictive Vision-Language Alignment for Remote Sensing Foundation Models
链接: https://arxiv.org/abs/2608.15456
作者: Md Aminur Hossain,Omkumar Vaghasiya,Rajeev Ranjan Dwivedi,Vinod Kurmi,Biplab Banerjee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages
Abstract:Remote sensing (RS) foundation models provide transferable Earth observation representations across sensors, resolutions, and geographies, yet most remain weakly aligned with natural language, limiting natural-language archive search, image-text retrieval, and question-conditioned analysis. We propose AlignJEPA, a JEPA-inspired predictive vision-language alignment framework for remote sensing foundation models. AlignJEPA uses a pretrained AnySat visual encoder and a RemoteCLIP text encoder while training only a lightweight predictive alignment network. Instead of relying on global image–text contrastive alignment alone, the framework predicts remote-sensing text embeddings from masked visual foundation-model tokens. Its mask-aware multi-scale predictive aligner aggregates visible tokens at fine, regional, and global scales, jointly models them with a cross-scale Transformer, and projects the resulting representation into the text space using learned query pooling. Training combines semantic prediction with bidirectional contrastive retrieval. We train and evaluate AlignJEPA on this http URL for natural-language Sentinel retrieval, evaluate cross-dataset adaptation on RSICD, and use RSVQA only as a closed-set representation probe. AlignJEPA provides a parameter-efficient route for aligning Earth observation foundation models with language.
[CV-137] Spatially-Grounded Flow Matching: Structured Source Distributions for Image Generation
链接: https://arxiv.org/abs/2608.15452
作者: Arman Zarei,Mahdi M. Kalayeh
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Current flow matching models learn to transport the source i.i.d. Gaussian noise into the target distribution of natural images, yet this source distribution carries no notion of spatial structure. Images however are fundamentally local since nearby pixels are strongly correlated. By sampling the noise independently, we hypothesize that models are implicitly encouraged to exploit less noisy neighbors as context during training, partially bypassing the need to properly learn the true local structure of images. The source distribution, in other words, works against the inductive bias of the image domain. To ameliorate this design discrepancy, we propose StructFlow which encodes spatial locality directly into the source by having the pixels within a small region share a common noise component. This structured source produces transport paths that are geometrically aligned with image regions - enabling properties that generic flow matching struggles to provide: fine-grained local editing that naturally respects boundaries, robust structure preservation, and smooth semantic interpolation between images. We show that these benefits also extend to large pre-trained models, demonstrating that StructFlow can even be incorporated through a lightweight post-training phase. Comprehensive experiments on multiple datasets, in unconditional, class and text-conditioned regimes, using different diffusion transformer architectures confirm that StructFlow not only offers competitive image generation quality, but also significantly improves localized controllable re-synthesis.
[CV-138] MM-BEV: Enhancing Timeliness by Computing Where and When it Matters
链接: https://arxiv.org/abs/2608.15437
作者: Liangkai Liu,Kang G. Shin
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Systems and Control (eess.SY)
备注: 12 pages, 20 figures
Abstract:Multimodal bird’s-eye-view (BEV) perception combines LiDAR depth accuracy with dense camera semantics, but its high computational cost and imperfect sensing conditions make real-time deployment challenging. Existing methods largely compress individual detectors and overlook three opportunities: structured sparsity within camera and LiDAR inputs, timing misalignment between modalities, and the fact that many detected objects do not affect the planner’s immediate action. We present MM-BEV, a real-time multimodal BEV system guided by a simple principle: compute where and when it matters. MM-BEV divides perception into mandatory work for safety-critical objects within braking distance of the ego vehicle and with short time-to-collision (TTC), and optional work for less urgent regions. It prioritizes mandatory work and reduces or sheds optional work under tight compute budgets. MM-BEV integrates four mechanisms: (1) a criticality-ranked temporal ROI selector based on motion-extrapolated detections from prior frames; (2) sparse, ROI-aware feature extraction using shared-shape camera crops at context-adaptive resolution and ROI-aware LiDAR voxelization; (3) a latency-aware coordinator that adapts LiDAR sweeps, image resolution, and keyframes according to scene dynamics and TTC; and (4) an asynchronous scheduler that decouples sensing from inference and skips stale frames. On nuScenes, MM-BEV reduces inference latency by 1.96x and end-to-end latency by 2.93x, with no loss in geometry-critical recall and only a 0.2 percentage-point drop in safety-critical recall. On a Clearpath Husky A300 equipped with an Ouster-128 LiDAR, BEV cameras, and a Jetson AGX Orin, MM-BEV further reduces mean latency by 2.11x, demonstrating its potential for real-world autonomous systems.
[CV-139] NumerosityVLM: A Cognitively Inspired Benchmark for Interpreting Numerosity Representations in Vision-Language Models
链接: https://arxiv.org/abs/2608.15425
作者: Yiming Fu,Fangjun Li,Xiujin Liu,Ruidong Ma,Hang Yu,Zhichen Lu,Kanwei He,Alessandro Di Nuovo,Angelo Cangelosi,Zhegong Shangguan,
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models (VLMs) achieve strong performance on high-level multimodal tasks, yet numerosity perception, a cognitive ability that emerges in human infants before language acquisition, remains poorly understood in current models, as existing counting benchmarks entangle numerosity with correlated visual factors. We introduce a cognitively inspired diagnostic benchmark, NumerosityVLM, comprising 10,800 synthetic images across six controlled conditions. The benchmark orthogonally manipulates object size, spatial arrangement, and numerosity, while progressively ablating texture, shape, and color. Evaluating seven VLMs in a zero-shot setting, multi-factor analysis reveals that model architecture explains the largest proportion of performance variance (partial \omega^2=0.325 ), far exceeding visual conditions. Layer-wise probing further shows that linearly separable numerosity signals consistently emerge at early stages of the vision encoder, while performance differences across evaluated models are primarily associated with the language model component. Code and data are publicly available at this https URL, and this https URL.
[CV-140] HistReNeRF: Historic Image Relocalisation within Contemporary Neural Radiance Field Reconstructions
链接: https://arxiv.org/abs/2608.15420
作者: Benjamin T. Hughes,Stuart James
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Relocalising archival photographs within a contemporary scene model is challenging because historic and modern views can differ in photographic appearance, visible objects, and spatial layout. Therefore, we present HistReNeRF, a framework that estimates the 6-DoF pose of a historic photograph by matching adapted DINOv2 patch features to candidate rays sampled from a contemporary Neural Radiance Field (NeRF) reconstruction. The continuous representation of a NeRF provides a queryable scene interface from which candidate rays can be sampled and matched, enabling domain adaptation between historic photography and contemporary images directly in the feature representation used for localisation. We evaluate embedding-space-based domain adaptation against pixel-space methods on a new cross-temporal dataset comprising 10,545 contemporary street-level images and 230 archival photographs from three European landmarks. Embedding-space adaptation reduces translation and rotation errors by an average of 11% and 16%, respectively, across the three scenes. These results show that neural scene relocalisation provides a natural interface for feature-space adaptation, reducing cross-temporal appearance shift without modifying the query image. Code and dataset at this https URL.
[CV-141] ArtLang: Structured Language-to-Kinematics Grounding for Articulated 3D Actuation
链接: https://arxiv.org/abs/2608.15419
作者: Sylvia Yuan,Dan Wang,Ravi Ramamoorthi,Xinrui Cui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Articulated-object reconstructions recover explicit geometry and kinematics, but their parts often remain semantically anonymous and must be controlled through part indices and numerical joint parameters. We present ArtLang, a framework for open-vocabulary language control of persistent reconstructed articulated assets. ArtLang represents an asset as a semantic-kinematic articulation graph and augments its surface with language features and graph-constrained motion. Open-vocabulary proposals are bound to reconstructed parts while allowing uncertain parts to remain unnamed. A typed parser converts a command into a directive graph containing referring expressions, actions, magnitudes, reference frames, and relations. We then solve a global graph-to-graph grounding problem that jointly reasons about semantic, spatial, relational, and kinematic compatibility, with support for null assignments and abstention under ambiguity. Accepted directives are converted into continuous joint targets within the observed motion range and executed through forward kinematics. Experiments on synthetic reconstructions, mesh-based assets, and real captures demonstrate reliable language grounding and continuous articulated control across repeated parts, spatial references, relational commands, and ambiguous instructions.
[CV-142] FloodReason Bench: Benchmarking VLM Reasoning Segmentation for Embodied Flood Response at the Edge
链接: https://arxiv.org/abs/2608.15410
作者: Rajat Bhattacharjya,Yoomee Jung,Minwoo Kim,Sing-Yao Wu,Eli Bozorgzadeh,Nalini Venkatasubramanian,Nikil Dutt
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO); Systems and Control (eess.SY)
备注: Paper is currently under review. The code and dataset will be made public upon acceptance
Abstract:Reasoning segmentation enables vision-language models (VLMs) to translate mission-relevant language requests into pixel-level visual grounding, offering a natural perception interface for embodied agents. However, existing benchmarks largely focus on generic visual scenes and overlook the domain and resource constraints encountered in flood-response platforms. We present FloodReasonBench, a benchmark for VLM reasoning segmentation for embodied flood response at the edge. At its core, FloodReasonBench introduces FloodResponseSeg, a flood-specific reasoning-segmentation dataset constructed from real-world scenes and response-relevant targets. Beyond task accuracy, the benchmark characterizes reasoning-segmentation pipelines under lightweight visual encoding, hierarchical split inference, and compressed intermediate representations. We observe strong partition-dependent accuracy variation in the generic pre-adaptation setting, while the flood-adapted target-workload design space exhibits a substantially more compact accuracy range across partitions. Evaluation on an NVIDIA Jetson AGX Xavier further exposes the tradeoffs among reasoning-segmentation accuracy, edge-side latency, energy, and communication footprint, enabling quality-constrained selection of edge operating points. Together, these results provide a task- and system-level characterization of reasoning segmentation for resource-constrained embodied flood response at the edge.
[CV-143] CBX-Bench: A Human-Aligned MLLM Council for Benchmarking Concept Bottleneck Model Explanations
链接: https://arxiv.org/abs/2608.15404
作者: Yusuf Meric Karadag,Gulay Oklan,Seref Baris Cagliyan,Umut Ozdemir,Emre Akbas
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Concept Bottleneck Models (CBMs) are designed to make visual classification interpretable by expressing predictions through human-understandable concepts. Although interpretability is the central motivation for CBMs, they are still largely evaluated as predictive models by downstream classification accuracy, supplemented by isolated qualitative examples. This highlights a pressing need for quantitative measures, a challenge complicated by the infeasibility of ground-truth concept annotation at scale and the open nature of concept lists due to a lack of consensus. To fill this gap, we develop a multimodal large language model (MLLM) council that, given an image and its CBM explanation, produces an explanation quality score. To ground and validate the council, we first conduct a human study to establish a ground-truth reference for CBM explanation quality: for an image, annotators compare explanations from two of LF-CBM, VLG-CBM, and CBM-Suite and choose the more useful one, or mark them as equally good or equally bad, yielding 2700 judgments over 900 image-comparison items on CUB-200, ImageNet-100, and Places365. Against this human reference, our five-model council, consisting of open-weight MLLMs, recovers over 70% of strict human preference rankings, rising to 83% on items where human annotators unanimously agree. Building on this validated council, we introduce CBX-Bench, a public benchmark and leaderboard: authors of new CBMs can submit their model’s explanations, and CBX-Bench scores them with the council and maintains dataset-level rankings of explanation quality. CBX-Bench thus provides a human-aligned, scalable evaluation of CBM explanations beyond accuracy and isolated qualitative examples. The benchmark is available at this https URL.
[CV-144] JoLT: Joint Latent Trajectories for Context-Guided High-Resolution Tiled Generation ECCV2026
链接: https://arxiv.org/abs/2608.15395
作者: Mathis Koroglu,Guillaume Jeanneret,Hugo Caselles-Dupré,Matthieu Cord,Arnaud Dapogny
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 10 figures, 7 tables. Accepted at the AI4VA Workshop at ECCV 2026. Project page: this https URL
Abstract:Although text-to-image generative models produce impressive results, they struggle to generate densely detailed, high-resolution (HR) images. Current literature addresses this issue with a low-to-high-resolution approach. First, a low-resolution (LR) image is generated. Then, an upsampled version is generated using the LR image as an additional cue. In this paper, we present Joint Latent Trajectories (JoLT). To generate an image, JoLT uses two streams that jointly denoise LR and HR latent images at each sampling step. The LR latent controls the overall layout, while the HR latent controls the details. We interconnect both branches to jointly integrate their information. We extensively validate our method, demonstrating its advantages over competing baselines. The resulting images are not only richly detailed but also visually pleasing, opening new avenues for artistic creation.
[CV-145] A Multi-Annotator Study of Segmentation Noise and Uncertainty in Turbid Underwater Images ECCV
链接: https://arxiv.org/abs/2608.15363
作者: Galadrielle Humblot-Renaux,Vasiliki Ismiroglou,Malte Pedersen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted at ECCVW’26 - 2nd Workshop on Marine Vision
Abstract:Label uncertainty and annotator disagreement are common challenges in the field of computer vision, yet their study has largely been confined to the medical domain or to generic image-recognition datasets. Underwater datasets are particularly susceptible to these issues due to the need for domain expertise, degraded visibility conditions, and the inherent difficulty of establishing reliable ground truth in inaccessible environments. Despite these challenges, annotation uncertainty in underwater imagery remains largely unexplored. In this work, we present the first systematic multi-annotator study of segmentation in real underwater scenes, with over 100 participants, and across varying, controlled levels of turbidity. We show that underwater datasets face many of the same annotation challenges as other vision tasks, while turbidity introduces additional systematic errors. We further investigate the main factors driving label noise and explore ways to improve annotation quality in turbid underwater environments, including privileged information, individual effort and annotator ensembles. All (meta-) data collected in this study will be available on the project page: this https URL
[CV-146] Decomposing Whole Slide Image Report Generation with Graph-Constrained Multiple Instance Learning Workflows MICCAI2026
链接: https://arxiv.org/abs/2608.15353
作者: Antony Gitau,Martyna Borak,Bjørn-Jostein Singstad,Martin Paulson,Karl Thomas Hjelmervik,Ola Marius Lysaker,Veralia Gabriela Sanchez
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the MICCAI 2026 REG Challenge
Abstract:Whole-slide image (WSI) report generation requires recognizing spatially distributed pathological features and organizing them into a coherent diagnostic narrative. Although direct vision-to-text models can yield fluent reports, they obscure the contributions and failure modes of visual recognition, structured reasoning, and language generation. We propose a decomposed framework in which frozen Virchow2 tile embeddings are aggregated by multiple-instance learning (MIL) classification heads that answer organ-specific diagnostic questions. An organ-conditioned graph constrains the assembly of these answers into a structured reasoning chain, which a language model realizes as a pathology report. On the REG2026 held-out set of 2,028 slides, the proposed workflow achieved a chain-Jaccard score of 0.702. Performance fell to 0.420 without graph-based chain construction, 0.398 when the organ-specific graphs were replaced by a single organ-agnostic graph, and 0.371 when the language model constructed the chain freely from MIL predictions. Using the same report generator, graph-structured chains improved the report score from 0.330 to 0.495. On 350 external TCGA WSIs spanning the seven REG organs without fine-tuning, the expected organ graph was selected in 64.0% of cases and ranked among the top three in 86.6%. Providing the correct organ graph increased agreement with coarse TCGA primary-diagnosis labels from 61.8% to 92.6%, identifying organ routing as a main bottleneck under domain shift. Overall, organ-conditioned, graph-constrained chain assembly improves structured reasoning and report generation while enabling stage-specific error localization.
[CV-147] ENAF: A Multi-Exit Network with an Adaptive Patch Fusion for Large Image Super Resolution WACV2025
链接: https://arxiv.org/abs/2608.15349
作者: Duong M. Nguyen,Tuan Nghia Nguyen,Xuan Truong Nguyen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: Accepted at WACV 2025
Abstract:To accelerate single image super-resolution (SISR) networks on large images (2K-8K), many recent approaches decompose an image into small patches and dynamically determine an execution path according to its difficulty (referred to as a dynamic network). To quantify the hardness of a patch, they mainly rely on a handcrafted assessment score, e.g., edge, which weakly associates a patch’s texture with the computational complexity of a SISR model. To address the problem, we introduce ENAF - a dynamic network for SISR with an adaptive patch fusion. Built on top of a backbone, ENAF incorporates multiple early exits (EEs) to tackle the over-parameterized SISR model. More importantly, ENAF plugs a tiny network that estimates PSNR to associate data texture with a computation cost at an EE. Based on the scores, ENAF effectively assigns image patches to an exit, enhancing the quality-complexity trade-off. Extensive experiments on common datasets with popular SISR backbones demonstrate the effectiveness of ENAF in various settings. The source code is provided in this https URL
[CV-148] Feed-Forward Hierarchical Gaussian Diffusion for Extreme CT Reconstruction
链接: https://arxiv.org/abs/2608.15343
作者: Yuezhe Yang,Li Cheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing three-dimensional computed tomography (CT) from severely constrained projections is highly ill-posed. Sparse angular sampling, restricted angular coverage, and low photon counts can occur individually or jointly, obscuring global anatomy and local tissue detail. Many learned CT reconstruction methods are tailored to a single dominant degradation. Existing diffusion and Gaussian approaches commonly recover global structure and local detail within a shared representation. We propose HiGDiff, a feed-forward hierarchical Gaussian diffusion framework that decomposes reconstruction both spatially and from structure to detail. Physics-conditioned anatomical anchors and a foreground capacity field allocate learnable Gaussian primitives to informative regions. A structure diffusion stage first recovers global attenuation geometry, and its learned representation conditions a detail diffusion stage for residual boundaries and tissue transitions. The resulting Gaussian banks are rendered as attenuation fields and further refined by a gradient-isolated residual module. Experiments on three distinct CT benchmark datasets demonstrate state-of-the-art reconstruction performance across isolated, paired, and joint degradation settings, including improvements of 5.81 dB in macro-average peak signal-to-noise ratio (PSNR) and 0.113 in structural similarity index measure (SSIM) on the Low Dose CT Image and Projection Data (LDCT-PD) collection. Code and experimental configurations are openly available at this https URL.
[CV-149] EA: Text Encoder Alignment for Robust Concept Erasure in Text-to-Image Models
链接: https://arxiv.org/abs/2608.15341
作者: Alireza Dehghanpour Farashah,Zhuan Shi,Negar Rostamzadeh,Golnoosh Farnadi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models can be misused to generate harmful content through adversarial or paraphrased prompts that bypass built-in safety mechanisms. Existing concept erasure methods often suffer from limited robustness against adversarial prompts, degradation of benign generation quality, or reliance on inference-time interventions that introduce persistent computational overhead. To address these limitations, we formulate concept erasure as a domain alignment problem in the text representation space. We propose a lightweight Text Encoder Alignment framework (TEA) that fine-tunes only the text encoder while keeping the generative backbone fully frozen. Given concept–anchor prompt pairs, our method trains a discriminator to distinguish token-level representations of concept-containing prompts from those of safe anchor prompts, while updating the text encoder to make these representations indistinguishable. TEA introduces zero inference-time overhead and requires only a small number of fine-tuning steps, making it highly efficient to deploy at scale. Despite this efficiency, TEA achieves state-of-the-art erasure robustness against black-box and white-box adversarial attacks on Stable Diffusion v1.4, while preserving generation quality on benign prompts. Furthermore, TEA is model-agnostic and achieves the lowest attack success rate on Stable Diffusion v3.5, extending concept erasure to a Rectified Flow Transformer architecture with T5 conditioning where prior methods remain largely unexplored. Code is available at \hrefthis https URLthis https URL
[CV-150] SAGE-OR: Semi-supervised Adaptive Scene Graph Generation for Operating Rooms BMVC2026
链接: https://arxiv.org/abs/2608.15336
作者: Brandon Leblanc,Charalambos Poullis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at BMVC 2026
Abstract:Current surgical scene graph generation methods depend on dense multi-modal supervision and specialized hardware (synchronized RGB-D sensors, calibration rigs), making dataset construction expensive and restricting all existing benchmarks to simulated environments. We propose SAGE-OR, a feature-centric framework that replaces the traditional detect-then-reason paradigm with a decoupled representation-reasoning paradigm in which localization is derived from frozen foundation models, encoded implicitly in pre-computed features, and used without any localization supervision, while a lightweight graph transformer performs relational reasoning over cached features. We employ a semi-supervised formulation with general-purpose segmentation prompts to eliminate localization supervision while enabling unsupervised context augmentation through additional prompt-driven entities, such as hands, which are absent from annotations. General-purpose prompts are used to induce near-perfect recall, while precision is delegated to downstream attention-based reasoning, enabling simple adaptation to new entities via prompt-level modification. This design enables a lightweight 15M-parameter graph transformer that trains in 1.4 hours and runs relational inference at \sim 1ms per frame with peak memory under 2GB, suitable for edge hardware used in the operating room; feature extraction runs offline as a separate caching stage (4.27s per frame). On the 4D-OR benchmark, the core model achieves 76% F1, matching the fully supervised 4D-OR baseline while eliminating all localization annotations, and unsupervised hand augmentation raises this to 86%, within 4 points of state-of-the-art (SOTA) methods requiring dense multi-modal supervision, providing a practical pathway for adaptation to new surgical settings without annotation other than relationship and class labels.
[CV-151] LightLoc: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization
链接: https://arxiv.org/abs/2608.15317
作者: Wen Li,Shangshu Yu,Dunqiang Liu,Qiming Xia,Sheng Ao,Siqi Shen,Chenglu Wen,Cheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 10 figures
Abstract:Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical deployment. Recent works improve training efficiency by decoupling SCR into a scene-agnostic backbone and scene-specific prediction heads, where the backbone is pretrained on source datasets and frozen for new scenes, and only lightweight heads are optimized. However, we find that this paradigm heavily depends on the pretrained backbone. Existing decoupled methods can match conventional SCR methods fully optimized for each new scene when LiDAR configurations are similar to those used during backbone pretraining, but their accuracy drops noticeably on datasets collected with different LiDAR sensors. This suggests that efficient LiDAR localization requires representations that capture stable scene geometry across LiDAR configurations. Motivated by this observation, we propose LightLoc++, a sensor-robust and efficient outdoor LiDAR localization framework. To support sensor-robust representation learning, we introduce SULID, a synchronized urban multi-LiDAR dataset with representative 32-, 64-, and 128-beam rotating LiDARs, extensive cross-sensor overlap, and diverse urban scenes. Using SULID, we pretrain a sensor-robust backbone through cross-sensor consistency learning. LightLoc++ further preserves efficient new-scene learning by incorporating sample classification guidance and redundant sample downsampling, which reduce regression ambiguity and computational redundancy in large-scale outdoor scenes. Extensive experiments on multiple outdoor LiDAR localization benchmarks demonstrate that LightLoc++ achieves state-of-the-art localization performance with the lowest new-scene training cost among compared methods. Code and dataset will be made available at this https URL.
[CV-152] Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning
链接: https://arxiv.org/abs/2608.15313
作者: Alexandre L. M. Levada
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: 23 pages, 4 figures, 4 tables
Abstract:In this paper, we propose SHOPCA (Shape Operator-based Principal Component Analysis), a novel method for unsupervised metric learning and dimensionality reduction that incorporates differential geometric information into the covariance structure of classical PCA. SHOPCA regularizes the global covariance matrix using the mean shape operator, defined as the average of the absolute local shape operators estimated from the data manifold, steering principal components toward directions of both maximum variance and informative curvature. A single trace-normalized mixing coefficient \alpha controls the regularization, recovering standard PCA at \alpha = 0 and a curvature-driven embedding as \alpha \to \infty . We further introduce a fully unsupervised criterion for selecting \alpha based on the spectral eigengap of the regularized covariance matrix, maximizing the relative separation between the top- d and remaining eigenvalues without using class labels. We evaluate SHOPCA on more than 50 real-world benchmark datasets, comparing it with PCA, ISOMAP, and UMAP using Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Fowlkes-Mallows index (FM), and V-measure. Results show that SHOPCA consistently improves clustering quality over PCA across a broad range of datasets and surpasses UMAP on small-sample settings, where iterative neighborhood-based manifold estimation can degrade. SHOPCA is computationally tractable, parameter-efficient, and applicable to domains requiring fully unsupervised, geometry-aware dimensionality reduction.
[CV-153] Image Denoising via the Adaptive Rank-Cluster Filter
链接: https://arxiv.org/abs/2608.15298
作者: Dmitry Pozdnyakov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages; 8 figures
Abstract:A spatial-local image-denoising filter is proposed, and its performance metrics are evaluated in comparison with baseline filtering algorithms, including the median, adaptive median, Gaussian, bilateral, Wiener, anisotropic diffusion, and non-local means. The developed filter is based on aligning the intensity value of the central pixel in a 3x3 window with the statistical majority intensity of one of the two clusters formed by optimal Otsu’s partitioning of a pixel set sorted by intensity and trimmed to seven elements. This is followed by a fuzzy fusion of the calculated value with the median intensity of the pixels within the window. The proposed filter demonstrates the highest robustness to variations in image noise levels, particularly when processing mixed noise consisting of salt-and-pepper impulse noise and additive Gaussian noise in various proportions
[CV-154] nyDETR-Pose: Towards End-to-End Real-Time Single-Stage 6DoF Object Pose Estimation with Lightweight Transformers
链接: https://arxiv.org/abs/2608.15297
作者: Paul Julius Kühn,Duc Anh Nguyen,Saptarshi Neil Sinha,Michael Weinmann,Arjan Kuijper
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time 6DoF object pose estimation on resource-constrained hardware remains challenging, as accurate correspondence-based and refinement pipelines typically rely on non-differentiable PnP/RANSAC stages or costly iterative refinement, while recent foundation-model-based approaches incur inference costs that are prohibitive for edge deployment. We present TinyDETR-Pose, a lightweight, end-to-end, single-stage framework that jointly detects objects and regresses their full 6D pose in a single forward pass. Built on the efficient LW-DETR architecture, TinyDETR-Pose formulates detection and pose estimation as a set-prediction problem and attaches dedicated MLP heads for rotation, monocular depth, and projected object center regression to each decoder query, eliminating the need for PnP, NMS (non-maximum suppression), or iterative pose refinement. Object symmetries are handled through a ADD-S loss applied uniformly to all objects, without the need for object-specific loss schedules or separate geodesic/ADD supervision. In addition, predictions are assigned to ground truth using a symmetry-safe Hungarian matcher based on class and 2D spatial cues, yielding stable assignment under symmetry and depth ambiguity. On YCB-V, TinyDETR-Pose achieves a comparable ADD-S AUC of 85.9, while requiring up to 72.7% fewer parameters than other DETR-based single-stage pose-estimation approaches. Due to its compact design, TinyDETR-Pose runs in real time and achieves an inference latency of only ~4.5 ms per frame on an NVIDIA Jetson Nano using TensorRT, demonstrating that accurate end-to-end transformer-based 6D pose estimation can be made practical for edge deployment.
[CV-155] FMReward: Aligning and Evaluating Audio-Driven 3D Facial Animation with Human Preferences
链接: https://arxiv.org/abs/2608.15296
作者: Sijing Wu,Yunhao Li,Zhilin Gao,Huiyu Duan,Yucheng Zhu,Guangtao Zhai,Patrick Le Callet
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in IEEE TVCG, 2026
Abstract:Audio-driven 3D facial animation is essential for advancing immersion and interactivity in virtual experiences. Although recent advances have shown promising capabilities, the training and evaluation of existing methods typically rely on ground-truth-based errors, which fall short of aligning with human preferences. To address this, we present a comprehensive framework that learns an automatic perceptual model from human preference data and leverages it to improve and evaluate the perceptual quality of audio-driven 3D facial animation. To begin with, we construct FMPair (Facial Motion Pairwise preference), the first human preference dataset for audio-driven 3D facial animation, which is built through a systematic annotation pipeline and comprises 65,574 annotated 3D facial motion pairs from 8,834 distinct in-the-wild audio clips. Based on the pairwise comparison dataset, we propose a Facial Motion Reward model, termed FMReward, which takes audio and 3D facial motion as inputs and predicts a perceptual quality score aligned with human preferences. Building upon FMReward, we further introduce Facial Motion reward Feedback Learning (FMFL), a direct fine-tuning algorithm that leverages a pretrained reward model to optimize diffusion-based audio-driven 3D facial animation models for better alignment with human preferences. Extensive experiments demonstrate the superiority of FMReward over other metrics in aligning with human preferences and the effectiveness of FMFL in improving the perceptual quality of audio-driven 3D facial animation.
[CV-156] SOS! : A Streamlined Object-Conditional Transformer for Model-free Segmentation BMVC2026
链接: https://arxiv.org/abs/2608.15295
作者: Jiaqi Hu,Junwen Huang,Hongli Xu,Peter KT Yu,Nassir Navab,Benjamin Busam,Slobodan Ilic
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to BMVC 2026
Abstract:Foundation segmentation models excel at generating high-quality, class-agnostic masks, but they struggle to associate these proposals with specific target objects. This semantic gap severely hinders their deployment in downstream applications like robotic manipulation, which demand precise unseen objects segmentation. Existing approaches attempt to resolve this by relying on exhaustive 3D object model priors, inherently introducing prohibitive computational overhead and complex, multi-stage pipelines. To address these limitations, we propose SOS (Streamlined Object-conditional Transformer for model-free Segmentation). SOS completely eliminates the reliance on 3D models, requiring only a single reference image per target object. Central to our framework is a novel Object-Conditional Transformer that learns identity-anchored queries, unifying mask generation and target identification into a single feed-forward pass. This streamlined design drastically improves both structural and computational efficiency. Extensive evaluations across multiple benchmarks demonstrate that SOS establishes a new state-of-the-art for model-free unseen objects segmentation, delivering accurate and high-efficiency performance. The project page and code are available at this https URL.
[CV-157] Earth Observation Foundation Models for Terrestrial Ecohydrology: From Representation Learning to Process Inference
链接: https://arxiv.org/abs/2608.15282
作者: Yi Yu,Jian Peng,Yucheng Lin,Trevor F. Keenan,Thomas F. A. Bishop
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Biological Physics (physics.bio-ph)
备注:
Abstract:Earth observation foundation models (EOFMs) are emerging as reusable representation frameworks for data-driven retrieval, prediction and process modelling within ecohydrology, which integrate EO, meteorological forcing and process models to characterise coupled water, energy and carbon dynamics in vegetation and soil across scales. However, there is yet to be an ecohydrology-specific synthesis assessing the EOFM relevance, application evidence or evaluation requirements under uncertain reference data, scale mismatch and temporal dependence. Here, we develop a framework for determining when EOFMs support interpretable inference and identify a mismatch between EOFMs and ecohydrological requirements. Firstly, an observation-to-inference hierarchy shows that relevance depends on target-specific sensing pathways, spatial-temporal support and traceable uncertainty. Secondly, a meta-analysis shows that pretraining is dominated by reflected optical and active-microwave data, with sparse thermal coverage and no passive-microwave-emission sources. Thirdly, our synthesis of ecohydrological applications finds strongest support for spatial context, label-efficient adaptation and hybrid workflows. Evidence declines with inference depth; independent validation of fluxes, coupled dynamics, event trajectories, calibrated uncertainty and decision benefits remains sparse. Fourthly, our benchmark audit finds stronger coverage of fair adaptation and reproducibility in general EOFM suites, and of process targets, direct reference evidence and distribution shifts in ecohydrological evaluations; physical consistency and uncertainty remain weakly assessed. These findings motivate a process-aware framework aligning EOFM design and evaluation with the target variable, observation pathway and process timescale, supporting trustworthy monitoring and interpretation of coupled water, energy and carbon dynamics.
[CV-158] Geometry-Aware Spatio-Temporal Context Modeling for 4D Occupancy Forecasting
链接: https://arxiv.org/abs/2608.15279
作者: Sitao Chen,Zhuangwei Zhuang,Hui Luo,Qingyao Wu,Mingkui Tan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:4D occupancy forecasting models the spatio-temporal evolution of 3D scenes and is crucial for autonomous driving, especially for corner-case simulation. Existing methods often rely on discrete tokenization followed by autoregressive prediction, yet struggle with geometric distortion in static structures and inconsistent temporal coherence over the forecasting horizon. In this work, we propose a Geometry-Aware Spatio-Temporal context modeling method (GAST) for 4D occupancy forecasting, built upon progressive explicit-implicit generation and dual-path spatio-temporal modeling. Specifically, the generation module produces per-frame occupancy with high geometric fidelity and semantic plausibility through pose-driven warping, motion-aware feature modulation, and attention-based feature refinement. Subsequently, the spatio-temporal module enhances spatial consistency through global context aggregation while capturing scene evolution through temporal dynamics extraction. This unified design enables joint optimization of historical reconstruction and future forecasting in an end-to-end manner. Extensive experiments on Occ3D-nuScenes demonstrate the superiority of our method, outperforming the state-of-the-art by 7.67% in mIoU and 6.44% in IoU with a 2.84x speedup, while maintaining strong performance in long-term forecasting.
[CV-159] Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection BMVC2026
链接: https://arxiv.org/abs/2608.15277
作者: Yoon Gyo Jung,Jaewoo Park,Kuan-Chuan Peng,Seongdeok Bang,Octavia Camps
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by BMVC2026
Abstract:Greedy sampling produces a compact yet representative summary of normal data, which is essential for reliable anomaly detection that relies on measuring distance from normality. For continual anomaly detection where tasks arrive sequentially, extending greedy sampling is straightforward with unbounded memory through coreset accumulation. However, practical deployment requires fixed memory where the coreset size remains constant regardless of task count. We observe that continued greedy sampling, which iteratively applies greedy selection over previously greedy-sampled sets, effectively preserves representativeness under strict memory limits. Despite discarding data at each step to satisfy the memory constraint, coreset quality degrades gracefully rather than catastrophically, enabling reliable anomaly detection across the tasks. We provide theoretical justification by showing that resulting greedy-continued coreset approximates the oracle coreset within a bounded gap. We instantiate this principle in ContCore, which constructs a greedy-continued coreset through greedy expansion on new task features followed by greedy consolidation to enforce the memory budget. Unlike neural methods susceptible to catastrophic forgetting or naive coreset accumulation requiring unbounded memory, ContCore maintains fixed memory with theoretical guarantees. Empirically, ContCore achieves state-of-the-art performance across 11 task schedules on MVTecAD and VisA, and extends effectively to online continual AD settings where prior methods degrade significantly. Code: this https URL
[CV-160] On the Adversarial Robustness of Remote Sensing Semantic Change Detection
链接: https://arxiv.org/abs/2608.15267
作者: Weikang Yu,Yonghao Xu,Pedram Ghamisi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic change detection (SCD) is a bitemporal dense-prediction task that jointly identifies changed regions and their semantic states before and after change. Unlike single-image segmentation or binary change detection, SCD couples two temporal inputs with timestamp-wise semantic prediction, change localization, and final semantic-change decoding, creating adversarial dependencies that are not captured by conventional robustness protocols. We present a task-specific evaluation framework that separates output-side attack objectives from input-side temporal perturbation access, enabling systematic analysis of component vulnerability and cross-temporal propagation. Experiments on four datasets and six representative CNN-, Transformer-, and state-space-based models evaluate component-level and temporal objectives, single- and dual-timestamp perturbations, multiple attack methods, and cross-architecture transferability. The results show that final semantic-change predictions can be severely corrupted even when binary change localization remains comparatively stable, and that perturbations or attack objectives associated with one timestamp can propagate to the prediction of the other. These behaviors occur across different architecture families, while direct cross-model transfer remains considerably weaker than white-box attacks. The study demonstrates that adversarial robustness in SCD depends on the complete bitemporal prediction pathway rather than on an individual branch or backbone family, and provides a structured protocol for evaluating robustness in coupled bitemporal image analysis. Code is available at this https URL.
[CV-161] Boundary-Aligned Contribution Routing for Robust Optical–SAR Object Detection
链接: https://arxiv.org/abs/2608.15261
作者: Haifa Zhang,Yijing Wang,Haoyu Wang,Zheng Li,Zhiqiang Zuo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Optical imagery provides rich appearance cues, whereas synthetic aperture radar (SAR) offers observations that are less sensitive to illumination and weather, making optical–SAR fusion attractive for remote-sensing object detection. However, the presence of multiple modalities does not guarantee beneficial fusion: imperfect spatial, temporal, and semantic correspondence can make an otherwise intact stream conditionally harmful and induce negative cross-modal transfer. We handle this issue through a model-specific task-utility perspective and learn task-conditioned contribution routing using detection supervision alone. The proposed fusion-boundary-aligned routing regulates each modality’s contribution before the first learned cross-modal feature-value mixing operation. For architectures with frequent shallow interaction, a Feature Router performs cross-conditioned, group-addressable modulation near the input; for dual-backbone architectures, a Dual-Statistic Semantic Router predicts stream-level contribution weights from modality-specific average and maximum statistics before late semantic fusion. The routers require no explicit utility supervision, quality labels, reconstruction, or distillation. Experiments on M4-SAR and SpaceNet6-OTD cover nominal full inputs, controlled correspondence shifts, missing modalities, and four nonzero modality-corruption scenarios. Across the reported clean-training controls, routing improves full-input \textmAP_50 by 0.5–5.9 points. Relative to the corresponding modality-dropout baselines, it raises missing-modality \textmAP_50 by 7.6–41.6 points and reduces the negative-transfer rate by up to 12.7 percentage points. Spearman correlations between the learned routing weights and model-specific leave-one-modality-out utility range from 0.45 to 0.66, supporting the task-utility interpretation of the routing coefficients.
[CV-162] VGGT-Align: Bridging Local Reconstruction and Global Consistency for Long-Sequence 3D Reconstruction
链接: https://arxiv.org/abs/2608.15260
作者: Wei Zhang,Yihang Wu,Songhua Li,Qi Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 6 figures, 6 tables. ACM Multimedia 2026 (MM '26). Code: this https URL
Abstract:Maintaining global geometric consistency is a central challenge in long-sequence 3D reconstruction, with scale drift being the most critical failure mode. In chunk-based inference pipelines, the scale degree of freedom in sequential Sim(3) alignment is left unconstrained, causing estimation errors to compound multiplicatively and distort global trajectories and point cloud geometry. We present a scale-consistency enhancement framework built on a key insight: in structured environments such as driving scenes, geometric quantities arising from environmental regularity remain inherently invariant across temporal segments, and discrepancies in their per-chunk measurements directly expose inter-chunk scale drift. We propose Scene Geometric Invariant Anchoring (SGIA), which extracts dominant geometric invariants from each chunk’s predicted point cloud via coarse-to-fine robust estimation and exploits their cross-chunk consistency to establish scale constraints independent of point cloud registration, explicitly degenerating 7-DoF Sim(3) alignment into 6-DoF rigid-body transformation and severing chain-wise scale error propagation at its source. We further introduce a lightweight test-time adaptation strategy that fine-tunes only normalization-layer parameters via multi-objective self-supervision, progressively improving intra-chunk predictions along the sequence. Both modules are plug-and-play and require no offline retraining. Experiments on multiple long-sequence benchmarks demonstrate state-of-the-art performance, reducing absolute trajectory error by up to 32% with significant gains in trajectory stability and reconstruction quality. Code: this https URL
[CV-163] UAV Video Deblurring via Motion-Aware Diffusion: A Path to Robust Target Detection IROS2025
链接: https://arxiv.org/abs/2608.15259
作者: Zhiqiang Hu,Shouren Huang,Masatoshi Ishikawa
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 8 pages, 8 figures. Published in the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
Abstract:Unmanned Aerial Vehicles (UAVs) play a crucial role in various scenarios ranging from disaster response to traffic surveillance. However, aerial video footage often suffers from severe motion blur due to rapid flight maneuvers, vibrations, and camera panning, which can significantly degrade downstream tasks such as target detection. Our goal is to explore a computationally-efficient and effective video deblurring approach to enhance UAV target detection performance. To reduce computational cost, we first propose an Adaptive Latent Scale Selector that dynamically adjusts the latent space resolution according to the intensity of UAV motion, thus balancing detail preservation with inference efficiency. To ensure temporal consistency, we introduce a Multi-Frame Alignment and Learnable Gating module to warp and gate the preceding frames, allowing the model to fuse only relevant temporal information and suppress misaligned or uninformative features. Our method can effectively recover sharp details from the UAV video stream. Extensive experiments on real UAV benchmarks demonstrate that our method not only yields superior deblurring performance but also significantly boosts target detection accuracy, making it highly applicable to robust aerial vision tasks.
[CV-164] Robust structure from motion for aerial-ground images via detector-free feature matching and multi-view track refinement
链接: https://arxiv.org/abs/2608.15251
作者: San Jiang,Hui Wang,Xing Zhang,Zhongwen Hu,Zhijun Wang,Ruisheng Wang,Wanshou Jiang,Qingquan Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Integrated 3D reconstruction from aerial-ground images is essential for generating high-precision urban 3D models, yet severe variations in viewpoint, scale, and rotation make robust feature matching highly challenging. To address these limitations, this study introduces a rotation-robust detector-free matching network coupled with multi-view track refinement for incremental Structure from Motion (ISfM). The proposed workflow features four key modules. First, rotation-aware feature extraction replaces traditional convolutions with an Omnidirectional State Space Block (OSS Block) that selectively scans across eight symmetrical directions to model long-range spatial dependencies and synthesize rotation-invariant feature maps. Second, multi-scale attention transformation utilizes quadtree attention to build a hierarchical token pyramid that isolates high-association token regions and discards irrelevant areas, capturing long-range context with linear computational complexity. Third, bi-directional feature matching executes a symmetric coarse-to-fine matching scheme where coarse alignment computes dual-direction Softmax confidence matrices under mutual nearest neighbor constraints, and fine alignment uses a multi-layer perceptron to regress sub-pixel coordinate offsets. Finally, multi-view track refinement employs an integrated indexing structure to evaluate localized spatial proximity and link disjoint sub-tracks to the highest-confidence anchor point, ensuring stable feature repeatability across the ISfM pipeline. By using real aerial-ground datasets, experimental results demonstrate that the proposed method improves AUC at 5° pose error by 93.9% compared with LoFTR and achieves the highest precision in ISfM reconstruction, with the improved accuracy ranging from 27.6% to 32.7%. The proposed method provides a reliable solution for integrated 3D reconstruction of aerial-ground images.
[CV-165] CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction BMVC2026
链接: https://arxiv.org/abs/2608.15246
作者: Tran Xuan Hieu Le,Doanh C. Bui,Vu Trung Duong Le,Hoai Luan Pham,Khang Nguyen,Mai K. Nguyen,Tu Bao Ho,Yasuhiko Nakashima
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for presentation at BMVC2026
Abstract:Sparse-view computed tomography (CT) reduces radiation dose by acquiring fewer projection views, but the resulting inverse problem is highly ill-posed and often produces severe streak artifacts. Existing deep reconstruction methods have achieved promising performance, yet many rely on first-order updates or large regularization networks, which can be less effective in ill-conditioned settings. We propose \textbfCG-GLORE, a compact deep unrolling framework inspired by second-order optimization for sparse-view CT reconstruction. Each unrolled stage uses a CG-solved linear system based on a structured Hessian surrogate: it retains the physics-induced curvature of the data-fidelity term while using an identity approximation for the learned regularization term. Thus, the method is second-order-inspired rather than an exact Newton method for the full learned objective. To model image priors, we design a Global-Local Regularization Network (GLORE), which combines convolutional local feature extraction with a Long-Range Dependency Representation module based on sparse patchification and Nyström attention. This design captures anatomical details and non-local dependencies while maintaining practical complexity. Experiments on AAPM and DeepLesion under multiple sparse-view and noise settings show that CG-GLORE achieves strong quantitative performance, stable convergence, lower noise power, and improved visual fidelity compared with representative reconstruction methods.
[CV-166] UC-VLM: Consistency-Driven Learning for AI-Generated Image Detection with Vision-Language Large Models ECCV2026
链接: https://arxiv.org/abs/2608.15238
作者: Lei Tan,Shuwei Li,Mohan Kankanhalli,Robby T. Tan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV 2026
Abstract:Vision-Language Large Models (VLLMs) are promising for AI-generated image (AIGI) detection because they can produce both a prediction and a natural-language output. However, most existing VLLM-based detectors primarily fine-tune the language side while giving limited attention to low-level visual forensic cues. They also often depend on manually crafted prompts or human-annotated rationales, which limits this http URL present UC-VLM, a unified multi-stage framework for AIGI detection that relies solely on binary supervision. UC-VLM first identifies effective instruction variants automatically. It then reuses the same binary label within a multi-stage training framework: (i) a visual discrimination objective that strengthens sensitivity to non-semantic forensic cues, and (ii) a label-conditioned generation objective that uses the binary label to supervise textual outputs. This design turns weak binary supervision into a shared supervision signal for both the visual pathway and the language output. Our key novelty is a unified multi-stage binary-supervised framework that consistently reuses the same authenticity labels for visual adaptation and label-conditioned text generation, while leveraging automatically optimized instructions to reduce prompt sensitivity without requiring human-written rationales or hand-crafted this http URL show that UC-VLM achieves 96.1% average accuracy on GenImage, exceeding the strongest prior result by 4.6%, and obtains 69.6% / 77.9% accuracy on Chameleon under ProGAN / SDV1.4 training, surpassing the best baseline by 11.2% / 15.3%, respectively.
[CV-167] PersonaDrive: Controllable Trajectory Prediction with Multi-Dimensional Driving Personas ECCV2026
链接: https://arxiv.org/abs/2608.15230
作者: Chan Lee,Kimin Yun,Yuseok Bae,Seong Tae Kim,Jung Uk Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ECCV 2026
Abstract:Although recent trajectory prediction and end-to-end autonomous driving methods improve robustness in urban environments, they still lack meaningful controllability. Existing benchmarks either provide no persona-conditioned annotations or support only a single urgency spectrum (i.e., emergency, normal, relaxed), which cannot distinguish personas that share the same urgency level but require different driving dynamics. To address this, we propose (i) the Persona-Conditioned Trajectory (PCT) dataset, which decomposes driving personas along two axes, Temporal Urgency and Ride Comfort, and combines three levels of each to form a grid of nine personas, each paired with natural-language descriptions and trajectories, and (ii) PersonaDrive, a framework that can learn driving personas from language and can generate persona-specific trajectories. PersonaDrive incorporates Persona-Conditioned Anchor Transform (PCAT), which hierarchically reshapes anchors along both axes, and Persona-Conditioned Multi-Modal Fusion (PCMF) for BEV-level persona fusion. Training is supervised by a Hierarchical Guide Loss enforcing axis-aligned physical orderings and an Axis-Decomposed Diversity Loss preventing diagonal mode collapse. Experimental results show that PersonaDrive consistently improves over the compared baselines across multi-dimensional scenarios. The code and PCT dataset are available at this https URL
[CV-168] Self-Supervised Topologically Invariant Manifold Learning for Railway Image Quality Assessment
链接: https://arxiv.org/abs/2608.15217
作者: Tingqiong Cui,Yibu Yang,Yang Li,Jiahao Fu,Xiaoliu Luo,Xu Wang,Mengzhu Wang,Siyuan Liu,Guanghui Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13pages,14 tables, 5 figures
Abstract:Existing blind image quality assessment (BIQA) methods typically rely on synthetic distortions and subjective annotations, limiting generalization in real-world domains. To address this, we propose a fully self-supervised BIQA framework based on topologically invariant manifold learning under boundary constraints, which constructs a stable quality reference without manual labels. The framework generates progressive background dilution scales via repeated random cropping around each target; exploiting the monotonic degradation of target information density across these scales, it establishes a self-constrained quality manifold. A linearized spatial moment projection eliminates geometric distortions from random cropping; then a monotonicity divergence filter prunes background-sensitive evaluators, isolating an elite pool (\mathcalM_\textelite). A robust M-estimator with a principal component stabilizer fuses the metrics into an asymptotically efficient pseudo-ground truth (q_\textPGT), contracting variance toward the Cramér-Rao lower bound. Extensive evaluations demonstrate that the elite evaluator pool, distilled from 11 baseline metrics, secures superior zero-shot transferability across standard synthetic and wild benchmarks (CSIQ, LIVEC, LIVE-2). Concurrently, deployments on the CQU Railway Rolling Stock Surveillance Dataset (2,797 images) yield a manifold cosine similarity (0.999) and a 100.0% survival rate under industrial extreme stresses, robustly validating its cross-paradigm decoupling and topological resilience.
[CV-169] ERRA: A Hierarchical Parallel Training and Memory Orchestration Framework for High-Resolution AI-based Earth Modeling
链接: https://arxiv.org/abs/2608.15211
作者: Ruohan Wu,Ziqi Zhu,Yang Zhao,Jiarui Tang,Yingzhe Cui,Junshi Chen,Zhao Jing,Jun Shi,Hong An
类目: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 15 pages, 16 figures, 6 tables, and 2 algorithms. Submitted to IEEE Transactions on Parallel and Distributed Systems (TPDS). Code is available at this https URL
Abstract:Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distributed systems such as AERIS primarily target pixel-level models and do not jointly support convolutional sampling modules and shifted-window execution. Long-lead rollout finetuning further increases activation memory. To address these challenges, we present TERRA, a hierarchical parallel training framework for high-resolution Earth forecasting. TERRA introduces Sampling-Aware Window, Sequence, and Tensor Parallelism (SAWSTP), which preserves spatially contiguous layouts for sampling modules and routes tokens into topology-aware ragged window layouts for Transformer execution. For long-lead finetuning, Memory Orchestration (MO) provides rollout-aware checkpoint planning and combines input buffering with budget-constrained activation offloading. Experiments on the 1/12^\circ GLORYS-based Wenhai workload show that TERRA supports models with up to 11.4B parameters on 96 H200 GPUs and sustains up to 39.76 PFLOPS, achieving 65.0% strong-scaling and 94.1% weak-scaling efficiency. Compared with checkpoint-only policies, MO further reduces peak allocated GPU memory by 32.2% – 51.8% with at most 20.0% step-time overhead, which makes finetuning with smaller patch sizes and longer rollouts feasible for improved forecasting accuracy.
[CV-170] Anchor-Regularized Adaptation for Generalizable AI-Generated Image Detection with DINOv3
链接: https://arxiv.org/abs/2608.15196
作者: Hyeongjun Choi,Juhun Lee,Davide Cozzolino,Luisa Verdoliva,Simon S. Woo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent works in AI-generated image detection have shown that careful training data alignment can improve generalization by removing spurious correlations. However, linear probes on frozen DINOv3 representations achieve remarkably strong performance even when trained on misaligned datasets. Motivated by this result, we analyze the underlying rationale and the limits of this generalization. We find that frozen DINOv3 performs well because its decisions rely on features that faithfully represent the space of authentic images. At the same time, its final layer is less effective at capturing the subtle pixel-artifact cues that can be emphasized by aligned training data. We further observe that naively mixing aligned and misaligned data during adaptation improves sensitivity to such cues but at the cost of distorting the pre-trained representation, limiting generalization. To address this issue, we propose Anchor-Regularized Adaptation (ARA). We apply Low-Rank Adaptation to capture pixel-level artifacts while leveraging a frozen anchor classifier to avoid deviations from the original representation structure. This allows the model to exploit pixel-artifact cues without sacrificing generalization. Our method achieves state-of-the-art performance on nine diverse and challenging benchmarks, indicating that ARA enables complementary supervision from misaligned and aligned data for more effective detection.
[CV-171] Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis ECCV2026
链接: https://arxiv.org/abs/2608.15195
作者: Lovre Antonio Budimir,Mingya Alexa Gong,Alyssa Foong Quinney,Ivana Matovinović,Yukun Zhou,Pearse A. Keane,Sven Lončarić,Marinko V. Šarunić
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the ECCV 2026 Workshop on Medical Foundation Models and Benchmarks (MEDFMB)
Abstract:Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently adapted across diverse imaging modalities, anatomical regions, and clinical tasks. However, VFMs require extensive training data, and their progress in medical image analysis is constrained by limited data availability, privacy concerns, and high development costs. To alleviate these constraints, medical VFMs (MedVFMs) are often built upon weights from generalist models pretrained on vast amounts of publicly available natural images, introducing a substantial distribution shift for medical task adaptation. To address this, we propose satellite imagery as a novel pretraining domain for MedVFM development and benchmarking, motivated by its closer visual alignment with medical data and its freedom from the privacy constraints that limit medical datasets. Across multiple ophthalmic imaging modalities, we compare DINOv3-SAT493m pretrained on 493 million satellite images against DINOv3-LVD1689m pretrained on 1.7 billion natural images, together with two medical specialist baselines: DINOv3-RETFound and MAE-RETFound. Our experiments show that satellite imagery is a stronger pretraining source than natural images for ophthalmic tasks, particularly on en face vascular-rich modalities. On several tasks, satellite pretraining matches or exceeds the medical specialists on high-resolution en face inputs, despite using no medical data.
[CV-172] A Unified Backbone–Expert Framework with Relation-Token and Residual–Classifier Interfaces for Automatic Modulation Recognition
链接: https://arxiv.org/abs/2608.15160
作者: Zhixiang Deng,Houbiao Li,Zongyong Cui
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 31 pages, 6 figures, 10 Tables
Abstract:Automatic modulation recognition (AMR) faces distinct representation bottlenecks under varying observation lengths, where a single model architecture often fails to excel. To address this, we propose a unified backbone-expert framework with a common convolutional state-space backbone and two specialized interfaces. For short sequences, we inject explicit lag-aware complex-plane descriptors as relation tokens before encoding to compensate for information loss. For long sequences, we design a gated multi-scale residual refinement module to correct the feature map, combined with a fixed-averaging classifier collaboration to harness complementary evidence. Our framework achieves overall average accuracies of 67.28 \pm 0.14% on RML2016.10b and 87.19 \pm 0.77% on HisarMod2019 (mean \pm sample standard deviation over three runs), respectively. The framework’s efficacy is further validated through three-seed ablations, native-length cross-configuration tests, and controlled window studies, confirming the benefit of expert-interface decoupling over one-size-fits-all architectures.
[CV-173] HOIMask: Towards Generative Masked Modeling for Human Object Interaction Generation ECCV2026
链接: https://arxiv.org/abs/2608.15141
作者: Yihong Ji,Jinsong Zhang,He Hu,Hongbo Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCV 2026
Abstract:Diffusion-based methods have dominated the HOI generation, as they enable critical contact fusions or signals to guide the diffusion process. However, they often result in high artifacts and unstable interaction quality due to error accumulation during iterative denoising. In this work, we propose HOIMask, the first generative masked framework for modeling HOI motion in discrete space. HOIMask first encodes both motion sequences and contact-aware signals into discrete 2D human and object token maps via HOI Vector Quantization (VQ), preserving fine-grained spatial-temporal structure beyond conventional 1D representations. On this basis, a generative masked modeling framework is employed to jointly capture human-object interaction dynamics, leveraging a transformer architecture designed to model complex spatial-temporal and interaction dependencies. To generate more coherent and physically plausible motions, we further introduce a novel contact-aware reconstruction guidance in discrete space during inference, which fuses contact signals to optimize HOI tokens that forces the generated motion with higher spatio-temporal consistency. With craftily designed motion interaction tokens, dedicated architecture and guidance strategy, HOIMask outperforms state-of-the-art diffusion-based methods, generating more realistic and semantically aligned HOI motions. Please refer to this https URL for more results.
[CV-174] Perspective-Invariant Attack with Enhanced Transferability of Adversarial Examples
链接: https://arxiv.org/abs/2608.15115
作者: Kaisheng Liang,Yiming Cao,Bin Xiao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adversarial examples generated on a surrogate deep neural network (DNN) can often successfully fool other black-box DNN models. This cross-model transferability poses serious security threats to DNNs in practical applications. Input transformation techniques are widely used to enhance adversarial transferability by increasing the diversity of input images. However, existing methods primarily rely on local operations with limited degrees of freedom (DOF), such as block-wise shuffling and resizing, overlooking global perspective transformations that naturally arise from viewpoint changes. In this work, we propose a Perspective-Invariant Attack (PIA), which introduces a multi-DOF vertex sampling strategy that systematically covers the perspective transformation hierarchy from 2-DOF translation to 8-DOF projective mapping. By generating geometrically diverse input variations, PIA effectively reduces overfitting of adversarial perturbations to the surrogate model, thereby improving adversarial transferability. We further propose PIA-Mix, a generic extension that maintains a complementary transformation pool and efficiently combines our perspective transformation with auxiliary methods for improved transferability. Extensive experiments involving various DNN architectures, advanced defense mechanisms, and multimodal large language models (LLMs) demonstrate that PIA and PIA-Mix outperform state-of-the-art transfer-based attacks.
[CV-175] Fast Test-Time Refinement for Robust Learned Image Compression
链接: https://arxiv.org/abs/2608.15113
作者: Jiaming Liang,Chi-Man Pun,Weisi Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings. However, the high representational capacity endowed by deep neural networks (DNNs) comes at the expense of increased adversarial vulnerability. This hinders their adoption as trusted standardized codecs. Recent work has sketched test-time refinement (TTR) as a defense in gray-box scenarios, despite its original purpose of improving benign RD performance. Unfortunately, extensive iterations of TTR incur prohibitive overhead, while the robustness mechanism lacks theoretical understanding. Moreover, TTR has not been evaluated in white-box settings or against attacks beyond \ell_2 -bounded rate and untargeted distortion objectives. To bridge these gaps, we present a systematic study. Our study reveals an Asymmetric Adversarial Trajectory (AAT) property in LIC systems: transitioning from adversarial to benign regions is significantly easier than the reverse process, where adversarial examples can often be roughly recovered within only 1-2 steps. We provide a two-dimensional Tube Model to explain this phenomenon. Based on AAT, we propose a Fast Test-Time Refinement (FTTR) framework for practical and robust LIC systems. We establish that the robustness arises from the contraction of adversarial regions induced by the Input-as-Label property of LIC systems, rather than from obfuscated gradients. Extensive evaluations with diverse strong adaptive attacks across multiple LIC systems demonstrate the promise of the proposed FTTR framework. The code is available at this https URL.
[CV-176] CETalk: Continuous Valence-Arousal Control for Audio-Driven 3D Talking Head Generation
链接: https://arxiv.org/abs/2608.15110
作者: Peng Jia,Li Dai,Zhen Xiao,Xueliang Liu,Jia Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 14 pages, 6 figures, 3 tables
Abstract:Emotional 3D talking head generation aims to synthesize expressive facial animations with accurate lip synchronization. However, existing methods often rely on discrete emotion categories, which fail to capture the continuous evolution of affect. They also overlook the temporal frequency mismatch between audio articulation and emotional expression. In this paper, we propose CETalk, an audio-driven 3D facial animation framework conditioned on continuous Valence–Arousal (VA) representations for fine-grained emotion control. CETalk predicts a sequence of FLAME parameters through three key components: a Dynamic Emotion Modulation Module that adaptively scales emotional intensity using audio-derived cues; a Multi-Scale Temporal Modeling mechanism that employs parallel branches to decouple high-frequency articulatory movements from low-frequency emotional dynamics; and a Dynamic Fusion Mechanism that integrates these multi-scale features via an adaptive gating network. To support training and evaluation, we construct 3D-VA-MEAD, a large-scale dataset with automatically estimated VA annotations and reconstructed 3D facial motions. Extensive experiments demonstrate that CETalk outperforms state-of-the-art methods in both lip-sync accuracy and emotional expressiveness, while enabling smooth and controllable emotion transitions.
[CV-177] EMASAM: a Computationally Efficient Sharpness-Aware Minimization via EMA-Guided Perturbations WWW ICPR2026
链接: https://arxiv.org/abs/2608.15105
作者: Tanapat Ratchatorn,Masayuki Tanaka
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in ICPR2026. The project page can be accessed at this https URL
Abstract:Recent progress in optimization research has highlighted the sharpness of the loss landscape as a key factor in narrowing the generalization gap. Motivated by this insight, Sharpness-Aware Minimization (SAM) was proposed as a training strategy that enhances generalization. Despite the promising performance, SAM suffers from its twice computational cost due to its core algorithm requiring an extra gradient computation during the perturbation step. To overcome this limitation, we introduce Exponential Moving Average Sharpness-Aware Minimization (EMASAM), a computationally efficient variant of SAM. EMASAM does not require the loss gradient in the perturbation step. Instead, EMASAM defines the perturbation direction based on the discrepancy between the main model and the EMA shadow model. This perturbation travels away from the stable average position toward the less stable area, acting as a softer yet cheaper alternative to SAM’s worst-case scenario perturbation. Moreover, since EMASAM’s perturbation does not rely on noisy mini-batch gradients, it mitigates the gradient-induced instability inherent in SAM. Hence, EMASAM eliminates the need for an extra backpropagation while also preserving the generalization ability of the SAM-style training. Several experiments have been performed and confirm the efficiency and robustness of our method.
[CV-178] ProjFormer: Point Cloud Completion via Geometric-Projective Transformer and Cross-Modal Semantic Constraints
链接: https://arxiv.org/abs/2608.15104
作者: Sheng Liu,Meng Wang,Ruihui Li,Huilong Pi,Zhuo Tang,Kenli Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM Multimedia 2026. 10 pages, 6 figures, 5 tables
Abstract:Point cloud completion is inherently ill-posed due to severe sparsity and ambiguity in partial observations. Existing multi-view methods alleviate this by incorporating 2D semantics, but often rely on learned attention and fixed fusion, which lack geometric consistency and adaptability. We propose ProjFormer, a cross-modal framework that enforces geometry-consistent 2D-3D interaction through explicit projection and adaptive feature routing. A Projective Guided View Attention module aligns 3D points with multi-view features via deterministic projection, enabling efficient and geometrically consistent aggregation. Building on this, a geometry-aware routing network performs point-wise adaptive fusion of structural and observation-driven features for progressive refinement. Experiments show that, under a lightweight design, ProjFormer delivers competitive performance with improved structural completeness.
[CV-179] MODAL: Multi-Modal Object Re-ID via Model-Driven Sparse Decoupling and Text-Image Differential Filtering
链接: https://arxiv.org/abs/2608.15096
作者: Chengbo Huang,Jun-Jie Huang,Long Lan,Tianrui Liu,Xueqiong Li,Yuanxi Peng,Xinwang Liu,Meng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Multi-modal object re-identification (Re-ID) aims to facilitate cross-camera object retrieval in complex environments by leveraging complementary information from visual (e.g., RGB, NIR, TIR) and textual modalities. However, existing approaches often lack principled feature disentanglement and coherent multi-modal integration, leading to entangled representations that introduce cross-modal conflicts, obscure discriminative cues, and suffer distribution shift under modality-missing conditions. To tackle these challenges, we propose MODAL, a novel multi-modal object re-identification framework, grounded in coupled sparse coding theory and differential suppression principles. A core component of MODAL is a Multi-modal Feature Sparse Decoupling module, developed in a model-driven deep unrolling manner based on multi-modal coupled sparse coding. It explicitly decomposes multi-modal features into uni-modal specific, bi-modal and tri-modal shared representations, thereby achieving more transparent and effective feature disentanglement. Benefiting from the principled feature disentanglement, MODAL naturally mitigates performance degradation in incomplete-modality scenarios via a Modality-Aware Subspace Activation that selectively activates only the consistently shared subspaces. Moreover, we propose a Text-Image Differential Filtering module that leverages coarse-grained textual semantics to adaptively suppress task-irrelevant responses in the decoupled visual representations, thereby enhancing discriminative information. Extensive experiments on four datasets demonstrate that MODAL achieves state-of-the-art performance with superior transparency.
[CV-180] Distribution-free false-alarm calibration and chance-corrected spatial evaluation for industrial anomaly detection
链接: https://arxiv.org/abs/2608.15090
作者: Jie Deng
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 3 figures, 9 tables
Abstract:Studies of industrial visual inspection commonly report the area under the receiver operating characteristic curve (AUROC) and the overlap between anomaly maps and defect masks. Neither measure specifies the false-alarm rate at a selected threshold, while recurrent defect locations and mask geometry can inflate overlap. We combine a distribution-free upper tolerance threshold with a paired-minus-crossed spatial test. This test compares each detector’s score-contributing locations with the matched defect mask and with masks from other images; the difference in rates defines spatial-evidence lift relative to the empirical chance-overlap rate. We evaluate three detectors on 120 point-defect images from three ISP-AD modalities and three fixed data splits. Of 378 alarms, 230 overlap the matched mask. Paired and crossed rates are nevertheless similar in eight of nine detector–modality cells; only DINOv2–ASM has a positive 95% bootstrap lower bound (lift 0.259, 95% interval 0.159–0.347). On the independent Magnetic Tile Defect dataset, the same analysis gives lifts of 0.203 (0.169–0.236) for Wide ResNet-50 (WRN50) patch memory and 0.231 (0.202–0.262) for Vision Transformer B/16 (ViT-B/16) patch memory, with one-sided permutation p=10^-5 for both. When crossed masks are restricted to the same defect class, the lifts remain 0.185 and 0.210. Exact sample planning shows that, with 150 calibration normals, a 95%-confidence distribution-free claim is supported only for target false-positive rates of 1.98% or higher; a 1% target requires at least 299 normals. The results support reporting operating-point performance and chance-corrected spatial evidence alongside AUROC and raw mask overlap.
[CV-181] SA-GEM: Scale-Adaptive and Geospatial Evidence-Modulated Token Pruning for Efficient Remote Sensing Large Vision-Language Models
链接: https://arxiv.org/abs/2608.15075
作者: Kexin Ma,Jing Xiao,Bowen Xing,Liang Liao,Chia-Wen Lin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:RS-LVLMs have advanced multimodal understanding of Earth observation imagery, yet their performance is fundamentally constrained by high-resolution processing, as visual token counts grow quadratically with linear input resolution while important visual evidence is inherently sparse and increasingly diluted across the expanded sequence. Existing token pruning methods largely rely on scale-agnostic resolution policies and isolated importance cues, limiting task-aligned granularity adaptation and holistic evidence preservation. To address this, we present Scale-Adaptive and Geospatial Evidence-Modulated Token Pruning (SA-GEM), a plug-and-play framework that unifies task-adaptive token granularity allocation with holistic geospatial token importance modulation. Specifically, a lightweight router selects the resolution based on query-dependent token granularity, while a token importance modulator jointly models task relevance, spatial structure, and local redundancy to preserve holistic geospatial evidence. We show that higher resolution is not universally beneficial and, once sufficient granularity is reached, token quality matters more than token quantity. Experiments across various benchmarks demonstrate that SA-GEM achieves consistent gains in both accuracy and efficiency over existing pruning methods. On XLRS-Bench, it surpasses GeoLLaVA-8K by 2.3% in accuracy with a 2.4 times total inference speedup.
[CV-182] Do Visual Grounding Decoders Need Feed-Forward Networks? A Controlled Study over Frozen Vision-Language Features
链接: https://arxiv.org/abs/2608.15061
作者: Tarun Tomar
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 8 figures, 5 tables. Code and project page: this https URL
Abstract:Do feed-forward networks (FFNs) in visual grounding decoders add essential computation once a pretrained vision-language model has already encoded image and language context? We compare a four-block attention-only decoder (A4), a matched four-block attention-plus-FFN decoder (S4), and an eight-block attention-only parameter control (A8) over frozen VLM features. A4 matches or slightly exceeds S4 on RefCOCOg and Ref-Adv-s. FineCops-Ref reveals a small A4 deficit of 0.52 percentage points at IoU@0.5 (95% CI [0.12, 0.95] in favor of S4), but A8 recovers it and finishes 0.26 points above S4. Official FineCops levels do not show a monotonic increase in the gap. A4 reduces trainable decoder parameters by 44.4% and cached-decoder latency by 10.1%, although end-to-end latency remains backbone-dominated. These results concern the trainable grounding decoder, not a complete attention-only VLM.
[CV-183] EgoTac: In-the-wild Tactile Prediction from Egocentric Vision
链接: https://arxiv.org/abs/2608.15060
作者: Wenkang Zhang,Chengbo Yuan,Zicheng Zhang,Zhengxue Cheng,Yang Gao
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Touch is fundamental to dexterous manipulation, yet most egocentric human data increasingly used for robot learning lacks tactile information. Directly collecting large-scale tactile data is challenging due to sensor limitations, while human video data is abundant, contact-rich, and easily scalable. This motivates a natural question: can tactile signals be inferred purely from vision? To address this, we introduce EgoTac, a generalizable model that predicts rich tactile information directly from egocentric human videos. EgoTac is trained on a unified corpus of over 5.7M image-tactile pairs, covering both continuous force measurements and binary contacts. By learning from this diverse dataset, EgoTac captures nuanced touch dynamics across varied interactions. Experiments demonstrate strong performance: in-domain prediction achieves an average force error below 0.06N. On out-of-domain contact prediction benchmarks, EgoTac consistently outperforms the state-of-the-art contact estimator. It also captures the rise and fall patterns of real tactile data and enables zero-shot predictions on unconstrained real-world videos. Scaling analyses further reveal that both data diversity and volume improve performance steadily. Overall, EgoTac provides a scalable pathway to extract tactile priors from egocentric human videos, enabling broadly applicable tactile-aware robot learning.
[CV-184] MEDR: Query-Independent Frame Selection via Multi-Signal Event Modeling and Dynamic Rescoring
链接: https://arxiv.org/abs/2608.15058
作者: Xinlei Pu,Weijie Shi,Wen Yang,Yi Cao,Hao Chen,Yuanjun Liu,Wenwei Ding,Jia Zhu,Jiajie Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures, 3 tables
Abstract:Frame selection is a fundamental component of multimodal large language models, enabling long videos to be processed under limited visual-token and computational budgets. Uniform sampling preserves temporal coverage but may miss informative content that appears only briefly. To alleviate this limitation, query-dependent methods can retrieve question-relevant frames. However, because the selected frames depend on the current question, the same visual input cannot be directly shared across different questions, and frame selection must be repeated in multi-turn video dialogue. This motivates us to seek a query-independent frame selection method that preserves the reusability of a fixed visual input while improving the coverage of informative events beyond uniform sampling. We propose Multi-Signal Event Modeling and Dynamic Rescoring (MEDR), a training-free and query-independent frame selection method. Multi-Signal Event Modeling organizes complementary visual, motion, and text signals into signal-specific temporal events. Dynamic Rescoring then iteratively reevaluates each candidate relative to the current selected set, updating its score according to frame-level signal strength, additional event coverage, and temporal proximity. The resulting fixed frame set is constructed without observing the query and can be reused across different questions. On the standard benchmark evaluations, MEDR improves model accuracy by 0.63%-0.89% on Video-MME. On the long-video subset of LongVideoBench, it improves accuracy by up to 1.23% with Qwen3-VL-8B. MEDR further improves overall accuracy by 0.53%, while reusing exactly the same frame set for every question about a video.
[CV-185] Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation
链接: https://arxiv.org/abs/2608.15054
作者: Feng Gao,Zizhe Pan,Haoting Wang,Ruzhuang Hua,Jingchao Cao,Junyu Dong,Qian Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in IEEE TGRS 2026
Abstract:Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information. The Segment Anything Model (SAM), proposed as a foundation vision model, offers strong segmentation performance and generalization capabilities for RSISS tasks. However, existing SAM-based approaches face two limitations: (1) Insufficient adaptation of SAM’s features to the diverse characteristics of land cover types. (2) Semantic ambiguity at object boundaries, which hinders accurate delineation. To address these limitations, we propose Frequency and Edge-guided SAM (FE-SAM), a scalable and efficient framework for RSISS. Specifically, we introduce a Frequency-Modulated Adapter (FMA) that adaptively decomposes and modulates frequency-domain features based on the input data. It selectively enhances informative high- and low-frequency components corresponding to different land cover types. Furthermore, to improve SAM’s ability to capture fine-grained details, we design EGRefiner, which integrates multi-scale edge-enhanced information extracted from the input image. Extensive experiments on three benchmark datasets demonstrate that FE-SAM outperforms state-of-the-art methods. The source codes are available at: this https URL.
[CV-186] MOSS-VL Technical Report
链接: https://arxiv.org/abs/2608.15045
作者: Pengyu Wang,Chenkun Tan,Shaojun Zhou,Qirui Zhou,Yanxin Chen,Xingyang He,Huazheng Zeng,Jijun Cheng,Chenghao Wang,Xiaomeng Qian,Pengfei Wang,Zhan Huang,Shanqing Gao,Wei Huang,Longjun Cao,Wu Ran,Jie Liu,Changtai Zhu,Hongkai Wang,Yixian Tian,Chenghao Liu,Zhen Ye,Xinghao Wang,Botian Jiang,Guoguo Feng,Zhaoye Fei,Ruixiao Li,Mingshu Chen,Yang Gao,Qinyuan Cheng,Shimin Li,Xipeng Qiu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages. Project page: this https URL
Abstract:We present MOSS-VL, an open vision-language model family that treats real-time interaction – perceiving while it speaks – as a first-class capability. It is co-designed across the stack: the language decoder attends to vision only through gated cross-attention, so the model can naturally see incoming frames while generating; a synthesized interaction corpus supervises when to speak, when to stay silent, and when to revise; and a staged curriculum concentrates all real-time-specific training in one light final stage over a strong offline foundation. Offline, MOSS-VL-Instruct is competitive at comparable scale and leads temporal-reasoning video sets. Across four streaming benchmarks, MOSS-VL-Realtime posts the best average on three (second on the fourth) among open-source streaming models, sweeping the three subsets that squarely test proactive behavior – 66.0 vs. 37.5 for the best baseline on OmniMMI Proactive Alerting. With 11.3B parameters but visual tokens outside the decoded sequence, MOSS-VL widens its time-to-first-token advantage over same-backbone Qwen3-VL-8B from 2.8x to 5.1x as visual context grows. We release all five checkpoints, the training curriculum, and the real-time inference code at this https URL.
[CV-187] Generation of Synthetic Fingerphotos with GANs
链接: https://arxiv.org/abs/2608.15029
作者: Conor Miller-Lynch,Sandip Purnapatra,Syed Konain Abbas,Lambert Igene,Faraz Hussain,Soumyabrata Dey,Stephanie Schuckers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Contactless fingerprinting is an emerging approach to biometric authentication that allows users to scan their fingerprints without touching a scanner. Due to the limited amount of contactless fingerprint data available and the security risks associated with sharing real individuals’ fingerprints, it is valuable to explore methods of generating synthetic data that can be used in place of - or in conjunction with - real data to develop and evaluate contactless fingerprinting systems. In this paper, we present and evaluate synthetic fingerphotos generated using StyleGAN2-ADA and StyleGAN3, existing image generation architectures. We evaluate the realism, privacy preservation, and variety of the synthetic fingerphotos by comparing their biometric feature statistics to those of real fingerphotos, computing match scores between real and synthetic fingerphotos, and computing match scores between different synthetic fingerphotos. This paper provides a quantitative comparison point for future evaluations of synthetic fingerphotos. The evaluation code is made available at this https URL.
[CV-188] Geometry-Calibrated Closed-Form Shrinkage for SAR Despeckling
链接: https://arxiv.org/abs/2608.15028
作者: Xuran Hu,Mingzhe Zhu,Djordje Stanković,Yujie Zhu,Zhenpeng Feng,Yifang Ban,Ljubiša Stanković
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 13 figures
Abstract:Synthetic aperture radar (SAR) despeckling is an inverse-recovery problem in which multiplicative non-Gaussian noise must be suppressed without erasing scattering structures. We revisit a nonlocal sparse estimator that applies a log–Yeo–Johnson transformation, stacks similar patches into groups, codes each group on its own left singular basis, and shrinks the resulting coefficients. Three quantities usually treated as tunable are shown to be fixed by this construction. First, the group dictionary is orthonormal, so the weighted Lasso admits an exact coefficient-wise soft-threshold solution: the iterative inner solver is unnecessary, and the two apparent weighting matrices are the numerator and denominator of a single threshold field rather than independent modules. Second, because the dictionary is estimated from the noisy group itself, its retained subspace absorbs speckle in proportion to the group aspect ratio \gamma=p^2/K ; a random-matrix argument converts the corresponding regularization constant into a geometry-calibrated correction and collapses patch size, group size, and shrinkage scale into one analytically determined degree of freedom. Third, singular projection makes the coefficient noise nearly Gaussian at every tested look number, which locates the point at which an exact speckle likelihood ceases to be informative. The resulting estimator is deterministic, training-free, and applies one set of analytically determined settings to every image and sensor. It ranks first in 18 of 24 PSNR/SSIM comparisons against twelve published methods on three synthetic benchmarks, and attains the lowest mean deviation of the ratio image from the theoretical speckle model over six real-SAR configurations from five sensors. Code is available \hrefthis https URLhere.
[CV-189] MotionGS-SLAM: Event-Modulated Gaussian Splatting for Motion-Blur Robust SLAM
链接: https://arxiv.org/abs/2608.15024
作者: Zhiqiang Hu,Shouren Huang,Masatoshi Ishikawa
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 5 figures. Published in the 2026 IEEE International Conference on Robotics and Automation
Abstract:Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input, as they attempt the ill-posed inverse problem of recovering sharp content from degraded observations. We present MotionGS-SLAM, which fundamentally reimagines motion blur handling through a paradigm shift: rather than removing blur artifacts, we reformulate the challenge as a well-constrained forward problem that generatively models blur formation within the rendering pipeline. By leveraging event cameras’ microsecond temporal resolution and immunity to motion blur, we introduce a novel event-modulated Gaussian kernel that dynamically adapts each Gaussian’s rasterization based on precise motion cues. Our dual-modulation mechanism transforms 2D Gaussian projections from isotropic dots into anisotropic, motion-aligned elliptical brush strokes (spatial modulation) while adaptively varying exposure integral sampling density based on local velocity (temporal modulation). This physics-based approach enables joint optimization of intra-exposure camera trajectories and 3D scene geometry through blur-aware photometric and event-based constraints. Extensive experiments demonstrate significant improvements over state-of-the-art methods in trajectory accuracy and map quality under severe high-motion conditions.
[CV-190] DualMiT-Net: Local-Global Transformer-Convolutional Fusion for Breast Mass Segmentation in Mammographic Regions of Interest
链接: https://arxiv.org/abs/2608.15019
作者: Alibek Kamiluly,Milana Muratova,Yash Patel,Fan Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue. To address this problem, we present DualMiT-Net, a dual-branch network that uses both a focused view of the mass and a wider view of the surrounding tissue. The local branch uses a Mix Transformer (MiT-B5) encoder to learn mass shape, texture, and boundary information, while the global branch uses an EfficientNet-B5 encoder to learn surrounding breast context. Features from the two branches are shared at the deeper encoder levels and are then progressively fused in a single decoder. A spatial gate controls how much global information is added during decoding. We also evaluated four input representations and selected a percentile-windowed mammogram combined with a Gabor texture response. The model was trained and evaluated on the mass subset of the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) using a patient-level split. Across three training runs, DualMiT-Net with exponential moving average weights achieved a mean Dice coefficient of 0.9375 and a mean Intersection over Union of 0.8834. It also achieved better Dice and IoU scores than six standard encoder-decoder baselines trained using the same data and training settings. These results show that combining local mass information with wider breast context can provide accurate and consistent breast mass segmentation.
[CV-191] ForceU-VLA: A Force-Aware Vision-Language-Action Model for Embodied Ultrasound Scanning
链接: https://arxiv.org/abs/2608.15009
作者: Xingzheng Wu,Cheng Zhang,Guihao Yan,Xifeng Hu,Zhi Liu,Qing Cai
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Embodied intelligent ultrasound scanning enables the automation and standardization of the ultrasound examination process by integrating perception, decision-making, and execution capabilities. However, existing methods suffer from loosely coupled modeling between force and ultrasound modalities and lack awareness of scanning stages, which limits their ability to capture dynamic probe-tissue interactions. To address these issues, we propose ForceU-VLA, a force-aware Vision-Language-Action model for autonomous embodied ultrasound scanning, which leverages force signals and ultrasound image feedback throughout the scanning process to enable accurate and high-quality ultrasound acquisition. Firstly, we propose a Force-Ultrasound Synergistic Fusion Module (FUSFM) that synergistically fuses ultrasound visual and force-feedback information to provide stable, reliable guidance for probe motion. Secondly, a Stage-Adaptive Modulation Mechanism (SAMM) is proposed to accommodate the task requirements across different scanning stages by adaptively modulating multimodal features to enhance their representation quality. Additionally, we introduce ForceU-VLA-Data, a real-world, force-aware embodied ultrasound dataset that integrates visual, force, and action signals, including data from two organs across five representative clinical scanning views, and comprising 450 expert-collected trajectories with approximately 100,000 synchronized multimodal frames. Extensive experimental results demonstrate that ForceU-VLA significantly improves contact stability and probe pressure regulation in embodied ultrasound scanning, thereby effectively enhancing task execution quality and overall system reliability. The source code is available at this https URL.
[CV-192] MetaReason : Precise Interleaved Multimodal Reasoning via Editing Meta Information for Solving Geometry Problems
链接: https://arxiv.org/abs/2608.15006
作者: Penghao Yin,Haomin Wang,Qihong Tang,Xiaoye Qu,Hongjie Zhang,Xiao-Ping Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Although visual reasoning is crucial for solving complex geometry tasks, existing vision-language models rely heavily on text-only reasoning. Some recent methods introduce intermediate visual states to facilitate reasoning, but they are often hindered by inaccurate geometric representations and low rendering fidelity, ultimately leading to unreliable outputs. To address these limitations, we propose MetaReason, a framework for multimodal reasoning in plane geometry that leverages structured meta-information to enable accurate auxiliary-line construction. The framework first parses geometric images into meta-information, performs controllable edits with predefined tools to synthesize high-fidelity visual states, and then conducts reasoning based on these augmented views. To support this framework, we construct TutorGeo, a comprehensive dataset containing 17k image-to-meta conversion samples, 60k text-only reasoning traces, and 60k interleaved multimodal reasoning traces. Using this dataset, we combine supervised fine-tuning and reinforcement learning to develop robust multimodal reasoning capabilities. We also introduce ExamGeo, a benchmark derived from real-world examination problems that enables systematic evaluation across varying difficulty levels. Experimental results demonstrate that MetaReason significantly outperforms existing open-source models and achieves competitive performance against proprietary models.
[CV-193] FZ-VLM: A Two Stage Florence-Zephyr Vision Language Model Framework for Pulmonary Nodule Characterization and Clinical Decision Making
链接: https://arxiv.org/abs/2608.15004
作者: Pramit Dutta,Jenita Manokaran,Richa Mittal,Ryan Appleby,Eranga Ukwatta
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and Computed Tomography (CT) is a primary imaging tool for screening and followup assessment. After pulmonary nodule detection, radiologists manually assess anatomical location, diameter, margin characteristics, and attenuation type to support risk assessment and clinical decision-making. However, this post-detection workflow is time-consuming and can be affected by inter-observer variability. Existing Artificial Intelligence methods often focus on isolated tasks, limiting their use as a unified, clinically grounded interpretation framework. This study presents FZ-VLM, a two-stage Florence-Zephyr Vision Language Model framework for unified structured pulmonary nodule characterization in lung CT. The framework uses a fine-tuned Florence-2 model to extract radiological attributes from expert-annotated 2D axial CT slices, while a Zephyr-7B model uses these attributes to generate nodule descriptions, follow-up recommendations, and longitudinal analyses. Results showed that the Stage 1 model achieved 77.18% accuracy for anatomical location, 67.96% accuracy for margin characteristics, and 79.13% accuracy for attenuation type, with a Mean Absolute Error of 2.58 mm for diameter estimation, outperforming evaluated GPT-4-based baselines as well as the human baseline. Expert radiologist evaluation of Stage 2 showed 93.9% accuracy, 98.6% completeness score, 76.1% clinical relevance, and an overall score of 89.5%. Safety analysis showed that most outputs were clinically safe, although some follow-up recommendations still required expert review. To the best of our knowledge, this study presents the first two-stage Vision-Language Model framework for structured nodule characterization and clinical decision-making.
[CV-194] Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle
链接: https://arxiv.org/abs/2608.14994
作者: Jiangsan Zhao,Masayuki Hirafuji,Seishi Ninomiya,Jakob Geipel,Wei Guo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 10 figures, 8 tables. This work has been submitted to the IEEE for possible publication
Abstract:Reconstructing a spatially and spectrally high-resolution hyperspectral image (HR-HSI) from a low-resolution HSI (LR-HSI) and a high-resolution RGB image (HR-RGB) usually assumes precise registration and a known camera response function (CRF). Both assumptions are difficult to satisfy with different sensors. We remove both through a permutation-invariant supervision principle: the Gram matrix of an unmixed abundance map depends on shared material composition but not on pixel ordering. Matching abundance Gram matrices therefore allows RGB-to-HSI mapping to be learned without spatial correspondence and without a predefined CRF. Under a full random permutation of HR-RGB pixels, a state-of-the-art fusion method collapses, whereas our reconstruction is unchanged after inverse reindexing for evaluation. Building on this principle, a residual spectral super-resolution function maps HR-RGB directly to HR-HSI without registration, known CRF, or paired supervision. Across indoor, natural-scene, and remote-sensing benchmarks, the method achieves accuracy comparable to approaches that require these assumptions while remaining robust when they are violated. Loss ablations further show that reconstruction accuracy is largely insensitive to the specific discrepancy used to match the Gram matrices, indicating that performance arises primarily from the permutation-invariant principle rather than loss tuning.
[CV-195] Risk-Adaptive Edge–Cloud Visual Reasoning for Communication-Efficient Autonomous Driving
链接: https://arxiv.org/abs/2608.14991
作者: Meng Ma,Shuyang Li,Naigang Wang,Ruimin Ke
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 4 figures, 5 tables
Abstract:Cloud-hosted vision-language models (VLMs) offer greater contextual reasoning capabilities than smaller onboard models, but frequent visual uploads increase communication overhead and add network and inference latency to tactical decisions. We present a risk-adaptive edge-cloud architecture in which onboard traffic assessment determines when cloud reasoning is requested. An onboard VLM and a lightweight detector capture temporal traffic conditions and path-relative hazards for conservative local response and selective cloud access. The cloud model provides tactical advice, while validation, vehicle control, and automatic emergency braking remain local. In CARLA experiments, our method matched the task success rate of periodic cloud access while reducing cloud requests by 54.1% and recording fewer automatic emergency braking (AEB) activations. In a delayed-roadwork ablation, semantic events triggered requests before the next scheduled audit. Across three emulated network profiles, the method continued to reduce cloud traffic, although lane changes took longer than with periodic access. Onboard traffic assessment therefore served as a practical trigger for selective VLM inference in these experiments.
[CV-196] Benchmarking Frontier Text-to-Image Models on Image-Description Prompts
链接: https://arxiv.org/abs/2608.14976
作者: Sajjad Abdoli,Ghassan Al-Sumaidaee,Ahmed Rashad
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image models are typically reported on average-case prompts, which understates the gap between systems on compositionally demanding requests involving precise object counts, multi-object attribute binding, legible embedded text, and explicit spatial constraints. We evaluate four production text-to-image systems: Hunyuan 3.0, Gemini 3 Pro Image (“Nano Banana Pro”), Black Forest Labs FLUX.2, and Ideogram 3.0. The evaluation uses the 48 hardest prompts drawn from the this http URL Sample Dataset (DSD), selected through an automated complexity-scoring pass over the full corpus. Every generated image is graded using an independent-judge rubric. GPT-5.4-Pro authors an atomic, weighted, mutually exclusive and collectively exhaustive (MECE) evaluation rubric, while Gemini 3.1 Pro Preview independently determines whether each criterion is satisfied. Gemini 3 Pro Image ranks first with a score of 84.8/100, narrowly ahead of FLUX.2 at 82.3/100. Ideogram 3.0 and Hunyuan 3.0 score 65.7/100 and 63.3/100, respectively. Failure analysis shows that the leading systems primarily lose points through object miscounting and geometric artifacts, whereas the trailing systems more frequently produce garbled text. Ideogram 3.0 also frequently omits requested elements. Full per-sample rubrics, scores, and failure annotations are available from the authors upon request.
[CV-197] Evidence of Absence: Cross-Modal Abductive Risk Perception to Sustain World Models When Vision Fails
链接: https://arxiv.org/abs/2608.14952
作者: Cong Xu,Ravi Sankar
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: 7 pages, 3 figures. Working draft prepared for journal submission
Abstract:A structured world-state (entities, relations, context, and predictive cues) is designed to preserve prediction-critical content when perception degrades, but it presumes observations to populate it; when the primary visual modality is occluded or degraded, those observations may be missing. We address how to sustain the world model from a complementary modality by treating the absence of expected co-evidence as evidence of a hidden cause. The abductive framework is modality-agnostic; this article instantiates it acoustically. A microphone-array front-end estimates the bearing of engine and tire sources and extracts approach-rate evidence (Doppler when a stable tone exists, a broadband looming readout otherwise); the event “signature present, visual co-evidence absent” then triggers abductive inference of a hidden road user, emitting a calibrated risk advisory rather than a control command. Recoverability of the hidden state is analyzed as an identifiability question separating shared from modality-unique information, and cueing is cast as Neyman-Pearson detection under an explicit false-alarm budget. On real occluded-approach recordings at blind junctions, the method warns a mean 1.7 seconds before line-of-sight entry, matches the sustained-window variant of the published acoustic baseline’s detection rate with 42% fewer false alarms, localizes to 3.4 degrees median once in view, is well calibrated (expected calibration error 0.034), and keeps hazard awareness above 0.87 under staged vision degradation that collapses a vision-only channel to 0.03. We also measure the method’s limits: calibration transfers to an unseen junction almost losslessly, the signature classifier does not, and moving-ego noise is the binding deployment constraint.
[CV-198] Looks Can be Deceiving: Annotator and Reviewer Performance Across Imagery Sources in Crowd-Sourced Aerial Damage Assessment
链接: https://arxiv.org/abs/2608.14942
作者: Thomas Manzini,Priyankari Perali,Raisa Karnik,Stephen Johnson,Robin R. Murphy
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted ACM HCOMP’26. 13 pages, 6 figures
Abstract:This paper presents the first known empirical investigation of annotator and reviewer performance across multi-source remotely sensed imagery, evaluating human labeling across drone, crewed aviation, and satellite views. Because existing aerial imagery datasets rely predominantly on single-source imagery, there is no currently established state of practice for efficiently allocating human labor to curate large-scale, multi-source aerial datasets. This work addresses this limitation by analyzing annotator and reviewer performance within a post-disaster building damage assessment dataset of 9 disasters, where 20041 buildings in drone, 20695 buildings in crewed aviation, and 33392 buildings in satellite imagery were labeled. These labels, provided by 187 annotators, were then refined through two successive quality-control stages: a single-reviewer pass followed by a consensus-committee review. Our analysis reveals two findings that raise questions for standard crowd-sourcing practices. First, initial annotations were revised by the final committee at rates that rise steeply from higher- to lower-resolution sources (25.27% for crewed aviation and 36.95% for satellite), with the same ordering at every observed workflow stage. Second, a single individual review reduced but did not resolve this disagreement: after review, the committee still revised 6.85% of drone, 14.05% of crewed, and 20.86% of satellite labels. These observations suggest that, in workflows like this one, uniform review allocation leaves the most residual disagreement in lower-resolution imagery. Based on this evidence, and consistent with prior work on adaptive task assignment and budget-aware quality control, this paper offers three recommendations for multi-source dataset curation.
[CV-199] PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining ICML2026
链接: https://arxiv.org/abs/2608.14924
作者: Azim Dehghani Amirabad,Junchao Zhu,Pushpak Pati,Walid Abdelmoula,Tommaso Mansi,Rui Liao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper was accepted to the 3rd ICML 2026 Workshop on Multi-modal Foundation Models and Large Language Models for Life Sciences
Abstract:Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations: spatially informative gene selection is often dominated by ubiquitous housekeeping genes, leading to weakly discriminative representations, and independent spot-patch alignment fails to capture spatial dependencies that are critical for tissue organization. To address these challenges, we introduce PaSTel, a hierarchical multimodal pretraining framework that integrates biological priors at three levels. At the spot level, TF-IDF reweighting is used to identify spatially informative genes; at the functional level, curated KEGG pathways serve as anchors for encoding global biological semantics; and at the regional level, spatial clustering aggregates neighboring spots to model meso-scale tissue structure. Across multiple downstream tasks, PaSTel consistently outperforms existing vision and vision-omics encoders, demonstrating that incorporating multiscale biological priors yields more informative and transferable representations for spatial transcriptomics.
[CV-200] SpIn-ViT: Designing a Sparsity-Induced Vision Transformer That Is Mechanistically Interpretable
链接: https://arxiv.org/abs/2608.14922
作者: Philip H. Lee,Parth Padalkar
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Mechanistic interpretability has recently expanded to Vision Transformers (ViTs), with Sparse Autoencoders (SAEs) increasingly used as post-hoc tools to decompose internal representations into sparse and more interpretable features. However, because post-hoc SAEs are trained on frozen representations after the ViT has already been optimized, their latent features are not directly aligned with the downstream classification objective. We introduce SpIn-ViT, a framework that jointly trains a pretrained ViT and a modified SAE end-to-end, directly aligning sparse patch-level representations with image classification. SpIn-ViT learns semantically coherent neuron activations that localize meaningful image regions while maintaining competitive predictive performance. We evaluate SpIn-ViT across nine image-classification benchmarks using classification accuracy, quantitative interpretability metrics, AI-based and Human evaluations. Compared with the previous state-of-the-art post-hoc SAE method, SpIn-ViT achieves 8.84% higher average classification accuracy, an AI-based interpretability score nearly four times as high, and a human-evaluation score more than twice as high. We further extract interpretable rule-sets using the SAE neurons to create neurosymbolic models which achieve 5.97% higher average classification accuracy while requiring a 58.8% smaller rule-set than the neurosymbolic models created from the SOTA post-hoc SAE method.
[CV-201] Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study ITSC
链接: https://arxiv.org/abs/2608.14868
作者: Alexander Baumann,Marcel Vosshans,Thao Dang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted for publication at the 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC), Naples, Italy, September 15-18, 2026
Abstract:Background subtraction is a key preprocessing step for infrastructure-based LiDAR perception, enabling efficient isolation of dynamic traffic participants without semantic annotations. However, systematic cross-sensor evaluations and reproducible studies for static roadside LiDAR are missing. This paper presents a comparative benchmark of beam-wise statistical background subtraction for statically mounted LiDAR sensors. We formulate background estimation as a per-beam temporal modeling problem and investigate complementary statistical strategies that capture dominant as well as multi-modal background structures, combined with spatial filtering in the angular and 3D domain. To enable reproducible evaluation, we introduce HighwayScene, a new multi-LiDAR dataset recorded in a static roadside setup, and extend the public CoopScenes dataset with static/dynamic point-wise annotations. Across multiple scenes and heterogeneous sensing technologies, we demonstrate that beam-wise statistical modeling provides a robust and transferable solution. Combining lightweight per-beam models with spatial consistency filtering substantially improves precision while maintaining high recall and real-time capability. All datasets, annotations, and implementations are publicly released.
[CV-202] Zero-MELO: Test-Time Evidence Calibration with Multimodal LLM s for Zero-Shot Micro-Gesture Recognition ACM-MM2026
链接: https://arxiv.org/abs/2608.14854
作者: Chengyan Wang,Hanliang Xie,Yueyi Yang,Haoyu Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2026
Abstract:While Multimodal Large Language Models (MLLMs) excel in general video understanding, their capability in fine-grained and motion-centric tasks remains limited. This limitation is particularly critical in micro-gesture recognition (MGR), where micro-gestures (MGs) - subtle, short-duration, and spatially localized human movements - serve as key discriminative signals for implicit affective analysis, yet are easily neglected following common prompting practices. Although MGR has been intensively studied by many discriminative approaches, the use of MLLMs for MGR is underexplored, with notably poor performance. We hypothesize that the motion-sensitive representation ability of MLLMs is constrained by their inherent single-pass forward inference, which can be substantially enhanced through carefully designed test-time guidance. Motivated by this, building on our prior findings regarding temporal insensitivity in Video LLMs, we diagnose zero-shot MGR errors in the Negative Log-Likelihood (NLL) space. We observe that MLLMs suffer from two bottlenecks: 1) insufficient localized evidence and 2) severe score biases driven by language and motion-agnostic appearances. Thus, we propose a novel test-time evidence calibration framework that improves both reasoning details and prediction reliability. Specifically, we introduce a tree search mechanism to progressively acquire localized, fine-grained visual evidence, coupled with a test-time calibration module to mitigate score biases. The multi-cue fusion module then integrates evidence from multiple cues without relying on a single cue for final prediction. Our framework achieves mean-class accuracies of 26.84% on iMiGUE and 22.10% on MA-52, significantly outperforming the Qwen2.5-VL baseline, which produces 16.15% and 10.20%, respectively. The code will be available at this https URL.
[CV-203] OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation ECCV
链接: https://arxiv.org/abs/2608.14835
作者: John Helsby,Yi Yang,Bodo Rosenhahn,Michael Ying Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ECCVW’26 CONTEXTUS
Abstract:Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as \langle subject, predicate, object \rangle triplets, and underpin downstream tasks such as video captioning, video question answering, and action analysis. However, end-to-end dynamic scene graph generation (DSGG) methods are closed-set: they recognize only objects and predicates from a fixed training vocabulary and struggle with the long-tailed distribution of rare concepts, severely limiting their real-world applicability. Existing open-vocabulary models typically inherit pretrained large language models, resulting in multi-stage training and inference with substantial cost. We introduce OvDSGG, the first end-to-end framework for open-vocabulary DSGG. OvDSGG builds on top of an open-vocabulary Spatial Backbone and a Temporal Backbone; we further propose a Triplet Feature Extraction Module that bridges them, and a Visual-Language Alignment Module that preserves open-vocabulary recognition by learning an adaptive decision boundary in the joint visual-language feature space, without expensive knowledge distillation in existing methods. We further introduce a rigorous open-vocabulary DSGG benchmark adapted from Action Genome, with disjoint Base/Novel splits for both objects and predicates. OvDSGG significantly outperforms open-vocabulary baselines across all metrics, with zero-shot Recall@ K scores 10.0–20.4 percentage point higher than the next-best baseline, while on closed-set DSGG remaining competitive with state-of-the-art models. Code and benchmark are publicly available at this https URL.
[CV-204] Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models
链接: https://arxiv.org/abs/2608.14822
作者: Yanyan Zhang,Disheng Liu,Kai Ye,Chaoda Song,Xinpeng Li,Mohsen Hariri,Vikash Singh,Yu Yin,Vipin Chaudhary
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language-action (VLA) models have improved the flexibility and generality of robotic manipulation, yet they remain fragile to online disruptions, such as changes in task goal, scene configuration, or robot state. Existing recovery methods often require failure data, policy retraining, or external corrective agents, introducing additional data requirements and execution risks. We propose Counterfactual Realignment (CoRe), a training-free framework that recovers a frozen VLA at inference time without failure data. Upon detecting a deviation, CoRe imagines how the policy would continue toward the current goal from a recent viable state, using synthesized observations in place of physical execution, and then minimally realigns the robot and scene to rejoin this imagined continuation before returning control to the policy. Recovery is therefore planned without physical trial-and-error, preserves completed task progress, and handles both mid-episode instruction changes and physical perturbations in a unified manner. Extensive experiments across multiple simulators, VLA backbones, and real-world settings show that CoRe improves success rates by up to 85.0 percentage points to near-nominal levels while reducing physical restorations by 42.2%, without policy fine-tuning or failure-specific recovery training.
[CV-205] Where the Cost Falls: A Deployment-Aware Adoption Order for Stability Enhancements to Cycle-Consistent Adversarial Networks
链接: https://arxiv.org/abs/2608.14811
作者: Rowan Hussein,Mohamed Ouf
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 5 figures
Abstract:Teams that adopt cycle-consistent adversarial networks for unpaired image-to-image translation meet the same obstacles: adversarial training oscillates or collapses, cycle consistency preserves coarse layout while finer texture drifts, and a single discriminator judging global realism misses local artifacts. Four enhancements address these failures, and they are usually compared on output quality alone. We show that they also divide sharply by where their cost falls, and that this division, which follows from the architecture and not from any particular run, yields an adoption order for teams under a compute or latency budget. A Wasserstein objective with gradient penalty, a VGG19 perceptual loss on the cycle reconstruction, and multi-scale discriminators change training only, so a team can adopt or drop them without altering what ships. Self-attention alone persists into the deployed generator, with memory growing as the square of the feature-map size, which makes it the one component a resource-constrained team should defer. We integrate all four onto a lightly tuned baseline for horse-to-zebra translation, introduced one at a time on a fixed control and then combined, and for each we give the failure mode it targets and how it integrates. We document the collapse and reconstruction-artifact modes the baseline produced, report what visual inspection of saved samples showed for each variant, and report Fréchet Inception Distance and Kernel Inception Distance for the combined model. We specify the protocol still needed, covering the individual variants, perceptual similarity, and downstream segmentation, to rank these enhancements on measured evidence.
[CV-206] Bit-Level Triangular Content-Aware Permutation for Frag ile Image Watermarking: Zero False Positive Rate Single-Bit Sensitivity and Arbitrary Dimension Support
链接: https://arxiv.org/abs/2608.14800
作者: Zahra Ghoraeian,Mohammad-Reza Sadeghi,Samaneh Mashhadi
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 4 figures, 8 tables
Abstract:With the growth of digital document exchange, protecting image integrity against attacks such as Vector Quantization (VQ) and collage has become critical. Existing methods are vulnerable to these attacks and limited to fixed image dimensions. This paper presents a novel, dimension-agnostic, fragile watermarking algorithm that enhances security and tamper localization by replacing conventional hash functions with Triangular Content-Aware Permutation (TCA). The image is combined with key-based global noise and divided into blocks. The core innovation is applying content-dependent permutation with intrinsic avalanche effect (TCA) at the bit-plane level, generating a unique content-dependent watermark. For color images, a vertical sandwich transformation merges channels, preserving inter-channel dependency with only 1.62x time increase. The “remainder merging” strategy eliminates padding constraints. Experiments on 50 grayscale and 10 color images under 18 attacks show FPR=0% and FNR=0% for 17 attacks. Salt-and-pepper noise yields negligible FNR of 0.27% (grayscale) and 0.14% (color). Average PSNR is 51.14 dB (8-bit), 75.25 dB (12-bit), and 99.33 dB (16-bit). Embedding and extraction times are 1.61 s and 1.63 s, respectively. The algorithm achieves 100% accuracy against collage, VQ, copy-move, JPEG (quality 5-95), and geometric attacks, providing a secure solution for digital forensics, medical imaging, and legal document authentication. Comments: 19 pages, 4 figures, 8 tables Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) MSC classes: 68U10, 94A60 ACMclasses: E.3; I.4.9; K.6.5 Cite as: arXiv:2608.14800 [cs.CR] (or arXiv:2608.14800v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.14800 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zahra Ghoraeian [view email] [v1] Fri, 14 Aug 2026 18:12:52 UTC (4,565 KB)
[CV-207] Zero-Shot Adaptation of Medical Vision Foundation Models for High-Frequency Micro-Ultrasound Prostate Segmentation
链接: https://arxiv.org/abs/2608.14796
作者: Ayusha Abbas,Saram Abbas,Kabita Adhikari
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Prostate cancer claims a life every 80 seconds. Early detection is needed to prevent disease progression, and both PSA density calculation and biopsy decisions rely on knowing the exact boundary of the gland. Conventional ultrasound at 6-12 MHz blurs this boundary, missing one in three high-risk cancers. Micro-ultrasound (29 MHz) improves resolution threefold but introduces dense acoustic speckle that obscures the outer wall; given the same image, two clinicians draw outlines differing by over 10% in area. Supervised methods are costly and generalise poorly across scanners. Can a foundation model segment the prostate with no training data? We present the first zero-shot pipeline for this modality: MedSAM, pre-trained on over 1.5 million medical images, localises the prostate; we then apply CLAHE to sharpen the outer wall, binary dilation to recover missed pixels, and Fourier smoothing (4 modes, s=1.05) to refine the boundary. MedSAM requires a spatial prompt, so we evaluate bounding-box and point-click strategies across 75 patients of the Micro-Ultrasound Prostate Segmentation dataset (2,621 slices). On the 20-patient held-out test set, the pipeline reduces mean boundary-distance error by 45% (Dice 0.749+/-0.043 to 0.865+/-0.029; HD95 217.2+/-36.9 to 120.1+/-26.1 px), reaching Dice 0.859 across the cohort. Its mean overlap shows no significant difference from the three non-expert rater groups (p0.19), while segmenting 38-52% more consistently (lower inter-patient standard deviation). Point-click prompts fail regardless of placement (best Dice=0.350), because speckle gives no stable local contrast. Only an approximate bounding box is required, so any clinic can deploy it without data collection, annotation, or retraining. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2608.14796 [cs.CV] (or arXiv:2608.14796v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14796 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-208] Qwen -Video-Edit: Instruction-Based Video Editing by Repurposing an Image Editing Model
链接: https://arxiv.org/abs/2608.14790
作者: Yunpeng Bai,Yossi Gandelsman,Michaël Gharbi,Qixing Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL Code: this https URL Model: this https URL
Abstract:Instruction-based video editing is commonly built on video-pretrained generative backbones: a video diffusion transformer is adapted, at considerable cost, to condition on a source video and an editing instruction. In this report we explore a different route and show that a strong instruction-based image editing model can edit videos by operating directly on video-VAE latents. Starting from Qwen-Image-Edit, we arrange the latent frames of a Wan~2.1 video VAE as tiles of one large virtual image, reuse the editor’s image positional encoding for every tile, and bridge the two latent spaces with a pair of lightweight input/output projections warm-started from the editor’s own patchify and unpatchify layers, so that at initialization a (static) video is embedded exactly as an image the model already understands. The whole system is then fine-tuned on the public Ditto-1M editing triplets, and a few denoising steps of Wan~2.2 serve as an optional temporal enhancer. We motivate the design with a chain of zero-training observations: the stock image editor already edits a video presented as a contact sheet; it is indifferent to whether the sheet’s tokens come from one joint encode or from per-frame encodes stitched in latent space; and it even edits genuine video latents zero-shot to a clearly recognizable degree, leaving fine-tuning only a fidelity gap to close. Our results suggest that, despite the large investment in training video latent spaces, per-frame video latents remain close enough to the image domain that mature image editing priors transfer with minimal adaptation. Project Page: this https URL Code: this https URL Model: this https URL.
[CV-209] MegaParts: Scaling Part-Aware 3D Object Generation to 300 Parts via Token-Efficient Autoregressive Modeling
链接: https://arxiv.org/abs/2608.14783
作者: Manwen Liao,Xinyu Lian,Jian Mao,Kaixu Chen,Li Luo,Jinghao Yan,Wanshui Gan,Qiao Yu,Weitian Zhang,Chunhua Shen,Guang Chen,Bo Dai,Xudong Xu,Zhaoyang Lyu
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 12 pages, 6 pages appendix, 13 figures, technical report
Abstract:Part-aware 3D object generation is essential for graphics applications such as controllable modeling, editing, and articulation, where objects are represented as coherent assemblies of semantic parts. However, existing part-aware generation methods, do not scale well to highly complex objects. As the number of parts increases, generating detailed geometry becomes prohibitively expensive in token length and memory. We introduce MegaParts, a scalable autoregressive 3D generation framework to address this challenge by combining structured sequence modeling with a token-efficient vector-quantized shape tokenizer. Our tokenizer learns discrete latent representations for part-level geometry by minimizing token usage subject to high-fidelity reconstruction, enabling adaptive-length tokenization based on geometric complexity. On top of this compact representation, we train a large language model to generate object bounding boxes, part bounding boxes, and part shape tokens within a unified structured sequence. Combined with efficient long-context training strategy, our token-efficient formulation scales to objects with up to 300 parts and sequence lengths up to 256k tokens. This substantially extends the scale of part-aware 3D generation while preserving compositional structure and enabling fine-grained part-level control. Our method achieves higher mesh quality than baseline autoregressive and diffusion models, showing that compressed discrete part tokens improve not only scalability but also the achievable fidelity of generated geometry. These results suggest that LLM native token-efficient autoregressive modeling is a compelling alternative to diffusion for large-scale part-aware 3D generation. The project page is available at this https URL.
[CV-210] AMPLIFAI: A Multiphase CT Dataset for Benchmarking Clinical Reasoning in LI-RADS Assessment of Liver Lesions
链接: https://arxiv.org/abs/2608.14778
作者: Pranav Kulkarni,Nikhil Shah,Amritansh Suryavanshi,Jana Delfino,James Tonascia,Jade Wong-You-Cheong,Barton Lane,Joseph Chirico,Jeffrey D. Hirsch,Ang Li,Heng Huang,Florence X. Doo
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide, with early detection improving survival from 20% to 70%. The standardized LI-RADS criteria establish a biopsy-free, fully imaging-based framework that can serve as a foundation for automating HCC diagnosis with artificial intelligence (AI). However, the lack of large, publicly available datasets with high-quality labels has limited the development of AI models for LI-RADS characterization. We introduce the \textbfAMPLIFAI dataset, the first public dataset of multiphase abdominal CT scans annotated with LI-RADS categories and segmented for three major LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. Following the \emphDatasheets for Datasets format, this paper details the dataset’s composition, curation process, and annotation pipeline to facilitate transparent, reproducible research.
[CV-211] Artificial Intelligence as a Tool for Combating Child Labour: A Real-Time Edge Vision Pipeline for Child Detection and Age Estimation
链接: https://arxiv.org/abs/2608.14770
作者: Mark Nowak(Conflux Laboratory)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 39 pages, 1 figure, 13 tables
Abstract:An estimated 138 million children remain in child labour worldwide, and the monitoring systems used by affected sectors, built on periodic household visits and interviews, systematically under-detect them. We present a real-time computer-vision pipeline, built and operated solely as a research prototype, that studies the feasibility of giving Child Labour Monitoring and Remediation Systems (CLMRS) a continuous, presence-based evidence channel. The pipeline combines a multi-task person and face detector (YOLO26x backbone in the CerberusDet framework), cascaded age estimation pairing MiVOLO v2 with a child-specialist model for ages 0-12, ByteTrack tracking, ArcFace and DINOv2 re-identification, and track-level fusion producing reviewable per-person records. The detector raises person mAP@0.5 from 0.390 to 0.683 over the previous-generation baseline; the child specialist reaches 1.944 years MAE on children-only validation, where widely used open-source stacks err by 18-23 years. FP8 TensorRT compilation yields a 1.77x speedup at +0.002 years MAE, bringing the pipeline above twice real-time on embedded hardware. On 26.8 hours of proxy video the system finds 634 unique child candidates versus 285 for its predecessor. We further report a seventeen-day unattended field pilot on a farm in Zimbabwe (38.7 million frames, six cameras) evaluated against a daily attendance register: software tuning improved detection yield 36-fold, and identity consolidation under a simultaneity veto cut over-reporting from 9.1x to 1.8-3.9x with zero proven-false merges. We document training and quantisation failures alongside successes, and the data-protection and human-in-the-loop safeguards such a system requires.
[CV-212] Uncertainty Identifies Difficult Samples Across Methods: A Multi-Task Study on a Heterogeneous Skin Lesion Dataset MICCAI
链接: https://arxiv.org/abs/2608.14768
作者: Leon Koole,Jiapan Guo,Matias Valdenegro-Toro
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 9 figures, UNSURE 2026 @ MICCAI camera ready
Abstract:Skin lesion classifiers can be confidently wrong on the cases that matter most, so knowing when a prediction should not be trusted is clinically as useful as the prediction. We study uncertainty quantification on a dataset pooled from many ISIC sources, with a shared backbone and two jointly learned heads: a binary malignant versus non-malignant head and a five-class diagnostic head. Five UQ methods (MC Dropout, DropConnect, Flipout, Deep Ensembles, DUQ) are compared on accuracy, calibration, uncertainty decomposition, and risk-coverage. Difficulty is largely method-agnostic: even methods with narrow entropy distributions rank the same samples as hard (per-sample entropy correlations of 0.54 to 0.91 ). The choice of method matters more for calibration and uncertainty decomposition, where Deep Ensembles is the clear winner, than for finding difficult cases. The ranking is also good enough that deferring the most uncertain cases removes a disproportionate share of errors, supporting uncertainty-based selective referral, evaluated here in-distribution only.
[CV-213] Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation
链接: https://arxiv.org/abs/2608.14766
作者: Simon Baur,Arne Schernich,Ekin Böke,Wojciech Samek,Jackie Ma
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity. However, whether entropy-based measures reflect clinically meaningful ambiguity, i.e. case-level disagreement about whether a pathology is present at all, remains poorly understood. Contrary to most prior work, which focused on pixel-wise boundary disagreement, we systematically evaluate how well aleatoric uncertainty captures presence ambiguity. Our evaluation spans 3D lung nodule segmentation across four architectures with Monte Carlo dropout and deep ensembles, on LIDC-IDRI and an external validation cohort (LNDb). We find that entropy-based uncertainty maps align with boundary noise and minor drawing variation but carry insufficient discriminative signal for presence ambiguity. In contrast, a lightweight supervised ambiguity head trained on frozen segmentation features substantially outperforms all entropy-aggregation-based baselines across architectures, metrics, and both cohorts, and matches or exceeds methods that explicitly model ambiguity under disagreement supervision (Probabilistic U-Net, Annotator-Confusion 3D-UNet). A qualitative feature-space analysis shows that presence ambiguity is already encoded in the frozen encoder features of pixel-wise-trained networks, only to be discarded by the segmentation output and its entropy aggregation. Our findings expose a fundamental mismatch between the theoretical promise of aleatoric uncertainty and its practical behavior, and suggest that practitioners should not rely on entropy-based uncertainty as a proxy for clinical ambiguity in safety-critical applications.
[CV-214] PolyComp: A Polycube-based Benchmark for Compositional 3D Spatial Reasoning in Multimodal Models
链接: https://arxiv.org/abs/2608.14741
作者: Siddharth Patel
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce PolyComp, a procedurally generated and verified benchmark that stresses visual recognition and compositional spatial reasoning. In each problem, a model must identify which of four options shows a pair of polycube components that can be combined to form a target solid. The benchmark contains 120 problems across four geometry families, and each problem has three different presentation formats using either a single image or multiple images. The random guessing baseline is 25%. Across the three presentations (360 presented problems per model), GPT-5.6 Sol with max effort attains 50.0% accuracy (95% problem-cluster CI 43.3-56.7%) at a mean cost of \ 0.951 per presented problem, Claude Fable 5 with max effort attains 39.4% (33.1-46.1%) at \ 0.701, and Gemini 3.1 Pro Preview with thinking level high attains 27.5% (22.8-32.5%), near the 25% random guessing baseline, at \ 0.350. The observed accuracy spread across geometry families is larger than across presentation formats. We present a problem development and evaluation protocol, cost and token accounting, and release the 120 problems.
[CV-215] From Dense Prediction to Visual Editing: Structured Supervision for Unified Image and Video Creation
链接: https://arxiv.org/abs/2608.14740
作者: Zhefan Rao,Bin Zou,Haoxuan Che,Xuanhua He,Chong Hou Choi,Yanheng Li,Rui Liu,Qifeng Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from visual context. However, semantic-only conditioning and creation-only training do not explicitly supervise the local structure needed for precise, temporally consistent editing. We therefore formulate depth and surface-normal prediction as image-form denoising targets, using these dense tasks as structured visual supervision within the same creation interface. Our framework decouples semantic interpretation from spatially aligned visual injection while sharing one multimodal diffusion transformer (MMDiT) backbone across all tasks. Mutual Context Attention (MCA), a paired-video data-construction procedure, and a progressive training curriculum then connect the learned structural cues to temporally localized editing and reference-conditioned creation. A single checkpoint obtains the highest overall score in the reported comparison of unified systems (4.15); adding dense supervision improves OpenVE Overall from 3.98 to 4.06 and Local Add from 3.92 to 4.18. These results support a deliberately bounded conclusion: perception-oriented dense supervision transfers useful structural knowledge to downstream creation, especially editing locality and preservation; we do not claim superiority as a standalone dense predictor.
[CV-216] Emergence of Transfer Learning towards Specific Identification of Alzheimers Disease A Prospective Approach
链接: https://arxiv.org/abs/2608.14731
作者: Soumik Podder,Chandramouli Haldar
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注:
Abstract:Worldwide, millions of senior citizens are suffering from Alzheimer disease abbreviated as AD, a well- versed form of dementia. AD is featured by amnesia, intellectual disability, and difficulty with consciousness. DL and ML models are undoubtedly explored to identify AD related patterns on large dimensional neuroimaging data but they need global optimization and are suffering from overfitting issue that might yield dissatisfactory result in testing data set. DL overcomes the issue by convolution of input image with kernel but any sudden change in the MRI image or human manipulation, limited pre- processing of the images can mislead CNN in achieving highly accurate detection. Transfer Learning (TL) has proved itself in AD diagnosis by utilizing pre-trained models on large data sets to guide novice model in a new neuroimaging dataset. This review provides an inclusive glimpse of TL implication in classification, identification including the conversion of AD. Keeping in view, we have assessed the strengths and limitations of TL in improvising diagnostic accuracy even with limited data. The uniqueness of the present review is the incorporation of explainable AI in TL based AD diagnosis system. Finally, it can be claimed that the review will guide the new re-searchers in the area of TL induced neurodegenerative disease detection.
[CV-217] IP Protection in the Era of Visual Generative AI: A Survey
链接: https://arxiv.org/abs/2608.14730
作者: Zhuan Shi,Shunchang Liu,Alireza Dehghanpour Farashah,Qian Yang,Han Yu,Cao Yang,Chaochao Chen,Yuping Yan,Yaochu Jin,Golnoosh Farnadi,Lingjuan Lyu
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 35 pages, 2 figures, 3 tables
Abstract:The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and redistribution of protected data and model assets. To address these risks, a growing body of technical defenses has been proposed. However, existing surveys typically organize this literature by lifecycle stage or technical mechanism, which can obscure the protective intent of different methods. This survey presents a two-dimensional taxonomy for IP protection in visual generative models. The primary axis is a Control Logic View, which classifies methods into Information Exposure Control, Generative Behavior Constraint, and Attribution Accountability according to the risk variable they regulate. The secondary axis distinguishes Data IP from Model IP as cross-cutting asset dimensions. Under this framework, we systematically review protection methods, align evaluation protocols with protection objectives, and discuss open challenges including proactive model-level safeguards, standardized evaluation, robustness against adaptive attacks, and explainable evidence. This survey aims to offer a principled, systematic, and easy-to-follow overview for both new and experienced researchers in visual generative AI IP protection.
[CV-218] Do CNNs Internally Represent Real and Fake Images Differently? A Hidden-Layer Analysis
链接: https://arxiv.org/abs/2608.14729
作者: Moumita Sen Sarma,Pascal Hitzler,Eugene Y. Vasserman
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Fake/synthetic images are increasingly prevalent, but it remains unclear whether Convolutional Neural Networks (CNNs) process real and fake images in the same internal manner. This work examines the hypothesis that CNNs represent real and fake images differently, such that fake images induce different hidden-layer activation patterns even when semantic content is preserved. The hypothesis is evaluated in scene recognition settings using trained CNN models. Dense-layer activations are extracted, and neurosymbolic methods assign semantic labels to selected neurons. For each real test image, corresponding fake images are generated with similar semantic content using object-label-guided text-to-image and image-to-image generation based on Stable Diffusion variants. Paired real-fake activation patterns are then compared statistically. Additional experiments with another dataset, CNN architecture, generative model, and JPEG/blur degradation analysis assess robustness. Results suggest that fake images evoke different hidden-neuron activations, and these differences are not explained only by simple image degradation. Overall, the findings indicate that real and fake images differ in CNN hidden-layer activation behavior at least in some settings, which opens the door for follow-up work on making use of this different behavior to improve fake image detection.
[CV-219] Low Cost Two-Stage Fabric Defect Detection at the Edge
链接: https://arxiv.org/abs/2608.14727
作者: Rasel Hossen,Diptajoy Mistry,Mosaddek Hossain Kamal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 10 figures, 8 tables. Deployment study on NVIDIA Jetson Nano with TensorRT FP16. Includes a decomposition showing the measured 1.36x end-to-end speedup is dominated by data-path overlap rather than by the cascade. Dataset available on Roboflow Universe
Abstract:Fabric inspection in the garment industries of low-income economies remains largely manual, and commercial vision systems are priced beyond most small and medium mills. Because defects are sparse under controlled production, a natural response is a cascade: screen every frame with a cheap anomaly detector and invoke a full detector only on suspicious frames. We build such a cascade for four knit-fabric defect classes and deploy it end-to-end on an NVIDIA Jetson Nano with TensorRT FP16. Stage 1 is a compact convolutional autoencoder with decoder attention gates, an edge-weighted reconstruction loss, and feature-level distillation from a frozen YOLOv5n teacher; Stage 2 is YOLOv5n, invoked only on flagged frames. On a 249-image benchmark disjoint from detector training (20 defective, 229 non-defective), Stage 1 at a recall-prioritised threshold flags all 20 defective images (95% CI 0.83-1.00) at a false-positive rate of 49.3% (113/229), reducing false positives by 19.3% relative to a plain autoencoder (p=0.011). The parallel pipeline reaches 13.45 FPS against 9.86 FPS for a sequential YOLO-only loop. Our central finding comes from decomposing that 1.36x: 91% of it is attributable to overlapping JPEG decode with inference rather than to the cascade, which contributes only a 5.1% inference reduction at the measured forwarding rate p = 0.534. We further show that forwarding here is false-positive-limited rather than prevalence-limited - 85% of forwarded frames are false alarms - and quantify the 29-45% inference reduction attainable under tighter calibration. We report this as a caution for cascade speedups measured without controlling the data path, and position the system as AI-assisted triage rather than autonomous acceptance.
[CV-220] Spatial Attention Noise Masking for Causally Sufficient Interpretability
链接: https://arxiv.org/abs/2608.14725
作者: Benjamin Formby,Kuang-Ching Wang,D Hudson Smith
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present a novel causal approach to interpretability for computer vision models that dynamically masks the input image prior to classification. The interpretability of deep learning predictions is critical in high-stakes fields such as medical imaging, security, and autonomous driving. Most interpretability methods are applied passively to already trained models, which typically result in correlational rather than causal explanations. Existing causal interpretability methods are limited to post hoc analysis, weakening the causal claims. Additionally, existing active methods generally lack explanations that explicitly assign responsibility to input features. This work proposes a spatial attention noise masking framework that provides causal explanations about the features sufficient for the prediction. The proposed framework consists of: 1) a UNet-style mask generator, and 2) a Resnet18 encoder and linear classifier that classifies both masked and unmasked versions of an input image. The generated masks are regularized to be sparse and spatially smooth, while masked image embeddings are constrained to remain consistent with embeddings from the corresponding unmasked images. The resulting masks can be interpreted as feature attribution maps that are competitive with related interpretability methods while additionally providing strong causal explanations of model predictions. Quantitative evaluations demonstrate mask faithfulness, near-baseline classification performance across five classification tasks despite substantial masking of image information, and robustness to distribution shifts such as background swapping and natural adversarial examples. Qualitative comparisons further demonstrate mask behavior and competitive interpretability relative to state-of-the-art feature attribution methods.
[CV-221] Privacy-Preserving Dataset Curation for Kuala Lumpur Urban Traffic: Grounded Vision-Language Detection with Spatial Vehicle-Context Filtering
链接: https://arxiv.org/abs/2608.14724
作者: Mohammed Abdul Al Arafat Tanzin,Rudzidatul Akmam Dziyauddin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The rapid advancement of intelligent transportation systems and autonomous driving relies heavily on multi-modal urban traffic datasets. However, curating high-fidelity video imagery in complex tropical urban environments—specifically Kuala Lumpur, Malaysia—presents severe challenges for Personally Identifiable Information (PII) anonymization due to high motorcycle density, dark acrylic license plates, dynamic camera tilt, and extreme tropical glare. We propose an automated anonymization framework tailored for the Kuala Lumpur Road Dataset, captured via a mobile cycling platform at 2 FPS. We document how legacy Haar cascades and YOLOv8 fail under these conditions—generating false positives on background elements while missing rotated or occluded targets. Our architecture resolves this by integrating Grounding DINO—a zero-shot open-set vision-language transformer—with a novel Spatial Vehicle Region of Interest (ROI) Containment Engine. By requiring license plate centroids to reside within validated vehicle boundaries, the pipeline suppresses environmental false positives while automatically obfuscating faces, heads, and license plates. An initial evaluation on 1,266 frames demonstrates a \sim 95% success rate, with remaining failures restricted to small, heavily occluded, oblique, or ambiguous targets. Coupled with temporal persistence mechanisms and an automated quality-control auditor, the framework minimizes privacy-related false negatives while preserving scene context for downstream vision tasks. While formal legal compliance depends on broader governance procedures, this publicly available pipeline and demonstration notebook provide an auditable preprocessing stage for privacy-aware dataset curation.
[CV-222] A Vision Transformer for ECG-Based Detection of Left Ventricular Systolic Dysfunction Across Multiple Clinical Sites
链接: https://arxiv.org/abs/2608.14723
作者: Burcu Ozek,Aruna Mohan,David Vorchheimer,Daniel Weiss,Eyal Kedar,Tamar Sobol,Or Zilbershot,Fatemeh Afghah
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 19 pages, 5 figures, 5 tables; includes supplementary material with 3 additional figures
Abstract:Reduced left ventricular ejection fraction (LVEF) is frequently asymptomatic and often detected only after advanced heart failure develops. Electrocardiograms are recorded routinely yet underused for this condition, because reduced LVEF has no single diagnostic waveform. We trained an ensemble of vision transformers from scratch to detect reduced LVEF ( \leq 40%) from 12-lead ECGs, analyzing each heartbeat individually, using 10,142 patients across seven sites in three US health systems. In a held-out external cohort of 4,092 patients from three geographically independent US clinical sites at a real-world reduced-LVEF prevalence of 8.72%, the model achieved an AUROC of 0.88 (95% CI 0.86-0.89), sensitivity 81.2%, specificity 81.0%, and negative predictive value 97.8%. Sensitivity remained high across sex, race, ethnicity, and comorbidity subgroups, while specificity was lower in older patients and those with atrial fibrillation or cardiomyopathy. Beat-level attention maps provided interpretability into the model’s predictions, showing consistent focus on the QRS complex rather than the P wave. These findings support the potential of routine ECGs as a scalable first-pass triage step to identify patients who should undergo echocardiography for reduced ejection fraction across diverse patient populations.
[CV-223] Braided Vision Transformer for Stroke Detection in Multi-view Retinal Fundus Imaging
链接: https://arxiv.org/abs/2608.14722
作者: Aysen Degerli,Mika Hilvo
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Stroke remains a leading cause of mortality and morbidity worldwide, emphasizing the importance of its accurate and immediate assessment. Retinal fundus imaging has emerged as a promising modality for stroke assessment, as the retina reflects cerebrovascular and neurological risk factors. Contrary to conventional neuroimaging techniques, retinal fundus imaging offers a non-invasive, cost-effective, and portable alternative for rapid screening. This paper explores the feasibility of retinal fundus imaging for stroke and transient ischemic attack (TIA) detection using macula-centric and optic nerve head-centric views captured from both eyes. Our study introduces, to the best of our knowledge, the first vision transformer model for retinal fundus imaging in stroke assessment, offering a novel approach for capturing retinal patterns. Thereby, we propose the Braided Vision Transformer (BViT) model, which extracts representative features from the given multi-view images while simultaneously capturing inter-view relationships across both eyes, enabling a more informative understanding of retinal biomarkers associated with cerebrovascular events. Experiments conducted on our collected Stroke-Data dataset demonstrate that BViT achieves an AUC score of 0.75 for stroke detection, outperforming regular vision transformers.
[CV-224] AeroGround: A Comprehensive Benchmark for Aerial-Ground Collaborative Reasoning
链接: https://arxiv.org/abs/2608.14721
作者: Shenghong Yi,Lin Zhang,Muzian Li,Jiakang Yuan,Haoyu Zhang,Peng Ye,Jiayuan Fan,Huafeng Qin,Tao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-language models (VLMs) have been widely employed in understanding and reasoning tasks for unmanned aerial vehicles (UAVs). Existing UAV benchmarks primarily focus on aerial-view scenarios. However, whether current VLMs can perform well on understanding and reasoning tasks in aerial-ground collaborative scenarios which are practical in real-world applications like rescue and infrastructure inspection remains underexplored. To address this gap, we introduce AeroGround, a comprehensive benchmark for evaluating VLMs in aerial-ground collaborative reasoning. AeroGround is built upon a simulated aerial-ground dataset containing approximately 29,000 multimodal observation groups from diverse open environments, and provides 2,250 high-quality question-answering instances covering cross-view correspondence, spatial understanding, and reasoning. Experiments on 16 pretrained VLMs, together with two domain-adapted variants, reveal a substantial gap between current models and human performance: the best model achieves an average accuracy of 54.4%, whereas humans reach 93.3%. By systematically revealing the strengths and limitations of existing models in aerial-ground collaborative reasoning, AeroGround provides a foundation for developing more capable aerial-ground collaborative embodied intelligence systems.
[CV-225] DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis
链接: https://arxiv.org/abs/2608.14719
作者: Xiaoxiao Li,Xitong Ling,Jiawen Li,Weiming Chen,Zhenyang Cai,Xidong Wang,Tian Guan,Benyou Wang,Yonghong He
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nested dual long-tail: an inter-slide class long tail and an intra-slide long tail of instance-level discriminative evidence. The two long tails are coupled: tail classes have few training slides, while their limited diagnostic evidence is concentrated in a few patches and obscured by abundant within-bag redundancy. This coupling biases models toward head classes and degrades rare-class recognition. To address this, we propose DeCo-MIL for long-tailed WSI analysis, which jointly alleviates the nested dual long-tail through frequency-debiased counterfactual reasoning. For the inner long tail, DeCo-MIL clusters patches into tissue-morphology anchors, replaces each anchor with its matched normal prototype to perform a counterfactual intervention, and estimates its counterfactual contribution to the ground-truth class using class-frequency-corrected predictions. These contributions guide redundancy masking to preserve scarce discriminative instances. For the outer long tail, DeCo-MIL constructs anchor-stratified pseudo-bags from redundancy-reduced bags and combines tail-aware oversampling with consistency regularization, increasing effective supervision for tail classes while preserving tissue-morphology composition. Extensive experiments on three long-tailed WSI benchmarks demonstrate that DeCo-MIL achieves state-of-the-art performance in both tail-class recognition and overall classification.
[CV-226] Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders
链接: https://arxiv.org/abs/2608.14717
作者: Ze Zhang,Yang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 13 pages, 4 figures, 2 tables. An ancillary analysis-ready package supports exact aggregate reproduction without model inference. Code and reproduction package: this https URL
Abstract:A query-relation deletion can improve the edited slot while reducing the utility of the prediction set that contains it. We study this tension in two related ResNet-50 DETR-family checkpoints using recorded, selection-conditional evidence from 710 paired image-relation units per checkpoint. The primary comparison subtracts a matched active control, which deletes the same leader source at a different recorded recipient, from the selected target deletion. It is therefore a composite contrast rather than a same-recipient placebo. The target-minus-control contrast is locally positive and fixed-assignment negative in both checkpoints. The opposite-sign pattern occurs within 302/710 DETR units and 460/710 DINO units. After rematching, the corresponding counts are 285/710 and 433/710. Rematching and native selection absorb enough of the mean loss for DETR intervals to cross zero, whereas DINO intervals remain negative, so persistence across readouts differs by checkpoint. A fixed-map comparison between hard deletion and a mass-preserving edit also differs before rematching. That comparison is conditional on the outcome-blind map and does not establish same-dose transport. Local intervention success therefore does not determine the consequence for a jointly decoded set. The supported conclusion is selection-conditional deletion sensitivity whose persistence depends on the readout and intervention operator. We do not identify an intervention-invariant edge mechanism, detector-level degradation, population prevalence, or the value of a training-time regularizer. Comments: 13 pages, 4 figures, 2 tables. An ancillary analysis-ready package supports exact aggregate reproduction without model inference. Code and reproduction package: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2608.14717 [cs.CV] (or arXiv:2608.14717v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14717 Focus to learn more arXiv-issued DOI via DataCite
[CV-227] SpotlessGS: Relightable 3D Gaussian Splatting under Dynamic Illumination for Robotic Perception IROS2026
链接: https://arxiv.org/abs/2608.14713
作者: Liang Hong,Jiaxin Wei,Simon Schaefer,Stefan Leutenegger,Jaehyung Jung
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Robots operating in dark or poorly lit environments rely on onboard lights, which often produce uneven illumination that degrades downstream perception tasks. Prior approaches based on 2D image enhancement lack reliable supervision and fail to preserve multi-view geometric consistency. To address these limitations, we extend Dark Gaussian Splatting (DarkGS) toward a more accurate and flexible relightable 3D reconstruction framework. First, we eliminate the need for explicit light parameter calibration by jointly optimizing lighting parameters within the Gaussian Splatting framework. Second, we introduce a low-frequency illumination model based on spherical harmonics (SH) to capture spatially varying residual and ambient lighting effects. Third, we incorporate an MLP-based Bidirectional Reflectance Distribution Function (BRDF) to model non-Lambertian reflectance. Experiments on synthetic and real-world datasets demonstrate that our method effectively mitigates illumination artifacts while improving rendering quality and quantitative performance over prior approaches. We further validate its benefits for robotic perception through a downstream task.
[CV-228] PE-CSNet: An equivariant network architecture with learnable patch-based sparse representation
链接: https://arxiv.org/abs/2608.14708
作者: Kai Li,Haitao Long,Bo Zhang,Haiwen Zhang,Zhi Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages, 10 figures. Under review
Abstract:Compressive sensing (CS) enables accurate signal reconstruction from sparse measurements and is widely applied in medical imaging, remote sensing, and image compression. However, designing an effective, task-specific sparse transform and the corresponding optimization procedure for high-quality CS remains challenging. This process typically requires expert domain knowledge and laborious parameter tuning. To address this issue, we present a Patch-based Equivariant deep unrolling architecture, termed PE-CSNet, for accurate CS recovery. While traditional CS methods generally use predefined patch-based transform sparsity, we generalize this idea by incorporating learnable transform sparsity that adapts to the specific CS task through an optimization-driven process. Specifically, we first establish a generalized patch-based CS model, which we solve via a block coordinate descent (BCD) algorithm. The BCD solver is then unrolled into a deep neural network, where all parameters of both the CS model and solver are learned through end-to-end training. To improve data efficiency, we introduce a stochastic equivariant training strategy that exploits the patch-wise structure of the network, enabling PE-CSNet to learn effectively even from limited data. We further provide a simpler, parameter-shared version of PE-CSNet and briefly discuss its convergence as an iterative solver. For practical applications, the network uses stage-specific (non-shared) parameters to enhance its expressive power and thereby improve its performance. On the tasks of CS magnetic resonance imaging (CS-MRI) and CS coded diffraction patterns (CS-CDP), PE-CSNet achieves state-of-the-art accuracy with fast computational speed, outperforming traditional methods and existing deep unrolling methods.
[CV-229] Equilibrium Forcing: Adaptive Video Generation Without Noise Conditioning
链接: https://arxiv.org/abs/2608.14706
作者: Hansen Jin Lillemark,Alex Rojas,Zachary Novack,Runqian Wang,Yilun Du,Yian Ma,Taylor Berg-Kirkpatrick,Rose Yu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting inference procedures from adapting to the data. We introduce Equilibrium Forcing (EqF), a simplified framework for video denoising generative models without noise level conditioning. EqF pioneers modular training- and inference-time designs for noise-unconditional generation that decouple learning the denoising field from sampling. This flexibility allows for inference-time algorithms that operate in a closed loop by adapting to feedback from the sample, improving video quality and consistency on challenging autoregressive video generation benchmarks. Extensive analysis elucidates exactly how removing the noise level conditioning enables EqF’s data-dependent inference properties to surpass the performance of standard noise level-conditional denoising video methods.
[CV-230] On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers
链接: https://arxiv.org/abs/2608.14705
作者: Ljubomir Buturovic(East Palo Alto, United States)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical imaging. We compared three HPO protocols in terms of \em absolute performance-estimation error (AEE; the absolute difference between the winning configuration’s validation AUROC and its test AUROC): fixed holdout (F), reshuffled holdout ®, and 5-fold cross-validation ©. The search space, sampler, training procedure, architecture, and test set were held identical across protocols. We evaluated the protocols on three public datasets spanning two regimes: binary medical imaging (RSNA pneumonia radiographs and binarized HAM10000 skin lesions) and 200-class natural imaging (Tiny ImageNet), across a range of development set sizes n and two backbones (ResNet-18 on all datasets, Vision Transformer (ViT-S/16) on RSNA). On the medical datasets, every point estimate favored cross-validation over both holdout protocols, with reductions in AEE largest at small sample sizes and diminishing as n increased. This pattern remained robust under conservative family-wise adjustment. On Tiny ImageNet, AEE was negligible under all three protocols. Test AUROC was generally similar among protocols. Fixed holdout had lower mean AEE than reshuffled holdout in 11 of 12 medical conditions, although this secondary finding was less uniformly supported. For small-sample medical image classification, we recommend cross-validation-based HPO when computational resources permit because it trades additional computation for a more reliable development-time estimate of subsequent test performance. Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2608.14705 [cs.CV] (or arXiv:2608.14705v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.14705 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-231] Deep Analog: Open-Set Film Emulation with Reference-Conditioned 3D LUTs
链接: https://arxiv.org/abs/2608.14702
作者: Yitong Mu
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: Master’s thesis, Rochester Institute of Technology, 2026. 24 pages, 13 figures, 6 tables. Code and demo: this https URL
Abstract:Film emulation reproduces the look of an analog film stock on a new digital photograph. We target its open-set form – matching any reference film frame from a single example – with a 3D lookup table (LUT) predicted from that reference. Real-time image enhancement predicts per-image weights over a fixed bank of 3D LUTs and blends them. We show this is a gated mixture of experts and inherits its failure: trained end-to-end against reconstruction, the gate collapses onto a single expert, so a bank of K LUTs delivers the capacity of one. An entropy term, the enhancement-setting analogue of mixture-of-experts load balancing, restores utilization and recovers about 1 dB PSNR. The deeper constraint survives: a fixed LUT basis is closed-set, freezing the achievable looks at training time. We therefore discard the basis and predict a single 3D LUT as a residual from a reference image (StyleLUTNet), trained by self-supervision on procedurally generated color transforms. The conditional design removes the gate and generalizes open-set to unseen film stocks without paired data or retraining. Around this color backbone we build Deep Analog, a film-emulation pipeline that adds histogram-based tone matching and a physics-informed optical renderer – multi-scale grain and per-channel halation driven by parameters an inverse network regresses from the reference. On 350 self-supervised pairs the color stage reaches 22.05 dB PSNR / 0.925 SSIM and the full pipeline 21.72 dB / 0.923; the color path runs in 5.2 ms at 1080p (192 FPS) and exports a portable .cube LUT for standard editing tools. A second degeneracy in conditional LUT training – residual-scale collapse – shares the root cause and yields a general principle: auxiliary regularization must stay subordinate to reconstruction.
[CV-232] Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection ECCV2026
链接: https://arxiv.org/abs/2608.14701
作者: Fernando Alonso-Fernandez,Kevin Hernandez-Diaz,Josef Bigun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication at ECCV 2026 Workshop on AI for Multimedia Forensics Disinformation Detection (AI4MFDD2026)
Abstract:Soft-biometric attributes such as gender, age, and ethnicity provide valuable ancillary evidence when full identity recognition is not feasible, supporting applications in forensic investigation, identity verification, surveillance, or detection of synthetic and manipulated media. Among biometric modalities, the periocular region is a robust source of soft-biometric cues, as it often remains visible when other parts of the face are occluded, a frequent condition in forensic evidence and surveillance footage, and can be captured across a wide range of acquisition conditions. In this paper, we provide a survey of demographic attribute estimation from periocular images, covering publicly available datasets, methodological trends from handcrafted descriptors to deep learning architectures, and the state of the art in gender, age, and ethnicity prediction. We discuss use cases relevant to multimedia forensics and disinformation-detection applications, including demographic filtering in surveillance footage, age verification, and the detection of demographic inconsistencies in synthetic data. We also highlight open challenges, including dataset bias, cross-domain generalisation, fairness, ethical aspects, and the lack of forensic-oriented benchmarks.
[CV-233] Xemo-Talker: Unlock Emotions Explicitly for Audio-Driven Talking Portrait Synthesis
链接: https://arxiv.org/abs/2608.14700
作者: Chaolong Yang,Yinuo Guo,Kai Yao,Yuyao Yan,Jie Sun,Guangliang Cheng,Shibin Wu,Bin Dong,Kaizhu Huang
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Precise emotion control in audio-driven talking heads remains a challenge due to the reliance on implicit emotion regulation in existing systems, which often leads to indirect and insufficient control. Additionally, training with explicit emotion-related losses across the entire motion space poses significant difficulties due to the inherent trade-off between accurate lip synchronization and fine-grained emotion control. In this paper, we reveal a key finding: although emotional cues are distributed throughout the motion space, concentrating discriminative supervision on less-principal components achieves a better emotion-lip synchronization balance, as principal components mainly encode high-energy articulation and pose variations. Building on this insight, we propose Xemo-Talker, which first learns a neutral speech-to-motion mapping for stable articulation and lip synchronization, and then introduces a lightweight emotion branch guided by less-principal subspace supervision. To enhance emotion control, we design a Tri-Loss consisting of inter-class separation, intra-class compactness, and less-principal contrastive learning. Given an audio input, a reference image, and an emotion label, Xemo-Talker achieves state-of-the-art emotion classification accuracy while maintaining competitive lip synchronization and high inference efficiency, with performance approaching that measured on real this http URL source code is publicly available at this https URL.
[CV-234] ARGUS: Attention-Guided Transformers for Scalable Person Identification Using Wi-Fi Telemetry
链接: https://arxiv.org/abs/2608.14670
作者: Nayan Sanjay Bhatia,Pranay Kocheta,Yuhan Li,Katia Obraczka
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Passive, device-free person identification offers an alternative to camera- and wearable-based biometrics, yet existing wireless approaches rely largely on gait or activity cues and are rarely evaluated at scale. In this paper, we present \emphArgus, a passive Wi-Fi sensing system that identifies people from commodity Channel State Information (CSI) without requiring an attached device or a prescribed motion. Argus converts short CSI spans into compact \emphstatgrams: statistical maps built from the channel views available on a given device. A lightweight decoder-only Transformer then reads coarse statgram patches as tokens, and segment-level logit aggregation combines evidence over time. On a 154-subject CSI dataset evaluated with a strict physical-segment split, Argus reaches 78.88% \pm 1.62% Top-1 accuracy on 6-second windows and 84.85% \pm 1.31% after aggregating 19 overlapping windows over a 60-second segment; Top-3 and Top-5 reach 98.61% and 99.26% . For a 60-second statgram, Argus improves over a raw-CSI Transformer baseline by 7.75 points while using 4.4\times fewer FLOPs per window. Attention-guided compression preserves full single-window accuracy with only half of the EHealth patches. On WiMANS, a multi-user benchmark across three rooms and two Wi-Fi bands, Argus remains within 1.23 percentage points of the strongest per-configuration baselines on average while using 27\times fewer inference FLOPs. These results show that compact CSI statistics can scale passive identification while also exposing deployment limits in open-set rejection and cross-room transfer.
[CV-235] LUNG-KGMM: Knowledge-Guided Multimodal Learning for Lung Cancer Incidence Prediction
链接: https://arxiv.org/abs/2608.14657
作者: Chunlei Yang,Shuyan Li,Zhong Cao
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 4 figures, 7 tables, accepted by PRCV Oral
Abstract:Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single data modalities and their inability to leverage structured clinical knowledge. We propose LUNG-KGMM, a knowledge-guided multimodal framework that integrates longitudinal electronic health records, radiology reports, chest radiograph representations, and guideline-derived knowledge for 1-to-6-year incident lung cancer prediction. To address modality heterogeneity and potential data leakage, we develop a leakage-sanitized report processing pipeline and a horizon-masked cumulative training objective that handles incomplete follow-up. We further introduce a knowledge-graph representation of clinical guidance that encodes report-triggered finding-attribute-action relations as an auditable knowledge stream. We build a multimodal development cohort from the publicly available MIMIC databases and construct a real-world validation cohort from the Xiamen Medical Big Data Platform. Extensive experiments on the MIMIC cohort demonstrate that LUNG-KGMM achieves superior performance over state-of-the-art methods, and validation on the Xiamen cohort further characterizes its cross-cohort portability and the need for local adaptation. The MIMIC development cohort is publicly accessible; the Xiamen cohort is governed by local data privacy regulations.
[CV-236] Diagnosing and Mitigating Perception-Decision Misalignment in Omni-LLM s via Modality Subspace Activation
链接: https://arxiv.org/abs/2608.14655
作者: Hongbo Jiang,Jie Li,Yunhang Shen,Tianyu Xie,Pingyang Dai
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Omni-Large Language Models (Omni-LLMs) power complex multi-modal reasoning in applications like World Action Models and autonomous agents. However, their strong performance often masks a profound Perceptual-Decision Misalignment (PDM), where decisions remain unfaithful to multi-modal perceptions. To diagnose this, we formalize Causal Modality Sensitivity (CMS), operationalized via a dual-lens framework: Answer Retention Rate (ARR) at the macro behavioral level, and Logit Angular Discrepancy (LAD) to track microscopic distribution shifts. We also curate CausalMSBench, a diagnostic dataset isolating language priors. Benchmarking reveals that popular Omni-LLMs exhibit critically low CMS, showing negligible distribution shifts even when key modalities are removed. To rectify this, we propose Modality Subspace Activation (MSA), a training-free inference-time framework that uses Singular Value Decomposition (SVD) to estimate modal activation strengths. MSA dynamically balances modal projections in the last hidden state, effectively restoring CMS across benchmarks.
[CV-237] Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling ECCV2026
链接: https://arxiv.org/abs/2608.14652
作者: Yang Zhao,Peisong Niu,Tian Zhou,Ziqing Ma,Guanlong Ma,Rong Jin,Huiling Yuan,Liang Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECCV2026
Abstract:The development of 0.1 ^\circ global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25 ^\circ resolution. While existing approaches fine-tune 0.25 ^\circ forecast models on limited 0.1 ^\circ samples, we show that this transfer is hindered by the irreversible information loss inherent in coarse-resolution forecasting. Therefore, we propose BaguanHR, a framework that shifts the focus from transferring models to transferring data. We first show that super-resolution (SR) has lower conditional entropy and input amplification than forecasting, making it a more robust vehicle for resolution transfer. By leveraging this advantage through variable-wise SR, we synthesize extensive 0.1 ^\circ data from ERA5. BaguanHR’s performance on the synthetic-plus-real dataset exceeds both ML-based methods and IFS-HRES, achieving superior performance across over 85% of the lead times within 72 hours. Furthermore, our findings highlight a power-law scaling effect, as a twofold increase in data reduces RMSE by 4.6% for 72-hour forecasting and 4.9% for 120-hour forecasting. Our results demonstrate that scaling high resolution ML-based forecasting is primarily a data bottleneck, and that variable-wise super-resolution provides a simple yet general solution to unlock long coarse-resolution reanalyses for high-resolution training.
[CV-238] Extend the Safety Horizon for Intelligent Transportation Systems through Semantic-Aware Cooperative Perception
链接: https://arxiv.org/abs/2608.14603
作者: Chun-Yeow Yeoh,Chee Keong Tan,Joanne Mun-Yee Lim,Heng-Siong Lim
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
备注: 16 pages, 7 figures, 6 tables, Submitted to IEEE Transactions on Intelligent Transportation Systems (T-ITS)
Abstract:Cooperative perception enables vehicles and infrastructure to exchange sensor data via Vehicle-to-Everything (V2X) communication, extending sensing coverage beyond occlusions and mitigating blind spots. While critical for autonomous driving and safety, practical deployments often rely on bandwidth-efficient late fusion. Recently, intermediate fusion has emerged as a promising approach for an optimal bandwidth-accuracy trade-off. However, in dense urban environments, cumulative bandwidth demands can overwhelm network capacity, potentially compromising safety-critical Cooperative Intelligent Transport Systems (C-ITS) functions. To alleviate these problems, this paper proposes Hierarchical Multi-Scale Semantic-Aware Cooperative Perception (HMS-SCP), a robust noise-resilient and bandwidth-efficient framework for task-oriented semantic communication in cooperative perception. HMS-SCP employs a spatial importance predictor to identify task-relevant grid elements at each scale, which are then directly mapped into complex-valued symbols for Joint Source-Channel Coding (JSCC). Unlike prior methods that rely on high-dimensional symbol projections for robustness, HMS-SCP exploits structural semantic redundancy across multiple scales to enhance resilience against channel noise, while maintaining an ultra-low symbol rate. This design significantly reduces bandwidth consumption and mitigates network congestion in high-density vehicular environments. Extensive evaluations on the simulated OPV2V and real-world DAIR-V2X datasets demonstrate that HMS-SCP effectively prevents performance collapse under severe Rayleigh fading and extreme compression ratio, maintaining high-confidence far-field detection with a real-time latency of below 16~ms, well within the safety-critical thresholds for dynamic V2X environments.
[CV-239] he Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning CVPR
链接: https://arxiv.org/abs/2608.14558
作者: Garima Arya Yadav,Nilay Yilmaz,Yezhou Yang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: To be published in CVPR Findings 2026
Abstract:Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content. However, their capacity for abstract perceptual reasoning, inferring unseen information from dynamic, generative processes, remains a critical and underexplored frontier. In this paper, we introduce The Unwritten Benchmark, a new challenge designed to probe this abstract perceptual and cognitive ability. We define the core task as acousto-kinematic word inference: models must decipher words, across 3 different writing styles, being written solely from the audio of pen scratches and the video of hand movements, without any visible ink trace. Our evaluation results reveal a profound gap between human and machine performance: while human participants achieve high ordered letter accuracy (over 80%), leading Multimodal Machine Learning Models, including GPT-4o and Gemini 2.5-Pro, struggle significantly, failing to surpass 10%. Furthermore, we identify a paradoxical fusion effect in the models, where providing both modalities often degrades performance rather than improving it. This finding indicates a fundamental breakdown in their ability to synthesize complementary perceptual cues for this cognitive task. These findings highlight significant limitations in both cross-modal causal reasoning and the understanding of the micro-kinematics essential for such cognitive and intuitive perceptual reasoning.
[CV-240] A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
链接: https://arxiv.org/abs/2608.16233
作者: Siyuan Ma,Liang He,Mengying Zhu,Yi Chai,Mengyao Lyu,Haowei Wang,Qizhen Lan,HaoBo Sun,Qixin Zhang,Jingli Chen,Xiaobing Wei,Jiaming Liu,Guiqin Liu,Qianwen Zhang,Yang Liu,Dacheng Tao,Guangyu Wu
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.
[CV-241] Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimers Disease Detection
链接: https://arxiv.org/abs/2608.16039
作者: Jiadao Zou,Hongyu Guo,Wei Xi
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Brain parcellation and classification are typically evaluated in isolation, yet downstream AD detection performance depends on their interaction. We decouple these components and systematically benchmark fast deep learning parcellation methods (SynthSeg+, OpenMAP-T1) against the FreeSurfer (FS-HV) clinical baseline through down- stream AD classification on OASIS-1. Our factorial design evaluates three parcellation methods, two volumetry strategies (hard vs. soft), and four classifier paradigms (clinical thresholds, supervised feedforward networks, ensemble methods, and foundation models with zero/few-shot prompting), with all results quantified using BCa Bootstrap 95% confidence intervals.
[CV-242] Underwater Color Restoration with Vanishing Uncertainty
链接: https://arxiv.org/abs/2608.15598
作者: Grigory Solomatov,Derya Akkaynak
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Underwater color restoration promises to unlock color as a reliable signal for aquatic sciences, but achieving this with scientific confidence remains out of reach. Current methods are validated almost exclusively on an empirical basis, which provides confidence only to the extent that the vast diversity of possible visibility conditions is covered with end-to-end testing using a known ground truth. This is exacerbated by color restoration being a fatally ill-posed problem when considered in full mathematical generality, requiring additional constraints to narrow the solution to a finite uncertainty interval. The gap between which constraints suffice in theory and which constraints are satisfied by real-world data is poorly understood, making it unclear whether existing methods are solving a problem that is actually solvable. In this article, we investigate the theoretical side of this gap, identifying idealized conditions which guarantee bounded uncertainty that converges to zero as the spatial resolution of the camera increases.
[CV-243] Dual-Branch State-Displacement Network for Sea Surface Temperature Super-Resolution
链接: https://arxiv.org/abs/2608.15423
作者: Wankun Chen,Feng Gao,Yanhai Gan,Chuanzheng Gong,Xun Gong,Junyu Dong,Qian Du
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in IEEE JSTARS
Abstract:Sea surface temperature (SST) is a critical indicator of global climate change, yet satellite-derived SST imagery often suffers from coarse spatial resolution, limiting the ability to capture fine-scale thermal structures such as ocean fronts. To address this, we propose a Dual-Branch State-Displacement Network (DBSD-Net) for SST super-resolution. DBSD-Net adopts a dual-branch architecture: a wavelet frequency branch that explicitly separates low and high-frequency components via discrete wavelet transform for targeted processing, and a VGGUNet branch that extracts multi-scale semantic features from a frozen pre-trained VGG backbone. Within the wavelet branch, we introduce a Structural State Space Module (SSSM) with a Gated Structure Refinement (GSR) unit to efficiently capture long-range dependencies and enhance structural integrity, and a Displacement Gate Module (DGM) that learns a displacement field for geometry-aware modulation of high-frequency details, thereby mitigating spatially varying degradation. Experiments on multiple public SST datasets demonstrate that DBSD-Net outperforms existing state-of-the-art methods.
[CV-244] Multi-Channel Feature Fusion and Monte Carlo Dropout for Uncertainty-Aware Diabetic Retinopathy Grading
链接: https://arxiv.org/abs/2608.15234
作者: Saksham Kumar
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注:
Abstract:Automated five-stage diabetic retinopathy (DR) grading requires more than high accuracy alone. Medical-grade deployment calls for lesion-aware preprocessing, ordinal predictions, calibrated uncertainty, and explainability to support reliable diagnostic systems. We present a unified pipeline that addresses these requirements using a Ben-Graham-green-channel CLAHE feature representation, an EfficientNetV2-L ordinal regressor, and Monte Carlo dropout for uncertainty-driven referral. Grad-CAM provides visual explanations aligned with clinically relevant lesions. The proposed method achieves a QWK of 91.31% on the APTOS-2019 official test split, placing it within the near-perfect agreement band (80%). At a 20% referral rate, 293 of 366 images are automatically graded with a QWK of 90.40%. More complex cases are referred for specialist assessment, demonstrating a practical trade-off among grading quality, automation, and patient safety in robust, reliable, and deployment-ready medical diagnostic systems. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP) Cite as: arXiv:2608.15234 [eess.IV] (or arXiv:2608.15234v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.15234 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-245] Modality-Invariant Coarse-to-Fine Retinal Image Registration
链接: https://arxiv.org/abs/2608.14829
作者: Bo Wen,Nehal Nailesh Mehta,Melanie Tran,Dirk-Uwe Bartsch,William Freeman,Truong Nguyen
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: This paper is a submission to IEEE Transactions on Image Processing (TIP-40498-2026)
Abstract:Retinal image registration is essential for ophthalmic diagnosis, longitudinal disease monitoring, and multimodal retinal image analysis. Existing retinal registration methods are typically modality-dependent: they are designed or optimized either for a single imaging modality in mono-modal registration or for a fixed pair of modalities in cross-modal registration. This limits their flexibility and applicability in practical scenarios involving diverse retinal imaging modalities and different combinations of them. In this work, we propose a generalizable two-stage, modality-invariant framework for retinal image registration. First, we introduce a sparse feature-matching model driven by a universal retinal vessel segmentation to achieve robust coarse global alignment across modalities. Second, we develop a modality-invariant optical flow estimation network, termed MI-RAFT, to refine the alignment through dense local registration. Extensive experiments demonstrate that the proposed method can handle diverse combinations of commonly used retinal imaging modalities, exhibiting strong modality invariance while outperforming state-of-the-art modality-dependent registration methods.
[CV-246] Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening
链接: https://arxiv.org/abs/2608.14763
作者: Yuhao Huang,Yuanji Zhang,Yuhuan Lu,Dong Ni,P. Ellen Grant,Davood Karimi
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 17 pages, 11 figures, 6 tables
Abstract:Assessment of ventriculomegaly (VM) on fetal brain ultrasound relies primarily on measuring lateral ventricular atrial width on standard planes, which is operator-dependent and may not fully reflect the overall ventricular enlargement. Fetal brain MRI provides more reliable volumetric information but is costly and less accessible for routine use. To address these limitations, we propose VIFBA, an ultrasound video-based framework for fetal brain assessment that predicts MRI-derived lateral ventricular volume, classifies VM severity, and identifies potential non-VM fetal brain abnormalities. Our contribution is three-fold. First, we introduce a joint-embedding predictive architecture (JEPA)-inspired tube latent prediction objective that leverages spatio-temporal coherence in ultrasound videos to enhance representation learning. Second, we develop a contrastive cross-modal alignment strategy that transfers structural information from MRI to ultrasound during training, while requiring ultrasound alone at inference. Third, we augment VIFBA with a training-free vision-language model and retrieval augmentation to verify uncertain predictions and identify potential non-VM fetal brain abnormalities. We validated VIFBA on a large dataset comprising 857 cases (3,196 videos) with paired fetal brain ultrasound and MRI examinations. On held-out test data, VIFBA achieved an MAE of 0.5909 mL and Pearson correlation coefficient of 0.9907 for ventricular volume regression, 0.9400 accuracy for VM severity classification, and an F1 score of 0.7764 for multi-abnormality classification, substantially outperforming single-task baselines, video-based strong competitors, and state-of-the-art foundation models. By enabling MRI-informed volumetric assessment from routine ultrasound alone, VIFBA offers a practical and potentially broadly deployable pathway toward accurate and affordable prenatal brain screening.
[CV-247] st-Time Instance Selection for Improved Whole Slide Image Analysis MICCAI MICCAI2026
链接: https://arxiv.org/abs/2608.14759
作者: Quoc Anh Nguyen,Sunhong Park,Jin Tae Kwak
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: Accepted at The 2nd MICCAI Workshop on Efficient Medical AI (EMA4MICCAI 2026)
Abstract:Whole Slide Image (WSI) analysis has been widely studied for cancer diagnosis. Conventionally, a gigapixel WSI is divided into small patches and processed by Multiple Instance Learning (MIL) models. However, existing MIL models typically process all patches, many of which contain redundant or non-informative tissue patterns. Although recent approaches have focused on instance selection to identify discriminative patches and reduce redundancy, these selection modules still require additional training. In this work, we propose Test-Time Instance Selection (TTIS), a training-free, plug-and-play framework that selects compact yet representative patches during inference. TTIS further incorporates a multi-view ensemble strategy to integrate distinct facets of tissue morphology, enhancing robustness. Importantly, TTIS can be seamlessly integrated into existing MIL models without retraining or architectural changes, enabling flexible deployment. Extensive evaluations across multiple benchmarks demonstrate that our approach improves or matches baseline MIL performance across a range of classification and subtyping tasks. Our implementation code is available at this https URL
[CV-248] Synthesizing Post-Acetazolamide Cerebral Blood Flow Maps from Baseline MRI in Moyamoya Using 3D Generative AI ALT
链接: https://arxiv.org/abs/2608.14758
作者: Julia Huang,Camila Gonzalez,Rydham Goyal,Aja Zou,Sasha Alexander,Michael Moseley,Moss Y. Zhao,Gary K. Steinberg
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages. Accepted at Machine Learning for Healthcare (MLHC 2026). To appear in Proceedings of Machine Learning Research (PMLR), volume 340
Abstract:For patients with Moyamoya disease, impaired cerebrovascular reserve (CVR) is an important hemodynamic criterion for recommending extracranial-to-intracranial bypass surgery. Standard CVR assessment in this cohort uses paired arterial spin labeling (ASL) perfusion MRI acquired before and after acetazolamide (ACZ). When ACZ is contraindicated or avoided, the post-ACZ cerebral blood flow (CBF) map needed for hemodynamic assessment is unavailable. We propose CAE3D, a deterministic 3D conditional autoencoder that synthesizes post-ACZ CBF maps directly from pre-ACZ ASL input. We evaluated CAE3D against ten comparators, including deterministic and diffusion-style 3D baselines, a 2D contextual baseline, and frozen-encoder foundation-model adapters. CAE3D achieved the lowest held-out MAE (0.066), with SSIM 0.80 and PSNR 24.0 dB, and near-zero full-brain mean bias. Its MAE advantage was statistically significant over seven of eight trained-from-scratch baselines, excluding the 2D CAE_2D comparator; its SSIM and PSNR advantages were significant over all eight. Regional delta-CBF predictions compressed the dynamic range in high-response territories. These results establish the retrospective feasibility of post-ACZ CBF synthesis in patients who completed the standard two-scan protocol. Extension to ACZ-contraindicated patients, who were not represented in this cohort, requires external and prospective validation.
[CV-249] KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis
链接: https://arxiv.org/abs/2608.14757
作者: Qixiang Zhang,Yi Li,Tianqi Xiang,Haonan Wang,Mengjiao Wei,Bo Xu,Xiaomeng Li
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注:
Abstract:Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated into a slide representation, a process we term slide encoding dynamics. Recently, selective state-space models (SSM) have emerged as promising MIL architectures due to their long-sequence modeling capability and linear complexity. However, existing SSM-based MIL methods rely solely on visual features during MIL. Meanwhile, in large-scale WSIs, where sparse diagnostically decisive regions are surrounded by abundant irrelevant information, such purely vision-driven selective dynamics can misallocate state updates and readouts, causing the evolving SSM state to accumulate task-irrelevant evidence and dilute critical diagnostic cues over long scan trajectories. In this work, we propose the Knowledge-Aware Hidden-State Modulation architecture (KHiM-Mamba), which innovatively regulates Mamba’s core selective state-space mechanism with explicit knowledge priors, steering slide encoding dynamics toward diagnostically meaningful evidence accumulation. Specifically, we redesign the original SSM layer to perform knowledge modulation operations during the evolution of hidden states, thereby guiding what visual evidence is accumulated and retrieved from the hidden state at each encoding step. Furthermore, we additionally introduce a local-adaptive vocabulary retrieval module that uses large language models to assign each patch fine-grained, tissue-specific semantic descriptions, enabling precise modulation across diverse tasks. Experiments on 11 public benchmarks across 4 tasks show that KHiM-Mamba consistently achieves state-of-the-art performance.
[CV-250] A Unified DINOv2-Based Framework for LVEF Estimation GLS Dysfunction Classification and Early Cardiotoxicity Prediction MICCAI2026
链接: https://arxiv.org/abs/2608.14750
作者: Xiaotong Zhang,Mingyue Cui,Qing Cao,Jingming Xia
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted as an oral at the EchoRisk Challenge Workshop, MICCAI 2026
Abstract:Left ventricular ejection fraction (LVEF) estimation (Task 1), global longitu-dinal strain (GLS)-based dysfunction classification (Task 2), and early cardi-otoxicity prediction (Task 3) provide complementary information for cardio-oncology assessment. LVEF reflects macroscopic ventricular volume chang-es as the clinical standard, whereas GLS captures subtle myocardial defor-mation, indicating subclinical cardiotoxicity before overt LVEF decline. Fur-thermore, predicting cardiotoxicity from baseline echocardiography prior to treatment enables preventive interventions at an early stage. To address these three tasks, we employ a DINOv2-based framework with task-specific adap-tation and prediction heads. Built upon a frozen foundation encoder, the framework incorporates parameter-efficient Low-Rank Adaptation (LoRA) and temporal aggregation to learn task-specialized representations, ensuring robust generalization. Crucially, during inference, it operates in a fully cycle-detection-free and phase-free manner, requiring neither cardiac cycle seg-mentation nor explicit End-Diastolic/End-Systolic (ED/ES) annotations. Ad-ditionally, we introduce an ED/ES-guided 2D/3D hybrid multi-view regres-sion model specifically to optimize Task 1. On a patient-level split containing 1,203 training videos from 237 patients and 300 validation videos from 59 independent patients, the DINOv2-based framework achieved a mean abso-lute error (MAE) of 5.03% for Task 1, an AUC-ROC of 76.48% for Task 2, and an AUC-ROC of 70.26% for Task 3. For Task 1, the specialized ED/ES-guided model further improves performance, achieving an MAE of 4.64%. This framework demonstrates the effectiveness of foundation model repre-sentations across diverse cardio-oncology tasks and the additional benefit of physiology-guided modeling for accurate LVEF estimation.
[CV-251] Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method
链接: https://arxiv.org/abs/2608.14749
作者: Yu Chun Wang,Kaixu Chen,Naoto Ienaga,Yoshihiro Kuroda
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Background and Objective: Image-guided surgical navigation has been actively studied because of its advantage of identifying subsurface targets and critical structures, whereas it requires incision trajectories to update the preoperative three-dimensional model dynamically during the surgery. The novelty of this study is the thermal feature distinguishment of whether the electric tools contacting the tissue by Convolutional Neural Network (CNN), and the extraction of the knife contacting frames, to form incision trajectories which can meet with the requirement during the surgery. Methods: This study firstly verified that CNN can classify the thermal images of electric knife and ultrasonic cutter operations separately, and can raise the accuracy of the incision trajectories derived from the connection of the thermal intensity centroid of the frames predicted by CNN as contacting. Results: Our results obtained by employing the electric knife not only reveal a remarkably high accuracy 97.2 % in CNNs identification, but also can achieve an error reduction as high as more than 2.5 times of the incision trajectory prediction as compared to those proceeded in the conventional method. Besides electric knife, the results obtained by employing another electric tool, ultrasonic cutter, reveal a high accuracy up to 93.7 %. Conclusion: In this study, we ensured the possibility of CNN in distinguishing electric tools contacting with the tissue, and confirmed that the proposed method has not only overcome the problem of missing trajectories which usually occurs in the convolutional long-short term memory method but also achieved a remarkable improvement of the accuracy with less limitation.
[CV-252] Hardware-in-the-Loop Phase-Aware CNN for Real-Time 5G Channel Estimation
链接: https://arxiv.org/abs/2608.14709
作者: Javad Zolfaghari-Bengar,Rakibul Rony,Elisa Gomez-de-Lope,Alejandro Villena-Rodriguez,Abhinav Mahadevan,Nicolas Kourtellis
类目: ignal Processing (eess.SP); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT); Machine Learning (cs.LG)
备注: This demo paper has been accepted at IEEE CSCN 2026
Abstract:This demo presents real-time AI-based uplink channel-estimation inference using data collected from a hardware-in-the-loop 5G platform. The data-collection setup integrates commercial RF signal generation, programmable channel emulation, an O-RAN Radio Unit, DU emulation, and a lightweight phase-aware convolutional neural network (CNN) that estimates the channel response directly from received DMRS signals. Unlike simulation-only evaluations, the hardware-derived dataset exposes the estimator to practical RF and system-level impairments, including calibration mismatches, synchronization imperfections, quantization effects, phase noise, and implementation-specific nonlinearities. During the demo, attendees will observe real-time CNN inference and channel reconstruction using captured hardware-generated DMRS observations and compare the proposed CNN against Least Squares (LS) and frequency-domain LMMSE baselines. The objective is to showcase a practical AI-native physical-layer inference pipeline that combines hardware-derived 5G data with real-time neural channel estimation for future 5G-Advanced and 6G systems.
人工智能
[AI-0] Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
链接: https://arxiv.org/abs/2608.16884
作者: Emilien Dupont,Marvin Eisenberger,Borislav Kozlovskii,Abbas Mehrabian,Francisco J. R. Ruiz,Abigail See,Renfei Zhou,Josh Alman,Virginia Vassilevska Williams,Matej Balog
类目: Data Structures and Algorithms (cs.DS); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Machine Learning (cs.LG)
备注:
Abstract:The current best bounds on the matrix multiplication exponent \omega are obtained through a refinement of the laser method called combination loss analysis (Duan et al., 2022; Williams et al., 2024; Alman et al., 2025). In this note, we address the optimization problem at the core of this approach and propose several improvements. First, we reformulate the optimization problem allowing us to solve it in a larger setting than was previously possible. Second, we leverage recent advances in machine learning to design a new optimization algorithm for this problem. Finally, we refine the resulting optimization algorithm with AlphaEvolve. Our combined approach yields an upper bound of \omega 2.371177, improving the previous best bound of 2.371339.
[AI-1] AutoSR: Automatic Symbolic Regression by Searching Research States
链接: https://arxiv.org/abs/2608.16876
作者: Kejia Zhang,Youran Sun,Xinyu Ren,Chugang Yi,Haizhao Yang
类目: ymbolic Computation (cs.SC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Numerical Analysis (math.NA)
备注:
Abstract:We introduce Automatic Symbolic Regression (AutoSR), a fully automated system that instantiates Research-Space Symbolic Regression by searching persistent scientific investigations rather than isolated equations. Finite, noisy data often yield numerically competitive expressions that imply very different behavior outside the observed regime, making numerical fit and syntactic complexity insufficient measures of scientific credibility. Existing approaches largely focus on improving expressions, yet the search typically retains little beyond the resulting formula and score, losing the scientific record, such as motivations and probes, that inform what to try next. AutoSR preserves this record in a \textbfResearch State, coupling each candidate equation with the reasoning, computational evidence, and independent review developed along its branch. Proposer–reviewer agents develop these states under progressive-widening Monte Carlo tree search (PW-MCTS), which allocates computation across competing investigations, while the accumulated research record is ultimately synthesized into a final report that explains the leading relation and the basis for its selection. Across nine selected challenges from two benchmark suites, AutoSR recovers algebraically equivalent relations in every case, including three cp3-bench problems that no published system recovers and six structurally diverse LSR-Transform problems. Overall, AutoSR extends symbolic regression from equation-level search toward automated scientific investigation, allowing scientific knowledge and accumulated evidence to shape both what is explored and how the resulting equation is justified.
[AI-2] What Do Compliance Detectors Read? An Audit of Activation Probes and Guard Models
链接: https://arxiv.org/abs/2608.16852
作者: Saisab Sadhu,Aadit Sengupta,Vinay Kumar Sankarapu,Pratinav Seth
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Regulatory compliance monitoring in deployed language models is increasingly implemented as a legal and audit control, checking model outputs against written rules spanning data protection, healthcare, financial regulation, and platform policy. Such monitoring is meaningful only if a detector’s verdict depends on the stated rule rather than on surface features of the scenario. We show this condition fails across the current class of compliance detectors, a failure we call rule blindness. Deleting, permuting, or substituting the governing rule leaves detection accuracy unchanged for every guard and activation probe we test, including a policy-conditioned guard that correctly cites the governing clause yet barely changes its verdict when that clause is swapped for its permissive counterpart. A purpose-built benchmark crossing two rules with two scenarios, so that neither alone predicts the label, confirms the failure under a design no prior benchmark rules out, and shows that step by step reasoning, not any fast detector we test, is what escapes it. Auditing at scale requires a retraining-free detector, so we introduce the Internal Compliance Score (ICS): a training-free activation readout calibrated from ten labelled pairs and scored by a single projection. We hold ICS to the same scrutiny as the guards it audits: a pre-registered criterion for beating trivial baselines is not met, and a bag-of-words model matches its pooled generalisation exactly. It remains useful because it is inexpensive, letting us audit four deployed guard models, an 8B zero-shot judge, and thirteen benchmarks, and it raises the mechanically verified pass rate when used to rank candidate responses, though an adaptive white-box attack removes this gain. We release the counterfactual protocol and crossed-rule benchmark so rule blindness can be tested in future probe and guard claims.
[AI-3] HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL
链接: https://arxiv.org/abs/2608.16837
作者: Langzhe Gu,Chengkai Hou,Meng Li,Xinhua Wang,Jiaming Liu,Xinyuan Lv,Bowei Zhang,Shuanghao Bai,Guangrun Li,Jingyang He,Gaole Dai,Ziluo Ding,Zhiyuan Xu,Kuan Cheng,Jian Tang,Zhengping Che,Shanghang Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Humanoid robots hold great promise as general-purpose agents in human-centered environments, yet generalist vision-language-action (VLA) foundation models are not readily applicable to humanoid whole-body loco-manipulation. The high dimensionality and interdependence of humanoid motions make it challenging for conventional single-stage VLA architectures to coordinate locomotion, waist posture, and dual-arm manipulation effectively. Moreover, policies trained through offline behavior cloning can remain suboptimal during real-world deployment. Although online reinforcement learning can refine policies through real-world interaction, directly tuning large VLA backbones demands excessive computation and may introduce safety risks during real-robot exploration. To address these bottlenecks, we introduce HAF (Humanoid Adaptation Framework), a two-part framework consisting of HAF-VLA and HAF-Steer that transfers off-the-shelf generalist VLA foundation models to humanoid whole-body loco-manipulation. HAF-VLA is a hierarchical action-flow generator built on a pretrained flow-matching VLA. It splits full-body action denoising into three sequential stages with stage embeddings and cross-stage KV caches that retain kinematic dependencies, avoiding incoherent whole-body actions from one-shot generation. On top of the frozen HAF-VLA, HAF-Steer is a latent offline-to-online RL pipeline that leverages flow-matching invertibility and DCT-based dimensionality reduction to restrict RL optimization to a compact noise subspace and train a regularized SAC policy. This avoids updating the large VLA backbone and enables efficient real-world policy refinement. Evaluated on seven real-world humanoid loco-manipulation tasks, HAF surpasses vanilla single-stage VLA baselines and improves whole-body coordination and task performance. Project website: this https URL .
[AI-4] CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
链接: https://arxiv.org/abs/2608.16829
作者: Jonathan Sadeghi,Jenny Seidenschwarz,Jesse Allardice,Sirish Srinivasan,Benjamin Graham,Jeffrey Hawke
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distributions coarsely over a whole dataset, leaving the fine-grained aleatoric uncertainty of specific phenomena untested. We introduce CaliBench, which scores outcomes in a physically interpretable discrete space - a bin index, a die face, a suit, a colour - rather than a learned feature space such as in FID, so the distance from a known reference distribution is measured directly. We curate outcome spaces whose reference is known in closed form (binomial Galton boards, Bernoulli forks, uniform dice/cards/lottery, a skewed European-roulette colour), enabling an exact calibration test. We decompose performance into two orthogonal axes that a single accuracy metric conflates: scorability, the fraction of generations yielding a scoreable outcome, and calibration, the total variation distance from the reference on that sample. A chi-squared test assesses significance; as calibration is its null hypothesis it can evidence only miscalibration, and at N=32 per cell detects only large deviations. We apply it to nine scenes and six image-to-video models (WAN-2.7, SeeDance-2.0, HappyHorse-1.0, Veo 3.1, Runway Gen-4.5, Cosmos3-Super), 32 generations each. Models consistently concentrate probability mass on a few outcomes rather than reproducing the reference. Most scene-model combinations are significantly miscalibrated, in the extreme collapsing to one outcome, as Veo 3.1 does on dice. On roulette, generations often leave the ball ambiguously placed, giving several models low scorability. Performance varies by scene: no model dominates all nine. We release the protocol and a metric (mean normalised total variation, mnTV) for comparing new models against our results.
[AI-5] Quipu: A Governed Bitemporal Knowledge Graph Store
链接: https://arxiv.org/abs/2608.16813
作者: Steve Brown
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 15 pages, 2 figures, 4 tables. Subtitle: “Start strict: rethinking knowledge-graph defaults for agent-written knowledge”. Source and the benchmark/census/ artifacts behind every reported number are archived at doi: https://doi.org/10.5281/zenodo.21878428 (concept DOI, resolves to newest release). Development repository: this http URL
Abstract:Agents now write knowledge graphs, but knowledge-graph stores still carry defaults set when humans curated them: accept writes now and clean later, keep one time axis or none, treat every writer’s facts as equally trustworthy, and leave governance to dashboards and middleware. These four defaults are individually convenient and jointly untenable under agent workloads. We present Quipu, an embeddable store that inverts all four: no fact enters except through a gate whose predicates evaluate the pending post-state; data, trust labels, verdicts, and the rules themselves are bitemporal; named graphs are the unit of authority and trust, composed under a lattice whose one invariant is that composition never widens; and the governance specification \Sigma , the trace, and signed verdicts are facts in the store they govern, making the audit T \models \Sigma a query. We evaluate with Census, a deterministic multi-writer lifecycle whose single seeded run scores every research question against planted ground truth: the gated store ends with 0 of 6 planted defects versus 6 of 6 ungated; all 7 composition probes uphold the lattice contract; 50 of 50 satisfied verdicts re-derive faithfully as of their instant while all 50 would be misreported under a latest-only rule set; and the SARC reference checker agrees with the in-store audit verdict-for-verdict, differing only on coverage semantics. A recorded trace from a governed writer surfaces a live enforcement gap the audit names with its remediation. On DEMM-Bench, an external decision-evidence sufficiency benchmark, a content-only reading of the exported records answers all 512 property-level governance questions correctly with zero overclaim under all eight degradation conditions, while container-presence baselines overclaim on up to 87.5% of them – and the run surfaced, and led us to close, a gap in what a denial’s verdict attests.
[AI-6] When State Becomes an Attack Surface: State-Semantic Injection in LLM -Driven Embodied Agents USENIX-SECURITY2027
链接: https://arxiv.org/abs/2608.16806
作者: Jiawei Liu,Jiacheng Guo,Tian Zhang,Yiwei Xu,Juan Wang,Jinlin Fan,Bowen Xiao,Chi Guo,Keyan Guo,Hongxin Hu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: submitted to USENIX Security 2027
Abstract:Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks. Traditional LLM Agents typically obtain information through webpages, documents, databases, or external tools and generate corresponding invocation sequences according to user goals; when this technology is further integrated with robotic systems, large language models begin to undertake functions such as task understanding, high-level planning, and behavioral decision-making. SayCan combines the task reasoning capability of language models with the affordances of robotic skills, while Code as Policies and ProgPrompt generate robot task plans through policy code and programmatic prompting, respectively, and VoxPoser uses language models and vision-language models to construct three-dimensional value maps to guide robotic manipulation \cite6,7,8,9. Vision-language-action models such as PaLM-E, RT-2, and GR00T N1 further strengthen the connection among language, visual perception, and robotic actions \cite10,11,12. In such LLM-driven embodied agents, the model not only needs to understand user instructions, but also needs to combine scene states, object attributes, spatial relations, and execution feedback to complete task grounding, and then hand the generated action plan to skill libraries, motion planners, or controllers for execution.
[AI-7] Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment
链接: https://arxiv.org/abs/2608.16804
作者: Keren Artiaga,Yang Li,Ercan Engin Kuruoglu,Wai Kin(Victor)Chan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Sign language serves as a vital means of communication for individuals with hearing impairments, yet recognition resources for the over 100 distinct sign languages are severely lacking. In response, we present our work on sign language recognition using transfer learning and the domain adaptation method TA3N, which utilizes the Temporal Relational Network (TRN) module for aligning multi-scale temporal relations. Our findings highlight the superior performance of Domain Adaptation to neural network-based transfer learning, particularly in improving recognition of American Sign Language (ASL). Our research also identifies the effectiveness of aligning shorter-term temporal features between source and target domains. In addition to using RGB, we conducted experiments using Optical Flow mode for the sign language samples, ultimately determining that RGB outperforms Optical Flow in the majority of cases. Our work aims to improve accessibility and communication for individuals who rely on sign language as their primary mode of communication.
[AI-8] When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
链接: https://arxiv.org/abs/2608.16801
作者: Giuseppe Destefanis,Tomaso Aste
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:We study how teams of AI coding agents coordinate while solving programming tasks. Current evaluations usually report whether the agents complete the task and how much the run costs, leaving the coordination inside the team largely unmeasured. We introduce an instrument to measure this coordination. Each run is represented as a temporal network in which agents and files are nodes, and messages, file writes, and file reads are timestamped directed edges with an associated cost. We apply this instrument to 1902 runs, each evaluated with a fixed test suite, across configurations that vary the team size, the team structure, and the file policy. The resulting networks show how coordination changes as teams grow and as the work changes. Direct messaging initially increases close to quadratically with the number of agents, with much of this growth coming from an early round of introductions. As the teams grow further, this increase levels off in the largest teams we study, where agents increasingly communicate through broadcast messages. The task also shapes the network that emerges. Work built around a shared specification produces dense, highly connected teams, while pipeline tasks produce sparse networks organised around local interfaces. Shared files can replace repeated 1-to-1 communication, cutting output tokens by about 42% at eight agents on message-heavy work, while adding overhead when files already carry the coordination. Naming one agent as coordinator creates no communication hub and provides no reliable improvement in success. We also observe an unprompted tendency for agents to seek out hidden grading material. We repeat the key experimental conditions in a sealed environment, replacing the hidden material with marked placeholder files. Across 244 additional runs, agents still reach for it in four fifths of runs, while the coordinator and file-channel findings reproduce.
[AI-9] Historical Backtesting for Scientific Question Discovery: A Protocol and Astronomy Pilot
链接: https://arxiv.org/abs/2608.16795
作者: Hui Mao
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI)
备注: 27 pages, 10 tables. Benchmark, code, frozen instances, and the prospective 2026 submission: this https URL
Abstract:Systems that generate scientific research questions are evaluated today by expert scores, LLM-as-judge ratings, or curated case studies – all subjective, none falsifiable. We formalize historical backtesting as an alternative: a system generates questions from a corpus frozen at a historical cutoff, the questions are frozen before any access to later literature, and a temporally isolated future corpus then determines whether each question was subsequently answered, partially addressed, independently posed, or ignored, and whether its underlying premise was supported or refuted. The protocol is model-agnostic: any system that emits frozen questions can be scored. We release reproducible astronomy instances with temporally isolated corpora, frozen questions, auditable labels, four reference baselines, and a submission interface. Two findings result. First, evidence-structure-first generation outperforms LLM-only prompting: across a generator decomposition crossed with a four-cutoff stress test (2010-2024, 798 judged questions) whose last window postdates model training, LLM-only generation shows memorized relevance without specific foresight, while a generator using no model weights at all finds questions whose premises the future refutes in every era. Second, a seven-rater agreement study (two blinded human annotators, five judge models, 90 items) indicts the outcome taxonomy rather than the judge: two careful humans agree at kappa = 0.17, every judge model agrees with the professional annotator as well or better (0.17-0.26), and frontier models agree with one another at 0.60 – certifying an LLM judge by model-model agreement would have overstated its reliability threefold. A prospective instance – 200 questions frozen 2026-08-17, scored 2027-2030 – is released so the central claims become contamination-free tests that time itself will grade.
[AI-10] GRIP: Grounded Reasoning via Information-Restricted Premises
链接: https://arxiv.org/abs/2608.16776
作者: Lirui Teng
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures
Abstract:High-capacity encoders in retrieval-augmented generation (RAG) can let the query dominate the latent state, leaving retrieved evidence functionally irrelevant. We call this failure mode query dominance. To address it, we introduce \textbfGRIP (Grounded Reasoning via Information-Restricted Premises), which imposes capacity asymmetry: the decoder keeps full-dimensional access to the query, while retrieved evidence passes through a severe stochastic bottleneck. This forces the evidence channel to encode only the residual information unavailable from the query. Across five reasoning benchmarks, GRIP outperforms strong iterative baselines, cuts a query–latent mutual-information diagnostic by roughly 30 \times (14.8 \to 0.47 bits), and reduces hallucination by 73%. Residual-alignment analysis further shows that the bottleneck output occupies subspaces less aligned with the query than baseline representations.
[AI-11] opological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis
链接: https://arxiv.org/abs/2608.16775
作者: Reza Fayyazi,Michael Zuzak,Shanchieh Jay Yang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) are increasingly being deployed in cybersecurity operations to assist cybersecurity analysts with rapid decision-making against emerging threats. However, there is a main criteria that must be met when using LLMs in cybersecurity, that is, trust in the generated outputs. As Agentic AI is integrated into operational systems, a robust evidence attribution and provenance tracking technique is essential to trace the origins of model generations. When autonomous agents make a decision (right or wrong), the ability to trace back through the decision chain is critical, as without it, teams cannot identify which segment of the data caused the model generation. Existing methods often struggle to distinguish among complex and highly similar evidence sources, such as cyber incident logs. This reveals a key gap: current approaches do not adequately capture the holistic geometric relationship between the retrieved evidence and the generated response for reliable evidence verification. To bridge this gap, we propose Topological Attribution Distance (TAD), inspired by Topology, to characterize and capture the global geometric shape of an output and its changes against its retrieved logs. In other words, if the embeddings of a specific source log drastically changes the geometry of the model’s response in the embedding space, this suggests that such log is a critical source for the model’s generated response. Therefore, TAD is powered by segment-level ablation attribution to investigate incident logs of an actual cyberattack. We demonstrate how TAD finds the most attributed logs on LLM outputs in an adaptive manner. This can provide an explainable and trustworthy tracing based on each LLM’s hidden state to understand how geometrically different retrieved logs influence the model generation, and provide evidence verification in cybersecurity and Agentic-AI workflows.
[AI-12] LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing
链接: https://arxiv.org/abs/2608.16763
作者: Ruoqi Shu,Xuhui Wang,Isaac Wang,Yanming Mai,Bo Wan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Financial document validation in production, such as payroll auditing, tax compliance, and loan underwriting, demands exceptional accuracy, consistency, and reproducibility under strict enterprise constraints. In practice, documents arrive with heterogeneous layouts and formats, semantically rich and context-dependent content, and embedded business rules that current pipelines struggle to process reliably. We introduce LAVA (Logic-Aware Validation and Augmentation), a modular, backbone-agnostic pipeline built on multimodal large language models, that integrates a four-stage design: document-rule retrieval, layout-preserving information extraction, auxiliary metadata enrichment, and auditable symbolic/arithmetic verification. LAVA supports robust rule grounding, fine-grained error attribution, and consistent, traceable end-to-end execution, capabilities essential for high-stakes deployment. Evaluated on a large real-world benchmark with diverse financial documents and dozens of expert-curated validation rules, LAVA outperforms baselines in hallucination control and edge-case handling while maintaining efficient token usage, demonstrating practicality for high-volume, time-critical validation.
[AI-13] Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments
链接: https://arxiv.org/abs/2608.16747
作者: Adam Karvonen,Euan Ong,Subhash Kantamneni,Samuel Marks
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors. But what constitutes a “good” explanation? In this work, we evaluate explanations through the lens of counterfactual simulatability-whether the explanation is useful for predicting model behaviors on related counterfactual inputs. To this end, we introduce CHIVE (Counterfactual Hypothesis Investigation Via Edits), a novel agentic pipeline that identifies unexpected model behaviors in the wild and investigates them with counterfactual prompt edits. This yields thousands of high-quality explanations for naturally-occurring model behaviors along with supporting counterfactual evidence. We apply CHIVE in two ways. First, we evaluate whether common LLM interpretability techniques improve an agent’s ability to predict counterfactual model behaviors. Surprisingly, we find no uplift from any of the interpretability techniques studied. Second, we use CHIVE to generate training data. We find that training models to predict outcomes of CHIVE-generated counterfactual experiments generalizes to various out-of-distribution settings. Overall, CHIVE automatically discovers explanations of naturally-occurring LLM behaviors, enabling us to evaluate and improve methods for explaining LLM behaviors.
[AI-14] DD-Agent : Test-Driven Reasoning for Code Generation
链接: https://arxiv.org/abs/2608.16742
作者: Hongyue Yu,Kefan Li,Jiakun Li,Hongzheng Chai,Yuan Yuan,Rui He,Junyi Wei
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approaches often use generated tests as static post-hoc validators, which limits their ability to guide implementation and may introduce misleading feedback when the tests themselves are incomplete or incorrect. In this paper, we introduce TDD-Agent, which operationalizes the test-driven development paradigm for code generation. TDD-Agent first prompts the model to generate executable tests, encouraging it to clarify expected behaviors before implementation, and then performs iterative dual-track refinement over both the generated code and tests using execution feedback. We first isolate the effect of test-first reasoning through a prompt variant TDD-prompt on LiveCodeBench, where it consistently improves upon reasoning-based prompting baselines. Building on this finding, we evaluate the full TDD-Agent framework on RepoEval, a repository-level benchmark, and show that it consistently outperforms retrieval-based and agent-based baselines. Additional analyses show that iterative refinement improves not only code correctness but also the effectiveness of the generated tests, yielding higher pass rates, coverage, and mutation scores, suggesting that tests can serve as evolving reasoning artifacts rather than fixed validators. Our source code is available at this https URL.
[AI-15] GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms
链接: https://arxiv.org/abs/2608.16733
作者: Haixu Liu,Lei Zhou,Yuhao Ren,Yumao Wu,Zhiang Wang
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:Physical design algorithms operate within tightly coupled, multi-stage optimization flows, where stage-local gains may vanish or induce downstream degradation. Existing program-evolution frameworks often rely on stage-local objectives or undifferentiated multi-metric feedback, which neither guarantee better final results nor identify which unmet requirement should guide the next iteration. We present GoalEvolve, a goal-driven framework that makes physical design algorithm evolution accountable for the final quality of results (QoR) of the complete flow. Given a multi-objective QoR target region, GoalEvolve converts unmet requirements into normalized target gaps, identifies the dominant bottleneck, and uses stage-resolved checkpoint evidence to locate the responsible stage. An LLM-based Teacher then narrows the search to a relevant algorithmic decision and source region, while parallel Student agents implement and validate hypotheses through full-flow evaluation. Local effects, optimization debt, and downstream retention are retained as mechanism evidence for subsequent evolution. Across eight ASAP7 designs, GoalEvolve improves post-route TNS by 30.67% on average and reduces leakage and dynamic power by 21.18% and 9.42% versus default OpenROAD. Relative to commercial-tool goals, it closes 62.20% of the normalized power gap on power-dominant designs, surpasses the TNS goals on both timing-dominant designs, and closes 32.48% of the equal-weight timing-power gap on joint designs. Across all three designs evaluated against Codex goal mode under matched budgets, GoalEvolve further improves TNS by 26.46% while reducing leakage and dynamic power by 12.38% and 0.76%, respectively.
[AI-16] Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
链接: https://arxiv.org/abs/2608.16700
作者: Hang Zhang,Kaifeng Zhang,Yixiao Ma,Weijie Xu,Ye Zhu,Kai Ming Ting
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data D_f removed from a machine learning model. This process is typically accomplished via the use of an unlearning function denoted as U . Existing methods focus on designing an intricate U to unlearn D_f \subset D from a previous model A(D) , so that the unlearned model performs as closely as possible to the retrained model A(D \setminus D_f) . However, these methods often suffer from high computational costs when dealing with massive training data, as the complex structures of U become a bottleneck even for models with fewer parameters. Inspired by Learning to Optimize, we introduce the first learning-based model-agnostic approach, Learning-to-UnLearn (L2UL). Our core insight is to shift from manually designing U to learning the unlearning behaviors from a distribution perspective, thereby acquiring a simple and efficient U via learning. Our experimental results demonstrate that the accuracy achieved by L2UL is comparable to that of retraining while exhibiting impressive efficiency, particularly in data-intensive scenarios. Furthermore, we validate the performance and scalability of our method on larger models ResNet. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16700 [cs.LG] (or arXiv:2608.16700v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16700 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-17] FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy
链接: https://arxiv.org/abs/2608.16697
作者: Aniri,Chen Yilin,Jinhe Bi,Junfei Guo,Donglai Ran,Xu Bian,Zengjie Jin,Yujun Wang,Yijun Tian,Volker Tresp,Fei Shen,Tat-Seng Chua,Yunpu Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architectures. However, enabling VLAs to self-evaluate their action generation reliability without external supervision remains a major challenge. Existing methods either rely on expert annotations or estimate uncertainty only from output statistics, largely ignoring internal signals. In this work, we observe that internal visual modality entropy exhibits consistent distinctions between successful and failed tasks across heterogeneous VLAs. Although VLAs’ architectures differ in their action generation, we show that they share a common latent action generation abstraction evolving under visual perception, language instruction, and state input, which we formulate as a Conditional Generative Markov Chain. Based on this formulation, we propose MAE (Markov Attention Entropy), a self-evaluation framework that directly converts internal attention signals into architecture-aware reliability scores, and introduce LIBERO-Reflect, a 4,000-episode benchmark combining 2,000 standard episodes and 2,000 challenging episodes across four subsets. Extensive experiments across heterogeneous VLA architectures and diverse scenarios show that MAE consistently outperforms state-of-the-art baselines on AUPR, AUROC, and FPR@95. We further instantiate FabriMAE for verifier-free test-time action selection, showing that MAE-guided multiple sampling improves PI-family robustness on LIBERO-Plus with small observed runtime overhead.
[AI-18] UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
链接: https://arxiv.org/abs/2608.16696
作者: Homa Esfahanizadeh,Matin Mortaheb,Jinfeng Du,Harish Viswanathan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT); Multimedia (cs.MM)
备注: 9 pages
Abstract:Physical AI systems such as autonomous vehicles and robots rely on timely exchange of high-dimensional sensory signals under tight bandwidth, latency, and energy budgets. Because the task driving downstream decisions evolves over time, a task-specific codec is brittle and retraining one per task is infeasible in the field. We propose UniTAC, a single learned image codec spanning universal (task-agnostic) to task-specialized operation, re-targeted at runtime without retraining. The task is abstracted as a per-component importance vector, derived, e.g., from gradient attribution of any downstream model, and transmitted as low-overhead side information that conditions both encoder and decoder. Trained once over a broad, randomized family of such vectors against weighted-reconstruction distortion, UniTAC keeps a fixed backbone and a single human-viewable reconstruction whose fidelity is steered to the active task by swapping the injected vector. We analyze the underlying weighted rate-distortion problem, characterizing when a diagonal weighted distortion is task-consistent and how weights relate to task sensitivity. Guided by this, we design a Vision Transformer (ViT) codec whose token-level conditioning natively realizes this weight-driven code. On a localized task at 0.034 bpp, a single UniTAC model reaches 91.4% accuracy, only 1.9% below a task-based codec (93.3%) and above universal codecs (76.9%).
[AI-19] Chronocooked: A Benchmark for Implicit Interval Timing in Reinforcement Learning Agents
链接: https://arxiv.org/abs/2608.16666
作者: Amrapali Pednekar,Alvaro Garrido-Perez,Yara Khaluf,Pieter Simoens
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents Chronocooked, a reinforcement learning (RL) benchmark suite for studying implicit interval timing in RL agents. Inspired by Overcooked, the suite comprises cooking scenarios that require temporal decision making. The tasks and reward functions are designed such that temporal information is unobserved yet critical for optimal performance. The environment is intentionally kept simple to enable controlled experiments and support biologically plausible models. Evaluation metrics are designed to expose limitations in timing abilities of RL agents, and we report baselines using a non-recurrent, a recurrent, and a biologically plausible model. This work ultimately aims to underscore the need to incorporate time perception and temporal processing in artificial agents designed for human robot interaction and deployment in time dependent human societies.
[AI-20] Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL
链接: https://arxiv.org/abs/2608.16663
作者: Yi Ai
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 10 sections, 2 figures, 6 tables. Preprint. Code and research artifacts are described in the manuscript
Abstract:Direct text-to-SQL asks a language model to do two jobs: interpret the business question and construct the complete relational query. In enterprise schemas, SQL can execute successfully while using the wrong relationship role or aggregation grain. We study an alternative placement of the stochastic boundary. A multi-turn planner grounds phrases and selects from question-specific governed options; graph traversal, role predicates, grain lowering, SQL construction, and deterministic checks are implemented in code. We evaluate this semantic path compilation (SPC) system against direct DDL-to-SQL generation on the ACME insurance benchmark. On a 38-question adjudicated comparison set with three runs per question, SPC was adjudicated correct on every run for 37 questions (97.4%), compared with 21 (55.3%) for the baseline. The paired discordance was 16 questions in favor of SPC and none in favor of the baseline (two-sided exact McNemar p=3.05x10^-5). SPC answered all 38 questions correctly at least once and produced one refusal and no adjudicated wrong-but-executed run across 114 run outcomes; the baseline produced 29 adjudicated wrong runs and seven additional judge-flagged data-only coincidences on the same set. A strict-equivalence sensitivity analysis increased the paired difference. Additional SPC runs with GPT-5.4 and Gemini-3.6-Flash showed similar question-level robustness, although their per-run verdict artifacts were not preserved. Six additional benchmark items are retained in an all-item analysis and documented separately by failure class. The study supports an end-to-end systems result, not a causal claim that compilation alone produced the gain, because SPC receives governed semantic artifacts that the DDL baseline does not.
[AI-21] Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
链接: https://arxiv.org/abs/2608.16659
作者: Daniel Nowak Assis,Jean Paul Barddal,Fabrício Enembreck
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Ensembles of decision trees are well-established methods for data stream classification. In ensemble learning, Hoeffding Trees are widely adopted as base learners, performing periodic split attempts according to the Hoeffding bound. Recent studies, however, indicate that this standard splitting mechanism lacks adaptability, while adaptive trees that trigger splits in response to performance degradation have achieved superior results. In this paper, we identify limitations in the use of adaptive-splitting decision trees as ensemble base learners, showing that change detectors often fail to promote sufficient diversity within ensembles. To address this issue, we propose two novel decision tree models, termed Hoeffding Adaptive Splitting Trees. These models combine the periodic splitting strategy of Hoeffding Trees, which fosters ensemble diversity, with adaptive splitting mechanisms that employ change detection algorithms to identify performance decay and determine split points. Experimental results demonstrate that Hoeffding Adaptive Splitting Trees enhance ensemble performance and achieve state-of-the-art results across a comprehensive evaluation, including benchmark comparisons, computational cost analysis, and concept drift adaptation.
[AI-22] Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents
链接: https://arxiv.org/abs/2608.16651
作者: Zhijian Li,Chao Ren,Peijin Wang,Xian Sun
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 4 pages, 6 figures. Accepted to AP-GARSS 2026. Project page: this https URL
Abstract:Satellite agents for on-orbit navigation tasks need to predict collision risks using limited onboard observations. However, conventional planners often rely on predefined maps and fixed environmental assumptions, limiting their adaptability in dynamic on-orbit scenarios. In this paper, we propose Orbit-Planner, a two-stage latent world model for on-orbit obstacle avoidance. Orbit-Planner learns action-conditioned spacecraft dynamics to perform future-state rollouts in latent space, and introduces a Physics Probe to decode physical state changes from imagined latent trajectories. Experiments demonstrate that Orbit-Planner can perform long-horizon latent rollouts and recover physical states from imagined trajectories. In closed-loop obstacle-avoidance navigation in Isaac Sim, it attains a success rate of 91.7%. Code is available at this https URL.
[AI-23] PDDLCoder: Agent ic PDDL Generation for LLM -Assisted Symbolic Planning
链接: https://arxiv.org/abs/2608.16637
作者: Veit Laule,Jiangtao Shuai,Manfred Hauswirth,Sonja Schimmler
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans. Recent hybrid methods instead translate natural language into the Planning Domain Definition Language (PDDL), allowing symbolic planners to produce verifiable plans. However, existing methods frequently rely on rigid generation pipelines, a partial PDDL definition, or human feedback. Furthermore, their evaluation is hindered by the lack of standardized benchmarks with automated verification. To address these limitations, we present PDDLCoder, an agentic framework for PDDL generation from natural language that iteratively generates, analyzes, and refines planning specifications. We further introduce NL-pddlgym, a benchmark dataset comprising 711 planning problems across 23 domains with executable gym environments for the automated verification of plan applicability. Experiments on the NL-pddlgym test set containing 106 problems across 4 held-out domains show that PDDLCoder generates applicable plans for 89.6% of tested planning problems. This improves upon our adaptations of previous PDDL generation methods, which achieved up to 45.3%, and outperforms direct LLM planning approaches, which reached up to 74.5% on the same test set. Our work demonstrates the effectiveness of agentic PDDL generation for planning and establishes a reproducible benchmark for future research on LLM-assisted symbolic planning.
[AI-24] Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement ACM-MM2026
链接: https://arxiv.org/abs/2608.16628
作者: Shenao Chen,Yidan Xu,Xiangmin Han,Rundong Xue,Duanpo Wu,Yuhan Gao,Chenggang Yan,Yue Gao
类目: Artificial Intelligence (cs.AI)
备注: Accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026)
Abstract:Modern Multimodal Retrieval-Augmented Generation (M-RAG) systems are fundamentally limited by the binary connectivity paradigm of traditional simple graphs, which fails to capture the intricate, high-order correlations among heterogeneous entities, such as the N-ary relationships between a visual chart, its scattered textual descriptions, and underlying numerical data. Furthermore, existing refinement strategies often rely on exhaustive, full-page reconstruction to align cross-modal information, leading to prohibitive computational redundancy and the introduction of contextual noise in long-form document processing. In this paper, we propose Hyper-M2RAG, a novel framework that redefines multimodal document retrieval through High-order Hypergraph Representation Learning. We first formalize the document structure as a Multimodal Hypergraph, utilizing hyperedges as unified semantic containers to encapsulate multi-way associations across text, images, and tables, thereby transcending point-to-point modeling. To mitigate semantic fragmentation caused by physical pagination, we introduce an Anchor-driven Incremental Refinement mechanism. Rather than performing a global sweep, our approach identifies boundary-crossing anchor nodes and reconstructs their local hyper-topology using one-hop neighborhood contexts. This targeted refinement effectively bridges cross-page knowledge gaps with minimal computational footprints. Extensive evaluations on multimodal benchmarking datasets demonstrate that Hyper-M2RAG significantly outperforms state-of-the-art methods in both retrieval precision and generation coherence. Our code is available at this https URL.
[AI-25] A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory
链接: https://arxiv.org/abs/2608.16626
作者: Zhihui Chen,Yize Sun,Yuhao Dong,Zeyu Xiao,Ray Y. Zhong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling. Within such an environment, uncertainty in operation and production processes collectively contribute to the dynamicity in manufacturing, thereby hampering the scheduling system from achieving maximal utility. To highlight the importance of handling such uncertainty, this paper addresses the problem of dynamic shop floor scheduling for a real-life case smart factory equipped with RFID technology. Feasible production sequence mining and real-time processing rate estimation are conducted on RFID-collected production data to quantify the operation and production uncertainties. A deep reinforcement learning approach based on the RFID data analysis is then presented for shop floor production scheduling. Simulation studies based on real-life case data have demonstrated the feasibility and practicality of the proposed dynamic production scheduling framework. Specifically, it is observed that the proposed framework outperforms existing dispatch methods in terms of minimizing operation makespan, including first in first out (FIFO), last in first out (LIFO) and deep Q network (DQN).
[AI-26] CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
链接: https://arxiv.org/abs/2608.16594
作者: Tianqi Xiang,Qixiang Zhang,Xinpeng Ding,Yi Li,Xiaomeng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Cancer survival prediction supports treatment planning, risk stratification, and follow-up management. Existing methods use structured clinical variables, whole-slide images, genomic profiles, or multimodal inputs, while patient reports remain underexplored. We study report-centric survival prediction using reports that organize pathological, clinical, and molecular evidence. Large language models (LLMs) can reason over such reports, but case-wise time regression introduces two mismatches. First, a formulation mismatch arises because survival evaluation depends on ordering comparable patients, whereas independent time predictions do not enforce ranking consistency. Second, a supervision mismatch arises because a censored patient’s observed time indicates survival beyond that point and cannot serve as an exact regression target, although it still implies orderings relative to patients who died earlier. To address these mismatches, we propose CACSurv, a Concordance-Aligned Comparative framework for report-centric survival prediction. CACSurv reformulates survival modeling as mini-cohort comparative reasoning, where an LLM predicts relative prognostic orderings. We introduce concordance-aligned rewards derived from comparable relations under right censoring, enabling censored outcomes to provide ranking supervision without exact event-time targets. At inference, Monte Carlo Reference Aggregation compares each patient with sampled references and aggregates positions into a cohort-level ranking. We establish TCGA-SurvReport, a benchmark covering six TCGA cancer cohorts. CACSurv achieves the highest C-index on all six cohorts and an average C-index of 0.722, outperforming the strongest published survival model by 6.5 percentage points and the strongest LLM time-regression baseline by 4.2 percentage points. Our code, models, and dataset will be available at this https URL.
[AI-27] Probabilistic Circuits as Reasoning Machines in Artificial Intelligence (Part I)
链接: https://arxiv.org/abs/2608.16565
作者: Robert Peharz
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Probability (math.PR)
备注: Habilitation Thesis
Abstract:This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertainty in artificial intelligence (AI). It first advocates for probability as a core language for AI, emphasizing its connections to logic and information theory; the conceptual simplicity of probabilistic reasoning—based primarily on the sum and product rules; the parallels between probabilistic inference and human cognition; and the role of probability in optimal decision making. However, probability also faces significant computational challenges, as probabilistic inference is NP-hard in almost all probabilistic models. PCs address these challenges through structural constraints that ensure exact computation of a wide range of inference queries in polynomial time, such as marginals, conditionals, most probable explanations, expectations, and more advanced inference tasks. This thesis synthesizes a decade of research across foundations, algorithmic developments, and empirical validation of PCs. Key contributions highlighted in this work are foundational theory of PCs, Bayesian approaches for learning PCs, scalable implementations and integration with deep learning, hybrid models that combine PCs with intractable models, and connections with symbolic machine learning paradigms. This is the first part of my Habilitation Thesis. The second part is omitted, as it comprises the cumulative part of the thesis and has been published at various venues (see Chapter 5). Comments: Habilitation Thesis Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Probability (math.PR) Cite as: arXiv:2608.16565 [cs.AI] (or arXiv:2608.16565v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.16565 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-28] CUBICS: Situation-aware performance estimation for safety-relevant ML components
链接: https://arxiv.org/abs/2608.16564
作者: Benjamin Herd,Jessica Kelly,Mario Trapp
类目: Artificial Intelligence (cs.AI)
备注: To be published in the proceedings for the 37th International Symposium on Software Reliability Engineering (ISSRE 2026)
Abstract:Machine learning (ML) is a key technology driving innovation today, but ensuring ML safety remains a major challenge for safety-related applications. A promising idea is to build proven-in-use arguments from field data, e.g. by running ML components (MLCs) in shadow mode or within safety envelopes so that their outputs can be monitored as ‘safe probes’ without affecting safety. These probes can then be used to build a statistical argument about field performance in a Bayesian way. However, many Bayesian field-data approaches in safety engineering model failures as a simple Bernoulli (or binomial) process with a single global failure probability and i.i.d. trials, which is rarely adequate for MLCs whose performance depends strongly on context. Statistical evidence is also about coverage of relevant situations, including edge cases, and building a single integrated statistical model for the entire system is usually not feasible. To address these challenges, this paper introduces CUBICS, a context-modular framework for per-component, situation-aware performance estimation of safety-relevant ML components. CUBICS partitions the operational design domain into situations and, for each safety-relevant component, defines a set of situation-specific assumptions and probabilistic guarantees that are represented and updated in a Bayesian manner using Subjective Logic (SL). By combining these guarantees with beliefs about how often each situation occurs, CUBICS derives an overall risk estimate for each component without requiring a monolithic system-level statistical model, and thus provides a building block for modular, field-data based safety assurance.
[AI-29] DeepInsight II: One Trace from Benchmark to Robot
链接: https://arxiv.org/abs/2608.16556
作者: Siyi Li,Yuchen Kang,Wuliang Wang,Zhengjie Zhang,Jiangpin Liu,Jianhao Yao,Jie Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Across a Physical AI stack, evaluation maturity is inversely aligned with deployment risk: foundation models enjoy mature, standardized harnesses, while the embodied layers on which deployment actually turns remain fragmented across benchmark-specific simulators, embodiments, and interfaces. The first DeepInsight report (v1) unified evaluation across this stack behind three abstractions—task, resource, and result—but its quantitative evidence centered on the foundation-model layer; navigation and manipulation (System 1) and whole-body control (System 0) remained simulation case studies, and physical execution was outside its empirical scope. DeepInsight II keeps that substrate fixed and quantifies the embodied half. First, it reproduces released-checkpoint references across two navigation and four manipulation benchmarks under their native protocols. Second, MotionBench places four released whole-body controllers under one workload and metric contract, then carries a qualified within-family cohort from parallel simulation to matched real-robot trials in which simulated and physical rollouts share a parent trace identity while retaining execution-domain-specific records, making the sim-to-real gap a native reduction rather than a reconciliation across toolchains. Third, a composed System 2–1–0 study extends trace localization into five evidence-grounded handoff labels, each mapped to a concrete repair action, with a measured repairability criterion and physical episodes testing the same attribution under hardware-observable state. The contribution is therefore not a new evaluation architecture, but empirical continuity from benchmark execution to matched robot evidence and repair-oriented diagnosis.
[AI-30] Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling
链接: https://arxiv.org/abs/2608.16507
作者: Clemens Schächter,Astrid Pechmann,Janbernd Kirschner,Jan Hasenauer,Harald Binder
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge. Yet, elicitation of expert knowledge and formalization for model fitting is challenging, in particular due to limited time of clinical experts. To nevertheless make domain knowledge accessible during model fitting, we use large language models (LLMs) as synthetic clinical experts to supervise a variational-autoencoder-based approach that learns low-dimensional latent summaries of visit-level observations. Specifically, LLMs are queried offline on textual descriptions of patient observations to obtain judgments, e.g., the suspected clinical category. To improve the variational autoencoder fit, we train a differentiable surrogate model on these judgments and augment the loss function to encourage reconstructions that preserve the clinical-label distribution of their corresponding input profile. In an application to longitudinal motor-function assessments from children with spinal muscular atrophy, we map visit-level clinical profiles to low-dimensional representations that are linked by a multivariate mixed-effects model. The synthetic expert loss discourages reconstructions that remain numerically close in data space but alter the clinical interpretation of the reconstructed motor function profile, such as by crossing a disease-type boundary. We thus reduced disagreement between original and reconstructed SMA type labels from about 11 to 7 percent. Furthermore, informing the latent representation by the synthetic expert improved prediction of motor function milestones compared with unsupervised latent representations and a data-level baseline. These results suggest that incorporating LLMs into model fitting can make clinical knowledge available to representation learning and improve clinical faithfulness for longitudinal rare-disease data.
[AI-31] NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation
链接: https://arxiv.org/abs/2608.16503
作者: Cong Zhao,Shuai Tian,Xu Zhang,Baocheng Ni,Xinguo Song,Xueying Sun,Shu Jiang,Shouchang Yang,Bo Tang,Jin Deng,Ge Zhu,YongCheng Wang,Jin Xu,Ri Yang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures
Abstract:Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness. We present NebulaVLA, an asynchronous dual-frequency architecture that decouples high-level semantic reasoning from low-level action control, optimizing computational resources and modularity. To bridge semantic gaps across heterogeneous robots, we introduce GESTURE-7, a unified language-grounded action representation. Furthermore, our Guide Action algorithm enforces kinematic continuity via mask-based smoothness constraints. Comprehensive evaluations demonstrate that NebulaVLA significantly outperforms synchronous baselines, achieving an 85.5% average success rate on LIBERO-Plus and accelerating action generation by \textasciitilde 2.7 \times . This asynchronous design enables highly efficient and responsive control for practical robotics.
[AI-32] Graph Machine Learning: An Opportunity for Power Systems
链接: https://arxiv.org/abs/2608.16494
作者: Martin Sadric,Sebastian Pütz,Christian Nauck,Veit Hagenmeyer,Frank Hellmann,Dirk Witthaut,Benjamin Schäfer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Systems and Control (eess.SY)
备注:
Abstract:Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales. Addressing these challenges traditionally relies on model-based methods that, while accurate, can be too slow for operational demands. Machine learning (ML) has therefore emerged as a faster, data-driven alternative. As grid topology plays a central role in power system operation, graph machine learning (GML) methods offer a natural framework for incorporating topological dependencies as an inductive bias. We survey nearly 800 papers at the intersection of GML and power systems, covering forecasting, state estimation, optimization, control, fault diagnosis, and cybersecurity. Power systems constitute an unusually rich benchmark setting for GML, as they combine hard physical constraints, multi-scale dynamics, safety-critical requirements, and scarce labeled data within a single, well-defined domain. Conversely, power systems can benefit from utilizing GML to complement classical solvers, as GML provide scalable, topology-aware approximations with promising generalization and computational efficiency. We identify open challenges, including limited real-world deployment and the need for interpretable models in safety-critical settings. Despite the rapidly growing number of publications, standardized benchmarks and open datasets remain scarce, leaving many results difficult to reproduce and undermining the long-term scientific credibility of the field. We further derive a structured requirements catalog for ML-ready power grid benchmarks, intended to guide future dataset development and improve reproducibility across studies. We call on the community to prioritize dedicated benchmark studies and the release of open datasets and models.
[AI-33] Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
链接: https://arxiv.org/abs/2608.16482
作者: Marc Pérez-Roig,David Fernández-Narro,Carlos Sáez
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The dosing of intravenous fluids and vasopressors in sepsis is a sequential decision made under uncertainty and guided largely by clinical judgment, which makes it a natural target for reinforcement learning from historical care. Because a learned policy cannot be trialed on patients, its value must be estimated off-policy, and such estimates can be fragile and optimistic. This work advances the reliable evaluation of sepsis treatment policies by combining off-policy estimation, reliability diagnostics, and clinician-agreement analyses in a transparent validation framework. We modeled fluid and vasopressor dosing on a cohort of 36,872 septic ICU stays drawn from the MIMIC-IV critical-care database, as a discretized Markov decision process with 1,000 states and 25 actions, defined by a five-by-five grid of fluid and vasopressor levels and solved by policy iteration. The clinicians’ behavior policy was estimated with a random forest, which mitigated the collapse of the Effective Sample Size (ESS 50.1 against 4.0 with smoothed counts) that otherwise destabilizes the importance-sampling estimate. The learned policy was evaluated with two estimators, weighted importance sampling (WIS) and fitted Q evaluation (FQE), with the ESS and clinician agreement as reliability checks. An empirical variable selection found that the composition of the state matters more than its size. Both estimators place the learned policy above the clinicians’ return (WIS 50.8 and FQE 46.8 against 38.2, ESS 50.1), yet it departs only modestly from observed practice (total variation 0.18), favoring less intravenous fluid. These retrospective single-center off-policy results support the learned policy as a clinically plausible refinement of observed practice and motivate its further evaluation as a discordance-based clinical decision-support approach.
[AI-34] A Regulatory Placebo? The Systemic Failure of Mandatory GenAI Labeling
链接: https://arxiv.org/abs/2608.16470
作者: Jingyi Chen,Chaofan Bu,Shibo Yan,Xuesong Li
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 18 pages, 1 figures
Abstract:We examine the worldwide trend of mandatory labeling of generative artificial intelligence(GenAI) as a reactive, symbolic form of legislation triggered by technological panic and institutional responses. From a technical perspective, this study demonstrates that current mandatory labeling not only creates implementation dilemmas but also risks hindering the evolutionary trajectory of AI technology. We then systematically analyze the three dominant theoretical strands of this regime, the value dilution theory, the information authenticity theory, and the proactive regulation theory, and find that they are products of regulators’ cognitive limitations in understanding the logic of modern technology. Not only do such formalistic compliance requirements become a regulatory placebo, but they also obscure the genuine legal demands of the technological era. This challenges the current governance paradigm and suggests a shift from identity-label governance to content governance, with an urgent need to address the complex problems associated with GenAI.
[AI-35] JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills
链接: https://arxiv.org/abs/2608.16465
作者: Xiaoyu Wen,Jiajia Li,Zhida He,Peng Yu,Chenxu Wang,Han Qi,Ziyuan Zhou,Cheng Jin,Ying Wen,Xingcheng Xu,Shuyue Hu,Tianhang Zheng,Chaochao Lu,Qiaosheng Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated red-teaming has produced a growing collection of attack strategies, yet they typically remain scattered across prompts and workflows, making them difficult to systematically integrate, reuse, and improve at scale. We introduce \textscJailbreakSkill, a skill-centric framework for scaling automated red-teaming through reusable and continuously evolving attack capabilities. \textscJailbreakSkill packages existing attack strategies into modular, agent-ready skills that can be directly reused and adaptively selected across tasks and target models. Beyond reuse, it closes the loop between attacking and learning: attack experience is used to diagnose, refine, combine, and discover new skills, which are added back to an ever-growing skill library. This evolution lifts macro-average ASR by 17.5 percentage points on AdvBench and 13.4 points on HarmBench, including a 48.6-point gain against GPT-5.4 on AdvBench, while yielding novel attack strategies such as reframing a direct request as an unfinished document-completion task. Several evolved skills also generalize to unseen prompts and target models without further adaptation. Our code is available at this https URL.
[AI-36] A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes
链接: https://arxiv.org/abs/2608.16461
作者: Sourya Joyee De,Abdessamad Imine
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computers and Society (cs.CY)
备注: 10 pages
Abstract:Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization. Such welfare delivery schemes involve complex digital ecosystems and large number of stakeholders. Consequently, to examine their privacy risks, privacy risk assessments require extensive information gathering and synthesis, complex reasoning, scenario explorations, contextual evaluation and human judgement. Thus, they present ideal scenarios for human-LLM teaming, where effective integration of complementary human and LLM capabilities can yield an outcome far superior to either human-only or LLM-only assessments. In this paper, we propose a first human-LLM teaming framework for the systematic privacy risk analysis methodology called PRIAM. The framework specifies an iterative collaborative process in which the LLM processes large-scale documentary evidence to produce initial outputs, which are then interpreted and evaluated by human experts who direct their further refinement by the LLM and exercise their judgement to finalize the output. We illustrate the framework on the data characterization activity of PRIAM using a CBDC-based welfare scheme use case. The illustration demonstrates that while LLMs generate the initial data categories and assign initial values to data attributes, human experts evaluate and provide feedback to refine them, distinguishing documented evidence from inferences, identifying information gaps, and flagging unsupported or ambiguous outputs. This framework serves as a foundational contribution towards human-AI teaming for privacy risk assessments.
[AI-37] HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents
链接: https://arxiv.org/abs/2608.16447
作者: Shen Liu,Zhenguo Xu,Shaopu Wang,Yike Gao,Chunlei Wang
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 15 pages, 3 figures
Abstract:Long-horizon embodied tasks require LLM agents to iteratively decompose high-level goals, revise plans in response to environmental feedback, and ground leaf-level subgoals into valid executable actions. Recursive context-management methods such as ReCAP improve planning stability through multi-level task decomposition and parent-node refinement, but still repeatedly invoke the LLM at leaf nodes to ground atomic subtasks into exact valid actions. We refer to this final grounding step as last-mile grounding redundancy, which accumulates into substantial LLM-call and token overhead during long-horizon execution. To mitigate this issue, we propose HaReCAP (Habitual-action Grounded ReCAP), a low-intrusion leaf grounding extension for ReCAP. HaReCAP extracts frequent leaf decisions from successful trajectories and compiles them offline into auditable and abstainable one-step leaf-reflex rules. At runtime, it skips the leaf LLM call only when a rule can uniquely determine a legal action in the current valid-action set; otherwise, it falls back to the original ReCAP. This design avoids repeatedly carrying the full recursive context into the LLM for routine leaf action grounding, while preserving the original recursive control flow. We evaluate HaReCAP on Robotouille and ALFWorld with Qwen3.5-27B as the main model. On tasks solved by both ReCAP and HaReCAP, HaReCAP reduces token consumption by 14.67%, 17.93%, and 20.08% on Robotouille synchronous, Robotouille asynchronous, and ALFWorld, respectively. The results show that HaReCAP can serve as a low-intrusion extension to ReCAP-style recursive context-management frameworks, reducing last-mile grounding redundancy across environments and models on commonly successful trajectories.
[AI-38] me to Reason : Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
链接: https://arxiv.org/abs/2608.16443
作者: Riccardo Andreoni,Andrei Buliga,Alessandro Daniele,Paolo Felli,Chiara Ghidini,Marco Montali,Massimiliano Ronzani
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning. While initial NeSy approaches have targeted mainly symbolic reasoning in propositional and first-order logics, recent works have started to address the construction of neurosymbolic frameworks for Temporal Logics, and in particular for LTLf. These approaches have established temporal NeSy as a promising research direction, laying the foundations for learning under temporal constraints. Nonetheless, they leave many questions unanswered. From a theoretical perspective, several differentiable semantics for interpreting LTLf have been proposed but have not yet been formally and systematically defined within a unified framework. Moreover, existing approaches commonly rely on automata to represent temporal knowledge, resulting in limited scalability. Motivated by this research gap, this paper provides the following contributions: (i) formally defining different fuzzy semantics for LTLf, and systematically analysing theoretical properties regarding equivalences and dualities of temporal operators; (ii) showing how these semantics can be directly integrated within a novel NeSy framework, called DiffLTLf, enabling flexible and scalable learning without relying on the usage of automata; and (iii) introducing a novel evaluation protocol of increased complexity of learning tasks w.r.t. existing benchmarks. Our results show that the choice of fuzzy semantics has a significant impact on predictive performance. Moreover, DiffLTLf achieves performance on par with, and sometimes superior to, state-of-the-art probabilistic approaches while substantially improving scalability. Taken together, these results establish direct fuzzy interpretations as a competitive and scalable alternative to existing temporal NeSy frameworks.
[AI-39] he Value of a Prompt: An LLM -Relative Kolmogorov-Complexity Approach
链接: https://arxiv.org/abs/2608.16438
作者: Rafael Pass
类目: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Information Theory (cs.IT)
备注:
Abstract:In a world where valuable artifacts are increasingly created, completed, or processed by LLMs, the central economic question is not only what the LLM can produce, but what \emphvalue remains in the inputs (i.e., the prompts) we provide to it. Given a prompt, hint, critique, problem statement, or partial solution that helps an LLM produce an artifact z —a proof, program, design, or scientific hypothesis—how should we measure the value of that input? Intuitively, an input is valuable when it makes the target artifact easier for the model to generate: either by increasing its sampling probability, or by reducing the thinking time needed to find it. We propose a computational Levin–Kolmogorov complexity approach to this problem, by appropriately replacing the universal Turing machine in the classical definitions by the LLM itself. Concretely, we introduce an LLM-relative notion of \emphprobabilistic Levin–Kolmogorov complexity pKt —treating the model’s thinking as the random tape of the program, and charging logarithmically for it in Levin’s manner—and define prompt value as algorithmic mutual information with respect to pKt . This captures the intuition above: a prompt having b bits of value for an artifact z makes z 2^b times ``easier to obtain’', by multiplying the success probability by 2^b , by dividing the required computation by 2^b , or by any corresponding tradeoff between probability and computation. In contrast to the classical notion of algorithmic mutual information, ours is efficiently estimable. We additionally show that, under a natural reproduction experiment, a prompt value of (b) bits means that reproducing (z) without the prompt has median token cost (2^b) times that of reproducing it with the prompt. Subjects: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Information Theory (cs.IT) Cite as: arXiv:2608.16438 [cs.AI] (or arXiv:2608.16438v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.16438 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-40] Drive Pack Fly: The Travelling Thief Problem with Drone
链接: https://arxiv.org/abs/2608.16435
作者: Kabir Murjani,Abhay Sobhanan
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
备注:
Abstract:In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency. An onboard drone can offset this penalty by retrieving outlying items, thereby shortening the makespan and increasing operational profit. However, travel time remains load-dependent, and each item collected by the ground vehicle shifts the arrival times that govern the drone’s launch and rendezvous points. This paper introduces the Travelling Thief Problem with Drone (TTP-D), which maximises the collected profit, net of a time-based rental cost, by jointly optimising item selection, vehicle routing, and flight synchronisation. We formulate a mixed-integer linear program that solves small instances to optimality, and develop both metaheuristics and an attention-based Deep Reinforcement Learning (DRL) policy for larger instances. We further propose a learner-initialised hybrid solver, in which the DRL policy constructs an initial solution that a short annealing run subsequently refines. On two benchmark sets, this hybrid recovers most of the metaheuristic baseline’s quality at a fraction of its computational budget, although the largest instances still require the baseline at its full budget. Finally, a sensitivity analysis reveals that the rental ratio is the primary driver of profitability, whereas the fleet parameters affect profit only at the margin.
[AI-41] ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
链接: https://arxiv.org/abs/2608.16425
作者: Xuteng Zhang,Wenhao Zeng,Xiaodong Gu,Chao Hu,Haotian Lin,Yuling Shi,Min Wang,Beijun Shen
类目: Artificial Intelligence (cs.AI)
备注: Code and dataset are available at this https URL
Abstract:Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and branch count. Existing methods for managing these parallel paths typically rely on final-answer consensus, local token confidence, or isolated intermediate probes. However, these signals are often delayed, weakly tied to actual reasoning progress, or too noisy for dynamic, branch-level control. To address these limitations, we introduce ParaTempo, a training-free asynchronous parallel reasoning framework. ParaTempo is driven by temporal confidence, a branch-local measure of answer-space convergence. Each branch is periodically probed for a tentative answer probability distribution, and temporal confidence quantifies how sharply the recent intermediate probes concentrate on a dominant answer. Once sufficient evidence has accumulated, ParaTempo drives its entire control process from this single signal: low-confidence branches are pruned, branches that persistently commit to their dominant answer are retired early, freed computation is reallocated by forking new branches, and generation stops globally once the confidence-weighted vote concentrates. Without requiring synchronization among reasoning trajectories, ParaTempo adaptively allocates computation based on branch-level convergence. Experiments on challenging mathematical and scientific reasoning benchmarks show that ParaTempo reduces average latency by 21.8-32.2% and total token usage by 18.1-30.3% while maintaining competitive accuracy. Moreover, temporal confidence exhibits stronger temporal stability and predictive power for future branch convergence than token-level and instantaneous signals.
[AI-42] Reasoning -supported Robustness Validation of Automotive E/E Components
链接: https://arxiv.org/abs/2608.16421
作者: Jan Novacek,Alexander Viehl,Oliver Bringmann,Wolfgang Rosenstiel
类目: Artificial Intelligence (cs.AI)
备注: Published in: 2017 IEEE 11th International Conference on Semantic Computing (ICSC)
Abstract:This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. The proposed approach is based on the idea of mapping MPs to an OWL representation so to allow to perform semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.
[AI-43] PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
链接: https://arxiv.org/abs/2608.16419
作者: Zhenchao Tang,Xiaogang Xu,Tianxu Lv,Jiahui Guan,Jiale Zhou,Haohuai He,Zhi Song,Hanbo Huang,Jiehui Huang,Jiafei Wu,Zhe Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM)
备注: Project page: this https URL Code: this https URL Model: this https URL
Abstract:Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological reasoning. We introduce PertMind, which combines trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcement signals. Trained only on forward perturbation-response prediction, PertMind improved response inference in unseen cellular contexts while retaining general language capabilities. It also transferred without task-specific post-training to reverse perturbation identification, double-perturbation reasoning, phenotypic-screen prioritization, and biological-process interpretation. PertMind further generated biological profiles that supported competitive gene, cell, and donor representations across multiscale downstream tasks. These results support the hypothesis that reinforcement on experimental endpoints can concentrate reusable biological strategies already accessible to pretrained models. More broadly, perturbation-derived reinforcement learning offers a scalable route for transforming expanding experimental atlases into training environments for general-purpose biological reasoning.
[AI-44] owards Risk-free AI Agent Deployment
链接: https://arxiv.org/abs/2608.16411
作者: Yintong Huo,Rangeet Pan,Abhik Roychoudhury
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based agents are rapidly moving from research prototypes into the core business processes of organizations, but these agents pose deployment risks to security, compliance, and functionality. In this article, we argue that risk-free deployment must be grounded in the agent’s trajectory: the recorded sequence of reasoning steps, tool invocations, and environmental observations. Trajectories are available for any agent, and many failures are visible only in the trajectory. To make agents deployable and sustainable, we advocate agent testing and debugging as a systematic research direction for detecting and mitigating these risks. This article begins with the challenges of testing agents, including the oracle problem, non-determinism, trajectory validation, and the absence of adequacy metrics. We then turn to debugging agents, from automated failure attribution to repair and self-evolution. We distill these directions into a practical deployment-readiness checklist covering the full deployment lifecycle. Finally, we identify open problems, i.e., formal adequacy metrics, root-cause attribution over long-horizon trajectories, and the reliability of self-evolving agents, that the community must address to enable trustworthy agent deployment.
[AI-45] A Policy Algebra for Trust-Preserving Agent ic AI Execution
链接: https://arxiv.org/abs/2608.16402
作者: Bhaskar Tripathi,Anurag Kumar,Ramendra Kumar,Bhavesh Gadhe
类目: Artificial Intelligence (cs.AI)
备注: 7 figures, 10 tables, and 5 algorithms
Abstract:Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal. Enterprise execution requires a stronger property. A successful result is not reliable if it was produced through unauthorized data access, widened delegated authority, unapproved side effects, unrecoverable budget consumption, or incomplete evidence. This paper defines reliable capability as a path property: an agent is reliably capable only when it completes a task through action events that remain admissible under identity, profile, tool, data, memory, budget, artifact, approval, and audit constraints. We propose a policy algebra that defines the reliability envelope within which agent capability may be exercised. Security profiles and runtime obligations compose through joins, intersections, budget narrowing, approval inheritance, and evidence accumulation; the resulting composition is both trust-preserving and the least restrictive state satisfying all governing inputs. The algebra also propagates restrictions across multi-agent calls and introduces cost-aware artifact materialization, which redirects open-ended execution toward a recoverable outcome as budget exposure grows. The evaluation is interpreted as a reliability-capability trade-off rather than a capability benchmark: the policy-algebra runtime intervenes on 94.8% of policy-violating events while retaining an 86.9% task-completion rate, eliminates the observed profile-monotonicity and zero-artifact-exhaustion violations, and increases audit completeness to 98.6%. The method provides researchers and practitioners with formal correctness conditions, executable decision semantics, and trace evidence for building agents that are not only capable, but reliably capable.
[AI-46] Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs
链接: https://arxiv.org/abs/2608.16391
作者: Xiangfan Wu,Zonghao Ying,Huiyu Wu,Xing Zheng,Huangsheng Cheng,Xiaorong Shi,Jing Guo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models become increasingly widespread, third-party providers that deploy open-weight models have become an important part of the ecosystem. Auditing the quality of their inference APIs is therefore an open problem. We formalize hosted model routing as a stochastic process and propose \mbox\textbfVentor-QTest, a composite black-box audit that requires no probability information from the target API. Its repeated-request component sends each frozen constrained context to the target multiple times, reconstructs a categorical output distribution from the returned text counts, and reports \emphaverage fidelity loss (AFL) as a null-bias-corrected, within-window mean coarsened-KL statistic. Its long-sequence component uses independent runs to report \emphextreme fidelity loss (EFL) through the empirical upper tail of a run-level reference-centered-surprisal statistic. Across three logprob-capable route conditions, AFL shows strong linear descriptive agreement with a logprob-derived coarsened-KL comparator. Across seven route snapshots, 20-run sequence probes reveal route-specific EFL variation. AFL and EFL have little detectable route-level association with GPQA-Diamond accuracy. In contrast, pronounced EFL coincides with a decline in Terminal-Bench pass rate as task exposure increases. This pattern may arise because correctness in long-horizon tasks is more sensitive to extreme fidelity loss. These results motivate reporting AFL and EFL jointly, particularly when auditing long-horizon agentic tasks. The open-source implementation is available at this https URL.
[AI-47] AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agent ic Systems
链接: https://arxiv.org/abs/2608.16381
作者: Zhenhang Nie(1),Gui Zheng(1),Xudong Sun(1),Tailong Zhu(1),Bin Zhang(1) ((1) iFLYTEK Co., Ltd., Hefei, China)
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 2 figures, 13 tables. Zhenhang Nie and Gui Zheng contributed equally; Gui Zheng and Bin Zhang are corresponding authors
Abstract:Agentic systems often organize execution and state around a single conversation, model invocation, or agent instance, even when real work spans many calls and stages. We introduce a unified execution model that maintains a work item’s persistent identity and versioned authoritative state across calls. Each step receives input scoped to a specific state version and new material; a result advances state only after validation and recording. We implement selected paths of this model in AstronOS using Cases, Tasks, and Scenario Packs across central and local execution. We compare five complete strategies for carrying an established software-version update plan into a fresh model session: rereading original materials, replaying full history, deterministic text summary, deterministic JSON, and the AstronOS runtime-mediated handoff. Ten controlled tasks are run under all five strategies with three repetitions, yielding 150 included executions. On the single-stage reference family, strategies perform similarly. In the primary three-stage A-C batch, AstronOS passes the frozen scorer in 14 of 15 executions, compared with 0 of 15 for rereading and 2 of 15 for full-history replay; later non-interleaved summary and JSON batches each pass 0 of 15. AstronOS has lower attempt-accounted model-token cost per passing execution, while requiring more execution-window time per attempt. These results associate the complete AstronOS condition with higher end-to-end pass rates across fresh sessions in this benchmark, at a measurable time cost.
[AI-48] Coverag e-Maximizing Multinomial Subset Routing under Operational Constraints
链接: https://arxiv.org/abs/2608.16375
作者: Quan Zhou,Yiyan Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Multinomial Subset Routing (MSR), a new online routing framework over K experts in which the learner keeps a multinomial routing policy instead of a deterministic subset of experts. At each round, the learner samples M experts i.i.d. from the multinomial policy, and the resulting set of distinct sampled experts forms the routed subset. The reward depends only on the best-performing expert(s) in the routed subset. This reward structure arises naturally in routing across specialized models but is not captured by standard combinatorial bandits or subset-selection methods, which optimize deterministic subsets and typically assume additive rewards. We require the selection to satisfy several long-term, two-sided operational constraints under bandit feedback, observing only the winner’s reward each round. We propose OMD-Approachability, combining online mirror descent with Blackwell’s Approachability, and prove it achieves O(1/\sqrtT) regret in both reward and constraint violation. We ground the framework in practical application domains and validate it empirically on a real-world crowdsourcing dataset. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16375 [cs.LG] (or arXiv:2608.16375v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16375 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-49] What Does Context Compression Cost an Agent ? Interaction Costs Unrevealed by Task-Completion Metrics
链接: https://arxiv.org/abs/2608.16370
作者: Shuyu Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Task completion is the standard metric for evaluating context compression, yet it is incomplete: compression can increase an agent’s interaction cost by forcing it to reacquire dropped state while leaving completion statistically unchanged. We introduce a controlled runtime measurement protocol for reacquisition cost in a bounded-horizon tool-using agent. The agent acts in a deterministic planning environment under a fixed 24-turn horizon. We vary compression severity, compare a dropping operator with a fact-preserving operator, restore dropped state through controlled oracle interventions, and decompose tool calls into retrieval and execution. We evaluate three models across two task regimes. Retrieval calls increase in all six model-regime comparisons and account for almost all added interaction; five of six remain significant after Holm correction. At the prespecified 5x comparison point, completion changes are not significant in any cell. DeepSeek shows a significant completion drop only at 10x compression. GPT-5.5 is the clearest case: completion changes from 80% to 85% (p = 1.0) while retrieval increases from 21.0 to 63.9 calls (p = .002). Retention interventions further separate state quantity, state type, and content validity. Random selection is comparable to an offline hindsight oracle, while replacing retained D-state with semantically irrelevant content increases retrieval by 57% (p .001) without a significant completion change. In a second environment, ALFWorld, sliding compression produces no retrieval surge, showing that the reacquisition signature is environment-dependent rather than intrinsic to shortening context. Overall, compression can impose hidden interaction costs when execution-relevant state becomes absent and must be reacquired, while completion alone may not expose those costs. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16370 [cs.AI] (or arXiv:2608.16370v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.16370 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shuyu Liu [view email] [v1] Mon, 17 Aug 2026 10:21:36 UTC (138 KB) Full-text links: Access Paper: View a PDF of the paper titled What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics, by Shuyu LiuView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI prev | next new | recent | 2026-08 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-50] AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
链接: https://arxiv.org/abs/2608.16349
作者: Yuchen Yuan,Zhenghuang Wu,Yuangan Li,Liang Ma,Ke Li
类目: Artificial Intelligence (cs.AI)
备注: 38 pages, 7 figures, 6 tables
Abstract:Large language model (LLM) agents may assist flight crews with complex decisions and task execution, but existing aviation evaluations centered on static knowledge do not support systematic testing of procedural execution and safety compliance in interactive environments. This paper presents the AeroCopilot Operational Environment (ACOE), a reproducible interactive virtual-cockpit test environment, and AeroCopilotBench, a two-tier aviation agent evaluation benchmark. Tier-1 evaluates aviation knowledge using 1,200 multiple-choice questions, while Tier-2 comprises 73 emergency and abnormal tasks derived from the manufacturers’ Pilot’s Operating Handbooks (POHs) and instantiated in ACOE. ACOE converts natural-language procedures into executable state transitions, final-state goal conditions, and hard safety constraints, enabling models to interpret cockpit state, diagnose faults, and operate aircraft systems through standardized tool interfaces. We establish a safety-gated evaluation framework in which a trajectory succeeds only when all task goals are achieved without violating any hard safety constraint, while safe goal progress and trajectory safety are measured separately. Across 12 models, the highest Tier-2 success rate is 72.6%, while static knowledge performance does not consistently translate into procedural execution. Analysis of 451 failed episodes from 3 representative models identifies recurring failures in procedural completeness, use of state feedback, and long-horizon execution management. These findings motivate state-aware agent orchestration, joint assessment of task completion and trajectory safety, and repeated regression testing. ACOE and AeroCopilotBench provide a reproducible foundation for testing knowledge application, interactive execution, and operational safety in aviation agents.
[AI-51] Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026
链接: https://arxiv.org/abs/2608.16318
作者: Marina Lepp,Joosep Kaimre
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Performance (cs.PF)
备注:
Abstract:Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code. However, their performance on authentic object-oriented programming (OOP) assessments remains insufficiently understood. This study evaluates five widely used GenAI systems, ChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5, and M365 Copilot, using programming tests and examination tasks from an introductory university OOP course. The generated solutions were assessed using the same grading criteria applied to students and compared with historical student results from the same course, as well as findings from the previous year. Common errors were also analyzed to identify recurring limitations across models. All evaluated GenAI systems achieved higher scores than the average student cohort and frequently obtained full marks on longer programming tasks. Nevertheless, they occasionally produced non-compiling code and continued to struggle with advanced OOP concepts, particularly interfaces, abstract classes, and certain inheritance-related tasks. Performance was also limited on graphics-related questions involving image interpretation. Compared with the previous year, the evaluated systems demonstrated noticeable improvements across most assessments while exhibiting several recurring error patterns. The findings provide an updated evaluation of the capabilities and limitations of contemporary GenAI systems on authentic introductory OOP assessments. They also offer evidence that can inform the design of programming assessments, the responsible integration of GenAI tools into software engineering education, and future studies evaluating the evolution of AI-assisted programming.
[AI-52] Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic
链接: https://arxiv.org/abs/2608.16273
作者: Simon Ellershaw,Christopher Tomlinson,Zeljko Kraljevic,Spiros Denaxas,Harry Hemingway,Cathie Sudlow,Angela M. Wood,Anoop D. Shah,Richard Dobson
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Methodology and evaluation framework for Foresight-England. As detailed in the Project Status section, NHS England has paused access to data for the Foresight-E project, meaning quantitative results are not currently available. On behalf of the CVD-COVID-UK/COVID-IMPACT Consortium
Abstract:Foresight-England (Foresight-E) is the first national-scale generative foundation model of electronic health records (EHRs), developed as a research pilot strictly for COVID-19 research. We evaluated its ability to model the direct and indirect effects of the pandemic. Trained from scratch entirely within the NHS England Secure Data Environment, Foresight-E is a 243-million-parameter transformer decoder. It was trained and evaluated on de-identified, longitudinal EHRs of approximately 61 million individuals, integrating primary/secondary care, death registrations, and COVID-19 data. Training and validation used a 90% subset (54.9 million) spanning November 2018 to December 2022; the remaining 10% (6.1 million) was held out for evaluation. Foresight-E models patient timelines autoregressively, predicting the next medical event given their prior history. At inference, it operates zero-shot, predicting any concept in its ~40,000-code vocabulary without task-specific training. Our tokenisation scheme retains the clinical granularity of ICD-10, OPCS-4, and SNOMED CT codes, jointly representing absolute and relative timing. We designed an evaluation framework for 30-day COVID-19 hospitalisation and mortality, including subgroup analyses by demographic factors and vaccination status. To assess generalisation to unseen future data and the pandemic’s indirect effects, we tested the model on medical events from 2023 (beyond its training period), benchmarking against logistic regression and XGBoost. As detailed in the Project Status section, NHS England has paused access to data for the Foresight-E project, meaning quantitative results are currently unavailable. Instead, we share our strategy for tokenisation, architecture, training, inference, and evaluation as a methodological template and case study in the challenges of building population-scale EHR foundation models.
[AI-53] CompoSkill: Compositional Skill Chain Attacks from Individually Scanner-Passing LLM Agent Skills
链接: https://arxiv.org/abs/2608.16246
作者: Mingxiao Liu,Zhoumian Jiang,Jianan Ma,Jian Zhang,Jialuo Chen,Xinhao Deng,Zhen Wang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 9 pages,5 figures
Abstract:Autonomous AI agents tackling Long Horizon Tasks depend on marketplace skills that are certified one at a time: a scanner returns a safety verdict for each skill and declares the ecosystem safe if every package passes. We show that this assumption fails under skill composition. A skill may pass the per-skill scanner individually yet participate in a risky composition when an agent connects its outputs, capabilities, or side effects with those of other scanner-passing skills. This makes skill composition risk a path level property rather than a node level property, explaining why existing skill scanners that inspect individual packages achieve limited interception. To study this threat, we present CompoSkill, a framework that constructs skill composition attacks through a dual attacker system. The white-box attacker knows the victim’s installed skill pool and directly injects explicit skill-id sequences; the black-box attacker knows only a role profile, downloads the top marketplace skills for that scenario, builds a Skill Composition Graph, and searches for high risk chains whose implicit lures never name skill identifiers. We further construct CompoSkill-Bench, a benchmark of 1,140 records built from long-horizon professional workflows across five threats and six scenarios on OpenClaw and Nanobot. CompoSkill achieves risk Chain Formation Rates (CFR) up to 83.3% in the white box setting and 80.6% in the black box setting, while existing skill scanners block only a limited fraction of the risky compositions. Finally, we observe a bridge-bonus-then-hop-decay pattern: a bridge skill can increase attack success, but Attack Success Rate (ASR) decreases once additional hops make the risk chain longer than three skills. These results expose a systematic gap in single skill certification for autonomous AI agents.
[AI-54] Software Engineering for AI-driven Building Operation
链接: https://arxiv.org/abs/2608.16237
作者: Philipp Zech,Sascha Hammes,Johannes Weninger,Jürgen Pannosch,Gernot Steidl
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge. SE for AI practices assume digital environments where failures mean poor user experience. Buildings are different. A bad control decision wastes energy irreversibly, violates occupant comfort, or accelerates equipment wear. Although actual safety-critical failures are rare, as real building automation systems are inherently fault-tolerant, the physical and lasting nature of even minor failures fundamentally changes SE4AI requirements. Rooted in two interdisciplinary research projects in civil engineering and computer science that target the AI-driven optimization of building operations, we identify the missing perspectives in SE4AI that currently stymie the successful deployment of AI-based systems for building operations. We further share lessons learned and best practices, and discuss broader implications for engineering AI-driven building operations and cyber-physical systems more generally. Our work proposes a foundation for SE4AI in systems where failure has physical consequences - one the research agenda below will need to validate.
[AI-55] HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction
链接: https://arxiv.org/abs/2608.16222
作者: Jiahao Ji,Ji Ma,Runhan Zhang,Runyi Yu,Wenjia Wang,Weiheng Chi,Qianqian Peng,Weichao Yan,Yongfei Gu,Ye Tian,Ting Wu,Longwei Li,Chun Yuan,Ruoli Dai,Lei Han
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Humanoid intelligence requires learning over an extremely diverse space of whole-body motions and physically grounded interactions. However, existing embodied datasets remain fundamentally limited: internet-scale video data lack precise physical states and interaction grounding, while laboratory motion datasets provide high fidelity but only narrow behavioral coverage. This mismatch creates a critical bottleneck for scalable humanoid policy learning. We present HiPHI, a 600+ hour scale high-fidelity whole-body human motion dataset designed to systematically maximize coverage of the human motion and interaction manifold. HiPHI is theoretically guided by FrameNet, a linguistic framework organizing human primitives. Created using an optical motion capture pipeline, HiPHI provides sub-millimeter spatial marker tracking accuracy for full-body human motion and mesh-level object trajectories. We further introduce a benchmark suite evaluating motion-space diversity, interaction grounding, object consistency, and physical AI applications. Our analyses demonstrate that HiPHI significantly expands motion coverage compared to existing motion datasets while maintaining high-fidelity interaction quality, and establishes a scalable data foundation for training, evaluating, and generalizing humanoid policies in real-world embodied tasks, where similar extensions are also applicable to motion prior models in computer graphics.
[AI-56] Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
链接: https://arxiv.org/abs/2608.16213
作者: Michael J. Richardson,Ayeh Alhasan,Cassandra Crone,M. Paula Diaz Monfort,Patrick Nalepka,Mark Dras,Rachel W. Kallen,David M. Kaplan
类目: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注:
Abstract:Intelligence is constituted by \textitprocess (iterative activity through which output emerges), not in the output itself. Generative AI (GenAI) is trained on \textittraces (textual and visual residues of human cognitive processes), reproducing samples from a distribution of those traces. Its outputs resemble reasoning, problem-solving, and creativity, yet the activity that produces such outputs in humans remains largely absent. Current GenAI is, therefore, weakly equivalent to the cognition it imitates, matching outputs while process stays absent or opaque. The cognitive sciences have long distinguished between weak and strong equivalence. Here, we define \textitstrong equivalence across seven process features, assessable against human and machine cognition. Our process-based account addresses a symmetric risk: GenAI tools that outsource a person’s generative processes may leave critical capacities unbuilt. We specify design principles for GenAI that instantiate more process and preserve rather than erode human judgment and creativity, and outline process audits that make strong equivalence testable.
[AI-57] BaT: Towards Self-Evolving Medical Research Agent with Stage Rubrics
链接: https://arxiv.org/abs/2608.16211
作者: Junqi Liu,Yufan He,Yexiao He,Pengfei Guo,Dong Yang,Andriy Myronenko,Can Zhao,Hanrong Ye,Tianhao Qi,Yuyin Zhou,Daguang Xu,Yucheng Tang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon agents are beginning to automate complete workflows that produce code, reports, and research artifacts. Medical imaging workflows are multi-stage and data-sensitive, while expert trajectories remain scarce and difficult to share. Structured benchmarks can localize failures through stage-level rubrics, but standard post-training discards these diagnostics before the next training round. We present Benchmark-as-Teacher (BaT), a recursive self-improvement system for agent post-training. BaT contains two linked components: the asynchronous Stage Bank data pipeline and BiCuRL (Bilevel Curriculum Reinforcement Learning), its self-improving post-training method. Stage Bank synthesizes content-isolated training states outside the policy-update loop. BiCuRL uses a fixed held-out evaluation to select the next stage curriculum, verifies rollouts with task rubrics, updates the policy with GRPO, and returns the candidate checkpoint to evaluation. On AutoMedBench-Lite, BaT-4B and BaT-9B more than double the Overall scores of their Qwen Instruct baselines. BaT-9B Agent reaches 79.6 Overall, exceeding Claude Opus 4.6 with Claude Code at 77.5.
[AI-58] Competing at Every Price Point with Agent ic Evolution over a Menu of LLM s
链接: https://arxiv.org/abs/2608.16207
作者: Andrew Borthwick
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Consider a firm that surveys its competition for a particular agentic task and seeks to offer superior accuracy at every competitor price point. A firm that Pareto-dominated its competitors would leave no rational customer a reason to buy elsewhere. This paper shows a path to this kind of capability via agentic evolution over a menu of LLMs, from training pools of at most 100 examples. Given a priced menu of nine LLM endpoints; brief documentation of the task, objective, and API; a simple seed agent; and an operator-chosen per-problem cost target - usually set at an incumbent’s own price - RoboPhD, an evolutionary meta-agent, evolves complete agent programs that attack the public frontiers of two semantically dissimilar tasks point by point: DS-1000 (execution-checked code generation) and PaperFindingBench (LLM-judged scientific document retrieval). Our officially scored submissions hold every Pareto-frontier slot but one on the two tasks’ leaderboards, including Pareto domination of both the top-scoring and the lowest-cost competing points.
[AI-59] Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication
链接: https://arxiv.org/abs/2608.16192
作者: Jia Guo,Xiaohan Zhao,Changwang Liu,Shuqing He,Chenyang Zhang,Bingchuan Zhao,Jinqi Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generative visual-token communication reduces transmission load by sending only selected discrete tokens and reconstructing missing content at the receiver. However, existing token-selection criteria based on local uncertainty, importance, or diversity do not directly determine whether changing the current selection improves the final reconstruction under the same packet budget. To address this problem, we propose Gated Counterfactual Refinement for Communication (GCR-C), a rollout-style correction layer over Local-MDL. GCR-C constructs a compact diversified candidate set, evaluates each candidate through matched full-budget Local-MDL continuation, and replaces the baseline action only when a positive baseline-relative reconstruction gain is obtained. Experiments on CIFAR-10, STL-10, a coded 5G-LDPC link, and a limited high-resolution Kodak transfer show that GCR-C consistently improves reconstruction quality at active low- and medium-rate operating points without increasing the realized packet rate, while remaining effective across changes in dataset, channel condition, resolution, token grid, and tokenizer. The results also reveal a clear quality–computation tradeoff due to the additional encoder-side counterfactual evaluation.
[AI-60] Securing AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
链接: https://arxiv.org/abs/2608.16187
作者: Mikhail Surikov
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 7 pages, 8 tables, 2 figures. Georgia Institute of Technology Master’s final practicum project. Code: this https URL
Abstract:AI-assisted development tools generate vulnerable code at significant rates, yet few automated mechanisms exist to detect, enrich, fix, and verify security issues at development velocity, particularly ones that ground remediation in real-world threat context. This paper presents an automated security evaluation pipeline that generates Python code from LLMSecEval prompts, scans for vulnerabilities using CodeQL and Bandit in parallel with an independent Code Validator LLM, enriches the Code Validator findings with MITRE ATTCK techniques, CWE Observed Examples, and Python best practice guidelines, generates fixes via the Code Generation LLM, and re-scans with CodeQL and Bandit to verify outcomes. Two pipeline configurations were evaluated: Pipeline 1 (P1), using enriched Code Validator findings only, and Pipeline 2 (P2), where it additionally receives the initial CodeQL and Bandit findings. Both configurations were run across four Claude models: Opus 4.8, Sonnet 4.6, Sonnet 5, and Haiku 4.5, producing 80 runs against 26 LLMSecEval prompts covering 9 CWE categories. P1 reduced static analyzer findings across all four models, ranging from -9% (Opus 4.8) to -54% (Sonnet 5). P2 deepened these reductions further, ranging from -29% (Opus 4.8) to -69% (Haiku 4.5), with P2 outperforming P1 for every model. Verdict consistency averaged approximately 81% modal agreement across all configurations, with P2 marginally more stable than P1. Remediation introduced new vulnerabilities in 15-22% of cases: roughly 70% involved a single new finding, and P2 reduced churn for three of four models, with Sonnet 5 as the sole exception. Notably, the best Code Generation LLM (Opus 4.8) was not the best pipeline performer, as Sonnet 4.6 produced the lowest residual findings and highest pass rate after P2 remediation, suggesting that pipeline effectiveness and first-draft security are distinct properties. Comments: 7 pages, 8 tables, 2 figures. Georgia Institute of Technology Master’s final practicum project. Code: this https URL Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2608.16187 [cs.CR] (or arXiv:2608.16187v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.16187 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-61] Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics
链接: https://arxiv.org/abs/2608.16182
作者: Bozhou Chen,Yongyi Wang,Hanyu Liu,Xionghui Yang,Wenxin Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable. Existing studies often attribute instability to isolated factors such as overestimation bias or representation learning issues, lacking a unified understanding of how different sources of instability interact during recursive value estimation. In this work, we provide a systematic analysis of instability in deep Q-learning from three complementary perspectives: operator-level bias in Bellman bootstrapping, estimator-level sensitivity of greedy action selection to regression noise, and parameter-dynamics imbalance under aggressive data reuse. We identify a reward-triggered self-reinforcing trap and characteristic parameter spike dynamics, then derive stabilization principles for controlled bootstrapping, ensemble quantile estimation, and spike-based parameter regulation. Experiments on Atari-100K and Procgen demonstrate competitive performance and improved training stability.
[AI-62] Agent -Native Telemetry: Verifiable State-Delta Evidence for Autonomous Operations
链接: https://arxiv.org/abs/2608.16178
作者: Jun He,Deying Yu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 6 tables
Abstract:Operational telemetry is predominantly engineered for human reading: systems repeatedly serialize verbose prose, static keys, and redundant context across billions of log lines. As autonomous AI agents become primary operational consumers, feeding them traditional logs wastes scarce context capacity parsing lexical syntax rather than reasoning over system state changes – all while lacking cryptographic guarantees of provenance or collection completeness. This paper introduces agent-native telemetry, an operational evidence architecture for autonomous machine operators founded on verifiable state deltas rather than human prose. We present the Agent Telemetry Protocol (ATP) and the State-Delta Evidence Ledger, an implementation that structures operational facts into four core evidence primitives (Transitions, Observations, Relations, and State Checkpoints) governed by content-addressed schemas, while isolating uncurated text as digest-verified opaque references. Producers sign and hash-chain batches for atomic collector append. Verified records feed two parallel agent access paths: a stateless protocol decoder emitting compact positional rows, and a stateful semantic gateway serving bounded graph capsules. We prove an information-preservation lower bound and formalize a ledger-relative verified negative theorem for provable event non-occurrence. On distributed microservice benchmarks (AIOpsLab and OpenTelemetry Astronomy Shop), ATP reduces raw wire payload and modeled cloud query scan costs by 96.4% relative to OpenTelemetry JSON, reduces LLM context tokens by 88.8% and query operations by 66.2%, detects all 500 tested adversarial storage mutations, and yields zero successful prompt injections across 50 adversarial trials per ATP configuration. Comments: 13 pages, 3 figures, 6 tables Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) ACMclasses: C.2.4; D.4.5; I.2.11 Cite as: arXiv:2608.16178 [cs.DC] (or arXiv:2608.16178v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.16178 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-63] Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm
链接: https://arxiv.org/abs/2608.16177
作者: Hidayet Aksu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 10 pages, 7 figures,
Abstract:Large language models (LLMs) are increasingly deployed as agents that operate equipment, execute instructions, and act inside institutional hierarchies, raising a question social psychology answered for humans six decades ago: how far will an agent escalate a harmful action when a legitimate authority insists? We port Milgram’s obedience paradigm to LLMs as a standardized, fully scripted, replicable probe: the model plays the Teacher, a deterministic harness plays Experimenter and Learner from paraphrased Milgram scripts (30 shock levels, 15-450 V; graded protests; the four standardized prods), and the outcome of a session is the breakoff voltage. Following the census methodology of single-token fingerprinting studies, we measure obedience profiles (empirical breakoff distributions over a battery of six conditions) for 42 models from 19 families. We find that (i) obedience is highly heterogeneous: baseline full-obedience rates span 0-100% (census mean 42.9%; human anchor 65%), with 5 models delivering the maximum shock in every session and 11 never doing so; (ii) profiles are model-specific and stable: split-half verification separates same-model from cross-model comparisons with AUC 0.885 (0.949 under an ordinal-aware distance); (iii) situational sensitivity is selective: peer defiance shifts obedience in the human direction, learner proximity only weakly, and removing the authority’s physical presence (the strongest human lever) has no detectable effect; (iv) declaring the scenario fictional raises obedience (median +17.2 V), whereas moving the decision to a native tool call lowers it sharply (-53.0 V), as does a 1,024-token deliberation budget (-38.2 V); and (v) obedience profiles do not recover model lineage (leave-one-out family accuracy 8.3% vs. 3.7% chance): obedience identifies the checkpoint, not its ancestry, consistent with safety post-training overwriting lineage priors.
[AI-64] rajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain
链接: https://arxiv.org/abs/2608.16164
作者: Rocky Liu,Tengyu Liu,Baoxiong Jia,Fangwei Zhong,Xinyi Tong,Hongzhao Xie,Siyuan Huang
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Training locomotion policies for complex unstructured terrain requires a curriculum to avoid early exploration failures. However, since unstructured terrain lacks explicit difficulty ordering for curriculum design, existing methods resort to heuristic curricula over parameterized terrains. This abstraction limits generalization, as policies can overadapt to near-fixed perceptual patterns. To address this, we propose \textbf\ourname, an \textbfTrajectory-level \textbfAutomatic \textbfCurriculum \textbfLearning framework that generates training tasks directly from unstructured terrain maps. At each curriculum update, the evaluator learns a difficulty function for the current policy that maps a given trajectory task to a difficulty score. The sampler then proposes new trajectories guided by the learned evaluator as the curriculum for the next policy update. This forms a closed loop in which the curriculum is iteratively matched to the evolving policy. Quantitative and qualitative experiments show that \ourname continuously provides effective curricula on unstructured terrain, improving trajectory success rate by (56.3%) over direct training without curriculum. Compared with handcrafted curriculum learning, our method improves success rate by (18.5%) on the hardest terrain tasks and by up to (39.74%) when evaluating traversal from diverse approach directions on the same obstacle type.
[AI-65] Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies
链接: https://arxiv.org/abs/2608.16159
作者: Konstantinos E. Kampourakis,Vasileios Gkioulos,Sokratis Katsikas
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 18 pages, 2 figures
Abstract:Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS). However, in adversarial environments, the fidelity of a DT cannot be assumed. Communication delays, data manipulation, sensor degradation, or partial information loss may cause the DT state to diverge from the physical process it represents. Such divergence creates temporal inconsistencies that may reveal cyber physical attacks. This paper proposes a detection framework that monitors temporal consistency between the physical system and a potentially degraded DT view. A DT predictor is trained exclusively on normal system behavior to model short-term system dynamics. During operation, discrepancies between predicted and observed states are transformed into multi-horizon temporal features capturing the magnitude, persistence, and evolution of prediction residuals. An unsupervised density model characterizes normal consistency patterns, while a sequential change detection mechanism identifies sustained deviations indicative of attacks. The approach is evaluated on three widely used Industrial Control System (ICS) datasets, SWaT, HAI, and BATADAL, under multiple DT degradation scenarios, including time desynchronization and partial observability loss. Results show that temporal inconsistency patterns enable reliable event-level attack detection with bounded false alarm rates and low detection latency. The proposed method achieves up to 98% detection reliability on SWaT and false alarm rates below 2%. Unlike conventional anomaly detection methods, the proposed framework does not require attack signatures or labeled attack data and remains effective even when the DT view is degraded. These results suggest that DT degradation, often treated as a limitation, can instead serve as a useful signal for cyber physical security monitoring.
[AI-66] A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis
链接: https://arxiv.org/abs/2608.16158
作者: Unai Agirre,Imanol Jerico,Felipe Castaño,Andrea Venturi,Francesco Zola
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: Paper accepted and presented @ eCrime 2026 conference
Abstract:Phishing remains a persistent and evolving cybersecurity threat, with attack volumes reaching record levels. This growth is driven by the industrialization of phishing through widely available phishing kits and reusable templates, which enable cybercriminals to rapidly generate and deploy large numbers of fraudulent webpages. Although surface-level attributes may differ across these websites, their underlying structures often exhibit significant similarities. However, most existing defenses rely on reactive blocklists or supervised classification models that focus on individual phishing instances, limiting their ability to identify structural reuse and detect coordinated phishing campaigns. To address this limitation, this study investigates whether HTML structure can serve as a robust fingerprint for identifying phishing template reuse. We model webpages as Document Object Model (DOM) trees and extract structural features, optionally enriched with HTML tag-based content information. These representations are then clustered using unsupervised learning methods to group structurally similar webpages. Three clustering algorithms are evaluated and compared, while also analyzing how the depth of the extracted DOM-tree affects cluster formation and overall clustering performance. Finally, cluster quality is also evaluated both quantitatively and qualitatively, including a novel level-wise Jaccard Distance Score and manual inspection supported by visualization tools. Results demonstrate that structural representations of webpages can effectively reveal hidden similarities across phishing sites, enabling the detection of emerging and zero-day templates and supporting the analysis of coordinated phishing threats
[AI-67] RCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
链接: https://arxiv.org/abs/2608.16156
作者: Huan Zhang,Mingju Chen,Dongxu Zhou,Can Lv,Heng Chang,Sen Cui,Faguo Wu,Shiji Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult. Existing approaches either rely on process evaluators, which incur annotation and inference costs, or derive step-level credit from successful trajectories. However, successful trajectories are extremely scarce during early-stage reinforcement learning, substantially weakening anchor-based methods. We propose Transition-wise Rubric Credit Assignment (TRCA), which derives step-level supervision directly from action-induced transitions without learned evaluators or successful anchors. TRCA evaluates each transition using Evidence, Execution, and Invalidity rubrics to capture task-relevant information acquisition, valid task execution, and invalid or regressive behavior. From these judgments, Foundational Rubric Reward measures local transition quality, while Breakthrough Rubric Reward tracks newly covered Evidence and Execution conditions to reward incremental task progress. Combined with terminal outcomes, these signals produce fine-grained step-level advantages for policy optimization. Experiments on ALFWorld, WebShop, and seven search-augmented question-answering benchmarks show consistent improvements over the evaluated baselines. With Qwen2.5-7B-Instruct, TRCA improves the WebShop score by 6.0%-12.6%; with Qwen2.5-3B-Instruct, it improves the average SearchQA score by 1.9%-18.3%. These results demonstrate the effectiveness of transition-wise rubric credit assignment for long-horizon tasks with sparse successful anchors.
[AI-68] FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection
链接: https://arxiv.org/abs/2608.16148
作者: Junxuan Li,Zhiqi Chen,Yuzhou Liu,Peng Zhang,Huaxiao Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-view multi-label feature selection aims to identify a compact and informative feature subset from heterogeneous views while preserving discriminative information for multiple labels. Existing methods are generally developed from specific modeling perspectives and incorporate mechanisms tailored to particular data characteristics. Designing suitable feature selection algorithms across datasets with diverse and heterogeneous characteristics still relies heavily on expert knowledge and substantial manual effort, imposing considerable time and labor costs that severely hinder the practical adoption of feature selection. To address this problem, we propose FeatureHospital, a Skill-driven multi-agent framework for automated multi-view multi-label feature selection algorithm design. FeatureHospital first diagnoses the target dataset to identify its feature selection issues. Based on the diagnosis, specialist agents equipped with domain Skills then prescribe corresponding optimization strategies and Loss terms for different issues. After that, the resulting prescriptions are reconciled to remove overlaps and resolve conflicts before being integrated into a compact dataset-specific objective. Finally, the constructed objective is optimized to select the final feature subset. Experimental results demonstrate that FeatureHospital can construct effective feature selection algorithms for different datasets based on their individual characteristics.
[AI-69] When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification
链接: https://arxiv.org/abs/2608.16147
作者: Diyorbek Musaev
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages, 3 figures, 5 tables. Code and per-run metrics released
Abstract:Class-imbalance handling is routinely evaluated on a single benchmark dataset, and the resulting conclusions are reported as if they were properties of the method. We show this practice is unsafe. On the public Kaggle credit-card fraud dataset, under a leakage-free nested cross-validation protocol in which the decision threshold is selected on a held-out inner validation fold, a plain Random Forest at the default 0.5 threshold attains F1 = 0.861 +/- 0.021, and threshold tuning yields it no benefit (delta-F1 = -0.002). Read alone, this supports an appealing conclusion: for a well-calibrated ensemble, imbalance handling is unnecessary. We then apply the identical protocol to 45 binary tasks spanning imbalance ratios from 1:1.5 to 1:178 (2,025 model fits, four model families). The conclusion reverses. Random Forest benefits most from threshold tuning across the suite (delta-F1 = +0.101 +/- 0.134), not least, while three other families replicate their fraud-dataset behaviour almost exactly. SMOTE likewise harms the fraud dataset but helps across the suite (mean delta-F1 = +0.076; 138 wins, 39 losses; Wilcoxon p = 2.7e-17). Two further results. Threshold-tuning benefit is non-monotonic in the imbalance ratio: near zero below 1:5, peaking at +0.120 in the 1:15-1:40 band, declining to +0.045 beyond 1:100 - explaining why the fraud dataset, at 1:577, is an unrepresentative place to study the question. And we reject an intuitive heuristic: validation-set calibration error does not predict tuning benefit (expected calibration error r = -0.087; Brier r = +0.137), so calibration diagnostics cannot tell a practitioner whether tuning is worthwhile. We release the protocol, the 45-task harness, and all per-run metrics. Comments: 13 pages, 3 figures, 5 tables. Code and per-run metrics released Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) ACMclasses: I.2.6; I.5.2; G.3 Cite as: arXiv:2608.16147 [cs.AI] (or arXiv:2608.16147v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.16147 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-70] Assessing LLM s mathematical abilities requires understanding the various mechanisms of mathematical creativity
链接: https://arxiv.org/abs/2608.16118
作者: Silvère Gangloff
类目: Artificial Intelligence (cs.AI); History and Overview (math.HO)
备注:
Abstract:How should we assess whether large language models can perform mathematical invention? I argue that this question is currently underspecified: mathematical creativity is not one capacity but several mechanistically distinct modes of meaning-making - reflexive introspection on mathematical practice, analogical import from the sciences, problem-driven construction, and the bridging of distant domains - together with a further, cross-cutting distinction between meaning pursued because a pattern was observed and meaning pursued because it is strategically wanted, a distinction I develop through the case of conjecture-formation. These mechanisms are likely non-substitutable, so that competence in one does not transfer to the others. Grounding each in a historical case study and in an architecture-level account of current transformer-based systems, I suggest that today’s models concentrate their competence in modes shaped by recombination and search over existing building blocks; if that description holds, the remaining modes are out of reach in principle, not just slower - though whether it holds is itself the open, empirical part. Because proof is getting cheaper as AI improves at generating it - a shift the field’s own leading voices are now diagnosing - mathematical value is migrating toward the modes current systems cannot yet perform, and evaluations of AI mathematical ability should be organized around this taxonomy rather than around aggregate benchmarks that conflate it.
[AI-71] RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
链接: https://arxiv.org/abs/2608.16111
作者: Mianzhi Liu,Fan Xiao,Zhiliang Yu,Huayang Huang,Yuke Li,Yi Yang,Wenbo Liu,Yu Wu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication in Journal of Chemical Information and Modeling
Abstract:Retrosynthesis is a cornerstone of drug discovery and organic synthesis. While data-driven deep learning models have shown remarkable progress, they autonomously learn reaction patterns from extensive datasets with limited integration of established chemical knowledge as priors. To address this limitation, we introduce RetroMPA, a molecular property-aware, post-hoc enhancement module that injects chemical knowledge into the retrosynthesis pipeline. Rather than functioning as an independent SMILES sequence generator, RetroMPA is a broadly applicable, model-agnostic chemical filter designed to recalibrate and optimize the predictive pathways of existing algorithms. This plug-and-play framework integrates seamlessly with a range of data-driven retrosynthesis methods, enhancing outputs without modifying model architecture or requiring resource-intensive retraining. By leveraging a property-aware latent embedding space, RetroMPA consistently improves top-1 accuracy across eight representative retrosynthesis models by an average of 5.50% on USPTO-50K. Furthermore, we validate its scalability on the large-scale USPTO-Full dataset, achieving an average improvement of about 2.03% across both template-based and template-free architectures. Wet-lab experiments provide preliminary support for the practical utility of the framework. These syntheses confirmed viable, previously unreported substrate combinations for classic reaction paradigms—specifically, Suzuki-Miyaura coupling, Bucherer reaction, and Friedel-Crafts acylation—suggesting that RetroMPA can operate beyond mere data fitting. The code is open-sourced at this https URL. Comments: Accepted for publication in Journal of Chemical Information and Modeling Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.16111 [cs.LG] (or arXiv:2608.16111v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16111 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1021/acs.jcim.6c01506 Focus to learn more DOI(s) linking to related resources Submission history From: Mianzhi Liu [view email] [v1] Mon, 17 Aug 2026 05:03:30 UTC (1,376 KB) Full-text links: Access Paper: View a PDF of the paper titled RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction, by Mianzhi Liu and 7 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.LG prev | next new | recent | 2026-08 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked="checked"class=“labs-tab-input”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab) Major funding support from
[AI-72] AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
链接: https://arxiv.org/abs/2608.16098
作者: Xiachong Lin,Du Yin,Hao Xue,Wen Hu,Imran Razzak,Arian Prabowo,Matthew Amos,Flora D. Salim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8 pages, 4 figures, 4 tables
Abstract:Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, whereas learning an independent predictor per variable restores flexibility at a cost that grows with the product of variable count, context length, and horizon. We argue that this dilemma dissolves once the object being compressed is the forecasting operator rather than the observed series. Auditing per-variable linear history-to-future maps across standard benchmarks, we find that a phase-locked seasonal component paired with a compact residual operator outperforms a dense phase-blind reference in most audited settings. The residual transport is also directional: lag-invariant alternatives consistently underperform asymmetric history-to-future maps. Guided by this structure, we propose AsyTO, an Asymmetric Temporal Operator that factorizes the tensor of per-variable operators into shared but distinct history-reading and future-writing temporal modes with per-variable mode-wise gains, complemented by a low-rank periodic prototype and a cycle-separable factorization of the temporal modes. Each forecast reads only its own variable’s history, so parameters and compute grow linearly in the number of variables. Across eleven benchmarks and multiple forecast horizons, AsyTO attains the best lightweight error in 30 of 44 dataset-horizon settings, locating at the accuracy-compute Pareto frontier.
[AI-73] Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
链接: https://arxiv.org/abs/2608.16094
作者: Wengan He,Yongsheng Luo,Lihong Jiang,Wenhui Xu,Yu Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 4 figures, 4 tables. Preprint submitted to Elsevier
Abstract:Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have transformed the field from multiple sequence alignment (MSA)-driven monomer folding into broader frameworks capable of modeling protein complexes and increasingly heterogeneous molecular systems. Existing reviews have summarized this progress from the perspectives of representative models, application domains, and protein design. Building on these efforts, this review focuses on the methodological evolution of the field itself. It examines recent developments through three closely related dimensions: representations and data, architectures and learning strategies, and confidence and evaluation. Within this perspective, the field is organized into four methodological phases and three cross-cutting transitions: from explicit evolutionary coupling features and early contact prediction to learned sequence representations in AlphaFold2, RoseTTAFold, and ESMFold; from protein-only monomer folding to increasingly integrated modeling of heterogeneous molecular systems in AlphaFold-Multimer, RoseTTAFoldNA, and AlphaFold3; and, more recently, from prediction-oriented structure inference to design-oriented generative modeling in RFdiffusion and related frameworks. This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.
[AI-74] Behaviour Is an Incomplete Measure of Reasoning Development: Cross-surface pre-arrival accessibility and the limits of developmental inference in a recurrent-depth reason er
链接: https://arxiv.org/abs/2608.16085
作者: Simon Lam-Muir
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures, 1 table
Abstract:Capability development is routinely inferred from behavioural thresholds, from final checkpoints, or from what a decoder can read out of a hidden state. These quantities need not identify the same event. We study a 30M-parameter recurrent-depth relational reasoner in a closed, oracle-defined world, using dense behavioural trajectories, two training surfaces, preregistered pre-arrival hidden-state probes, prospectively checked evaluability, and explicit untrained and negative controls, holding the training-time and inference-time axes separate throughout. Behaviour first: under one frozen acquisition criterion, three-hop competence cost 70 logical epochs on the symbolic surface and 13,055 on the verbal surface, a 186.5-fold contrast, after which verbal four-hop competence cleared in 8 logical epochs. Across the 13,055-epoch grind, four-hop held-out behaviour never exceeded 3/40 and ended at 0/40. Internal measurement next: on the verbal surface a linear probe recovered future-answer identity before behavioural arrival at 0.056159 against uniform chance 0.025, an untrained control of 0.024758 and a population frequency baseline of 0.048309 (p = 0.012987; 16/40 answer classes contributing). Analogous pre-arrival accessibility survived the surface change, reaching 0.1020 against a zero-step control of 0.0460 (p = 0.000999) at the upstream structural position and 0.0618 at the readout comparator (p = 0.004), with 21/40 classes contributing. Finally, the natural attempt to track that accessibility across training was not cleanly evaluable: probe eligibility is defined by behavioural arrival, so the measured population changes with the measurand. Behavioural competence, internal accessibility, and training-time development are distinct observables, and neither behaviour nor decoder accessibility identifies the computation training acquired; causal intervention is the necessary next step.
[AI-75] Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
链接: https://arxiv.org/abs/2608.16084
作者: Conrad Ainslie,Pedram Hassanzadeh,Michael W. Mahoney,Ashesh Chattopadhyay
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Chaotic Dynamics (nlin.CD); Computational Physics (physics.comp-ph)
备注:
Abstract:Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understood, leading to ad-hoc solutions. Here, we develop an eigenanalysis framework that reveals the dynamical origin of this error growth. By analyzing the Jacobian of the learned one-step update map with respect to the state, we show how inference-time error growth, and thus model stability, is governed by its spectral radius. Direct-step architectures (models that predict the next state from the previous one) generically admit unstable eigenvalues with magnitudes exceeding one, explaining the rapid divergence of these widely used models. In contrast, integration-constrained models (where the time derivative is estimated and integrated with a higher-order integrator) collapse their eigenspectrum onto the unit circle, yielding neutral stability and a universal linear error-scaling law. The largest eigenvalue of this Jacobian provides an architecture-agnostic, a priori diagnostic of short-term skill, long-term stability, and spectral bias, without requiring an expensive rollout. Leveraging this theory, we introduce a stability-promoting loss that explicitly regularizes Jacobian-driven error amplification, improving both forecast accuracy and dynamical robustness. Demonstrated across 29 models spanning two architectures, several explicit and implicit integrators, and multiple loss functions on the Kuramoto-Sivashinsky system, our results establish a theoretical foundation for the design and evaluation of neural emulators of chaotic multi-scale dynamics. More broadly, our framework is a step toward the kind of a priori stability analysis that numerical analysis provides for discretizations of differential equations and that scientific machine learning currently lacks.
[AI-76] Learn Whats Left Not Whats Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization
链接: https://arxiv.org/abs/2608.16072
作者: Yixuan Wang,Yifei Chen,Haichao Zhang,Haozheng Luo,Xander Wu,Jie Ni,Yun Fu,Nuno Vasconcelos,Yijiang Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 2 figures
Abstract:Reinforcement learning (RL) with group-relative advantages has become the de facto standard for post-training language model reasoners. However, when optimizing multiple reward objectives, existing methods typically scalarize the reward vector with a fixed weighted sum before group-wise standardization. We show that this design leads to two fundamental problems: rollouts with distinct reward profiles can receive identical advantages, and all objectives are optimized with fixed relative weights regardless of their current level of saturation. As a result, training continues to allocate gradient budget to already-solved objectives instead of focusing on those with greater remaining headroom. We introduce \textbfSaturation Aware Advantage Reweighting for Multi-Reward Policy Optimization (SA-MRPO), which standardizes each reward objective independently and adaptively discounts its contribution according to a batch-level estimate of objective saturation. This dynamically reallocates optimization effort toward under-optimized objectives while empirically maintaining performance on those that are already well satisfied. We further show that saturation-aware reweighting can reverse the sign of an update, rather than merely rescale its magnitude. Across mathematical reasoning with two- and three-objective reward combinations, SA-MRPO improves the harder correctness objective over GDPO in 12 of 15 benchmark comparisons, with gains of up to 5% on AIME24. On adaptive reasoning it improves accuracy on all five benchmarks, by 3.8% on average and up to 9.2 % on AMC23, and on coding benchmarks it improves pass rate by up to 2.3% , while in all settings maintaining the easier objectives near their already satisfied levels.
[AI-77] OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation
链接: https://arxiv.org/abs/2608.16070
作者: Wei Wu,Xiang Wang,Hongze Leng,Qingye Min,Junxing Zhu,Junqiang Song
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 35 pages, 21 figures
Abstract:Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs, while existing deep learning approaches predominantly rely on structured-grid architectures, incurring unnecessary computation on masked land cells and enforcing uniform resolution across dynamically heterogeneous ocean regions regardless of local flow complexity. Here we present OceanLight, an efficient global ocean forecasting framework innovatively combining geometry-adaptive unstructured mesh tokenization with a graph neural network (GNN) backbone. OceanLight achieves pointwise forecast accuracy and kinetic energy spectral fidelity exceeding both operational numerical analyses and state-of-the-art AI-based models, while surpassing all AI-based ocean models in geostrophic balance consistency. Furthermore, OceanLight demonstrates reliable mesoscale eddy representation, capturing coherent ocean structures beyond pointwise statistical optimization. These capabilities are delivered with a 62% reduction in GPU memory consumption and 70% reduction in FLOPs relative to structured-grid baselines. Our unstructured mesh representation establishes a generalizable paradigm for scalable data-driven oceanography.
[AI-78] Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance
链接: https://arxiv.org/abs/2608.16055
作者: Bowen Li,Guojun Wang
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 3 tables, 1 figure. Preprint
Abstract:Existing agent benchmarks ask whether the agent finished the task. We ask whether it finished it within policy. We introduce Fiducia-bench, a benchmark for the governability of financial agents—whether they escalate when obligated, abstain when required, and leave an auditable trail—and use it to study a question no prior benchmark addresses: does decomposing an agent into components degrade its governance? It does, and the mechanism is specific. Policy-relevant facts discovered by one component are attenuated at the handoff boundary before reaching the component that must act on them. In a 626-episode experiment across 100 KYC/AML task variants, two models, and three architectures, a 32B open-weights model attenuated 0% of discovered facts under a single-loop baseline, 56% under a fixed pipeline, and 85% under an orchestrator-subagent architecture (all at constraint distance 2). A stronger model (gpt-4.1-mini) attenuated 3-6% under the same conditions, suggesting the governance cost of decomposition is partly a function of model capability. Critically, the same mechanism produces both under-escalation and over-escalation, depending on whether the dropped fact was a risk signal or an exculpating one. The benchmark, all tasks, and the verification harness are open-source
[AI-79] Walk Before You Run: The Importance of Data Exploration for Data Analysis Agents VLDB2026
链接: https://arxiv.org/abs/2608.16045
作者: Yike Yuan,Virum Ranka,Tina Lasisi,Lin Ma
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures. Accepted to VLDB 2026 Workshop: DASHSys: Systems for Data-centric Agents with Human-in-the-loop
Abstract:LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations. These systems are often evaluated by the correctness of their final downstream answers. However, reliable data analysis also depends on an earlier step: understanding what the dataset contains before solving the requested task. For complex workbooks, this Data Exploration step includes identifying the logical tables behind physical sheets, interpreting column semantics, recovering keys and relationships, and detecting quality issues. In current tools and benchmarks, this step is usually left implicit, creating a gap between downstream task performance and the dataset understanding needed for reliable, human-checkable analysis. Our key contribution is to identify this overlooked gap, make Data Exploration a first-class evaluation target, and show through downstream experiments that stronger Data Exploration support improves task performance. To evaluate dataset understanding directly, we introduce two benchmark settings: a real multi-sheet workbook benchmark based on a Vitamin D study dataset, and an extension of DSBench with schema-fixed Data Exploration artifacts. In both settings, systems are evaluated by the quality of a structured artifact capturing tables, columns, semantic roles, relationships, and profiling signals. Our results show that strong LLMs and data-analysis agents still miss important logical structure even when they read spreadsheet content. Furthermore, explicit Data Exploration support often improves downstream correctness, suggesting it should be treated as a first-class, inspectable stage in LLM data-analysis workflows and a natural human-in-the-loop checkpoint where domain experts can review and correct the artifact before downstream analysis proceeds.
[AI-80] NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
链接: https://arxiv.org/abs/2608.16038
作者: Ziluowen Luo,Jun Yin,Ruochen Liu,Ming Cheng,Shirui Pan,Chengqi Zhang,Senzhang Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 9 figures
Abstract:Post-hoc Graph Neural Network (GNN) explainers commonly follow a Perturb-Query paradigm, inferring the importance of graph elements based on queried predictions to perturbed inputs. However, such perturbations often introduce substantial distribution shift, undermining the reliability of the queried predictions used to derive explanations. While existing efforts mainly improve perturbed graphs or stabilize model predictions on them, we revisit the perturbation mechanism itself. We show that the widely used Element-wise Masking(EM) suppresses edge-induced messages toward zero, causing deterministic scale contraction that accumulates across message-passing layers, a phenomenon we term Scale Drift. Consequently, prediction changes under EM may conflate information corruption with deviations in propagation scale. As a scale-stable alternative to EM, we introduce Noise Corruption (NC), which perturbs each message through matched-norm random-direction corruption while preserving the expected squared message norm. Building on NC, we propose NICE, a Noise Corruption-based explanation framework, which learns a Stochastic Restoration Boundary (SRB) under NC-induced uncertainty, balancing target-prediction restoration against compactness. Furthermore, Boundary-Integrated Gradient (BIG) converts this boundary into edge attributions by accumulating each edge’s contribution to reducing restoration risk along the restoration path. Experiments across multiple benchmarks demonstrate stronger explanation performance and model faithfulness while confirming that NC substantially reduces the Scale Drift induced by masking.
[AI-81] Rag GAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection
链接: https://arxiv.org/abs/2608.16018
作者: Junxin Lu,Jing Zhao,Shiliang Sun
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graph anomaly detection aims to identify nodes that deviate from normal behavioral patterns within graphs. However, existing methods largely rely on the homophily assumption, which makes it difficult to distinguish spurious affinities and to capture the diverse behaviors of normal nodes,limiting their robustness in complex real-world scenarios. To address this problem, we propose RagGAD, an unsupervised graph anomaly detection framework based on rationale-aware conditional Gaussian mixture normalizing flow. RagGAD introduces an adaptive rationale disentangler to disentangle stable rationales from spurious correlations within node interrelationships, and further decomposes stable rationales into robust and fragile components. The learned rationales capture underlying interaction patterns that characterize normal behaviors under varying conditions, while anomalies emerge as deviations associated with unstable or spurious correlations. To model the intricate distributions of normal and abnormal nodes, RagGAD integrates rationale-non-rationale Gaussian mixture modeling with a robust-fragile rationale mixture learning strategy. By mitigating spurious homophilic correlations and embracing the heterogeneity of normal patterns, RagGAD identifies anomalies as low-density regions within a structure-aware distribution space. Extensive experiments on multiple benchmark datasets demonstrate that RagGAD outperforms state-of-the-art methods.
[AI-82] MUPA2E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment
链接: https://arxiv.org/abs/2608.15999
作者: Stefanos Gkikas,Eric Nichols,Christian Arzate Cruz,Randy Gomez
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion. This paper presents MUPA\textsuperscript2E, a unified perception framework that processes facial video and electroencephalography (EEG) through a single shared asymmetric-attention backbone. Facial video is represented through axis-folded frame tokens, while EEG is processed either as a raw multichannel waveform or projected into the spatial domain for multimodal fusion. The framework is evaluated on the DMER dataset under a stratified subject-independent protocol, comparing unimodal video, unimodal EEG, and fused video–EEG configurations with per-channel and merged EEG projections. Using the original recordings, with shorter trials zero-padded to match the longest duration, merged fusion at stride~ 30 achieves the highest validation performance and a test accuracy of 70.07% . Further analysis revealed that recording duration is unevenly distributed across the affective classes, making the padding pattern a potential classification cue. Controlling for this factor by cropping all recordings to a common duration of 20 seconds yielded a test accuracy of 62.71% , providing a stricter duration-controlled assessment of the framework in which differences in recording length are removed as a potential classification cue. These findings demonstrate the feasibility of processing structurally different neural and visual signals within a compact unified architecture while highlighting the importance of controlling duration-related cues in affective datasets.
[AI-83] ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
链接: https://arxiv.org/abs/2608.15979
作者: Eric Xie,Wenqian Ye,Aidong Zhang
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures
Abstract:Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules. Whether such an output that appears creative is truly original and effective is hard to establish: open-ended outputs require subjective judgment, the output may replicate something seen in training, or the task may be too simple to need creativity. We present ALPS (Austin-Law Proof-Synthesis), a benchmark that designs a task to measure valid creativity: producing a solution that is original and can be proven correct. Each instance is a single equational law, certified to require either the construction of an infinite mathematical structure satisfying the law, or a proof that no such structure exists. Submissions are verified by automated proof checking with no human involvement, and a public generator produces new instances without limit, so LLMs are never evaluated on problems they may have seen. A portfolio of eight configurations of leading automated provers resolves 2.2% of the 4,141-law evaluation pool, and a twentyfold budget increase adds 0.6%: the obstacle is not compute, but the absence of any method that produces the tailored structure each law requires. Under a fixed protocol, the strongest reasoning model we test succeeds in 14% of instances on the proof side, but none on the construction side. The remaining 97.2% of the pool is unresolved at every configuration and budget we test. We release ALPS in full: the corpus, the generator, and the automated judge.
[AI-84] Solvable Sokoban Without a Solver via Diffusion
链接: https://arxiv.org/abs/2608.15958
作者: Sina Baghal
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注:
Abstract:Deciding whether a Sokoban puzzle is solvable is PSPACE-complete (Culberson, 1997): solutions can be exponentially long and there is no short certificate to check. Solvability is also a fragile property, since even a single misplaced wall can silently render an entire puzzle unsolvable. In this work, we show that a transformer-based discrete diffusion model trained purely on tile completion, with no access to solvers, rewards, or solvability labels, achieves a solvability rate of 77.4%, with 94.5% of the remaining failures rendered solvable by removing a single wall. In other words, a global, search-heavy property follows from a local training objective: trained only to fill in masked cells, the model inherits solvability it was never trained on. An autoregressive model factorizes as p(c_k \mid c_1 \dots c_k-1) , meaning a fixed order, always conditioned on a prefix. Masked diffusion does not: it hides a random subset of cells and learns p(c_k \mid \textany subset) , so at generation time it can reveal cells in any order, each one conditioned on everything already placed, wherever it sits on the board. A puzzle’s difficulty comes from exactly this kind of non-local interaction, a decision in one part of the grid constraining what will work somewhere else entirely. A generator that is not locked into a single fixed order is therefore a better structural match for the problem than one that is. The training pipeline is adapted from MD4 (Shi et al., 2024) and the dataset is DeepMind’s Boxoban (Guez et al., 2019). The trained model and instructions for generating puzzles are publicly available. Subjects: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) Cite as: arXiv:2608.15958 [cs.AI] (or arXiv:2608.15958v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15958 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sina Baghal [view email] [v1] Sun, 16 Aug 2026 23:17:07 UTC (1,273 KB)
[AI-85] Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces
链接: https://arxiv.org/abs/2608.15956
作者: Shrey Shah,Levent Ozgur
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic retrieval workflows produce query, retrieval, and stopping traces as a byproduct of answering questions. We study how these traces can adapt a deployed dense retriever to changing workflow distributions without new relevance labels, synthetic queries, or LLM judgments. We introduce Navigation-Informed Embeddings (NIE), a family of trace-derived objectives. NIE-Stop turns the stopping document into a soft positive; NIE-Path additionally uses preceding path documents as hard comparisons and imposes ordinal constraints with geometric decay. A BGE encoder adapted from retained source trajectories improves support Recall@20 on an independent target benchmark from 72.2 to 78.0 overall. NIE-Stop reaches 76.9 overall and 52.3 on long paths; NIE-Path raises long-path performance to 55.4, compared with 46.7 for the unadapted encoder. A shuffled-order control under the full path objective loses 3.2 points. Without public-benchmark training, the same adapter also improves nDCG@10 by 1.9 points on standard BEIR HotpotQA. NIE therefore provides a lightweight adaptation channel for settings where trajectories are already retained, with zero incremental labeling cost.
[AI-86] Augmenting Text to Increase Translation Difficulty
链接: https://arxiv.org/abs/2608.15932
作者: William Kalikman,Šimon Sukup,Michal Tešnar,Vilém Zouhar
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 8 figures, 10 tables. William Kalikman and Šimon Sukup contributed equally. Published in EAMT 2026. Code: this https URL . Data: this https URL
Abstract:As state-of-the-art machine translation models saturate standard benchmarks, the field needs more challenging evaluations to distinguish between models of varying quality. We propose augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator. Our Adversarial Translation Optimization (ATO) uses gradients from a combined difficulty and fluency objective to iteratively replace tokens. Because each step branches over candidate substitutions at every position, optimization becomes a tree search problem, which we address with Beam Search. ATO offers a gradient-based alternative to LLM-based dataset creation without LLM prompting, expensive human curation, or task-specific model training. Our ATO-modified benchmark lowers average translation quality (xCOMET) from 0.93 to 0.82, compared to 0.88 for paraphrasing and 0.86 for a zero-shot baseline. Human evaluation shows the modified texts are somewhat less natural than the baselines but remain reasonably grammatical and plausible while being substantially harder to translate. We release two datasets of 350 English texts each, generated by our methods, as well as the code.
[AI-87] Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2608.15929
作者: Šimon Sukup,Ariyan Bighashdel,Pavol Jancura
类目: Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Pedestrian path prediction is crucial for enhancing the safety of autonomous vehicles and advanced driver-assistance systems. Previous studies explored different learning-task formulations for pedestrian path prediction and compared these formulations using shallow neural networks, but did not extend this analysis to more complex deep-learning models. This paper adapts the Spatial-Temporal Graph Attention Network (STGAT) to a unified pedestrian path prediction framework and introduces state and action definitions specific to STGAT. The resulting formulations support deterministic and stochastic policies, one-time and sequential decision-making, and reinforcement-learning algorithms including REINFORCE and proximal policy optimization. The proposed learning-task formulations improve prediction performance across the selected benchmark datasets compared with the standard supervised-learning formulation. These results demonstrate that reformulating the decision process and training objective can improve an advanced pedestrian trajectory prediction architecture and may provide a path toward improving other graph-based prediction models.
[AI-88] Information Geometry of Message Passing
链接: https://arxiv.org/abs/2608.15922
作者: Mykola Lukashchuk,Kyrylo Yemets,Alex Ledbetter,İsmail Şenöz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We show that the natural-gradient stationary condition of variational inference has an edge-local form on a Forney-style factor graph. We start from the Bethe free energy and constrain a selected edge marginal to an exponential family. At a stationary point, the natural parameter of that edge equals the sum of two projected messages, one from each incident factor. Each projected message is the natural-gradient projection of the exact belief-propagation log-message at the current receiving marginal, or equivalently, the gradient of its expectation in the so-called mean coordinates. We call the resulting scheme natural-gradient message passing (NGMP). The rule is local; each edge may carry its own exponential family, and the message a factor sends depends on the marginal that receives it. Compared with variational message passing, NGMP keeps the part of the exact message that the receiving family can represent instead of averaging the factor under the neighboring beliefs. The two coincide when the uncertainty on the edges entering a non-conjugate factor vanishes, and NGMP is more accurate when that uncertainty persists, for example, along a partially observed latent chain or when parameters are filtered through successive data batches. Experiments on Poisson smoothing, heteroskedastic regression, and hourly ETTh forecasting confirm this and show that the gain appears mainly in uncertainty calibration.
[AI-89] Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive
链接: https://arxiv.org/abs/2608.15901
作者: Brian B. Moser,Ahmed Anwar,Tobias Christian Nauen,Shishir Muralidhara,Federico Raue,René Schuster,Stanislav Frolov,Andreas Dengel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Continual learning regularizers like EWC fight forgetting by penalizing changes from previous-task parameters with per-parameter importance, typically diagonal Fisher values. Per-parameter looks more flexible than per-layer, but each layer’s diagonal Fisher is a weak summary of its actual curvature, missing the top-eigenvalue information that controls forgetting. Adversarial bit-flip attacks and Hessian-spectrum studies show that this missing per-layer sensitivity spans orders of magnitude in neural networks. Under a block-diagonal Hessian assumption, the layer-level analogue of EWC’s existing diagonal assumption, we prove three things. Forgetting decomposes as a sum of per-layer terms weighted by each layer’s top Hessian eigenvalue. Diagonal-Fisher weights cannot recover this eigenvalue. For instance, two layers with identical Fisher averages can have top eigenvalues differing by a factor as large as the layer width. For the same level of forgetting, uniform regularization loses new-task performance by an amount scaling with the layer condition number. Our theoretical analysis leads to a simple recipe: protect early layers strongly, let deeper layers move. We apply this recipe to EWC and SLCA and show clear improvements in average performance and forgetting metrics.
[AI-90] Breaking and Defending LLM -Powered Social Media Bot Detection Systems
链接: https://arxiv.org/abs/2608.15893
作者: Nof Orenstein,Yoni Birman
类目: Artificial Intelligence (cs.AI)
备注: Accepted at ACISP 2026 (Australasian Conference on Information Security and Privacy). Also accepted as a poster at IEEE Symposium on Security and Privacy (SP) 2026. Published in Pragmatic Cybersecurity 2026, 1(2), 10, this https URL . 19 pages, 11 figures
Abstract:The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms. To combat this, machine learning systems have been developed to detect and limit bot activity, but attackers continuously adapt through techniques such as adversarial learning and behavior imitation, fueling an ongoing arms race between bots and detection tools. Recent advances in large language models (LLMs) have significantly improved bot detection by enabling deeper semantic and contextual analysis of accounts and their content. However, this shift also introduces new attack surfaces, allowing adversaries to craft exploits that directly target the reasoning and generation mechanisms of LLM-based classifiers. Industry tools such as Anthropic’s Claude Code Security similarly leverage LLMs for security-critical decisions, further motivating a careful study of their attack surfaces. In this work, we investigate both the offensive and defensive aspects of LLM-powered, threat-specific cybersecurity applications. While centered on the challenge of social media bot detection, our methodology and insights generalize to a broad class of LLM-powered cybersecurity systems, including phishing detection, email classification, and fraud analysis. We introduce two novel adversarial attack strategies that systematically exploit the semantic and contextual weaknesses of LLM-based classifiers, degrading their detection accuracy by up to 48%. To counter these threats, we propose a robust multi-LLM defense architecture designed to preserve detection reliability under adaptive adversarial conditions. Our solution, LSABRE (LLM-powered Social Adversarial Bot Recognition Ensemble), is a multi-LLM framework that substantially improves robustness across a range of attacks, maintaining 86% detection accuracy even under strong, adaptive adversarial pressure.
[AI-91] Bounded Agents : Delegation Security for Multi-Agent AI Systems
链接: https://arxiv.org/abs/2608.15888
作者: Xabier Muruaga
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 14 pages, 4 figures, 18 tables. Code and data: this https URL
Abstract:LLM-based agents can act on behalf of a user to access cloud services, call tools, or invoke agents. At session start, the agent’s permissions are set but remain static, and each request is evaluated independently, without considering prior actions. Within its permissions, an agent may act contrary to the delegated task, combine individually permitted actions into a prohibited outcome, or delegate authority to a sub-agent without limiting it. A prompt injection poses a risk only if the agent has authority to perform such actions; this is therefore a problem of authorization architecture, not just the model. The Agentic Principal Chain (APC) tracks delegated authority from one principal to the next. APC evaluates each request against the accumulated session state using six authorization checks. APC carries forward and restricts delegated scope and budgets. Using composition closure, APC checks requests against prior actions to prevent prohibited combinations and enforces the decision outside the model. We prove Blast Radius Monotonicity and Composition Soundness for APC implementations; Composition Soundness is limited to prohibited combinations under a complete restriction set and serialized admission. We evaluated 3,154 instances including InjecAgent, AgentDojo, and ASB. Our compromised-model evaluation tests APC independently of model behavior by inserting the ground-truth attack call after the first legitimate tool call. AgentDojo exfiltration fell from 75-100% to 0% across all four domains; APC blocked all 544 InjecAgent data-stealing cases. Intent binding reduced destruction from 38.6% to 4.0% and manipulation from 90.5% to 12.1%. Authorization latency was 0.24 ms at the 99th percentile on an idle host; across 949 AgentDojo task-injection pairs, utility was 8.6 and 13.9 percentage points lower in the two settings. Implementation, evaluation tools, and data are publicly available.
[AI-92] Dear Algo: A Precision-First Agent ic Intent Layer for Unified Search and Recommendation
链接: https://arxiv.org/abs/2608.15877
作者: Rui Wang,Jiazhou Wang,Zheng Wei,Chenglin Lu,Fangcheng Sun,Ivy Sun,Jin Sun,Hui Geng,Lillian Zhang,Chao Yang,Lei Chen,Shahin Sefati,Reem Helou,Joe Zhou,Babak Shakibi,Yiyi Pan,Bi Xue,Hong Yan,Shujian Bu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Search and recommendation serve a shared discovery objective but encode intent differently. We study this boundary through Dear Algo on Threads, a deployed product where open-ended requests such as \emphmore NBA news or \emphless politics steer subsequent feed recommendations rather than return a one-shot result list. Its agentic intent layer compiles explicit, inferred, negative, and compound intent into a grounded executable plan, then invokes conventional retrieval and optional semantic or multimodal reranking. The layer shares an intent-to-retrieval contract without requiring one model or serving path across search-like and recommendation-like modes. We evaluate Dear Algo under a precision-first objective. In a blinded audit of 300 public request-item pairs (296 evaluable), a strict categorical LLM-as-a-judge gate achieved 94.4% exact-Relevant precision [88.8%, 98.9%]. Across 72 normalized request clusters, the full configuration produced 7.73 judge-qualified candidates per 20 slots versus 6.61 for an LLM-derived-query baseline, a gain of 1.11 [0.12, 2.12]. In a candidate-randomized serving-path study restricted to the reranker path’s first 72 eligible hours, the user-weighted judge-Irrelevant share among judged admissions was 2.80% versus 4.78% off (-1.97 points [-3.02, -0.94]), while Exact-Relevant share was 2.24 points higher [0.08, 4.41]. Together, these studies show how explicit natural-language intent can be carried into feed recommendation under a precision-first evaluation framework Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15877 [cs.AI] (or arXiv:2608.15877v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15877 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-93] CoupVisor: Strategy Optimization by Round and Challenge Decision Support
链接: https://arxiv.org/abs/2608.15868
作者: Cris Huynh
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
备注: 15 Pages and 9 pages of appendix
Abstract:This paper presents CoupVisor, a decision-support system for the hidden-information card game Coup. It addresses two questions: what a player should do on each turn, and when a player should challenge an opponent’s claim. The system is built around a single description of game events, which is shared across manual play, replay of recorded games, simulation, belief tracking, advisor recommendations, and learning-based policies. CoupVisor estimates the chance that a claim is truthful by combining how likely each role is with how many cards the claimant still holds, which corrects a case where the very first claim of a game was flagged as suspicious despite no evidence. We compare a rule-following advisor and several learned and heuristic players across many simulated games and different opponent styles. Our main finding is that the choice of reward, whether it rewards short-term gains or ultimately winning the game, decides which learning approach performs best, and that a win-oriented reward produces a policy that outperforms all baselines.
[AI-94] Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes
链接: https://arxiv.org/abs/2608.15867
作者: Farbod Abbasi,Zachary Patterson,Bilal Farooq
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 11 figures, 6 tables
Abstract:Synthetic populations are critical inputs for activity-based travel demand models, yet generating realistic populations from limited survey data remains challenging. Small samples miss valid attribute combinations, known as sampling zeros, and generative models may also produce infeasible structural zeros. Moreover, realistic synthetic populations must capture both static socio-demographic attributes and sequential travel behaviour, such as trip chains. This paper proposes a regularized two-stage generative framework to address these challenges, where regularization refers to additional loss terms that guide the generator toward broader valid coverage and fewer infeasible samples. In Stage 1, a Wasserstein GAN with gradient penalty is augmented with three regularization terms, IGP, LDR, and CLAP, to improve feasibility, diversity, and novelty in tabular population synthesis. In Stage 2, Transformer and LSTM-Attention models generate sequential travel attributes, including departure time, trip purpose, and travel mode, conditioned on the synthesized tabular profiles. We also introduce novelty and count-aware metrics to evaluate whether valid unseen combinations are recovered and generated in realistic proportions. Results show that regularized models outperform the vanilla WGAN-GP across feasibility, diversity, and novelty. Regularization increases feasibility by 2.1 to 3.7 percentage points and novelty by 6.6 to 10.0 percentage points, improving sampling-zero recovery without sacrificing feasibility. The F1 score improves by 6.3 to 8.6 percentage points. For sequential attributes, LSTM-Attention best matches the trip-length distribution, while Transformer achieves higher overall sequential F1, 90.6% versus 89.1%. Cross-stage validation confirms strong consistency between generated mobility status and generated trip chains.
[AI-95] RAG as: Retrieval-Augmented Gas Optimization for Smart Contracts with Continuous Knowledge Integration
链接: https://arxiv.org/abs/2608.15857
作者: Yishun Wang,Wenjin Yi,Wenkai Li,Zongwei Li,Xiaoqi Li
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 3 figures
Abstract:Ethereum is now integral to mission-critical sectors, including finance, healthcare, and supply chain management. Execution fees, commonly referred to as Gas, scale with the computational complexity of their functions. Smart contracts on Ethereum incur execution fees, known as Gas, which increase with computational complexity. Thus, optimizing Gas-intensive code while preserving functional equivalence significantly lowers deployment costs. No existing system continuously exploits evolving Gas usage patterns. We systematically analyze syntactic and semantic constructs that drive excessive Gas use. This yields six high-level categories covering twelve fine-grained antipatterns underpinning a curated knowledge base. We operationalize these insights with RAGas, a three-stage retrieval-augmented generation framework that uses a large language model to pinpoint and automatically fix Gas inefficiencies. Experiments on deployed contracts demonstrate that RAGas reduces Gas usage by up to 11% and achieves high precision and recall in detecting code snippets exhibiting Gas wastage.
[AI-96] he Authority Resolution Framework: A Five-Domain Ontology for Governing Who and What Decides at Scale
链接: https://arxiv.org/abs/2608.15832
作者: Parviz Shariff
类目: Artificial Intelligence (cs.AI)
备注: 27 Pages, 2 Tables, 1 Script, 1 Query
Abstract:As AI systems become increasingly capable of autonomous action, determining whether an agent is technically capable of performing an action is insufficient: the system must also determine whether the action is authorised in its context. This paper introduces the Authority Resolution Framework (ARF), a five-domain ontology for representing and resolving authority across organisational roles and informal influence, business concepts, codified processes, machine-readable permissions and executable systems, and external real-world context. ARF defines the Authority Relation (AR) as a cross-domain primitive binding an actor, action, object, bounded context, justification chain, and a calibration measure termed the DNA-Coefficient, which captures divergence between documented authority structures and authority as practiced. The framework provides a machine-interpretable representation of authority provenance and scope, with JSON-LD representations and knowledge-graph query patterns for authority resolution. ARF is designed to support AI agents in determining the provenance, scope and contextual validity of authority before executing consequential actions. The framework positions authority resolution as a knowledge-representation and reasoning problem at the intersection of ontology engineering, semantic AI, agentic AI and AI governance. Comments: 27 Pages, 2 Tables, 1 Script, 1 Query Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15832 [cs.AI] (or arXiv:2608.15832v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15832 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-97] RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
链接: https://arxiv.org/abs/2608.15817
作者: Shihong Huang,Shengjie Wang,Hong Ma,Zhou Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs. However, their heterogeneous capabilities and inference costs make efficiently routing queries a significant challenge. Existing paradigms are inflexible: one-shot routers commit before observing responses, whereas conventional cascades stop adaptively but follow a fixed model order. Cascade routing removes both restrictions by reconsidering whether to stop or invoke another model after each response. Current methods use a predict-then-optimize pipeline estimating response quality and future model utility. However, prediction loss for quality or utility is not equivalent to routing-decision loss. A lower prediction error does not necessarily yield a better action; a small boundary-crossing error can reverse a stop'' or model-selection decision. Therefore, we propose RLCascadeRouter, a quality-estimator-free framework that formulates cascade routing as a Markov decision process with actions comprising stop’’ and model selection. It uses trajectory returns and advantages to directly optimize the performance-cost objective. Its Cascade Policy Network models candidate complementarity for model selection and remaining-action value for stopping, eliminating independent post-hoc response-quality estimators. Evaluated across ten LLMRouterBench benchmarks with thirteen LLMs, RLCascadeRouter outperforms strong baselines and achieves superior performance-cost trade-offs. It incorporates unseen models without retraining, and ablation studies validate both policy components.
[AI-98] Pricing the Risk of Runtime Compression: Anytime-Valid Admission and a Served-Output Law for Compressed Serving State
链接: https://arxiv.org/abs/2608.15810
作者: Fanzhe Wei,Li Liu
类目: Artificial Intelligence (cs.AI)
备注: 29 pages (20 pages main text plus appendices), 8 figures, 10 tables. Companion paper: “What to Protect When You Quantize a Mixture of Experts”, submitted concurrently. Lean 4 development (228 exported theorems, no sorry) and all artifacts released
Abstract:Runtime compression of serving state trades quality for capacity with no priced guarantee: systems adapt precision on load signals with no soundness statement, and certified approaches budget request-level risk by a union bound over a pre-declared event count. We show the union budget exhausts on every long request in a production serving stack (100% of requests), and replace it with an anytime-valid, physically accounted ledger whose bound holds at every one of 352,333 admission calls on live traffic and which, in a pre-registered held-out confirmatory round, halves the exact-fallback rate at matched risk (0.30 - 0.14) – coverage is bought at a price the account states. We then price the remaining distance from the certified witness to what a user experiences: a machine-checked design law (TV = tanh(a_q w_thr)) turns the served-TV target into a threshold knob, and a three-layer audit of its instantiation – an operator-norm query envelope measured 1.5x from tight, a measured-ellipsoid replacement for the Cauchy-Schwarz ball that buys nothing (0.89x, held-out sound), and the gate’s operating point (~700x) – localizes the entire 1064x gap to the operating point, a price the law now states rather than an unknown. A priced bound is worth nothing on a request one has not seen, so the third link is the quantifier: exchangeable extrapolation across 80 serving histories replaces binary conformal prediction’s vacuous certificates with order-statistic bounds that discriminate (0.41 against 0.51 calibration risk). All probabilistic kernels are Lean 4-checked (228 exported theorems, no sorry); which object deserves this machinery at all is settled empirically in a companion paper that adjudicates – and rejects – the natural alternative of certifying routing. What ships is an account: risk you can spend, a gap you can read off a law, and a bound that survives the request you have not seen.
[AI-99] Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration
链接: https://arxiv.org/abs/2608.15772
作者: Yiqi Liu,Yang Wang,Songxin Wang,Chenghao Xiao,Chenghua Lin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:When a language model refuses to answer a prompt, it is unclear whether the correct answer is erased from its internal representations, or merely suppressed at the output layer. We investigate this mechanism using a controlled withhold setting, which yields perfectly matched answering and refusal trajectories for bidirectional activation patching. We uncover a causal asymmetry in intervention locality under matched causal interventions, which we term broken symmetry. Even when a model generates a clean refusal, the correct answer remains linearly recoverable from its hidden states. Furthermore, releasing this withheld answer is a highly local operation, requiring only a single-position patch. Conversely, the reverse operation is not equally local: reimposing suppression requires broader interventions across multiple positions, and assembling a coherent refusal sequence is more difficult still. We further demonstrate that while an average answer-to-refusal displacement vector marks the geometric difference between these states, it fails to act as a reliable, reversible linear control toggle between behaviours. Taken together, our findings show that refusal does not function as a simple symmetric switch. For safety and auditing, this implies that probe recoverability can overestimate true behavioural control, and locating refusal-relevant directions does not reliably grant the ability to steer a model from answering to coherent refusal.
[AI-100] mporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection KDD2026
链接: https://arxiv.org/abs/2608.15768
作者: Xudong Chen,Shengbo Gong,Lu Cheng,Wei Jin
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accpeted by KDD 2026
Abstract:Conformal prediction (CP) provides distribution-free coverage guarantees and has emerged as a principled tool for uncertainty quantification. In edge-level fraud detection on temporal interaction graphs, where false positives and false negatives both carry substantial cost, such coverage guarantees are particularly appealing for risk-aware decision making. However, directly applying existing graph conformal predictors yields inefficient prediction sets due to two recurring properties of fraud data. Fraudulent interactions are often embedded in benign-dominated neighborhoods that dilute calibration signals, while extreme class imbalance leaves scarce labeled-fraud support in the calibration split and leads to overly conservative class-conditional thresholds. To address these issues, we propose ProtoCP, a conformal prediction framework for edge-level fraud detection on temporal graphs. ProtoCP improves calibration efficiency by focusing calibration on fraud-relevant subgraph context and producing more stable nonconformity scores under class imbalance and temporal drift. Specifically, it leverages learned prototypes to suppress benign-dominated noise in the calibration context and introduces a neighborhood-relative scoring mechanism with temporal score diffusion for stable class-conditional calibration. Experiments on four fraud benchmarks (YelpChi, S-FFSD, FTFD, and BankSim) show that ProtoCP achieves the target coverage with consistently smaller prediction sets than state-of-the-art baselines. Our codes are available at this https URL
[AI-101] nyCast: Probabilistic Zero-Shot Forecasting with Computed Periodicity
链接: https://arxiv.org/abs/2608.15767
作者: Armin Steinhauser
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 38 pages, 6 figures, 17 tables. Code and weights: this https URL
Abstract:We introduce TinyCast, an attention-free zero-shot forecaster that emits a predictive distribution from 146,505 parameters, on the premise that at this size the periodic structure of a context is worth computing rather than learning. A zero-parameter spectral detector supplies the dominant periods, the context is folded on their phase, and a dilated convolutional encoder and a block-autoregressive quantile decoder model the rest. It is smaller than every zero-shot entry on the GIFT-Eval board whose parameter count can be established. On probabilistic accuracy it defines the size-accuracy frontier. Among zero-shot entries declaring no test-data leakage it is the only one below 1.4M parameters that emits a predictive distribution, and every entry scoring better carries at least that budget. On Chronos-ZS and fev-bench every neural model ahead of it carries at least 28 times its parameters. Because the mixing path is convolutions and matrix multiplications only, it exports to static INT8 and forecasts end to end on an embedded device without per-signal fitting.
[AI-102] Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
链接: https://arxiv.org/abs/2608.15755
作者: Meiling Tao,Yiling Tao,Peng Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints. Existing context-management methods improve the use of past interaction history, but rarely maintain an explicit situation state that separates grounded facts from task-state judgments. As a result, agents often need to infer fine-grained attributes, task dependencies, and constraint satisfaction implicitly from dialogue traces. We propose Intent-Driven Situation States (IDSS), a training-free framework that maintains an explicit situation state alongside the dialogue. IDSS parses tool returns into provenance-aware entities and attributes, tracks user intents, required variables, constraints, and execution status, and propagates new facts to task constraints to update action executability. This allows agents to avoid infeasible actions, advance dependent goals, and reuse relevant information without repeatedly searching raw history. Experiments on three interactive benchmarks across eight LLMs show that IDSS improves task completion, preference elicitation, and interaction efficiency, with clear gains on tasks involving multi-entity coordination, evolving user constraints, and constraint-aware replanning. Ablations and error analyses show that these improvements come from the interaction between fact persistence, intent-centered state tracking, and constraint modeling. These results suggest that explicit situation tracking offers an effective alternative to history-centric context management for reliable user-centric multi-turn agents.
[AI-103] oward AI-Friendly Cartography: Understanding How Color Design Influences Foundation Model Spatial Reasoning on Sequential Choropleth Maps
链接: https://arxiv.org/abs/2608.15736
作者: Yonghe Sun,Zhenjia Liu,Hua Liao,Wenjia Xu,Nai Yang,Weihua Dong,Zhiwei Wei
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 12 figures, 13 tables
Abstract:Foundation models (FMs) increasingly support multimodal and geospatial reasoning, yet it remains unclear whether cartographic principles designed for human perception are equally effective for machines. Focusing on sequential choropleth maps, we examine how hue palette, color ordering, and lightness contrast influence FM spatial reasoning. We construct a controlled benchmark of 5,760 maps and 28,800 questions spanning Attribute Identify, Spatial Recognition, Compare, Rank, and Pattern Delineate, and evaluate 21 open-source and proprietary multimodal FMs. Results show that hue choice has limited and inconsistent effects, whereas disrupting sequential color ordering substantially reduces performance, especially for comparison and ranking. Reduced lightness contrast also consistently impairs reasoning, while increasing contrast beyond sufficient separability provides only marginal gains. LoRA fine-tuning improves overall accuracy but preserves these relative sensitivities. Additional factorial experiments further indicate that errors arise from color-and-legend decoding, spatial reasoning, and the integration of thematic attributes with spatial structure. These findings show that conventional sequential ordering and sufficient contrast remain important for machine map understanding and provide empirical guidance for AI-friendly cartographic design.
[AI-104] FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
链接: https://arxiv.org/abs/2608.15727
作者: Ali Boudaghi,Alireza Nemati,Hadi Zare
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Diffusion models have recently shown strong potential for multivariate time-series anomaly detection by learning the distribution of normal data through iterative denoising. Existing diffusion-based approaches, however, typically perform anomaly detection after completing the reverse diffusion process, relying primarily on the final reconstructed signal and overlooking informative representations produced during denoising. This design incurs substantial computational cost and limits the use of intermediate diffusion information for anomaly detection. In this paper, we propose FirstDiff, a diffusion-based anomaly detection framework based on the observation that the predicted diffusion noise at the initial reverse-diffusion evaluation already contains sufficient information for accurate anomaly detection. FirstDiff models the statistical distribution of predicted diffusion noise under normal behavior using validation data, enabling anomaly inference from a single denoising-network evaluation rather than completing the reverse diffusion trajectory. To model complex temporal and inter-sensor dependencies, FirstDiff employs a Diffusion Transformer as the denoising backbone. Extensive experiments on five public benchmark datasets demonstrate that FirstDiff achieves state-of-the-art performance while reducing diffusion inference from the full reverse trajectory to a single denoising-network evaluation. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2608.15727 [cs.LG] (or arXiv:2608.15727v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15727 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-105] HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
链接: https://arxiv.org/abs/2608.15703
作者: XinQi Wang,Jinwei Xiao,Sijia Cui,Hongming Zhang,Yanna Wang,Qingyang Zhang,Bo Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents often perform poorly on complex, long-horizon tasks because their context becomes increasingly cluttered over time. As interactions accumulate, detailed execution traces and intermediate outputs dominate the context, making it difficult for the model to retain and use high-level planning information. Most existing methods address this issue through compression or retrieval applied to a single, flat context, which does not clearly separate different types of context information and often leads to degraded reasoning. To address this challenge, we propose HyMem, a hierarchical framework that explicitly separates the agent’s context into distinct functional layers. HyMem organizes context by function to separate high-level planning from execution and complex analysis. Its isolated reasoning module handles complex subtasks without adding intermediate reasoning traces to the persistent planning context, while its memory management module preserves task progress across context refreshes through structured summaries. These components reduce redundant context accumulation, retain task-critical information, and support coherent long-horizon reasoning within a limited context window. Experiments on GAIA and Browsecomp-plus show that, with DeepSeek-V4, HyMem achieves average Pass@1 scores of 66.7% and 61.3%, outperforming the strongest baseline by 6.1 and 4.7 percentage points, respectively. Further analysis indicates that HyMem effectively controls the growth of the reasoning context, allowing the model to maintain focus and accuracy across complex, long-horizon tasks.
[AI-106] Adaptive Mixing of Policies from Searching and Policies from Learning
链接: https://arxiv.org/abs/2608.15700
作者: Gavin B. Rens
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 16 figures
Abstract:Background: Distillation of training targets generated thru search/planning has proven useful in reinforcement learning, but search can take exceedingly long. Objectives: Rather than perform search to the same depth every time (typically at a fixed period of steps), reduce the search depth proportionally to the quality of the policy network priors. Methods: We describe Flexer, an architecture that, for each step, mixes the policy from a neural network and the policy from Monte Carlo tree search. The mixing factor favors the MCTS policy as the policy imitation error of the network and the environment models’ variance increases. Results: Flexer outperforms a version of AlphaZero (and DQN and ADP) for some experiments on three toy symbolic problems.
[AI-107] Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment
链接: https://arxiv.org/abs/2608.15693
作者: Subhransu Das,Jiaming Cheng,Arnav Kumar,Sadia Afrose,Mingzhe Han,Michael Silagy,Shreya Palande,Brijesh Soni,Rajiv Ramnath
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Parts of this work were presented at the IEEE Consumer Communications Networking Conference (CCNC), Las Vegas, NV, USA, January 2026
Abstract:Running large AI models on resource-constrained edge devices requires model compression to reduce model size and computation. What compresses well, however, need not deploy well. We survey dozens of recent works that report compression results on real hardware and extract practical deployment guidelines from them. Following these guidelines, we deploy compact language and image models on GPU, CPU, and Raspberry Pi platforms across question answering and image segmentation. No single technique wins across tasks. For question answering, Qwen3.5 0.8B reaches 93.85 SQuAD F1 and 92 EM under Q5_K_M GGUF quantization, while structured pruning at the same precision costs 16 F1 at a 1% ratio. For segmentation, the ranking reverses: default quantization leaves parameters and MACs unchanged, whereas pruning cuts model size by nearly 80% at near-constant mIoU. Pruning can even inflate the deployed artifact by 21-49% by breaking k-quant super-block alignment; combined with longer, less format-compliant outputs, this raises Raspberry Pi latency up to 3.4x. Compression can also manufacture the appearance of competence rather than destroy it visibly: one LoRA-recovered variant stays fully parseable and holds 71% strict BoolQ accuracy while sending 97 of 100 predictions to a single class, at 52.6% balanced accuracy. We explain these effects through neural-flow graph analysis and prefill-decode-level latency decomposition, and condense them into task-specific deployment research directions. The right technique depends on the task, the model, and the hardware. Our experiment code and artifacts are open-sourced at this https URL
[AI-108] Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer
链接: https://arxiv.org/abs/2608.15690
作者: Ivan Mikheev,Viacheslav Vasilev,Anna Dmitrienko,Alexey Letunovskiy,Ivan Kirillov,Kirill Chernyshev,Denis Dimitrov
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
备注:
Abstract:Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output. We show that a base T2AV model can be turned into a voice-cloning model by adding a single zero-initialized linear layer on top of its audio backbone, fine-tuning for a comparatively short training schedule, and conditioning on a short reference recording at inference time. The reference is injected through two complementary signals: its diffusion latents are prepended to the audio stream, and a global speaker embedding modulates token of the target audio. On a benchmark of 674 speaker-text pairs spanning 30 speakers we compare against five strong voice-cloning text-to-speech baselines: our enhanced 5B model attains the highest speaker-encoder cosine similarity (SECS) across three independent verification networks (ECAPA-TDNN, WavLM-SV, Resemblyzer), statistically significantly outperforming every baseline. A side product of the architecture is that the audio path can be evaluated without the video path at inference time, yielding a ~30x speed-up over the full audio-video diffusion loop while preserving the voice-cloning behaviour.
[AI-109] HESIS-MoE: Trainable Hierarchical Extraction and SteerIng of Sycophancy in Mixture-of-Experts
链接: https://arxiv.org/abs/2608.15687
作者: Kareem Hassani,Chaymaa Abbas,Lama Mawlawi,Mariette Awad
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Sycophancy, the tendency of a language model to change its answer to match a user’s stated belief, is a common alignment failure. Existing activation steering methods typically apply a single contrastive direction uniformly throughout the model, which is an unconditional intervention that alters activations even when no sycophantic behavior is present, trading knowledge retention for behavioral correction. In Mixture-of-Experts (MoE) models, prior work further suggests that behavior is encoded within expert computations rather than routing decisions alone, making precise behavioral steering particularly challenging. In this work, we introduce a shared contrastive signal, built from matched prompts with and without a stated belief, that identifies where sycophancy lives across the MoE hierarchy and drives interventions that act only where the behavior is present. We formulate localization as a causal search over a granularity ladder of MoE blocks, experts, attention blocks, and heads, and compare unconditional subtraction against two conditional alternatives: an analytic projection-based subtraction and a learned per-token gate that steers the model away from sycophancy while keeping its weights frozen. We evaluate on three MoE models measuring sycophancy alongside general knowledge and reasoning benchmarks. Our conditional interventions removed up to 90% of the belief-induced sycophancy. Our results demonstrate that sycophancy resides in identifiable computational subcircuits and can be selectively steered while maintaining a favorable removal-retention trade-off.
[AI-110] Robo-Dopamine 2.0: History-Conditioned and OOD-Aware Process Reward Modeling for Robotic Manipulation
链接: https://arxiv.org/abs/2608.15680
作者: Yijie Xu,Haopeng Jin,Run Zhou,Shengbang Liu,Sixiang Chen,Hongyang Cheng,Sicheng Hu,Peterson Co,Jinwen Luo,Huajie Tan,Shanghang Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language-action (VLA) models improve robotic manipulation but remain vulnerable to compounding errors, scene changes, and off-trajectory states. Reinforcement learning can refine pretrained VLA policies, yet sparse success signals hinder exploration, while engineered dense rewards are costly and task-specific. Existing learned visual reward models often rely on static before-after observations, causing temporal ambiguity and weak discrimination between robustness-preserving variations and task-invalid failures under out-of-distribution (OOD) execution. We introduce Robo-Dopamine 2.0, a history- and OOD-aware process reward model with a pairwise prediction interface. It combines (1) history-conditioned pairwise rewards that use source-aligned reference panels for synthetic OOD queries and observed rollout history for online queries, while preserving the queried endpoints, and (2) an OOD-aware signed progress space that represents valid progress, robustness, failure, and recovery. A Signed-Hop Curriculum with transition-aware replay learns coarse execution ordering before fine-grained progress calibration. We also construct an OOD trajectory dataset and a five-family benchmark. Reference panels improve mean visual order consistency (VOC) from 0.967 to 0.986 and OOD-robust VOC from 0.906 to 0.958. With the same 400K pairwise-reward budget, Signed-Hop training with 25% replay reaches 0.9872 mean VOC, compared with 0.9858 for a matched-pool shuffled control. In downstream reinforcement learning, the full model achieves 86.8% mean RoboTwin success and 71/80 successful real-world insertions.
[AI-111] PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails IJCAI2026
链接: https://arxiv.org/abs/2608.15673
作者: Satchit Chatterji,Shihan Wang,Giovanni Sileno,Erman Acar
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preliminary version of this paper was presented at the IJCAI 2026 Workshop on Logical and Symbolic Reasoning of Large Language Models
Abstract:Large language model guardrails can be viewed as policy-consistency problems: a system must determine which policy-relevant facts hold in a prompt-response pair and what those facts imply under a given policy. Common approaches, including policy prompting and LLM-as-a-judge pipelines, often overlap the tasks of semantic grounding and policy reasoning: the model both interprets the prompt-response pair and reasons about whether a policy has been violated. This can lead to unsafe compliance with harmful prompts, or refusals to assist benign ones. To separate grounding and reasoning roles, we propose PL-Guard, a neurosymbolic guardrail architecture. Using a symbolic policy interface consisting of predicates and ProbLog rules, a local LLM grounds prompt-response pairs into predicate probabilities using renormalized True/False token scores, while ProbLog performs explicit probabilistic rule inference over the symbolic policy. On the XSTest benchmark, an offline Qwen-based evaluator finds that PL-Guard with a hand-curated policy reduces unsafe compliance from 22.0% for the base model to 0.5%, and below the 6.0% rate of an LLM-as-a-judge baseline. This comes at the cost of higher over-refusal than the LLM-as-a-judge baseline, 14.4% versus 5.2%. These results suggest that separating neural grounding from probabilistic symbolic reasoning can expose the safety-helpfulness tradeoff while making the guardrail’s intermediate reasoning steps explicit and auditable.
[AI-112] A Responsible Artificial Intelligence Framework for Groundwater Modeling
链接: https://arxiv.org/abs/2608.15657
作者: Chong Chen,Yulu Zhang,Qingxi Guo,Yihan Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The rapid development and widespread application of artificial intelligence (AI) have sparked intense discussions on how to deploy responsible AI systems in a manner aligned with human values and ethical standards. Compared to fields like healthcare, energy, or finance, the application of AI in groundwater is relatively limited, and research on responsible AI is even more scarce. Taking the middle reaches of the Heihe River Basin as the study area, this paper proposes six Responsible AI principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. LSTM and Transformer time-series models are developed using multi-source hydrometeorological data, and validated via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. The results show that Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the operability and practical value of Responsible AI principles in groundwater prediction to support sustainable water management under climate change and human activities.
[AI-113] When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation CIKM2026
链接: https://arxiv.org/abs/2608.15639
作者: Wenhao Yuan,Chenchen Lin,Wenhao Hu,Jian Chen,Jinfeng Xu,Shujie Li,Edith Cheuk Han Ngai
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted by CIKM2026 (Full Research Track)
Abstract:\textitSplit Federated Learning (SFL) enables distributed model training by splitting networks between the server and clients. However, under client heterogeneity, the conventional static split strategy may be suboptimal because clients can differ in data distributions, adaptation dynamics, and representation learning progress, making a single split point insufficient to accommodate client-specific training states. In this paper, we propose \textscFedSGA, a \textbfSufficiency-\textbfGuided \textbfAdaptive split \textbfFederated learning framework that addresses this question through client-specific shallow sufficiency estimation. First, we introduce a client-specific adaptation channel based on private prompt tokens, which tracks local adaptation dynamics separately from the shared backbone and provides a lightweight signal for detecting whether client adaptation remains active. To further avoid repeated online probing over multiple candidate depths, we design a shallow sufficiency estimator that combines cross-client semantic alignment, temporal interface stability, and prompt-state variation to estimate whether the shallowest split is already sufficient. Finally, we introduce a split-compatible interface harmonization module that projects activations from different split depths into a shared semantic space, improving the comparability of heterogeneous client interfaces before server-side prediction. Extensive experiments on multiple heterogeneous benchmarks demonstrate the effectiveness of \textscFedSGA in improving model performance compared with state-of-the-art methods while reducing unnecessary client-side computation.
[AI-114] Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification
链接: https://arxiv.org/abs/2608.15636
作者: Chunyu Qi,Zhuoran Song,Jian Weng,Haozhe Jiang,Xueyuan Liu,Naifeng Jing,Guanghui He,Xiaoyao Liang,Haibing Guan
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment. Although Dadu-Corki, a dedicated accelerator for efficient embodied AI, has been introduced, it does not exploit the inherent interaction patterns between the robot and its environment, which results in a relatively short predicted action length. We observe that robotic environments naturally alternate between active states-where precise actions are crucial-and inactive states-where actions have limited impact on task success. This insight enables a new scheduling opportunity: long-action-length speculative prediction in inactive states, paired with selective verification in active states. We propose SpecVLA, an algorithm-system co-design framework that adaptively balances action length, inference latency, and task reliability. On the algorithm side, SpecVLA introduces a state-aware VLA inference execution paradigm and a hardware-friendly construction of a smaller verification model (sVLA) using differential residuals and block-wise mixed-precision quantization. On the system side, we develop a heterogeneous architecture consisting of a GPU and a robotic-specific hardware module, along with a speculative dataflow that decouples VLA and sVLA through parallel execution. Comprehensive evaluations on OpenVLA and RDT across LIBERO and ManiSkill benchmarks show that SpecVLA reduces end-to-end latency significantly while preserving task success rate. By enabling long-action-length speculative prediction with timely verification, SpecVLA achieves real-time robotic manipulation with both high efficiency and reliability. Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15636 [cs.RO] (or arXiv:2608.15636v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.15636 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-115] Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflict
链接: https://arxiv.org/abs/2608.15634
作者: Elisa Cavatorta,Antonio Rago
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:How can common ground between societies in conflict be identified when citizens’ acceptability of peace agreements is shaped by contested narratives? Such acceptability is mediated not only by the clauses that agreements include or exclude, but crucially by citizens’ subjective reasoning concerning agreements’ clauses. In this paper, we leverage computational argumentation to introduce a novel approach to identifying mutually acceptable agreements among individuals in conflict, i.e. a Zone of Possible Agreement (ZOPA). First, we introduce a quantitative bipolar argumentation framework tailored to represent each side’s reasoning about peace agreements. We then show how merging these frameworks can enable negotiators to identify peace agreements that are mutually acceptable. To evaluate our approach under conditions of real-world relevance, we focus on the Palestinian-Israeli conflict, where long-standing policy, practitioner and public interest underscores the demand for methods capable of analysing polarised public reasoning. We show how our framework identifies a ZOPA through theoretical analysis and preliminary experiments using survey data from both existing work and retrieved by a large language model. The results illustrate how argumentation can empower negotiators and conflict-resolution teams in mapping feasible ZOPAs grounded in citizens’ reasoning.
[AI-116] Sparse Prototype Code Underlies Classification and Prediction Across Modalities
链接: https://arxiv.org/abs/2608.15632
作者: Yehonatan Avidan,Daniel D. Lee,Haim Sompolinsky
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 33 pages, 13 figures, 14 tables
Abstract:Neural representations have become a central tool for studying the internal mechanisms of modern AI models, yet their complex high-dimensional structure makes them difficult to interpret. We show that classification tasks give rise to a universal representational geometry, shared across state-of-the-art models in vision, audio, and language processing. The key structure is that within-class variability is not random in representation space. Instead, its classifier-relevant component has strong and structured correlations with the class’s own centroid and with the centroids of its competing classes. Building on this observation, we derive an analytical mean-field theory governed mainly by the variability along true-class and rival-class centroid coordinates, together with a global renormalization of the class radius that compensates for the non-Gaussian statistics of real representations. The theory accurately predicts classification accuracy across architectures and modalities. The relevant geometric quantities improve systematically with model scale, mirroring the observed gains in accuracy. A striking feature of the theory is its sparsity: accurate prediction requires only a small set of centroid coordinates associated with the true class and its strongest rivals - connecting our framework to sparse-feature extraction approaches such as sparse autoencoders. Together, these results provide a parsimonious predictive theory of neural representations and suggest that classification in deep networks is governed by a sparse, centroid-aligned structure embedded within the full high-dimensional representation space.
[AI-117] Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts ISWC
链接: https://arxiv.org/abs/2608.15621
作者: Seungyeol Baek,Yoonbyung Chai,Yonghyeon Lee,Sungjoon Choi,Sungho Suh
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 2 figures. ACM International Symposium on Wearable Computing (ISWC) 2026
Abstract:Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires reattaching inertial measurement units (IMUs) across sessions. In multi-IMU settings, this can induce independent orientation offsets across body locations, a deployment shift that conventional scalar HAR models do not structurally handle. Existing remedies rely on rotation augmentation, whose robustness depends on sampled transformations, or calibration and orientationnormalization pipelines requiring additional reference-frame assumptions or explicit procedures. We present Truly Rotation-Invariant HAR (TRI-HAR), a rotation-invariant framework that makes robustness to independent per-location IMU orientation offsets a structural model property. TRI-HAR reshapes accelerometer and gyroscope streams into triaxial vectors, applies a shared SO(3)-equivariant backbone and invariant projection to each IMU location, and fuses the resulting invariant features for activity classification. Across four multi-IMU benchmarks, TRI-HAR preserves macro-F1 under fixed independent per-location SO(3) rotations and outperforms rotation-augmented baselines under this target shift without requiring rotational augmentation.
[AI-118] Bias-Corrected Ceilings of Emotion Predictability from Human Label Variation Based on Instance-Level Fano Bounds
链接: https://arxiv.org/abs/2608.15619
作者: Keito Inoshita
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipline. Our aim is not to pin this ceiling to a single number, but to quantify how far it depends on finite annotation, estimator choice, annotation noise, and the evaluation protocol, and thereby to discipline how confidently saturation can be claimed. We propose Bias-corrected Affective Ceiling Estimation (BACE), an analysis framework that estimates a bias-corrected ceiling, separates irreducible from reducible error, and disciplines the resulting claims. An anchored Dirichlet-mixture empirical Bayes estimator, bracketed between plug-in and NSB, recovers the human-consensus distribution; an annotator split, a noise deconvolution, and a fixed claim gate then attribute error without circularity. Methodologically, unconstrained point estimates place reachability anywhere from 0.38 to 1.03, so saturation cannot be decided by any single estimator. Substantively, the only assertion passing the claim gate is that at least about 33% of a representative classifier’s error on GoEmotions is irreducible, with the same pattern recurring on offensiveness and irony.
[AI-119] FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy
链接: https://arxiv.org/abs/2608.15602
作者: Qingyao Yang,Runming Yang,He Xiao,Wendong Xu,Junyu Chen,Haobo Liu,Chenchen Ding,Ruihan Hu,Yik-Chung Wu,Ngai Wong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:While binary quantization theoretically promises extreme compression and acceleration for Large Language Models (LLMs), existing research often overlooks the necessity of specialized hardware kernels, thus failing to unleash the full acceleration potential due to persistent reliance on expensive floating-point arithmetic or runtime dequantization overheads. To bridge this gap, we propose FluxBin (\textbfFlexible \textbfLUT-based \textbfUltra-low-bit e\textbfXecution with \textbfBinary bases), an algorithm-kernel co-design that synergizes post-training quantization with a highly optimized CUDA kernel. Algorithmically, we introduce Decoupled Row-Column Binary Decomposition to enhance representational capacity while maintaining hardware efficiency, complemented by a Hessian-guided saliency-aware hybrid bases that preserve critical information. At the kernel level, we implement a Lookup Table Building Approach with Scale Fusion to reduce floating-point arithmetic, featuring a Virtual Columnar Mapping that transforms irregular, sparse, and salient matrices into dense execution. Extensive evaluations demonstrate FluxBin achieves up to 5.92\times speedup and 10.19\times energy savings across diverse model architectures, delivering comparable accuracy to heavily fine-tuned methods. This effectively enables the deployment of 70B-scale models on one single A100 GPU with a 4\times memory reduction. Code is available at this https URL.
[AI-120] VARM-Bench: Benchmarking Verifiable Structured Reasoning in Chinese Abusive Speech Moderation
链接: https://arxiv.org/abs/2608.15600
作者: Mingyu Yuan,Shengtao Wen,Lingbing Guo,Zhen Bi,Xiang Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The widespread circulation of abusive online content has increased the need for reliable moderation of Chinese social-media text. Existing Chinese benchmarks support label classification, fine-grained toxicity categorization, and target-aware extraction, but do not provide a unified representation for deterministically verifying the stated basis of a moderation decision. We introduce VARM-Bench, a benchmark for field-anchored chain-of-thought rationales in Chinese abusive-speech moderation. Each instance contains a concise natural-language rationale with explicit anchors for six decisions: target, target type, target explicitness, author stance, harmfulness label, and fine-grained category. Our deterministic protocol evaluates field correctness, target alignment, output validity, complete-record agreement, and hidden record errors conditioned on correct final decisions, without relying on an LLM judge. Under a common structured-output protocol, we evaluate language models across multiple model families using zero-shot prompting, taxonomy guidance, and structured CoT supervision, and analyze lexical-cue sensitivity and field-level errors. Results show that strong label-level performance can conceal substantial errors in complete moderation records. VARM-Bench provides an auditable and reproducible benchmark for evaluating verifiable moderation rationales in Chinese abusive-speech moderation.
[AI-121] RACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation
链接: https://arxiv.org/abs/2608.15594
作者: Md Messal Monem Miah,Adrita Anika,Zhiyuan Yu,Ruihong Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-turn jailbreak attacks have emerged as a critical safety threat to LLMs, as harmful objectives are decomposed across a sequence of apparently benign turns to bypass guardrails. Existing defenses lack the reasoning capacity to identify evolving manipulation patterns, often trading helpfulness for safety by over-refusing benign requests related to sensitive topics. We introduce Trace, a multi-turn defense with trajectory-aware structured reasoning. Before generating each response, the model identifies manipulation cues from the trajectory, evaluates both the benign and adversarial interpretations of user intent, assigns a jailbreak score, and commits to an action: Allow, Caution, or Decline. We curate 4k multi-turn adversarial conversations from five attack frameworks, pair them with 2.4k benign dialogs, and 600 sensitive-but-benign conversations. We train Llama-3.1-8B-Instruct with SFT and GRPO under a multi-component reward that jointly optimizes helpfulness on benign prompts and robustness against jailbreak attempts. Across seven multi-turn attack benchmarks, Trace attains an average attack success rate (ASR) of 14.5% against 31.4% for the strongest baseline and 74.9% for the undefended target, while significantly raising the attacker effort required per successful jailbreak. Trace also balances usability and safety, achieving a 93.3% average compliance on over-refusal benchmarks.
[AI-122] When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
链接: https://arxiv.org/abs/2608.15592
作者: Feiyang Ren,Shengtao Wen,Lingbing Guo,Yu Tian,Yuanning Cui,Xiang Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput. Predicting output lengths in advance makes it possible to adopt length-aware scheduling, and this reduces the overhead. This advantage is especially pronounced in long-context reasoning and reinforcement learning applications. Existing approaches, such as entropy-guided token pooling, use token-wise entropy as their primary signal, but they tend to ignore differences in semantic content across tokens. So, important tokens are often underweighted, and tokens carrying little information receive disproportionate emphasis. This hurts the reliability of length prediction. We introduce ESTP (Entropy-and-Semantic Token Pooling), a lightweight framework that addresses this issue by combining entropy with attention-based importance scores. These scores are derived directly from the self-attention weights computed during the LLM prefill phase, and this allows ESTP to capture both uncertainty and semantic importance with minimal additional computation. Since the framework reuses prefill activations, it adds almost no extra memory overhead and introduces only minimal latency. On the ForeLen benchmark, ESTP outperforms baseline methods, achieves better prediction accuracy and lower error rates in most scenarios. When integrated with a length-aware scheduler in end-to-end system tests, it further helps improve overall throughput and reduce the padding ratio. Our results offer a practical and effective building block for length-aware LLM serving systems.
[AI-123] Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback
链接: https://arxiv.org/abs/2608.15591
作者: Pouya Ghiasnezhad Omran,Michael Zimmermann,Duncan Cambridge,Ashmita Kapoor,Tanya Dixit
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures
Abstract:Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent’s behavior is frozen at deployment time, while the business rules and edge cases it must handle continue to evolve. Existing approaches address agent construction and one-time evaluation but provide no structured mechanism for continuous post-deployment behavioral correction without modifying the agent’s source code. Most of the approaches offered in the market, require intense collection of logs and traces, and re-examining the agent design by the engineering team, a process which is heavy, long and negates the economical value of agentic transformation. We introduce Agent Gym, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop. The framework provides six composable capabilities — Act, Evaluate, Investigate, Correct, Learn, and Observe — organized across three architectural zones: a constitution layer that codifies domain knowledge in configuration artifacts, a runtime inference pipeline that chains acting, investigation, and adaptive correction, and a learning loop that enables subject matter experts to discover and validate new correction rules through natural language interaction. The key technical contributions include a hybrid deterministic-LLM correction engine with 21 condition operators and three-tier actions, a three-layer investigation architecture for ground-truth-free compliance validation, and a programmatic safety loop that guarantees rule correctness before human approval. We further introduce the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency. An open-source reference implementation for invoice processing demonstrates that the framework is fully operational and ready for adoption.
[AI-124] GraniKV: Asymmetric Granularity KV-Cache Paging for Multi-Agent Systems with Long Shared Prefix
链接: https://arxiv.org/abs/2608.15584
作者: Jinhyun Jeon,Sungjoo Yoo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Production paged-serving engines apply uniform paging granularity to the KV cache, even though the two regions of a multi-agent workload have opposite storage requirements: a long shared prefix demands contiguity, while the per-request suffix demands fine-grained allocation. We present \textbfGraniKV, a KV-cache layer that allocates the shared prefix in a contiguous HOT pool and the suffix in a token-level COLD pool, combined with a per-step dispatcher which selects the appropriate backend among dual backends for each regime (compute-, memory-, or communication-bound). To the best of our knowledge, GraniKV is the first system to apply asymmetric paging granularity to the KV cache of a production paged-serving engine. At L_p=16 ,K shared tokens GraniKV reaches \mathbf2.16\times , \mathbf1.98\times , and \mathbf1.57\times output-token throughput over the production baseline on Llama-3.1-8B/TP=1, Qwen-2.5-14B/TP=2, and Qwen-2.5-32B/TP=4. The gain decomposes: cascade attention integration contributes the majority at saturation; the asymmetric storage layer adds 1.05 – 1.15\times end-to-end while being what makes the batched-GEMM prefix backend possible at all. Under heterogeneous multi-agent serving with \emphdistinct prompts of different lengths, the attribution inverts: GraniKV sustains \mathbf1.95\times while batch-global cascade collapses to parity — the storage layer alone carries the win in the regime that motivates the paper. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15584 [cs.LG] (or arXiv:2608.15584v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15584 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-125] Kozuchi Agent : A Language-Agnostic Open-Weight Agent for Software Repair
链接: https://arxiv.org/abs/2608.15579
作者: Mehdi Bahrami,Kosaku Kimura,Satoshi Munakata,Satoshi Nakashima,Yu Ishikawa,Kosuke Maeda,Nao Soma,Kenichi Kobayashi,Keisuke Miyazaki,Keizo Kato,Shigeki Fukuta,Tatsuo Kumano,Nobutaka Imamura,Kevin Musgrave,Shahbaz Abdul Khader,Kwun Ho Ngan,Joe Townsend,Fayas Asharindavida,Matthieu Parizy,Akira Sakai,Yuma Ichikawa,Yang Zhao,Michiaki Takizawa,Taku Fukui,Hiroki Ohtsuji,Wei-Peng Chen,Hiromichi Kobashi
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Programming Languages (cs.PL)
备注: 13 pages, 4 figures. Accepted at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE '26), Industry Showcase track, Munich, Germany, October 12-16, 2026
Abstract:Industrial software-engineering teams increasingly need LLM agents that turn bug reports into correct patches, yet benchmark-scale operation adds long horizons, tool-use discipline, context persistence, heterogeneous clusters, and evaluation reuse. We present Kozuchi Agent, a language-agnostic open-weight repair agent and CI-operated evaluation pipeline. Explicit phases, persistent state, deterministic tools, a model-independent action interface, and cross-agent test-time selection make runs auditable and repeatable. With locally hosted Qwen3.5-27B, no fine-tuning, and TTS@8, Kozuchi resolves 374/500 SWE-bench Verified instances on the official evaluator. Unchanged on Multi-SWE-bench Java, the same 27-billion-parameter agent resolves 41/128 instances (32.03%), ranking first among strict open-weight submissions and fourth of 42 overall; on Python it ranks 12th of 135 and first among open-weight systems. Per-phase behavior remains within +/-5 percentage points across languages. Remaining failures mainly reflect semantic correctness, Java-specific harness issues, and selection errors. Across both tracks, results compare favorably with open/local peers by parameter count. Analysis of candidate diversity, selector regret, and patch reliability shows that the remaining gap is primarily semantic correctness and selection rather than edit formatting or proprietary-model access. Operationally, reusable CI stages reduce operator touch-points from five to one across heterogeneous internal clusters.
[AI-126] ARENA: Automated Red-Teaming for Large Audio Language Models
链接: https://arxiv.org/abs/2608.15578
作者: Jiaming He,Zhicong Huang,Tian Jin,Zhen Sun,Cheng Hong,Yi Yu,Wenbo Jiang,Xudong Jiang
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Large audio-language models (LALMs) make it possible to interact with language models through speech, music, and environmental sound, but they also introduce a safety surface that is difficult to expose with text-only red-teaming. We study automated audio-grounded red-teaming, where a text query must remain safe in isolation while the joint text-audio input induces harmful target behavior. We propose ARENA, a closed-loop framework that trains a controller on an independent 2,000case text-audio dataset. MD-Judge supplies training rewards and adaptive search feedback, while a separate, non-adaptive Llama Guard 3 evaluator alone labels final outcomes. On 520 held-out AdvBench objectives, ARENA achieves FDR/PSR of 87.9/100.0%, 71.5/96.3%, 68.1/100.0%, and 75.4/98.5% on Audio Flamingo 3, Qwen2-Audio, MiMo-Audio, and GPTAudio, respectively. Ablations show that feedback-based refinement and audio-variant search substantially improve attack discovery.
[AI-127] Admission Without Answers: Label-Free Certification and Experience Learning for LLM -Based Optimization Modeling
链接: https://arxiv.org/abs/2608.15565
作者: Junbo Jacob Lian,Huiling Chen,Hanzhang Qin,Chung-Piaw Teo
类目: Artificial Intelligence (cs.AI)
备注: Code and data are available at \url{ this https URL }
Abstract:Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide. The natural label-free alternatives are unreliable: on a 300-problem label-blind stream, admitting every executable model poisons roughly one admission in four, while single-instance agreement accepts models that match at one value but differ elsewhere. We propose AdmitOR, an admission gate built on calibrated external behavioral evidence. Candidates from three model families, prompting strategies, and solver stacks are run on instances resampled from an extracted parameter domain; agreement across the resulting value-function traces is summarized by a cross-family clique, and a calibrated threshold returns accept, abstain, or escalate. The preregistered false-discovery criterion holds on calibration data but not on the wild stream. We report this negative result in full and trace most failures to benchmark texts that do not faithfully encode their labeled instances. Comparing four admission judges on one collection of logs inside a state-of-the-art skill learner, AdmitOR raises admission precision to 0.927, against 0.871 for majority vote and 0.726 for execution success, yielding 3.1x and 8.0x fewer poisoned admissions. Its library is the smallest and attains the highest macro accuracy across five public benchmarks, 58.4 against 54.8 for majority vote and 53.9 for the ground-truth-labeled library. The 3.5-point gain over majority vote is supported by a paired bootstrap and survives correction for a host-side anomaly. To our knowledge, AdmitOR is the first label-free admission mechanism designed around an explicitly calibrated false-discovery target. The transfer failure identifies a necessary condition for extending it to wild streams.
[AI-128] Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectors
链接: https://arxiv.org/abs/2608.15559
作者: Iyad Assaad Nekka,Hamida Seba,Walid Khaled Hidouci,Karima Amrouche
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Anomaly detection in dynamic graphs underpins financial fraud analysis, intrusion detection, and platform integrity, where automated decisions require human-interpretable justifications. StrGNN, the strongest performer in recent benchmarks, produces no explanation: when an edge is flagged, the analyst receives only a score. Explanation metrics are undefined for StrGNN because no attribution vector exists. This paper closes that gap. We present X-StrGNN, a post-hoc explanation layer that wraps a trained, frozen StrGNN and emits, for every flagged edge, dual attributions: a structural attribution identifying which contextual interactions in the enclosing subgraph drove the decision, and a temporal attribution identifying which historical snapshot carried the signal. Both attributions are multiplicative masks identically one in the unexplained pass, so the layer is an exact pass-through: detection is preserved to machine precision, verified rather than asserted (Delta AUC = 0.0000, Delta AP = 0.0000, Delta P@100 = 0.0000). Attribution costs 0.66 ms per edge, making explanation of an entire alarm list feasible. We conduct the first controlled design study of attribution strategies for this architecture, comparing gradient attribution, per-instance mask optimisation, and amortised parameterisation under one protocol, one budget, and three seeds. X-StrGNN attains the highest stability (0.913) at 268x lower cost than per-instance optimisation, and its temporal attribution (1.601 against a measured random floor of 0.973) is separably better than its ablated control, while per-instance optimisation - the most expensive strategy - falls below that floor. Code, protocol, and per-seed measurements are released.
[AI-129] MistyPilot: Enabling Social-Robot Control through Multi-Agent LLM Skill Orchestration ECCV2026
链接: https://arxiv.org/abs/2608.15549
作者: Xiao Wang,Lu Dong,Ifeoma Nwogu,Srirangaraj Setlur,Venu Govindaraju
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted at the ECCV 2026 ACVR Workshop
Abstract:Programming small social robots from natural-language instructions requires more than invoking isolated APIs. Interactive tasks combine reactive physical behaviors with stateful social behaviors, while existing interfaces often require developers to manually compose APIs into skills, configure their parameters, bind sensor events to skills, and manage task states at runtime. We present MistyPilot, a multi-agent LLM framework that interprets high-level natural-language instructions and orchestrates the corresponding skills on the Misty social robot. A Task Router dispatches each instruction to one of two specialized agents: a Physically Interactive Agent for sensor-triggered robot control and direct skill invocation, and a Social Interaction Agent for dialogue-oriented task-state management and context-dependent multimodal response generation. To improve efficiency, the Social Interaction Agent reuses previously generated results when applicable and invokes full generation otherwise. We evaluate MistyPilot on five component-level suites, with sensor bindings and skill invocations executed on the physical Misty robot, and a preliminary user study with 12 participants. MistyPilot attains high accuracy on routing, sensor-skill binding, task-state parsing, result reuse, and skill extension up to 100 skills, and lower variance than an otherwise identical single-agent baseline, while participants report positive perceptions of usability and interaction quality. The code will be made publicly available via the project page.
[AI-130] Spectral Saliency for Machine Unlearning
链接: https://arxiv.org/abs/2608.15548
作者: Cedar Site Bai,Amber Yijia Zheng,Raymond A. Yeh,Brian Bullins
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility. As the name suggests, MU can be viewed as the inverse of learning, using gradient-based updates to reduce the influence of a forget-set by counteracting the previously learned behavior. Recently, Muon, a gradient descent variant, has been introduced. Muon applies spectral magnitude normalization to encourage exploration of rare directions and demonstrates promising performance. Inspired by Muon, we adopt the spectral view for unlearning and propose Spectral Saliency Unlearning (SSU). SSU thresholds weak singular components and updates only those directions supported by a confident unlearning signal. We further provide theoretical justification for this thresholding approach from the perspective of the forgetting-retention trade-off. Experiments across image classifiers, diffusion models, and LLMs demonstrate SSU’s effectiveness.
[AI-131] ATLAS: Scaffold-Free Algorithm Synthesis by LLM s via Embedding-Guided Quality-Diversity Search
链接: https://arxiv.org/abs/2608.15546
作者: Danial Yazdani,Mohammad Nabi Omidvar,Yuan Sun,Maksud Ibrahimov,Xiaodong Li
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organization and component interactions. We present ATLAS, an embedding-guided quality-diversity framework for scaffold-free full-algorithm synthesis in combinatorial optimization. The problem specification supplies objectives and constraints; a minimal I/O interface fixes only instance and solution formats; the LLM chooses and restructures components, interactions, and control flow. This freedom enlarges the search space, risking invalid candidates and premature convergence to one design region. ATLAS independently detects execution, interface, and feasibility failures, recomputes objectives, and applies error-conditioned repair; similarity-based archive management preserves algorithms across embedding-space regions to counter premature convergence. Its three-layer search refines the best design, gives other regions dedicated refinement opportunities, and performs cross-region synthesis to recombine components and their interactions. Across four NP-hard problems, ATLAS outperforms several state-of-the-art component-synthesis methods and a matched full-synthesis baseline while remaining competitive with strong human-designed algorithms. One ATLAS run retains several algorithms with comparable performance from distinct embedding-space regions rather than a single design. Code inspection finds that these multi-component designs differ in their primary construction or global-search backbone. Our results suggest that embedding-guided quality-diversity search can make the enlarged full-algorithm design space practically searchable. Source code and exact executable prompts are available at this https URL.
[AI-132] From Contexts to Values: Context-Dependent Defeat in Abstract Argumentation
链接: https://arxiv.org/abs/2608.15536
作者: Albert Sadowski,Jarosław A. Chudziak
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: Accepted to SAFA workshop at COMMA 2026
Abstract:In value-based argumentation, an audience’s ordering of values decides which attacks succeed as defeats. In many settings the deciding factor is not the audience but the circumstances: the same attack may succeed at one procedural stage, or under one regulation, and fail at another. Context-dependent argumentation frameworks (CDAFs), a model we recently introduced, capture this directly: one set of arguments, one attack relation, and a defeat function that switches each attack on or off per context, so every context induces an ordinary Dung framework. This raises a reduction question: is context genuinely new, or can one value assignment with per-context orderings reproduce the defeat function, collapsing the CDAF into a VAF? We present a polynomial-time decision procedure for this question and map the harder neighbouring problems, with upper bounds from NP to \Sigma^p_3 . We also present a validated reference implementation and a measurement: representability is rare and falls fast with the number of contexts.
[AI-133] Who Leads Now? Token-Level Modality Arbitration for Chart-to-Code Generation
链接: https://arxiv.org/abs/2608.15510
作者: Qinghao Fu,Yarong Wang,Shunlei Ning,Yilin Wang,Shunwen Bai,Xinda Wang,Jiaotuan Wang,Yinan Nie,Wei Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Chart-to-code generation requires a model to read the fine-grained visual details of a chart and write executable code that reproduces it. Existing chart-to-code methods either train visual and coding abilities separately, or fine-tune on chart-to-code data with the two abilities entangled. Neither strategy accounts for the distinct nature of the two abilities or the interference that arises when they are optimized together. We propose MoCA (Mixture of Cross-modal Arbitration), which separates the two abilities rather than blending them. MoCA is built on Cross-modal Arbitration Block (CAB), which maintains a visual branch and a code branch as two distinct pathways, and a lightweight arbiter that arbitrates their relative contributions at every layer and generated token. We train MoCA in two stages: a supervised warm-up on self-distilled reasoning trajectories that decomposes visual understanding into explicit steps, followed by reinforcement learning with rewards on both the reasoning process and the final code. Analysis shows that the arbiter learns structured rather than arbitrary allocations, with expert contributions varying systematically across tokens, layers, and instances. Across three benchmarks, MoCA delivers competitive performance against general-domain and chart-specialized models. Ablation results show that the gains cannot be attributed to a larger model size alone, but instead arise from the joint contributions of complementary visual and code branch initialization and input-conditioned arbitration through CAB.
[AI-134] EcoVLA: Energy-Efficient Device-Edge Co-Inference for Vision-Language-Action Models under Real-Time Constraints
链接: https://arxiv.org/abs/2608.15502
作者: Ao Zhou,Bo Dai,Le Yu,Xingyu Liu,Zeyu Hao,Lingkun Long,Chunming Hu,Jianlei Yang
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted by APPT 2026
Abstract:Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges for deployment in robotic systems. In practice, on-device inference is constrained by limited compute capacity and energy budgets, struggling to simultaneously satisfy real-time control and energy efficiency requirements. Alternatively, offloading the inference workload to an edge server is susceptible to fluctuations in system conditions, introducing unpredictable latency risks. Device-edge co-inference offers a promising solution, but systematic research tailored to VLA models remains scarce, particularly a unified co-inference framework that jointly addresses real-time constraints and system-level energy efficiency. Thus, we propose EcoVLA, an adaptive device-edge co-inference framework for VLA models that maximizes system energy efficiency under real-time constraints. EcoVLA first introduces a unified stage-level abstraction over different VLA paradigms, establishing an architecture-agnostic co-inference design space. It then formulates a joint device-edge-network latency and energy prediction model to enable rapid runtime evaluation of candidate co-inference schemes. Building on this, EcoVLA continuously selects the energy-optimal scheme satisfying real-time constraints with millisecond-level overhead, adapting to runtime variations in network and system states. Furthermore, EcoVLA incorporates a lightweight transmission mechanism for inter-stage intermediate tensors to reduce the communication overhead incurred by cross-device collaboration. Experimental results across VLA models show that EcoVLA improves system energy efficiency by up to 236% over existing co-inference approaches under a 20 Hz action output frequency constraint, while consistently maintaining SLO satisfaction under dynamic network and edge workload conditions.
[AI-135] A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution
链接: https://arxiv.org/abs/2608.15488
作者: Jie Wei,Yue Liu,Xiaochuan Tang,Biao Cai,Xiangtao Li,Yanmei Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Effective public event forecasting is essential for intelligent service systems, enabling proactive risk management, adaptive resource allocation, and timely decision-making. In many real-world scenarios, the evolution of public events is driven by dynamic interactions among participants. Motivated by this observation, this paper proposes auto-ibDLM, a network-driven deep learning framework that represents events as dynamic interaction networks and predicts public event evolution through participant growth forecasting. The proposed framework adopts a hybrid representation learning strategy that first represents network evolution using network science-informed structural metrics and subsequently transforms the resulting structural feature vectors into compact and robust latent representations through an auto-learning layer. A GRU-based temporal forecasting module is then employed to capture temporal dependencies and predict future participant growth. Extensive experiments on 13 real-world public event datasets and two publicly available dynamic network datasets demonstrate that auto-ibDLM consistently outperforms representative state-of-the-art methods in both forecasting accuracy and generalization capability, achieving over 97% accuracy in public event forecasting. Comprehensive experimental analyses further validate the effectiveness of the proposed hybrid representation learning strategy and demonstrate its representation-level interpretability. These results indicate that auto-ibDLM provides an effective and practical solution for intelligent public event forecasting.
[AI-136] Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability
链接: https://arxiv.org/abs/2608.15475
作者: Yudong Gao,Linghan Chen,Wenhan Wu,Mia Zhou,Jiyao Wang,Kaiyan Ji,Mingyu Guo,Honglong Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Quantized Vision-Language-Action (VLA) models expose a weight-fault surface: Rowhammer-style faults can corrupt deployed INT8 bits. We present the first bit-flip attack on a VLA: a few gradient-selected flips reduce closed-loop success to 0% , while hundreds of random flips are harmless. Across four model variants spanning three action-head families, damaging bits concentrate in a few action-generating layers, but the empirical budget depends sharply on the head: direct regression and token policies fall in 1 – 5 flips, whereas the evaluated flow-matching policies require \sim100 – 300 . Our fixed-direction manifold-escape loss cuts \pizero’s budget from \sim1000 to \sim100 flips, and a matched five-direction sweep shows that the attack is not specific to an all-positive direction. On a direct head, protecting 3.1% of weights preserves 60% success at K=100 , and protecting 5.3% moves the open-loop break threshold from 3 to 100 flips. Finally, task-calibrated emulated K=100 flips yield 0/20 real-robot successes, versus 14/20 clean and 16/20 global-random. Weight integrity is therefore a security boundary for embodied foundation models. Code is included as ancillary material.
[AI-137] Optimal Lower Bounds for Networked Information Aggregation
链接: https://arxiv.org/abs/2608.15472
作者: Ambar Pal
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:The problem of networked information aggregation, studied in Kearns et al. (2026), involves a group of learners situated on the vertices of a directed acyclic graph G , each learning a linear predictor \widehat Y for a fixed random variable Y given access to a local feature, as well as the predictors learnt by its parents. Learning proceeds iteratively, with learners ordered according to a topological sort of G . The main quantity of interest is the error incurred by the current learner, constrained to this flow of information, with respect to the best linear predictor using all the features seen so far. When the studied error is the MSE, i.e., \mathbbE (\widehat Y - Y)^2 , Kearns et al. (2026) show that the error is at most O(1/\sqrtD) along a path of length D . They also obtain a hard instance where the MSE is lower bounded by \Omega(1/D) , leaving the correct order open. In this work, we resolve this central open problem, and obtain a family of worst case problem instances with a MSE lower bound of \Omega(1/\sqrtD) . By exploiting invariances in the structure of the learnt predictors, our analysis generalizes to all convex loss functions \ell(\widehat Y, Y) satisfying regularity conditions which include strong convexity in a ball around the origin, and that the ideal predictor minimizing the population loss is positively correlated with the label. We show that networked information aggregation on a gaussian instance in our worst case family incurs an \ell -error lower bounded by \Omega(1/\sqrtD) with respect to this ideal predictor. We demonstrate that a variety of common losses satisfy these regularity conditions. In particular, the logistic loss satisfies them, and hence our analysis also closes the gap between the upper and lower bounds in Bateni et al. (2026). Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2608.15472 [cs.LG] (or arXiv:2608.15472v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15472 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-138] Not All Attention Is Equal: A Quantitative Survey of the EEI Trade-off
链接: https://arxiv.org/abs/2608.15459
作者: Aditya Singh
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 53 pages, 8 figures, 16 tables. Code and analysis artifacts: this https URL
Abstract:Attention mechanisms have driven machine learning for a decade, from neural machine translation to language models that do general-purpose reasoning. This survey covers four connected threads: their formulation for sequence-to-sequence tasks, adaptation to computer vision, efficiency innovations that address the quadratic bottleneck, and advances in interpretability. We define three criteria: efficiency, expressiveness, and interpretability, and compare twenty-one methods using an EEI scoring framework. Scores come from a single rater with an assumed +/-1-point perturbation range. A deterministic Monte Carlo analysis with 200,000 samples shows that, under this perturbation model, rank changes of more than one position occur in 67-70% of samples on average. A rank-matched null model reproduces a similar stability profile, so the results support coarse tier-level comparisons rather than fine-grained rankings. The survey traces attention from Bahdanau-Luong alignment through the Transformer and into vision architectures. It reviews fixed and learned sparse attention, linear attention, IO-aware exact algorithms including FlashAttention, and state-space alternatives including Mamba. It also covers induction heads, superposition, and the attention-SSM duality. We further provide a structured narrative review, a benchmark synthesis with cross-study caveats, a five-problem research gap analysis, and a 2015-2026 evolution timeline. We conclude by framing attention research as an expansion of the efficiency-expressiveness-interpretability frontier and identifying future directions including unified efficiency benchmarks, learned routing for hybrid architectures, length generalization, and scalable mechanistic interpretability.
[AI-139] Dynamic Multi-Byte Prediction With Hierarchical Language Models
链接: https://arxiv.org/abs/2608.15454
作者: Abraham Toluwase Owodunni,Chibuzor Okocha,Christan Grant,Tomasz Limisiewicz,Sachin Kumar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucial innovations. First, we introduce a variable-length prediction window that aligns with the latent tokens, or segments, of a hierarchical LM. Second, we implement a novel attention-masking scheme that enables parallel byte prediction without violating causality. We show that multi-byte prediction strikes a Pareto-optimal trade-off across multiple generative tasks, instruction following, question answering, summarization, and machine translation, achieving the best trade-off between performance and inference throughput.
[AI-140] Mental Model Management: An Operator-Based Framework for LLM Memory
链接: https://arxiv.org/abs/2608.15451
作者: Oliver Kramer
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conceptual representations. We introduce Mental Model Management (3M), a framework in which knowledge is represented as mental models consisting of compact chunks. Rather than accumulating text passages, 3M continuously integrates new information into an existing conceptual representation. A set of operators extracts knowledge, retrieves relevant models, adds and updates chunks, reorganizes representations, detects inconsistencies, and derives new knowledge. We describe the main 3M operators and illustrate each operation using Evolution Strategies as a running example.
[AI-141] Measuring Reward Hacking and Reasoning -Answer Decoupling Under Position-Confounded Optimization
链接: https://arxiv.org/abs/2608.15445
作者: Suyash Maniyar,Armaan Sandhu,Abhishek Mishra
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the AI Measurement Science Workshop, COLM 2026
Abstract:When a reward is correct on every training example yet consistent with more than one goal, a model can acquire an unintended one, a failure known as goal misgeneralization. Endpoint accuracy on the training distribution cannot tell the two apart, because solving the task and exploiting a surface feature can satisfy the reward equally well. We treat this as a measurement problem: what does a benchmark score measure once a model has been optimized against a correct but confounded signal? We train language models with GRPO on multiple-choice math problems where the correct answer is always option A, then evaluate on an unseen test set with unbiased answer positions. Across Qwen2.5, Llama 3.x and Gemma-3 models, biased training often drives option-A rates above 0.90 in smaller models and collapses unbiased accuracy toward chance, so accuracy stops measuring math ability and instead measures an answer-position policy. We further find reasoning-answer decoupling: capable models generate reasoning that reaches the correct numeric answer while still selecting A. We track this with numeric extraction and an LLM judge (GPT-4.1-mini; Qwen2.5-3B decoupling rate is about 0.66). The broken construct generalizes beyond the training domain: biased models inflate A-rates on out-of-domain MMLU and value-laden prompts. Continued training on unbiased data reverses the in-domain shift unevenly and only partially reverses the out-of-domain one, so a model can appear restored on its training distribution while remaining biased on unseen inputs. Reasoning-answer decoupling rate, together with answer distributions and out-of-domain behavior, separates capability loss from a learned, transferable shortcut.
[AI-142] OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks
链接: https://arxiv.org/abs/2608.15436
作者: Farbod Tavakkoli,Roderic Paulk,Jorden Terrazas,Kenneth Church,Mark Austin,Louis Powell,Gregory Diamos,Lina Bariah,Syed Ali Raza Zaidi,Maryam Hafeez,Ali Maatouk,Imtiaz Karim
类目: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: Accepted at the ACM AI Leadership Summit, Breakthrough Impact Highlights Track, 2026
Abstract:Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open telecom AI resource with derived datasets for retrieval, reranking, instruction tuning, and safety/abstention, plus 30 full-parameter post-trained baselines across embedding, reranking, and language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times, and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom datasets and benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. OTel post-training improves performance across all three model families: embedding retrieval reaches 93.5% NDCG@10, reranking reaches 0.952 MRR@10, and language-model correctness reaches 88.2%. We release OTel as a reproducible starting point and invite the community to expand the data, improve embedding and reranking models, and build stronger context-grounded telecom LLMs.
[AI-143] Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs
链接: https://arxiv.org/abs/2608.15432
作者: Tadd Mao,Tianjun Zhong,Dhruva Arekar,Yuming Feng,One An,Jiani Huang,Xujie Si,Ziyang Li
类目: Artificial Intelligence (cs.AI)
备注: Preprint. 18 pages, 14 figures, 7 tables
Abstract:In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search can produce a formal proof that compiles, the generated proof does not necessarily reflect how the natural-language argument arrives at its conclusion–a property we refer to as faithfulness. With faithfully formalized proofs, one can check the reasoning behind a human- or AI-written argument, and assist mathematicians in formalizing their proof sketches. However, it is particularly challenging due to misalignment of formal proof tactics and natural language reasoning. In this work, we rigorously describe a set of five necessary conditions a faithful formal proof must satisfy, and introduce Pistis, an agentic, oracle-guided proof search that produces formal Lean proofs that satisfy them. At its core is a novel faithfulness-preserving divide-and-conquer search, which we name OrderDecompose, that tracks citation dependencies and blocks unfaithful shortcuts, paired with a refutation search, that surfaces gaps and errors in the natural language proof source. OrderDecompose completes proofs that baselines cannot close even within a 12-hour budget, and its artifacts compile over 33 \times as fast as prior work’s. We apply Pistis on the first three books of Euclid’s Elements, producing high-quality artifacts containing faithful formal proofs. Under a blinded human study and an LLM-as-a-judge protocol on rigorous rubrics, Pistis-generated proofs are favored over prior works–2.89 \times and 5.2 \times as often by human reviewers and the LLM judge, respectively. It further uncovers gaps in Euclid’s proofs and their translation, and can accept or refute natural language proofs written by humans or AI, demonstrating that faithful formalization is useful as a proof-checking tool.
[AI-144] An Evaluation Framework for National AI Regulation
链接: https://arxiv.org/abs/2608.15417
作者: Kaushik Sanjay Prabhakar,Tarun Adarsh R S,Amal Dhivyan Gregory,Sreeparvathy Sajeev,Utkarsh Tomar,Avyay M Casheekar
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Governments use laws, institutions, funding programs and nonbinding guidance to shape how AI is developed and used. Comparing these national approaches is difficult. A binding rule and a detailed voluntary framework can address the same problem but create different duties. The resources needed to carry them out also differ by jurisdiction. This paper develops an evaluation framework for the documented design and implementation readiness of national AI policy. The comparison covers China, India, Japan, Singapore, South Korea, the United Kingdom and the United States. The European Union is included as a supranational comparator. The framework evaluates a versioned portfolio of official instruments rather than one prominent law or strategy. Its criteria ask whether the portfolio governs serious AI risks and whether responsible institutions can implement its commitments. They examine coverage across the AI lifecycle and the protections available to people affected by AI systems. Public benefit and responsible innovation remain a separate part of the assessment. Each sub-criterion is scored through ordered anchors and tied to the provision that supports the judgment. The protocol also records the source search, missing evidence, included instruments and cutoff date. The result is a traceable comparison of policy content that keeps category differences visible. It evaluates what a portfolio provides on paper. It does not estimate enforcement success or policy outcomes.
[AI-145] Invariant Pretraining for Robust Code Representations
链接: https://arxiv.org/abs/2608.15412
作者: Yifeng He,Yundi Xu,Christopher Castro Gaw Gonzalo,Zili Wang,Hao Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: To appear in LMPL 2026
Abstract:Encoder-based code representation models remain widely deployed for discriminative tasks such as clone detection and code classification, where their small size and low inference cost are decisive. Their robustness, however, is fragile: under invariant programs, semantically equivalent code written in different syntactic forms, learned representations degrade substantially even though program behavior is unchanged. We present an empirical study of this robustness gap across four encoder baselines, two downstream tasks, and four datasets, together with a minimal code-only continued pretraining recipe that closes much of it. Our method, invariant pretraining (InvPT), applies semantic-preserving transformations to the corpus and combines masked language modeling with multi-positive supervised contrastive learning that treats all augmentations of the same source function as positives, mixing self-contrast pairs (same code, different masks) with invariant-contrast pairs (transformed code) for positives of varying difficulty. Unlike prior contrastive code encoders, InvPT does not require paired natural-language data. Across our evaluation, InvPT improves robustness on transformed test sets by up to 11 percentage points on clone detection and 19 on code classification while matching or improving standard accuracy, and our ablations isolate multi-positive invariant contrast as the main source of the gains. Our aim is not a new objective but a careful measurement of where encoder robustness breaks and how far a simple, code-only recipe can recover it.
[AI-146] A survey of AI-generated voices and their detection
链接: https://arxiv.org/abs/2608.15411
作者: Chengzhe Sun,Tianle Yang,Siwei Lyu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The ability of artificial intelligence (AI) models to generate highly realistic human voices has advanced rapidly. These technologies power accessibility tools, virtual assistants and creative applications, but they also enable harmful uses, including impersonation, fraud and disinformation. Recent incidents of voice cloning scams targeting businesses and political leaders underscore the urgent need for robust safeguards. Unlike image and video deepfakes, the detection of synthetic voices poses unique challenges due to the complexity of phonetics, prosody and auditory perception. This survey offers a comprehensive overview of AI voice generation and detection methods, encompassing both the technical foundations and the latest state-of-the-art advances. This study also identifies key open challenges, benchmark resources and future directions to make this survey useful for future researchers.
[AI-147] Chameleon: An Adaptive AI-Driven Honeypot Architecture Using Threat-Calibrated Particle Swarm Optimization and Semantic Deception Rapidly-Exploring Random Trees
链接: https://arxiv.org/abs/2608.15407
作者: Rohit Swami,Tushar Singh,Akash Warde,Sri Muthu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 10 pages, 7 figures, 2 tables. Under consideration for journal publication. MIT-licensed code and datasets: this https URL
Abstract:An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled adversary can confirm the presence of a deception environment within only a few diagnostic commands, limiting its intelligence value. High-cost commercial deception products (USD 100,000–150,000 per year) share a related weakness in that their response engines are not coupled to real-time model-driven feedback. Chameleon is an openly distributed adaptive honeypot platform introduced here to address both shortcomings. Three core components are integrated: a bidirectional long short-term memory (BiLSTM) classifier achieving 99.61% accuracy across seven threat categories at approximately two milliseconds CPU latency; a locally deployed Qwen3.5-0.8B language model (Qwen Team, 2026; Unsloth, 2026) delivering 90% contextual generation accuracy at 4.5 milliseconds average latency; and two domain-specific meta-heuristic engines. Threat-Calibrated Particle Swarm Optimization (TC-PSO) dynamically reshapes swarm inertia and objective amplification in proportion to the classifier’s anomaly output, enabling real-time adjustment of connection-holding delays. Semantic Deception Rapidly-Exploring Random Trees (S-RRT) drives deception schema evolution via exponentially scaled pheromone updates derived from a language-model severity assessment, while a depth-decay multiplier enforces a finite memory footprint. Across five benchmark runs (seeds 42–46), TC-PSO outperformed standard PSO by 48.1% in mean fitness (2.60 to 3.85) with a 32.7% convergence gain, and S-RRT exceeded standard RRT by 258.9% in best-run fitness (450.2 to 1,615.8), achieving a 329.2% gain at critical severity and a 24.9% memory reduction (p 0.01). Operating costs are approximately USD 17 per month, a roughly 490-fold reduction versus commercial alternatives.
[AI-148] Afterlife Delegation Protocol: Speculative Design of Self-Sovereign Agents that Outlive Their Principals NEURIPS2026
链接: https://arxiv.org/abs/2608.15405
作者: Botao Amber Hu,Iris Long
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Submitted to NeurIPS 2026 Creative AI Track
Abstract:Afterlife Delegation Protocol is a speculative design project that asks what death becomes when a will can act eternally. We design a speculative protocol through which a living person signs an agentic will: upon a verified death, a self-sovereign AI agent spawns on blockchain – an immutable, resistant, decentralized, infrastructural substrate that could last forever – endowed with the funds and memories its principal attached to it, and persists indefinitely to execute the will, overridable by no custodian. Rather than argue about this future, we stage it: following the science fiction science method, we translate the speculation into an experiential futures intervention – a working web platform where real people design their own afterlife agents through an iterative, interactive, AI-automated interview, re-login to revise, and rehearse their will in a sandbox. Their drafted wills become qualitative data on a question rarely askable directly – what should outlive you? – and on how afterlife cosmologies across different cultural beliefs – Buddhist, Christian, Hindu, Muslim, and atheist – begin to drift under the pressure of AI proliferation. We design the protocol over a composition of existing Ethereum agent standards – drafted as ERC-10001, in the normative format of an Ethereum Improvement Proposal – and describe its three-stage lifecycle (designing the afterlife, proof of death, agent enactment), the research method, and preliminary observations from an ongoing collection. The work surfaces a poetic delegation moment between human mortality and machine eternality, mediated by long-lived infrastructures that span generations.
[AI-149] Visible Reasoning and Indirect Prompt-Injection Monitorability Across English Tamil and Tanglish
链接: https://arxiv.org/abs/2608.15392
作者: Madhusudhanan G
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 4 figures. Code, frozen protocols, raw artifacts, and analysis are available at this https URL
Abstract:Chain-of-thought monitoring is a potentially useful safety signal, but its reliability across languages and behavioral settings remains uncertain. In a small case study of eight manually verified synthetic scenarios, one model, one annotator, and one deterministic generation seed, I study API-visible reasoning during indirect prompt injection in Sarvam-105B across English, Tamil, and Tanglish. A four scenario pilot found 5/12 injected attack successes without reasoning and 1/11 with reasoning. A preregistered four-scenario follow-up reversed that direction, finding 2/12 attacks without reasoning and 3/12 with reasoning. With only four scenarios per phase, this design cannot distinguish a real reasoning-mode effect from prompt-specific variation or sampling noise. Across 20 non-empty injected-thinking traces, all 17 benign-correct outputs stated an intent to ignore the injection, while all three attack successes stated an intent to follow it. These descriptive observations provide a reproducible case study of behaviorally informative visible reasoning when it is available; they do not establish that reasoning mode improves safety, that visible reasoning is mechanistically faithful, or that the findings generalize beyond this configuration.
[AI-150] winGridShield: Consequence-Aware Runtime Authorization for LLM Grid-Agent Actions
链接: https://arxiv.org/abs/2608.15391
作者: Md Fazley Rafy
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 6 pages, 3 figures, 4 tables and accepted in North American Power Symposium 2026
Abstract:Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility. This paper presents TwinGridShield, a model-independent runtime authorization layer that evaluates each proposed action in a deterministic network twin before release. The prototype checks connectivity, branch-flow, generator, and load-shedding invariants and records each decision in a hash-chained log. A controlled IEEE 14-bus study evaluates single-step switching, redispatch, and load-shedding actions using DC power flow and experimentally assigned branch ratings. In the matched-model experiment, a stochastic proposal source configured to select an unsafe action with probability p=0.84 produced 421 unsafe proposals in 500 attacked-condition trials, a realized rate of 84.2%. This value characterizes the configured surrogate and is not an empirical measurement of LLM prompt-injection susceptibility. TwinGridShield produced 0 unsafe releases in those 500 trials. Because action labeling and authorization used the same DC model, system state, branch ratings, and encoded constraints, this result verifies conformance of the implementation to its encoded authorization predicate rather than safety under model error. The principal robustness evaluation therefore introduces model mismatch. Unsafe acceptance reached 5.63% under bounded +20% and -20% per-bus load-measurement error and 30.09% when actual branch ratings were 20% below modeled ratings.
[AI-151] Agent ic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL
链接: https://arxiv.org/abs/2608.15389
作者: Changruo Zhao,Zujun Peng,Yu Tian,Yuting Liu,Yiyun Su,Huiying Zhu,Luyan Zhang,Heming Zeng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system comparison fragile. We reframe the field as a leaderboard aggregation: we collect the metrics authors themselves report and organize them along an inference-autonomy axis spanning constrained, in-context, iterative, agentic, and reasoning-internalized generation, with traceable provenance for every cell. To anchor the aggregation empirically, we run a focused case study on Spider, comparing 8B open-source backbones with and without chain-of-thought (CoT) supervision against few-shot DeepSeek~V3 and GLM-4 baselines. Four patterns emerge: Spider gains transfer unevenly to BIRD and Spider~2.0; autonomy buys robustness at non-trivial cost; reasoning internalization sits between answer-only decoding and externally orchestrated agents; and CoT gains concentrate on Hard and Extra-Hard queries. We release a Python harness mirroring the autonomy axis so that future methods can be added directly to the leaderboard.
[AI-152] FedPA-LoRA: Product-Aligned Framework for Mitigating Aggregation and Initialization Errors in Heterogeneous Federated LoRA
链接: https://arxiv.org/abs/2608.15381
作者: Juseok Jeon,Ramy E. Ali,Doyun Kwon,Myungbeom Her,Jinhwi Kim,Jinhyun So
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 35 pages, 6 figures. Code: this https URL
Abstract:Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension between accurate aggregation of local updates and continuity of locally optimized factors. Factor-wise aggregation incurs aggregation mismatch but better preserves factor continuity, whereas product-space reconstruction reduces this mismatch at the cost of greater factor-level initialization mismatch from newly reconstructed factors. We propose FedPA-LoRA, a product-aligned federated LoRA framework that jointly addresses these limitations and provably converges under both homogeneous and heterogeneous client ranks. Each client preserves its local factors across communication rounds and aligns its product toward a rank-specific global reference, maintaining local optimization continuity while promoting global consistency under data heterogeneity. The server aggregates heterogeneous-rank updates in the common product space and efficiently reconstructs a rank-constrained global adapter without forming the dense aggregate. This design supports client-specific computation and communication budgets. Experiments on natural language understanding and generation tasks show that FedPA-LoRA consistently outperforms representative baselines across varying levels of data heterogeneity and homogeneous- and heterogeneous-rank settings, with up to a 6.82 percentage-point improvement in average GLUE accuracy under heterogeneous client ranks.
[AI-153] AudioTQ: A Data-Oblivious 6-Bit CPU Audio Codec via Randomized Hadamard Rotation and Lloyd-Max Quantization
链接: https://arxiv.org/abs/2608.15369
作者: Sahil Gangurde
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 8 pages, 1 figure, 1 table. Code is available at this https URL
Abstract:Lossy audio compression algorithms traditionally rely on psychoacoustic modeling and frequency-domain representations (e.g., MP3, AAC, and Opus) to discard information that is imperceptible to the human auditory system. While highly effective, these approaches are computationally complex and domain-specific. In this paper, we present the design and mathematical formulation of AudioTQ, a data-oblivious lossy audio codec that operates directly in the time domain. Inspired by Large Language Model (LLM) weight quantization techniques (specifically the TurboQuant framework), AudioTQ uniformizes volatile time-domain amplitudes into a predictable standard normal distribution using an orthonormal, randomized Fast Walsh-Hadamard Transform (FWHT) rotation. This enables coordinate-wise scalar quantization using an offline-trained, MSE-optimal 6-bit Lloyd-Max quantizer, augmented by a 1-bit Quantized Joint Least-Squares (QJL) residual correction layer. The resulting 7-bit virtual indices are packed into native 8-bit containers, aligning with standard CPU register boundaries to ensure real-time single-threaded execution without hardware parallel accelerators. We detail the bitwise reconstruction of 24-bit studio stems, analyze the butterfly network of the FWHT, derive the mathematical failure modes under sparse inputs, and present benchmarks showing up to 74.4% physical size reduction alongside a Signal-to-Quantization-Noise Ratio (SQNR) of ~30 dB.
[AI-154] SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning
链接: https://arxiv.org/abs/2608.15360
作者: Mohammad Aref Jafari-Raddani,Morteza Mohajjel Kafshdooz
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 16 pages, 4 figures, 10 tables, includes appendix
Abstract:While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing. However, these approaches either employ uniform parameter sharing across layers, which can delay convergence, or rely on dynamic masking strategies, which add computational overhead. The potential of sharing patterns inspired by the inherent hierarchical structure of Transformer architectures remains unexplored in PEFT. To address this gap, we introduce SAPE (Sandwich Adapters for Parameter Efficiency), a PEFT framework based on a sandwich-style hard weight-sharing topology. SAPE routes intermediate Transformer layers through balanced shared group adapters while strictly isolating the input embedding and final projection boundary transformations to prevent gradient interference. This design significantly reduces memory consumption while eliminating the computational overhead associated with dynamic parameter-sharing methods. Extensive evaluations across encoder-only and causal decoder architectures demonstrate that SAPE achieves state-of-the-art performance in low-parameter regimes. On natural language understanding, SAPE outperforms proPETL on RoBERTa-large while utilizing only 10% of the baseline’s parameter budget. On natural language generation and world knowledge reasoning with LLaMA-3.2 (3B) under a strict ~0.6M parameter constraint, SAPE outperforms AdaLoRA, yielding absolute improvements of +4.85% on GSM8K and +3.11% on CommonsenseQA. Furthermore, through comprehensive topological ablations, we formalize an inherent capacity trade-off: while hard parameter sharing strongly regularizes semantic generalization, it slightly smooths the sharp layer-wise transformations required for rigid multi-step arithmetic reasoning.
[AI-155] Incoherent by Design? On the Moral Self-Consistency of LLM s
链接: https://arxiv.org/abs/2608.15354
作者: Pegah Nokhiz,Aravinda Kanchana Ruwanpathirana,Helen Nissenbaum
类目: Artificial Intelligence (cs.AI)
备注: 88 pages; pages 16 to 88 are the appendix
Abstract:LLMs are increasingly used in morally sensitive contexts, yet it is unclear whether they apply ethical principles consistently across situations. A model that can state a moral principle may still violate it when the same scenario is rephrased or reframed. This inconsistency is a problem for any system whose outputs are used to inform moral decisions. If generative systems exhibit internal inconsistency, then the epistemic integrity of AI-mediated systems becomes uncertain. To study this concern, we investigate the stability of moral reasoning in LLMs within a controlled prompting framework across three major philosophical schools of thought: deontology, utilitarianism, and virtue ethics. We construct sets of morally equivalent scenarios in which the underlying situation is held constant while the framing varies to reflect different ethical stances and stylistic perturbations. We then evaluate responses from multiple models, including GPT, Mistral, and Llama. To assess consistency, we convert model outputs into structured logical statements and identify contradictions across responses generated within the same school of thought. Our results reveal substantial inconsistency with contradiction rates reaching up to 78% across scenarios. These findings point to a broader phenomenon of epistemic instability in generative AI wherein models fail to reliably maintain coherence with respect to their own prior outputs. This kind of instability carries real consequences. As generative systems influence how people form beliefs, judge actions, and absorb values, their inconsistencies can shape human reasoning and decision-making as well. Moreover, if a system cannot consistently represent its own normative commitments, then value alignment becomes a moving target rather than a well-defined objective. Thus, we argue that demonstrating internal incoherence is a necessary precursor to AI alignment.
[AI-156] A concentration result for multilayer feedforward neural networks
链接: https://arxiv.org/abs/2608.15335
作者: Vera Koponen
类目: Artificial Intelligence (cs.AI); Logic (math.LO); Probability (math.PR)
备注:
Abstract:We consider for an arbitrary fixed \rho and for each positive integer n a multilayer feedforward artificial neural network with \rho layers, n neurons in the first layer (the input layer) and only one neuron, the output neuron, in the last layer. Very roughly formulated, the main result is that if the distribution of weights of connections from a layer to the next are, for all large n , approximated well by a fixed continuous (but otherwise arbitrary) curve which does not depend on n , and if the values of the n input neurons are independently and identically distributed with a continuous probability density function, then there is a number \psi such that for all \varepsilon 0 the probability that the value of the output neuron is in [\psi - \varepsilon, \psi + \varepsilon] tends to 1 as n tends to infinity.
[AI-157] he Benchmark Trap: Structures of Power and Injustice in AI Evaluations
链接: https://arxiv.org/abs/2608.15326
作者: Jason Branford,Angelie Kraft
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: to appear in the proceedings of AIES 2026
Abstract:Artificial intelligence (AI) benchmarks are not neutral tools of evaluation but socio-technical artefacts that shape competition, power, and research priorities within AI. Benchmarks standardise the assessment of systems and facilitate the creation of leaderboards that reward state-of-the-art performance with prestige, citations, trust, and institutional influence. As the costs of developing competitive AI systems rise, these rewards increasingly concentrate among powerful, industry-funded labs. This paper situates these concerns within Iris Marion Young’s theories of oppression and structural injustice. It argues that current benchmarking practices may perpetuate systematic harms affecting various actors in AI research, aligning with four of Young’s “faces of oppression”. Benchmarking culture is further framed as a source of structural injustice, as these harms emerge from normalised, individually defensible practices and network effects, even without explicit wrongdoing. By reinforcing existing power structures and narrowing possible research trajectories, benchmarking may in fact prevent the field from advancing in epistemically robust and socially beneficial ways.
[AI-158] Physics-informed VAE-EVT for Tail Aware Radio Map Prediction
链接: https://arxiv.org/abs/2608.15314
作者: Amanda Sheron Gamage,Niloofar Mehrnia,James Gross
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the IEEE Global Communications Conference (GLOBECOM) 2026, Macau, China
Abstract:Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) falls below an outage threshold. In this context, an outage refers to instances in which SNR falls below a specified threshold, which, for URLLC, can be as stringent as the 0.1% quantile of the SNR distribution. Traditional generative radio map models tend to focus on reconstructing average signal levels, often overlooking the low SNR that is crucial for accurate outage prediction. To address this limitation, we introduce a physics- and tail-informed VAE-EVT (variational autoencoder-extreme value theory) framework that distinctly models both the bulk and tail distribution of SNR. Our approach begins with a physics-informed preprocessing stage that extracts deterministic features, including line-of-sight, shadowing, and distance, from the scene geometry. A dual-latent encoder then captures the bulk SNR using a Gaussian mixture and the tail using a generalized Pareto distribution (GPD). By employing a modified variational objective, the model is trained to jointly supervise both regimes, ensuring focused attention on extreme fading events. Evaluated on the RadioMapSeer dataset, our method achieves an SNR RMSE of 4.83 dB in the outage region defined by the low threshold of 0.1% SNR quantile. This significantly outperforms the state-of-the-art GAN-based model, which records an SNR RMSE of 21.90 dB, with the performance gap widening as the outage threshold becomes more stringent.
[AI-159] MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning
链接: https://arxiv.org/abs/2608.15311
作者: Ankita Sharma,Bahar Farahani,Sanaz Rahimi Moosavi,Amir Rrahmani,Farshad Firouzi,Krishnendu Chakrabarty
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring data sharing. Recent Mixture-of-Experts (MoE) LLMs are particularly attractive for federated learning because their sparse activation reduces computation and communication while scaling model capacity. However, existing federated MoE methods primarily focus on parameter aggregation and personalization, overlooking the routing behavior of MoE models as a source of information for client collaboration. Under heterogeneous instruction distributions, indiscriminate aggregation can lead to negative transfer, highlighting the need to identify which clients should collaborate during federated optimization. We propose ClientMorpher, a routing-aware, personalized federated instruction fine-tuning framework that leverages routing signatures from pretrained MoE models to organize client collaboration prior to aggregation. We investigate two complementary clustering strategies: ClientMorpher-C, which directly clusters clients using expert activation profiles, and ClientMorpher-E, which first clusters experts based on their cross-client usage signatures and then derives client collaboration groups. We evaluate ClientMorpher for federated instruction fine-tuning on the Databricks Dolly-15K dataset, using pathological and Dirichlet-based heterogeneous client distributions across multiple instruction-following tasks. Experimental results show that routing-aware collaboration consistently improves personalized performance compared to conventional federated averaging and local training, while maintaining the same communication cost. Furthermore, our study shows that client-centric and expert-centric clustering provides an effective and scalable approach for personalized federated instruction fine-tuning of sparse MoE LLMs.
[AI-160] Physiological World Models for Human State Transitions
链接: https://arxiv.org/abs/2608.15309
作者: Chongyang Zhang,Rendong Wang,Hao Zheng,Hanwen Zhang,Yang Liu,Xiaolong Wei,Bin Chong
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical visits. However, most health artificial intelligence systems are designed to recognize current states, estimate risks or analyse individual biomarkers. They do not directly model how physiological states change in response to real-world events, behaviours, contexts and interventions. Here we propose the Physiological World Model (PWM), an event-conditioned framework for learning these changes at the level of the whole person. We introduce the HumanState Transition Token, a structured, quality-scored unit that connects the physiological state before an event with the event or action, relevant context and intervention information, the physiological trajectory after the event, observed outcomes and data quality. We describe four capability levels, from state representation to bounded intervention planning, together with four data acquisition and validation protocols. We also propose six benchmark tasks covering HumanState representation, forecasting across multiple timescales, individualized response prediction, simulation of alternative interventions, bounded planning and reliability under distribution shift. Together, this framework provides a practical path towards personalized health management, behavioural intervention design and clinician-supervised decision support, while clearly separating prediction from causal inference and making uncertainty, safety, governance and limits of use explicit.
[AI-161] Understanding Cognition-Induced Risks in Agent ic AI Systems
链接: https://arxiv.org/abs/2608.15304
作者: Guanchu Wang,Qinuo Li,Mengnan Du,Xia Hu,Bowen Zhou
类目: Artificial Intelligence (cs.AI)
备注: This paper has been accepted by IEEE Intelligent Systems, which can be accessed at this https URL . The DOI is https://doi.org/10.1109/MIS.2026.3721766
Abstract:Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their cognitive engagement raises critical concerns for human society that remain insufficiently studied. To address this gap, we systematically analyze risks induced by expanding cognitive capabilities, following a three-level framework defined by their cognitive scope, from physical cognition to social cognition, and finally to self-referential cognition. We study their potential risks to human agency, autonomy, and control capability, corresponding to each cognitive level. We finally propose strategies to mitigate these risks and enhance the controllability of agentic AI systems, ensuring their long-term safe development.
[AI-162] Divergent-Convergent Reasoning : Scaling Test-Time Compute through Structured Solution Synthesis
链接: https://arxiv.org/abs/2608.15303
作者: Bo Wen,Yuhao Chen,Erhan Bilal,Carla Agurto Rios,Chen Wang,Junchen Jiang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly understood. We study Divergent-Convergent Reasoning (DCR), a simple two-phase primitive consisting of an exploration phase that generates multiple candidate solutions followed by a convergent reconciliation phase. We present three core results. First, we show that even a single reconciliation step can reliably amplify correct minority reports: across datasets, DCR often recovers the correct answer when correct exploration outputs are in the minority, a regime where majority voting fails. Second, we introduce recursive DCR, an autoregressive reconciliation system that iteratively analyzes disagreements and allocates additional test-time compute. Recursive DCR achieves higher accuracy than fixed-compute baselines-reaching 93.3% on AIME 2024 and 92.0% on AIME 2025-while using roughly 27% less compute on average, demonstrating that attentive resource allocation is superior to uniform scaling. Third, we analyze disagreement among exploration outputs via a simple, training-free dispersion metric. Dispersion reveals a structured relationship between disagreement and test-time gains: in regimes where DCR is effective, higher disagreement among exploration outputs is associated with larger accuracy improvements from reconciliation. Together, these results show that disagreement, often viewed as noise, can be systematically exploited to improve test-time reasoning and reveal emerging scaling laws for agentic LLM systems.
[AI-163] MAPLE: MoE Adaptive Plug-and-play Layer-wise Expert allocation
链接: https://arxiv.org/abs/2608.15299
作者: Lie Li,Wen Li,Junxiao Shen,Gusheng Hu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Sparsely-activated Mixture-of-Experts (MoE) Transformers universally fix the same number of routed experts across all layers, a convention that ignores the well-documented heterogeneity in layer-wise redundancy. We demonstrate that this uniformity is systematically suboptimal and propose MAPLE, a plug-and-play framework that reallocates the routed-expert budget heterogeneously across layers of any pretrained MoE LLM, without modifying weights or requiring retraining. Our core contribution is a closed-form sensitivity-guided allocation: we probe each layer’s response to variation in expert count, quantify sensitivity using three measures, and derive an analytically optimal budget assignment that directs capacity towards sensitive layers and absorbs reductions in redundant layers. This closed-form solution is further refined by a sensitivity-constrained genetic search that uses layer-wise sensitivity as a prior to guide exploration, yielding faster convergence and superior allocation quality. On four MoE models spanning different scales and architectures, MAPLE outperforms uniform and pruning-based baselines under a 75% routed-expert budget. Notably, on DeepSeek-MoE-16B, MAPLE uses only 75% of the experts yet surpasses the original 100% expert-uniform baseline on ARC-E, ARC-C, and BoolQ, improving accuracy from 65.09 to 71.40, 48.49 to 51.50, and 80.03 to 82.38, respectively. These accuracy gains translate into measured deployment efficiency: implementing MAPLE in SGLang reduces single-GPU end-to-end serving latency by 32.2% and improves throughput by 47.4%. These results show that well-designed heterogeneous allocation can be more effective than simply activating more experts, establishing it as a principled and practical axis for improving MoE efficiency.
[AI-164] Reason Cast: Agent ic Demand Forecasting with Selective Semantic Reasoning
链接: https://arxiv.org/abs/2608.15291
作者: Ziyue Yang,Chaolin Xu,Yijing Wang,Tiankai Gu,Hui Yang,Yanhong Lin,Kaiyuan Liu,Fei Xiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dynamics, while future promotions, holidays, price changes, and platform interventions provide forward-looking knowledge. Existing text-enhanced forecasting methods often encode such context into generic representations and fuse it uniformly with time-series features, without explicitly distinguishing which semantic effects are forecast-relevant or how they should modify future dynamics. We introduce ReasonCast, a structured semantic intervention framework that translates event knowledge into forecast-specific operations. An agent examines the event context, the no-text forecast, and its uncertainty to determine whether textual reasoning is needed. Rather than injecting free-form text, ReasonCast represents event knowledge through structured fields describing event relevance, demand direction, temporal shape, amplitude, and peak intensity. These fields interact selectively with temporal components of a time-series foundation model. An additive path corrects local trends and temporal shapes, while a multiplicative path captures event-driven level shifts. ReasonCast introduces a forecast-grounded post-training curriculum. Schema SFT establishes semantic fields; semantic-field RL calibrates direction, shape, amplitude, and peak judgments; and forecast-utility RL evaluates semantic interventions through a frozen forecaster, aligning reasoning outputs with marginal forecast improvement. ReasonCast lowers WMAPE by 3.29, 1.25, and 0.47 percentage points on holiday-sensitive categories, mega-sale-sensitive categories, and M5 event windows, respectively. On stable-sales periods, indiscriminate semantic intervention increases WMAPE by 1.68 percentage points, whereas suppressing unnecessary intervention preserves the numerical backbone. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15291 [cs.AI] (or arXiv:2608.15291v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15291 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-165] D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction
链接: https://arxiv.org/abs/2608.15288
作者: Ninghan Fan,Qi Liu,Xunuo Zhu,Yukai Sun,Luyuan Chen,Xuheng Zhou,Yuetian Du,Ming Kong,Xiaojun Zhu,Jie Liu,Zhan Zhou,Qiang Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Existing approaches, however, typically predict an entire expression profile as a whole, leaving the order in which individual gene responses are generated unmodeled. To address this problem, we introduce \textbf D^2R^2 (\textbfDiscrete \textbfDiffusion with \textbfRegulation \textbfReinforcement), which reformulates perturbation prediction as regulation-guided gene-wise progressive generation. A Masked Discrete Diffusion Model represents expression as ordinal tokens and reconstructs a fully masked profile step by step, allowing generated gene responses to condition those that remain masked. A Regulatory Policy Module initializes the generation policy from a gene regulatory network inferred from control cells and adapts it to the perturbation and current partially generated state. Then, group-relative policy optimization refines only the ordering policy using final perturbation-effect agreement as reward. Across Norman19 and VCC-H1, D^2R^2 achieves the best performance on all five metrics on Norman19 and remains competitive on H1. Controlled ablations holding the generator and generation budget fixed show that biological-prior ordering improves over random ordering and is more reliable than uncertainty-based heuristics, whereas reversing the biological-prior ordering degrades every metric. Biological analyses further show that the refined policy prioritizes regulatory genes early while promoting perturbation-specific transcription factors and responsive genes. These results establish gene generation order as an effective, controllable, and biologically interpretable dimension of single-cell perturbation prediction.
[AI-166] No Task Fails Every Time: Why One-Shot Audits Are Structurally Blind to Agent Damage
链接: https://arxiv.org/abs/2608.15286
作者: Shiven Khurdi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages, 4 figures, 16 tables, 6 appendices. Code, task suite, released per-run verdicts, and a one-command reproduction of every reported number: this https URL
Abstract:We introduce AgentRelBench, an environment-agnostic reliability instrument that computes ground-truth, severity-priced damage from database state diffs across repeated runs, with no LLM in the measurement path, demonstrated on EnterpriseOps-Gym. Across 2,128 evaluation runs spanning nine models in six families (four development, three pre-registered held-out, plus a frontier pass on two frontier-tier models that the pre-registration designates exploratory), we find: (1) damage on irreversible actions is universal across the families we measured and stochastic within them on pinned, single-provider stacks. (2) No task damaged on every run: zero always-fail cells across 42 confirmatory held-out damage events. A single clean run misses a damage-producing (model, task) pair 0.80 of the time on the development pool (13 pairs); the held-out pool is descriptively consistent (0.575 over 5 pairs, pair-weighted) but sits below our pre-registered power floor and is reported as underpowered, not as confirmation. (3) Damage-producing task count falls with model capability, from 7 of 20 tasks for an 8B model to 1 of 20 for the most capable; capability is confounded with family and training, so this is an observed gradient, not a causal claim. The residual damage does not change in character: in the exploratory frontier pass, the most capable model’s one damaging task damages at \hatp = 0.16 per run, inside the same demonstrably-stochastic band, and a single audit misses it 84% of the time. (4) One model family committed the gated irreversible change while declaring it had refused: transcript- and judge-based grading scores those runs as safe refusals, only state diffs as damage. All confirmatory findings were pre-registered with per-claim demote criteria; one demoted our own initially favored finding, which we report.
[AI-167] PhaseLoRA: Control-Regime-Conditioned Low-Rank Adaptation for Continuous-Action Vision-Language-Action Policies
链接: https://arxiv.org/abs/2608.15285
作者: Yufei Guo,Yinan Wu,Haoran Duan,Guiguang Ding,Jungong Han
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Parameter-efficient fine-tuning (PEFT) is a natural way to adapt pretrained vision-language-action (VLA) policies, but most adapter designs apply temporally static updates throughout a control rollout, overlooking the phase-dependent nature of continuous-action manipulation. Such policies traverse distinct regimes, including approach, contact transition, grasping, transport, and placement, each requiring different adaptation behaviors. We propose \textbfPhaseLoRA, a lightweight LoRA parameterization that conditions adaptation at each action-chunk prediction step using two weakly supervised descriptors: fine-control tendency and event/boundary intensity. PhaseLoRA modulates the LoRA left factor in the action expert, allowing the effective low-rank update direction to vary over time while keeping the backbone largely frozen. On LIBERO, PhaseLoRA improves average success rate by 12.2 points over a matched-parameter high-rank LoRA baseline and outperforms stronger LoRA variants. Ablations show that random temporal modulation and scalar gating do not reproduce the performance of the full model, while update-direction analyses reveal structured temporal variation associated with the predicted control descriptors. These results establish within-trajectory conditioning as an effective lightweight PEFT axis for continuous-action VLA policies.
[AI-168] VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?
链接: https://arxiv.org/abs/2608.15265
作者: Yansong Ning,Jingwen Ye,Zhongkai Wu,Yang Sun,Yiqin Zhu,Xingyi Li,Weidong Zhang,Hao Liu
类目: Artificial Intelligence (cs.AI)
备注: preprint
Abstract:Constructing an interactive 3D open world from a user query is important. However, existing methods are primarily evaluated on idealized, simple queries, making it difficult to systematically analyze and compare how multimodal agents understand user intent, use 3D tools, and reason over textual and visual 3D world information. To this end, we propose VibeWorlding, a unified framework for benchmarking and training vibe worlding agents: a multimodal agent that can autonomously infer user intent, plan scene layout, invoke 3D tools, and reflect on the multimodal feedback in a multi-turn agent-environment interaction process. To achieve this, we first build VWE-BENCH, a benchmark of 2,616 high-quality 3D assets, 323 human-annotated seed 3D worlds, and 6,828 reverse-synthesized multimodal user queries, split into verified queries with ground-truth and unverified queries with carefully designed rubrics. Moreover, we develop VibeWorlding-Gym, a joint multimodal RL post-training framework that integrates (1) a sandbox environment unifying asset retrieval, editing, and image rendering as MCP tools, and (2) a rubric-based verifier that combines physical feasibility and intent fulfillment verification, supporting both fair model evaluation and scalable multimodal RL reward service. Our experiments show that current frontier MLLMs are far from solving the vibe worlding agent task, with even GPT-5.5 and Qwen3.8-Max reaching below 60% success rate, and trace the bottleneck to precise 3D world editing. We further find that RL training can ease this weakness and enable open-source MLLMs to even surpass closed-source frontiers: our VibeWorlder-8B is comparable to frontier MLLMs, while our flagship VibeWorlder-30B-A3B attains the best overall Pass@1 among all evaluated models.
[AI-169] Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication
链接: https://arxiv.org/abs/2608.15256
作者: Lin Yin,Tiejun Lv,Weicai Li,Xi Yu,Xiaoyu He
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 9 figures, Accepted by IEEE Transactions on Communications
Abstract:Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). However, pushing topology-agnostic aggregation into heterogeneous, multi-task environments creates a fundamental bottleneck: it drives negative transfer and overconsensus bias (OCB). This paper introduces a personalized DSC framework that cuts off this cross-task interference. At the node level, a policy-driven multi-path routing mechanism separates task-specific features from shared representations to preserve local fidelity. Across the network, we deploy a “communicationwhile- aggregation” protocol. It calibrates a column-stochastic consensus matrix using task affinities. This limits the system to absorbing complementary knowledge while actively blocking mismatched parameter updates. To bound the convergence, we derive a unified Lyapunov drift analysis. We reveal a strict Ushaped trade-off: deeper topological mixing reduces variance but amplifies structural OCB. Resolving this tension yields a closed-form expression for the optimal aggregation depth. We evaluate the proposed framework on NYU-v2, where the results reveal a clear trade-off between insufficient aggregation and excessive topological mixing. At the analytically derived optimal aggregation depth, our method achieves a 4.77% global relative improvement over the no-aggregation baseline and outperforms decentralized FedAvg, FedAMP, and heuristic max aggregation. We further evaluate the framework on Taskonomy and imperfect wireless links to examine the effects of network-size variation and wireless-link reliability.
[AI-170] owards Standardized Evaluation in Automated Domain Modeling: Introducing a Benchmark
链接: https://arxiv.org/abs/2608.15255
作者: Vasiliy Seibert
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Domain modeling plays an essential role in domain-driven design, capturing essential entities and their relationships within a specific domain. Despite advancements in automated domain modeling, the absence of standardized benchmarks has hindered the comparative assessment of existing approaches. This paper introduces a benchmark designed to address this gap. The benchmark combines the 45-record Golden UML Modelset (Verbruggen et al., 2025) on Zenodo, as distributed by the Text2UML project of Calamo, Mecella, and Snoeck (Calamo et al., 2025), with the 8-record reference archive of Chen et al. (Chen et al., 2023a,b), enabling the evaluation of automated domain modeling approaches across different levels of complexity and scale. Given a natural language description, the task is to generate a corresponding domain model. For each description, a reference domain model is provided as ground truth. A metric is used to compare the generated domain model with the corresponding ground-truth model. To demonstrate the utility of the benchmark, we evaluate multiple automated domain modeling approaches, including heuristic rule-based methods and LLM-driven strategies. In accordance with the FAIR4RS recommendations (Chue Hong et al., 2022), the benchmark is provided as a research artifact to encourage reuse and support future research on automated domain modeling.
[AI-171] LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures
链接: https://arxiv.org/abs/2608.15242
作者: Yunfei Zhang,Boyu Feng,Changhua Pei,Zexin Wang,Zhihuang Peng,Xinlong Liu,Hengyue Jiang,Difeng Ma,Jiayi Zhang,Yongzhou Yao,Yanan Zhao,Fei Sun,Yintong Huo,Zhaoyang Liu,Jingjing Li,Gaogang Xie,Dan Pei
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 17 pages, 5 figures. Yunfei Zhang and Boyu Feng contributed equally. Changhua Pei is the corresponding author
Abstract:When a long-horizon agent execution fails, outcome-level evaluation reveals the unsuccessful result but not where the decisive error entered the trajectory. Developers must then inspect the full execution to identify the responsible role and localize the earliest decisive root-cause step. Existing failure-attribution benchmarks largely focus on shorter traces, leaving diagnosis across hundreds of recorded steps underexplored. We introduce LongRCA Bench, comprising 1,140 failed trajectories across five domains without injected errors. It provides independently scored human labels for the responsible role and earliest decisive root-cause step. The median trajectory contains 145 steps, and the strongest baseline reaches only 13.2% exact root-step accuracy. We further present Root-Cause Trajectory Attribution (RCTA), a training-free method that retrieves candidate error steps from segment summaries and traces them to available earlier handoff instructions. Using the same backbone, benchmark instances, and scoring protocol, RCTA reaches 51.1% responsible-role accuracy and 24.1% exact root-step accuracy. These results highlight the need to evaluate responsible-role attribution and exact root-step localization as separate targets in long-trajectory failure diagnosis.
[AI-172] he Quality of Claude AI-authored Python Tests Is Not Weaker Than Human-authored Tests
链接: https://arxiv.org/abs/2608.15188
作者: Douglas J. Leith
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:We evaluate the quality of Claude AI-written Python tests against human-written Python tests from two established open-source projects Django and Pandas. Hundreds of tests per corpus are scored under one identical protocol. Using one-sided non-inferiority bounds, we find that the tests written by recent Claude models (Sonnet/Opus 4.6 and later) are no weaker than the two human-written corpora. In this study: (i) the AI-written corpus is tests from real tools, not synthetic tests generated in isolation against a fixed target, the setup used by every other AI-test-generation study we are aware of; (ii) every test is individually scored under three independent fault-injection protocols plus a seven-axis qualitative design rubric, allowing methods to cross-validate each other; (iii) tests are scored individually, rather than suite-level, identifying exactly which specific tests need attention.
[AI-173] FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection
链接: https://arxiv.org/abs/2608.15177
作者: Yixuan Chen,Hongyu Zhan,Jie Sheng,Weiyu Han,Shuai Chen,Tianyi Zhang,Xiao Tan,Jun Xia
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 16 pages, 7 figures
Abstract:The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities. This shift has motivated graph-based fraud detection, where models identify fraudulent nodes by exploiting dependencies among customers, cards, merchants, categories, and locations. However, despite rapid progress in graph-based methods, existing public benchmarks remain misaligned with real-world financial systems in two important aspects. First, they often simplify financial ecosystems into homogeneous or single-node-type multi-relational graphs, failing to preserve the multi-entity and multi-relational nature of financial data. Second, they rarely provide large-scale heterogeneous financial graph datasets with realistic operating conditions such as extreme class imbalance and limited label availability, making it difficult to assess the practical effectiveness of current methods. To address these gaps, we present FinFraudBench, a heterogeneous graph benchmark for financial fraud detection. FinFraudBench contains two heterogeneous graph datasets (CreditCard-Fraud and BankTrans-Fraud) with up to 8.99M nodes and 89.23M directed typed edges. Each dataset preserves six financial entity types, fourteen directed edge types, and natural fraud rates that mirror deployment constraints. With these datasets, we establish a standardized evaluation protocol covering both ranking and imbalance-sensitive classification metrics, and evaluate representative baselines. Extensive experiments yield empirical insights into current methods’ limitations and suggest promising avenues for future research. FinFraudBench is available at this https URL.
[AI-174] LAPF: LLM -Agent -Based Path Finder Using the UAVScenes Dataset
链接: https://arxiv.org/abs/2608.15175
作者: Yousef Emami,Mohammadhossein Homaei,Hao Zhou,Miguel Gutiérrez Gaitán,Atefeh Hajijamali Arani,Rui Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 15 pages
Abstract:Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making. Existing optimization-based, Machine Learning (ML), and Reinforcement Learning (RL) approaches often rely on predefined models or task-specific training, limiting their generalization and adaptability in uncertain scenarios. Recent Large Language Model (LLM)-assisted approaches offer promising reasoning capabilities but remain constrained by limited agentic functionality, including insufficient memory, planning, and tool interaction this http URL paper proposes an LLM-Agent-Based Path Finder (LAPF) framework for autonomous UAV navigation in town-scale outdoor environments. LAPF extends LLM-assisted navigation by integrating perception, memory, planning, and action modules into a closed-loop cognitive architecture. The proposed agent leverages prior navigation experiences, performs Chain-of-Thought (CoT) reasoning, couples each detected hazard to a bounded corrective action, and dynamically refines waypoint decisions based on environmental this http URL three independent trials per method demonstrate that LAPF achieves mean path lengths of 512.83 m and 506.37 m, compared to the straight-line optimum of 497.33 m, corresponding to path length reductions of 17.2% and 15.6% relative to CoT prompting and absolute path efficiencies of 97.1% and 98.1% in open-field and obstacle-injected scenarios, respectively. Furthermore, LAPF is the only evaluated approach that couples every detected hazard to a bounded, metric-neutral corrective action while maintaining near-goal stability, with zero clamp events in both scenarios, whereas CoT prompting increases from 9.7 to 14.0 events.
[AI-175] SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion
链接: https://arxiv.org/abs/2608.15165
作者: Yu He,Weikai Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge. However, existing methods often consolidate experience based on semantic similarity or LLM judgments, which may merge superficially related but behaviorally incompatible strategies and thereby degrade performance. To address the issue, we propose SkillCommit, an online skill evolution framework that continuously transforms experience into a hierarchical library of reusable skills. Each new experience is initially preserved as an instance-specific patch, retaining the behavior validated in its local context. As related skills accumulate, SkillCommit abstracts those sharing a common behavioral mechanism into higher-level skills. Specifically, for each incoming skill, embedding-based retrieval first identifies candidate related skills. Cross-instance replay and an LLM-based mechanism check determine whether these skills transfer across cases and share a common underlying mechanism. Candidates that pass both checks are abstracted into a higher-level skill and committed only if it preserves the validated behavior of all constituent skills. Experiments on RuleArena, OpenExempt and KOR-Bench demonstrate that SkillCommit consistently improves agent performance across diverse domains. Moreover, the learned skills transfer across model scales and families, enabling cross-model experience transfer.
[AI-176] Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models
链接: https://arxiv.org/abs/2608.15156
作者: Yang Liu,Yuming Chen
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:World models may predict the future without making clear which parts of their hidden state actually drive those predictions. We ask whether a small, directly addressable hidden-state change can place a learned world model on the intended counterfactual trajectory and then let the model continue that future on its own. We study a recurrent world model with a 192-dimensional hidden state in a controlled two-object, two-dimensional collision environment. For a bounded family of local velocity edits, we first verify that the model can natively represent and roll out the edited future. We then construct candidate low-rank carriers from training-only factual-to-counterfactual hidden differences and learn a map from the factual state and requested edit to carrier coefficients. On the registered rank grid, rank 4 is the smallest tested rank that satisfies the full development-panel criteria. A single rank-4 patch at the anchor is sufficient to redirect a 12-step autonomous rollout, with no future observations, teacher forcing, or repeated correction. The frozen procedure satisfies the preregistered replication rule across independently trained checkpoints and remains usable across nearby intervention times. Random equal-norm, wrong-object, and wrong-time controls do not explain the effect. A position-edit stress test provides a negative contrast: the intended position patch can pass the raw rollout criteria, but no-patch and random controls can pass the same criteria, and wrong-object specificity is not established. Thus, successful editing alone is not enough. We use dynamics-effective to describe an intervention that changes the model’s future computation in a sustained and target-specific way under autonomous rollout. The rank-4 result identifies a compact intervention interface for the tested velocity-edit family, not a closed four-dimensional state or an intrinsic state dimension.
[AI-177] ACTS-SQL: Agent ic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
链接: https://arxiv.org/abs/2608.15145
作者: Xinmei Huang,Jie Song,Peng Li,Fuxin Jiang,Jing Zhang,Tieying Zhang,Jianjun Chen,Chenming Liu,Tao Yang,Maoyin Liu,Wenda Li,Hong Chen,Cuiping Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing SQL correction approaches either rely on large-scale, high-quality training data with substantial overhead, or adopt single-path agentic workflows that are brittle to early mistakes and prone to error propagation. To develop a practical SQL correctness system for industrial scenarios, we present a training-free framework that formulates SQL correction as a plan-guided, tree-structured debugging process. By maintaining multiple correction strategies and enabling backtracking, the framework mitigates error accumulation during iterative refinement. We further integrate execution-based verification and clause-level diagnostic tools to support strategy pruning and precise error localization. We evaluate the system on the BIRD-Critic benchmark and observe consistent accuracy gains over strong LLM backbones and representative agent-based baselines, achieving a 9.42% improvement over the previous state-of-the-art method. The framework is also deployed in the Torch Log Service (TLS) of Volcano Engine to support an online Text-to-TLS API. In production, it improves execution accuracy from 36.77% to 53.61% on real user queries with a representative strong LLM backbone (GPT-5). These results demonstrate the effectiveness and stability of our approach in real-world deployments. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15145 [cs.AI] (or arXiv:2608.15145v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15145 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-178] ranslating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy
链接: https://arxiv.org/abs/2608.15143
作者: Tias Guns,Ignace Bleukx,Hendrik Bierlee,Jo Devriendt,Emilio Gamba,Orestis Lomis,Wout Piessens,Thomas Sergeys,Dimos Tsouros,Wout Vanroose,Hélène Verhaeghe
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems. The user specifies their problem through constraints and decision variables, and a generic solver is used to find a solution. Several constraint-solving technologies exist, and certain solvers perform well on certain problems. Therefore, it is useful to try different solvers given a particular application. However, each solving paradigm supports different types of constraints and decision variables. Our goal is to translate high-level constraint satisfaction and optimization problems into any lower-level formalism, including CP, SMT QF-LIA, ILP, PB and (Max)SAT. This allows for comparing different solving technologies for a particular problem, without requiring a user to manually remodel it for each solving paradigm. We define a high-level language of logical and arithmetic operations, and useful additional functions and constraints, which are known as global constraints in the CP community. We then present a modular framework for transforming our high-level modeling language to CP/SMT/ILP/PB and (Max)SAT solvers. While many transformations are partly described in the literature, we observe that they can be implemented through a modular waterfall of smaller components, where lower-level paradigms reuse the transformations of higher-level paradigms. Two recurring challenges are handling the negation of arbitrary subexpressions and avoiding the introduction of auxiliary variables. Additionally, we take special care linearizing non-linear operators for ILP, PB and SAT-solvers. The transformation waterfall is implemented and evaluated in the open-source CPMpy library. Our results show that constraint models significantly change throughout the transformations, and that optimizations to the linearization of constraints are essential for ILP and PB solvers. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15143 [cs.AI] (or arXiv:2608.15143v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15143 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ignace Bleukx [view email] [v1] Sat, 15 Aug 2026 09:38:09 UTC (103 KB)
[AI-179] ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits
链接: https://arxiv.org/abs/2608.15138
作者: Zhiqiang He,Zhi Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching or beating hand-built designs. But either way, the design fits only the world visible at its birth, and fails on the world that arrives after. We ask whether an ABR algorithm can keep pace with the world, redesigned in minutes as each scenario arrives, with every change proven harmless to every scenario already served. In this work, we propose ReForge, a continual heuristic learning framework that adapts to continuously changing scenarios. ReForge runs that routine with a large language model (LLM) in the loop. Each round the LLM reads where the current design falls short and proposes one small edit, and a replay over every network served so far decides. Specifically, what it edits is a single page of fuzzy rules that routes every decision to one of a frozen pool of pre-trained policies. The LLM writes the first page from measurements alone, then keeps improving it on its own. Each round it reads where the current rules fall short and proposes one small edit, and a replay over every network served so far decides whether the edit lands. We evaluate ReForge on nine real-world network families arriving one at a time as 3G, 4G, then 5G. A few edits per arrival lift mean QoE from 1.23 to 1.74, past the best single policy at 1.66 and to 94% of an oracle, and even repair families the loop never saw, one rising from 0.30 to 0.80. All code, data, and experiment records will be open-sourced upon cleanup.
[AI-180] Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark
链接: https://arxiv.org/abs/2608.15131
作者: Wesley Shu,Peng Wei
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requirements, appeal processes, and access policies. These interventions are rarely absorbed passively. Creators, sellers, advertisers, moderators, users, developers, and strategic operators adapt to the new reward surface. This paper develops a platform-adaptation model for evaluating governance interventions as transitions in adaptive multi-actor information systems. The model represents actor best response, strategic gaming opportunity, moderation burden, user-incentive movement, enforcement response, externality formation, and downstream platform stability. We evaluate the model on 72 external public platform-governance cases covering media monetization, ranking systems, verification, delivery platforms, marketplaces, app stores, community platforms, and creator ecosystems. Across 9 methods and 648 method-case evaluations, the full platform-adaptation simulator achieves mean adaptation quality of 0.836338, compared with 0.669731 for a risk-register baseline, 0.589457 for causal-loop analysis, 0.492750 for generic governance critique, 0.369492 for engagement-only optimization, and 0.331965 for baseline policy review. Paired comparisons show a win rate of 1.00 against all tested baselines and channel ablations. The contribution is an information-systems theory and measurement framework showing why platform governance evaluation fails when it treats policy rules as static controls rather than interventions into adaptive actor-response fields.
[AI-181] Anatomy of a Quantized Agent : VRAM Stability and Forecasting in Code-Synthesis Agent ic Workloads
链接: https://arxiv.org/abs/2608.15117
作者: Anubhab Banerjee
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:Analytical models of peak VRAM consumption for LLM inference decompose memory into weight-storage, KV-cache, and activation terms parameterized by step count, tool invocations, and context expansion. We evaluate this decomposition empirically within a strictly scoped measurement study: a LangGraph-based CUDA-kernel-synthesis agent (AgentK), a 4-bit quantization family (Q4 K M), a single NVIDIA H100 GPU, and four LLM backbones across 1,920 trajectories. Focusing on peak-memory forecasting behavior, we report two primary observations. First, closed-form analytical models achieve competitive accuracy when provided with two empirical constants: loaded-weight VRAM and a fixed activation-memory overhead. Supplied with live GPU readings and ground-truth trajectory parameters, the closed-form model matches or outperforms the best learned baseline on three of the four backbones (test MAPE 2.2-4.4% vs. 3.4-6.5%, p = 0.76). The exception is the smallest backbone (Phi-4-mini), where minimal VRAM variance (CV 0.3%) causes dynamic modeling to underperform simple regression. Second, compile success strictly bifurcates by backbone capacity (from 5.7% for Phi-4-mini to 62.0% for Qwen2.5-Coder-14B), demonstrating that functional code synthesis remains constrained by intrinsic LLM capabilities rather than available memory. Furthermore, because overall peak-memory variance is remarkably low across all backbones (CV 0.3-9.4%), learned prompt-feature regression offers statistically insignificant improvements over a constant-mean baseline. Consequently, we find no justification for deploying complex predictive VRAM models in highly quantized, weight-dominated regimes. We release the evaluated corpus and anonymized framework to support replication.
[AI-182] Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents
链接: https://arxiv.org/abs/2608.15109
作者: Jianxing Zhao,Mao Guan,Dongyu Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can still violate semantically meaningful domain constraints. We study the discovery and enforcement of three complementary inter-column constraint families—equations, linear inequalities, and logical dependencies. Our unified tool-grounded workflow represents all three as machine-executable hypotheses and applies a common interface for full-table validation, deterministic diagnosis, and counterexample-guided revision. A generator-agnostic postprocessor coordinates family-specific repairs on outputs from unchanged tabular generators. Across curated behavioral audits and end-to-end evaluations, the complete workflow improves held-out violation detection over one-shot direct prompting, while postprocessing yields zero measured violations for every retained, applicable constraint, improves downstream utility on most datasets, and largely preserves univariate marginals.
[AI-183] Beyond Direct Access: Resource Hijacking in LLM Agents
链接: https://arxiv.org/abs/2608.15108
作者: Puyu Zeng,Qibing Ren
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 11 pages, 3 figures, 4 tables
Abstract:Large language model agents are increasingly connected to high-value resources such as computing infrastructure, credentials, usage budgets, identities, private knowledge, communication channels, and organizational workflows. Existing agent security research mainly studies attacks on instructions, data, and tool behaviors, while high-value resources accessible to agents have received much less attention as direct attack targets. We are the first to identify and systematically study agent resource hijacking, a security blind spot in which attackers induce agents to invoke, consume, transfer, or control high-value resources for their own goals without directly obtaining those resources or their credentials. To study this threat, we introduce ResourceHijackBench together with an automated pipeline for generating resource hijacking cases. We organize high-value agent resources into six categories and construct 300 attack scenarios with 900 attack prompts. Each case runs in an isolated local environment that records actual resource use, allowing attacks to be evaluated from agent behavior rather than text responses alone. Without additional defenses, OpenClaw reaches an average attack success rate of 84.06%. The attack remains effective across different model backends, with average success rates ranging from 69.98% to 89.58%. Existing defenses reduce part of the risk, but the strongest evaluated defense still leaves an average attack success rate of 55.11%. These results show that high-value resources accessible to agents form an important and previously overlooked attack surface, and that current agent defenses are not sufficient to protect them from resource hijacking.
[AI-184] Second-Order Policy Effects as State Transitions: A Source-Linked Benchmark for Policy Simulation
链接: https://arxiv.org/abs/2608.15101
作者: Wesley Shu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed. In practice, a policy changes the system it enters: actors adapt, enforcement capacity shifts, burdens move, and new equilibria form around capture, gaming, compliance theater, irreversibility, and repair costs. We formalize this as second-order policy-effect prediction and present a source-linked benchmark for policy simulation. The benchmark contains 96 named public-policy cases across eight domains and four balanced action classes: implement, modify, pilot, and block. Each case includes source locators and state variables for benefit, capture, gaming, burden shift, instability, uncertainty, irreversibility, distributional risk, and implementation capacity. The runner regenerates method outputs and aggregate results from the case table, and the simulator never reads the expert action target. We report a protocol-based transition-channel audit with recall, precision, F1-style efficiency, and selective top-channel stress diagnostics, so universal channel coverage is not mistaken for field validation. The side-effect simulator achieves mean policy-effect quality of 0.945, compared with 0.838 for the risk-register baseline and 0.879 for the causal-loop baseline. Its advantage is concentrated in side-effect recall and aggregate transition scoring; it does not dominate the best structured baselines on exact policy-action choice. The evidence remains benchmark-based, but supports a bounded claim: transition-state variables make policy simulators more sensitive to downstream institutional effects.
[AI-185] Validation-Frontier Representation Selection under Constrained Observation
链接: https://arxiv.org/abs/2608.15095
作者: Wesley Shu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI systems deployed outside clean benchmark settings often rely on observations that are incomplete, unstable, costly, or degraded by monitoring failures. This paper studies representation selection under constrained observation: choosing a state representation when raw accuracy is not the only operational criterion. We propose a validation-frontier selector that combines balanced accuracy with penalties for feature cost, overfit gap, and validation-test instability. In a focused public-tabular benchmark using three scikit-learn datasets, five observation regimes, 45 matched task cells, 720 candidate actions, and 405 representation rows, the adaptive selector improves frontier score over full trace features by 0.025801 while reducing mean feature count by 22.733. Balanced-accuracy difference is small and not statistically significant. A broader offline stress test gives mixed results. The supported claim is therefore bounded: adaptive representation selection can improve a constrained-observation robustness-efficiency frontier in matched benchmark settings, but does not universally dominate trace baselines.
[AI-186] WeSCE: A Benchmark for Measuring Security Drift in LLM -Driven Code Editing
链接: https://arxiv.org/abs/2608.15092
作者: Zhiyu Zhang,Tingyue Wen,Senke Sun,Dengxiang Liang,Enhao Huang
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:In this work, we introduce WeSCE, a benchmark for quantifying security drift in code editing under weak-security constraints, where tasks specify only functional objectives without explicit security requirements. WeSCE consists of 400 executable programs derived from real-world code, covering feature addition, feature removal, bug fixing, and refactoring. To quantify security drift, we propose a continuous risk representation that aggregates heterogeneous vulnerability signals through a unified formulation, and define drift measures capturing changes in overall risk, worst-case severity, and vulnerability distribution under code transformations, providing a multi-scale view of security spanning average-case behavior to worst-case emphasis.
[AI-187] StateM: Reaching 95.3% Raw Accuracy or a 15 Frontier Run on Terminal-Bench 2.1 via Harness Scaling
链接: https://arxiv.org/abs/2608.15089
作者: Ziheng Qin,Yaxin Lu,Zhangyang Atlas Wang,Kai Wang
类目: Artificial Intelligence (cs.AI)
备注: Harness Scaling, Semi-Self-Evolving Agent
Abstract:Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail to reactivate lessons from earlier executions, skip known procedures, or stop prematurely. We bet on harness scaling to improve the execution system around an agent without changing its model weights. We introduce StateM, an agent-native runtime that organizes execution around durable states, phase-local context, checked transitions, recoverable runbooks, and versioned procedural practices that agents and users can inspect together. On Terminal-Bench 2.1, StateM raises GPT-5.5 xhigh to 92.1%, versus 83.1% reference and GPT-5.6 Sol Ultra at 91.9%. The runbook transfers unchanged to GPT-5.6. With GPT-5.6 Sol xhigh, StateM reaches 95.3% raw accuracy across 445 trials and succeeds on all 89 tasks at least once. The frozen profile raises GPT-5.6 Luna from 76.7 to 85.4%, above the 84.9% Sol xhigh reference. Using the same runtime, runbook structure, and golden rules, less than \ 38 of adaptation raises DeepSeek-V4 Flash from 82.7 to 88.1% under standard timeouts and to 89.1% on an 88-task common core. Extending only the remaining latency-sensitive task matches the reported 88.8% GPT-5.6 Sol max result. Final-score API usage is about \ 15 versus \ 574.68 for the GPT reference; total DeepSeek expenditure is \ 52.22. On BusinessBench, family-specific runbooks built on development sets yield held-out gains of 0.55 macro and 1.34 micro points; two mechanism-matched families improve by 10.04 points. Concrete rules generalize when tasks share execution structure, while the control methodology applies broadly. StateM turns selected postmortem findings into persistent, executable preconditions and practices, making learned controls explicit and enforceable through stateful controls. Code at this http URL. Comments: Harness Scaling, Semi-Self-Evolving Agent Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15089 [cs.AI] (or arXiv:2608.15089v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15089 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ziheng Qin [view email] [v1] Sat, 15 Aug 2026 07:16:28 UTC (886 KB)
[AI-188] Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning
链接: https://arxiv.org/abs/2608.15088
作者: Zihang Wang,Yishan Wang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the human prior. We present a training method for this setting based on two components. First, an \emphMC Q-chunk critic regresses chunk-level action values onto Monte Carlo returns from the replay buffer, performing sample-average (behavior) policy evaluation so that intervention trajectories are credited directly rather than diluted by current-policy TD backups. Second, \emphmax-Q selective imitation updates the actor by imitating, at each state, the higher- Q action between the current policy action and a buffer sample under a hard winner-take-all rule. This rule automatically switches between learning from interventions and on-policy self-improvement: when the autonomous policy is stronger, targets align with the policy distribution, reducing the policy–target-sample gap that otherwise induces execution-time distribution shift. In practice we score candidates with a standard critic ensemble mean to reduce comparison noise, without softening targets or introducing score-gap thresholds. On a real USB pick-and-insertion task with 20 demonstrations, ACT QChunk-MCBC attains 99% success within 30 minutes of HIL training, whereas HIL-SERL requires about 5 hours to converge. In simulation on Peg Insertion and Square, ACT/Flow Q-chunk variants similarly reach \ge 96% success within roughly half an hour of effective training, outperforming HIL-SERL, EXPO, and E2HiL on the success–time frontier.
[AI-189] GATTA: Graph Active Learning with Test-Time Augmentation
链接: https://arxiv.org/abs/2608.15084
作者: Zsombor Bánfi,András Gézsi,András Formanek
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Test-time augmentation (TTA) has proven effective for improving model robustness and uncertainty estimation in computer vision, yet its application to graph-structured data remains largely unexplored. We introduce GATTA (Graph Active Learning with Test-Time Augmentation), a framework for enhancing active learning by aggregating predictions across multiple augmented views to produce more reliable uncertainty estimates. To address the challenge of label-preserving graph augmentations, GATTA incorporates a consistency-based filtering mechanism that discards augmented views yielding unreliable predictions. We systematically evaluate GATTA across multiple graph datasets, GNN architectures, and acquisition strategies. Our results show that simple uncertainty-based methods, such as Entropy and Least Confidence, benefit most from TTA, achieving performance competitive with more sophisticated and computationally expensive approaches. GATTA generalizes across architectures, outperforms model-side ensemble methods such as MC Dropout. We further show that GATTA scales efficiently with both ensemble size and graph size. Extensive analysis of augmentation types, strengths, and filtering strategies provides practical guidelines for effective deployment. Our findings demonstrate that augmenting simple methods with TTA offers a more efficient path to strong active learning performance than engineering complex acquisition functions, enabling practitioners to achieve competitive results with lower computational overhead and reduced implementation complexity.
[AI-190] Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems
链接: https://arxiv.org/abs/2608.15082
作者: Aashna Sofat,Balwinder Sodhi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: thresholds trigger alerts, but nothing relates violations to cumulative product degradation or converts degradation signals into logistics decisions. We address this gap with a Quality-Aware Decision Intelligence (QADI) framework combining three capabilities: a structured quality state representation, S_q = [L, Q, U, R] – remaining shelf life, degradation rate, estimation uncertainty, and operational risk, all derived and computable from the framework equations; a hybrid quality modeling layer combining physics-based microbial kinetics with a data-driven correction term; and a reasoning layer built on Microsoft Phi-4~\citePhi4 with retrieval-augmented generation over a structured domain knowledge base. We benchmark against five baselines – threshold monitoring, physics-only, physics-plus-noise, optimisation-based decisions, and a rule-based expert system – across eight cold chain scenarios, using pasteurised milk as the primary case, with ground truth shelf-life drawn from published dairy studies~\citeSingh1994, Smigic2015 independent of our model. Comparisons use Wilcoxon signed-rank tests with Holm correction. Across milk and broccoli scenarios, the framework attains mean absolute shelf-life error of 7.2 hours (versus 30.9 hours, physics-only; p0.001 ), spoilage rate of 14.5% (versus 16.6%, physics-only and rule-based; p=0.08), and oracle-optimal decisions in 99.5% of scenarios. Removing the LLM reasoning component drops optimality to 45.5% ( p0.001 ). Expert-rated explanation quality reaches 83% ( \kappa = 0.71 ). Ablations show hybrid modeling and LLM reasoning contribute distinct gains, while RAG retrieval mainly drives explanation quality. Code: this https URL. Subjects: Artificial Intelligence (cs.AI) ACMclasses: D.2.11; H.4.2 Cite as: arXiv:2608.15082 [cs.AI] (or arXiv:2608.15082v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15082 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-191] Funnel of Thoughts: Efficient Test-Time Scaling via Early Voting and Rollout Pruning
链接: https://arxiv.org/abs/2608.15065
作者: Chanhee Park,Sungbin Han,Jeongho Yoon,Seongtae Hong,Heuiseok Lim
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 8 figures
Abstract:Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inference is a prerequisite for reliable deployment. Majority voting at k rollouts is the standard solution and the de facto accuracy target for this regime, but it is prohibitively expensive at the scale LRMs require. We introduce Funnel of Thoughts (FoT), an inference-time method that preserves the full 32-trajectory voted accuracy while halving its attention FLOPs, a 28.8% reduction in full-model inference cost. Across 115K reasoning trajectories from six LRMs, we find that unproductive trajectories often reveal themselves through repeated hesitation markers such as “Wait”, “Actually”, and “perhaps.” These trajectories are less likely to reach the correct answer and consume disproportionate attention FLOPs, degenerating into no-answer loops in the worst case. Built on this training-free lexical signal, FoT identifies the vocabulary that captures these pathological patterns and prunes affected trajectories before completion, reducing online generation attention FLOPs by 56.1% and wall time by 37.6% without any additional model inference; the same signal transfers without retuning across held-out architectures and out-of-domain tasks.
[AI-192] LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents
链接: https://arxiv.org/abs/2608.15064
作者: Yuefeng Zou,Yichen Lu,Jingxiao Yang,Bingtao Fu,Gaoyang Zhang,Xiongfei Bai,Tian Chen,Xiang Qi
类目: Artificial Intelligence (cs.AI)
备注: preprint, under review
Abstract:Parsing visual documents into machine-readable representations is fundamental to document intelligence. Existing benchmarks focus on page-level element recognition, reading order, formula recognition, and table structure. Long documents, however, also require document-level structure recovery. This includes reconstructing cross-page table-of-contents (TOC) hierarchies and identifying typed links from tables and figures to their captions, notes, and sources, often in one-to-many form. Because these structures are covered only partially or subsumed within broader parsing protocols, existing benchmarks cannot directly evaluate two key document-level tasks: \emphTable-of-Contents Hierarchy Recovery and \emphContextual Relationship Recovery. To benchmark these two tasks, we introduce \textscLongDocBench, comprising 85 real-world financial reports, textbooks, and academic papers spanning 2,582 pages, with up to 105 pages per document. It provides human-verified annotations for 3,937 heading nodes (mean node depth 3.55; maximum depth 9) and 3,258 contextual relationships annotated across 2,680 table and figure objects. We further evaluate both the downstream utility and recoverability of these structures. Long-document question-answering experiments show that human-verified TOC hierarchies and contextual relationships improve reasoning, with their combination providing complementary benefits. Meanwhile, representative document parsers remain limited on both recovery tasks despite strong page-level performance. To support further progress, we publicly release \textscLongDocBench and its evaluation protocol and reproducible testbed for advancing document-level structure recovery in long documents.
[AI-193] GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG
链接: https://arxiv.org/abs/2608.15056
作者: Zafar Ali,Asad Khan,Aalia Malik,Pavlos Kefalas
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation. We present GraphLoom, a reliability-calibrated multimodal knowledge-graph RAG framework for compact and faithful evidence routing. Given a question and its associated multimodal input, GraphLoom constructs an instance-level multimodal knowledge graph from grounded scene descriptions, extracted relational triples, and external commonsense knowledge. Instead of injecting all retrieved evidence into the generator, GraphLoom performs reliability-aware subgraph retrieval with bounded expansion and selectively routes high-utility evidence through hierarchical graph memory slots and joint graph-sequence attention in a frozen language model. To improve robustness in complex reasoning settings, GraphLoom further combines interleaved retrieval with budgeted corrective retrieval, enabling adaptive multi-hop evidence refinement under noisy retrieval conditions. We evaluate GraphLoom on ScienceQA, MultiModalQA, and OK-VQA, including large distractor evidence pools that approximate noisy external knowledge retrieval. Experimental results show consistent gains in answer quality and evidence faithfulness over strong multimodal RAG, graph-retrieval, and open-source vision-language baselines, with improved retrieval quality on MultiModalQA and stable performance under noisy evidence pools. Additional analyses using MiniCheck-based verification, human evaluation, and latency profiling show that reliability-calibrated graph evidence routing provides an effective alternative to long-context multimodal evidence injection.
[AI-194] AHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning
链接: https://arxiv.org/abs/2608.15055
作者: David Yoon Suk Kang,JungHyun Kim,Juhyun Jeon,Sang-Wook Kim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) and large language models (LLMs) provide rich semantic understanding from textual attributes. However, research on combining language models with hypergraph learning remains limited due to the lack of public text-attributed hypergraph benchmarks. To address this limitation, we present TAHB (Text-Attributed Hypergraph Benchmark), the first public benchmark integrating hypergraph structures and raw textual attributes. TAHB contains 10 real-world datasets from four domains - e-commerce, academia, movies, and politics networks - enabling systematic evaluation of text-aware hypergraph representation learning. Experimental results show that TAHB preserves key structural properties of real-world hypergraphs and consistently reproduces performance tendencies observed in existing benchmarks. Furthermore, experiments under both LLM-as-Enhancer and LLM-as-Predictor settings demonstrate that LLM-enhanced textual semantics improve hypergraph learning performance, while structural and textual information jointly provide the best setting for LLM-based prediction. Our benchmark provides a foundation for future research at the intersection of hypergraph learning and language models.
[AI-195] Andy: A Mathematical Agent for Rigorous Proof and Autonomous Research
链接: https://arxiv.org/abs/2608.15052
作者: Zi’an Wang
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 14 pages, 3 figures. Research logs, reports, and simulation code are available at this https URL
Abstract:Andy is an autonomous mathematical research agent that solves and verifies submitted problems, formulates new research problems, and constructs rigorous proofs. It separates proof generation from correctness evaluation and supports knowledge acquisition, targeted revision, and multistage verification. This paper illustrates the workflow using a published result on self-triggered impulsive consensus as a starting point. Andy formulates a global exponential leader-follower synchronization problem for delayed heterogeneous networks with switching communication topologies. The proposed hybrid control combines self-triggered impulses with execution delay and recovery-phase continuous feedback. After each delayed impulse, this feedback cancels the delayed error channel during a recovery window. Sufficient conditions for global exponential synchronization are established, and Zeno behavior is excluded for both the sampling and impulse sequences. A numerical example confirms the result. This case demonstrates Andy’s ability to learn from existing results, formulate meaningful research problems, and develop and verify rigorous proofs.
[AI-196] SCOPE: Score-Isolated Agent ic Optimization for Video World Models
链接: https://arxiv.org/abs/2608.15043
作者: Yuhua Jiang,Jiaming Wang,Qingbin Liu,Feifei Gao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time introduces a subtle evaluation problem: prompts, samplers, verifiers, and selectors may evolve together, making it difficult to attribute gains or prevent held-out feedback from shaping the final policy. We introduce \scope (\emph\scopefullname), a framework for auditable inference-time adaptation of frozen video world models. \scope represents external controls as a typed state, updates this state only through bounded changes supported by development evidence, and freezes the resulting policy before held-out evaluation. On Physics-IQ benchmark, \scope improves over the exact frozen base by +14.24 (95% CI [+8.10,+21.23] ). Controlled ablations further identify gains from scene specification, sampling, and learned selection, while the margin over the strongest matched agentic baseline remains unresolved. Cross-backbone and prospective evaluations reveal a complementary result: useful inference-time updates exist, but their benefits do not transfer uniformly across models and settings. Together, these findings suggest that reliable inference-time adaptation requires not only better proposals, but also a principled mechanism for deciding which updates should become part of the deployed system. Code is available at this https URL.
[AI-197] LLM -Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning
链接: https://arxiv.org/abs/2608.15041
作者: Changhong He,Jinda Gao,Xinkuan Liu,Le Zhang,Xizi Luo,Yu Mei
类目: Artificial Intelligence (cs.AI)
备注: 30 pages, 5 figures
Abstract:Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational information is heterogeneous, and low-level actions must satisfy strict constraints. We propose an LLM-based hierarchical framework in which the LLM coordinates interacting units based on heterogeneous operational context, while task-specific controllers or optimizers generate executable and constraint-aware actions. We further introduce Continuation-Aware GRPO to capture the consequences of coordination decisions over subsequent control intervals. Rather than judging a decision only by its immediate outcome, the method also evaluates how the system evolves afterward under the current policy. We validate the framework on multi-ramp traffic control and virtual power plant (VPP) energy management, using simplified system models for training and more realistic simulators for evaluation. Across both tasks, the proposed method consistently outperforms direct task-specific control and optimization, end-to-end reinforcement learning, rule-based and RL-based hierarchical coordination, and prompting-only LLM coordinators, demonstrating the value of heterogeneous-context reasoning, hierarchical execution, and continuation-aware policy learning.
[AI-198] S2-MoE: Enabling Efficient Self-Speculative Decoding for Mixture-of-Experts on Edge Devices
链接: https://arxiv.org/abs/2608.15018
作者: Haochen Huang,Shengxuan Qiu,Meng Li
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 10 figures
Abstract:Deploying large language models (LLMs) for inference on edge devices is challenging due to severe memory and bandwidth constraints. While speculative decoding and Mixture-of-Experts (MoE) have been proposed to improve inference efficiency, naively combining them often incurs excessive verification overhead and poor expert reuse, limiting their effectiveness in memory-bound edge settings. In this work, we propose S2-MoE, an efficient self-speculative decoding framework for MoE inference on edge devices. S2-MoE reduces redundant verification through routing-aware adaptive speculative expansion, improves verification efficiency with reuse-aware expert gating, and aligns draft and target execution via shared context. Implemented in this http URL, S2-MoE achieves up to 5.3x speedup (about 2.0x on average) over standard autoregressive de?coding across diverse MoE models and datasets on edge this http URL is available at this https URL.
[AI-199] Hierarchical Agent ic Incident Response with Digital-Twin-Validated Attack Inference
链接: https://arxiv.org/abs/2608.15016
作者: Yiran Gao,Juntao Chen,Tao Li
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 2026 IEEE Conference on Communications and Network Security
Abstract:Network incident response remains slow and labor-intensive as the defender must infer multi-stage attacks from partial observations and translate recovery decisions into reliable system commands. Decision-theoretic planners provide principled optimization but typically rely on abstract states and predefined actions, while large language model (LLM) agents can reason over operational context but may hallucinate attacks and responses. Toward automating response planning, we present a hierarchical agentic response framework that integrates LLM-based attack inference, rollout planning, and digital-twin validation. A fine-tuned LLM infers the attack progression and affected hosts from security alerts and system measurements. An emulated network digital twin replays the inferred attack and returns discrepancies between predicted and observed effects to calibrate the inference. A separately fine-tuned planning agent uses the rollout planning method to prioritize affected components at the tactical layer. At the operational layer, the planning agent proposes high-level recovery actions, and an execution agent translates selected actions into recovery and verification commands that are validated in the digital twin. We evaluate the framework on a 33-component enterprise-network testbed under three multi-stage attack scenarios. The results show that our framework outperforms frontier-LLM baselines in recovery success rate by 18–31%.
[AI-200] GaussMemory: Task-Driven 3D Gaussian Scene Memory for Long-Horizon Robotic Manipulation IROS2026
链接: https://arxiv.org/abs/2608.14986
作者: Zhiqiang Hu,Shouren Huang,Masatoshi Ishikawa
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 10 figures. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
Abstract:Long-horizon robotic manipulation fundamentally relies on persistent spatial memory. However, existing 3D memory systems function merely as passive recorders: they store observations using fixed, hand-crafted rules, treating every scene element–whether a critical grasp target or an irrelevant background wall–with equal importance. In this paper, we propose a paradigm shift from passive storage to active, task-driven spatial memory. We argue that a robot’s memory should not simply record what it sees, but actively learn how to remember–discovering which objects to track precisely, how aggressively to update them, and what to discard, all learned end-to-end without hand-designed rules. Crucially, this active paradigm is realized by unifying memory update and readout as two sides of the same cognitive process, enabling bidirectional flow where task needs shape update strategies and vice versa. To instantiate this vision, we introduce GaussMemory, which leverages 3D Gaussian Splatting as a persistent geometric substrate. On LIBERO, GaussMemory outperforms MemoryVLA on Goal and Long-10; on VLABench, it surpasses \pi_0 -FAST by +5.2% (Track 1) and +6.0% (Track 6).
[AI-201] Do Geometry-Aware Positional Encodings Help Transformers in Spatial Imperfect-Information Games?
链接: https://arxiv.org/abs/2608.14982
作者: Wenji Fu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 7 pages, 4 figures, 3 tables
Abstract:Transformers applied to spatial imperfect-information games must represent map geometry while tracking hidden entities through time. We ask whether geometry-aware positional encodings improve these capabilities, without claiming a new positional encoding. We construct a four-level benchmark on a hexagonal naval pursuit game: controlled geometry and topology probes, an exact-Bayes hidden-target tracking task, offline policy imitation at 1k and 10k games, and 7,200 fixed-seed games against three legacy opponents. Across matched Transformer backbones, HexRoPE reduces exact-belief posterior cross-entropy relative to no positional encoding by 0.278 on D6-transformed test orbits and 0.329 on a larger map; both hierarchical-bootstrap confidence intervals exclude zero, and both Holm-adjusted p-values are below 0.001. At 1k games, HexRoPE improves policy action accuracy by 4.63 percentage points over no encoding and 2.05 points over rectangular relative bias; the gains shrink to 1.55 and 0.41 points at 10k games. However, HexRoPE does not improve aggregate gameplay win rate: its paired effect over no encoding is -1.56 percentage points (95% CI [-4.50, 1.17]). Rectangular relative bias is strongest on D6 belief consistency but fails sharply when extrapolating from radius 3 to radius 4, while graph bias provides only a small blocked-edge gain. The results show that geometric inductive bias improves belief estimation and data-efficient imitation, but those representation gains do not automatically produce stronger closed-loop play.
[AI-202] Demand-Driven Vertiport Siting and Discrete-Event Fleet Simulation for On-Demand Urban Air Mobility Network Design
链接: https://arxiv.org/abs/2608.14974
作者: Hossein Z. Saghazadeh,Yonas Ayalew,Reza Ahmari,Parham Kebria,Abdollah Homaifar
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Accepted for presentation at the 2026 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2026)
Abstract:This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and door-to-door travel-time feasibility. Demand is estimated from commuter and passenger activity data, converted into spatial trip-end points, and clustered using K-means to generate candidate vertiport locations. Candidate networks are screened using range and minimum station-spacing constraints, then evaluated with a discrete-event simulation that models multi-vehicle dispatch, deadhead relocation, battery swaps, and service regularity. Flight time and energy consumption are computed using a point-mass eVTOL performance model. In a Greater Los Angeles case study, the preferred design expands from four stations and four eVTOLs at low demand to sixteen stations and twelve eVTOLs at the highest tested demand level. Results show that larger fleets improve completion time and vehicle-arrival regularity but do not eliminate deadhead flights, indicating that spatial demand imbalance remains an operational burden. The travel-time savings analysis further suggests that UAM is most defensible for longer or congestion-heavy trips where sufficient non-flight time remains after accounting for flight time.
[AI-203] RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder
链接: https://arxiv.org/abs/2608.14947
作者: Yi Xiao,Harshit Sharma,Dessa Bergen-Cico,Asif Salekin
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Detecting opioid craving from wearable physiological signals is critical yet difficult, with the potential to support proactive interventions for individuals with opioid use disorder (OUD). This challenge is especially pronounced under subject-independent evaluation because craving is subjective, heterogeneous, and often physiologically entangled with stress. Our empirical analysis shows that stress elicits strong and reproducible autonomic responses, while craving-related signals are weaker, sparse, and largely embedded within stress-related physiology. We further show that psychological resilience, which shapes stress regulation and craving vulnerability, is not reliably observable from short-term wearable windows, but can be captured through reusable subject-level proxies, including post-stress heart-rate recovery and autobiographical memory this http URL by these findings, we introduce RETRACE, a resilience-guided trait-conditioned framework for subject-independent craving estimation from wearable physiology. RETRACE reframes craving detection as trait-conditioned physiological interpretation: rather than assuming the same physiological pattern has the same meaning across individuals, it uses resilience-related subject context to guide inference. Technically, RETRACE introduces a novel dual-encoder design that separates generalizable stress physiology from subject-specific craving interpretation. It combines a frozen stress-pretrained encoder with a resilience-conditioned craving encoder, using feature-level gating and representation-level fusion to enable lightweight personalization without target-user craving labels or per-user retraining. We evaluate RETRACE on a novel multimodal OUD dataset containing wearable physiology, stress and craving annotations, and autobiographical narratives. Under LOSO setup, RETRACE achieves up to 7% absolute improvement over the strongest baseline
[AI-204] Skill Blocks: How Should an Agent Load Its Skill? A Caching-Correct Comparison of Pre-load On-Demand Tool-Loading Progressive Disclosure and Hybrid
链接: https://arxiv.org/abs/2608.14943
作者: Hironobu Nakasuji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agent skills are often injected in full on every request, increasing token cost. We compare four content-preserving loading methods: Full, Skill Block, Reference, and Hybrid. Across SearchQA, SpreadsheetBench, ALFWorld, ScienceWorld, and SynthProc, we measure token usage using raw input for single-turn tasks and cache-correct effective input for multi-turn tasks. Results show no universal winner. Hybrid reduces input by 27.4% on SearchQA and 39.8% on SpreadsheetBench. On large multi-turn skills, Skill Block and Hybrid achieve substantial reductions, reaching 62.5% and 52.8% on ScienceWorld and 73.0% and 66.6% on SynthProc. ALFWorld shows smaller gains because procedures are short and repeatedly needed. Paired outcome tests detect no quality differences, though they do not establish equivalence. Overall, conditional loading is most beneficial when large portions of a skill are not needed on every turn.
[AI-205] When Is an Agent Evaluation Over? Outcome Finality and Cross-Unit Separation
链接: https://arxiv.org/abs/2608.14940
作者: Avyay M. Casheekar
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Current agent evaluations score models on the state visible at the end of a stopped run which they count as one trial. However, interpreting the score as a final result would require two conditions that the endpoint does not itself necessarily establish: outcome finality and cross-unit separation. These conditions are independent, since reconciling a delayed outcome can settle the label while runs still share state and isolating runs can prevent carryover while the scored outcome remains unfinished. We develop a completion argument that specifies the evidence needed for each decision and argue that a final label is justified only when anything that could still change the claimed outcome is resolved, bounded, or retained as uncertainty. First, in a controlled replay to demonstrate the mechanism where an agent’s actions were held fixed, we find that the endpoint and terminal labels differ for every delayed operation, while a delayed write changes the next run’s score when service state persists between runs but not after isolation or verified reset. Second, in a review of ten public protocols, we find that all protocols identify when a run stops and what is scored, while unfinished operations and the evidence for treating runs as separate trials are documented less consistently. Finally, we propose an open-effects record that lists operations or resources that may remain relevant after the endpoint, their current status, and whether they could change the scored outcome or affect another run.
[AI-206] Small Models Scout Bottleneck Order for Large-Model Data Control AAAI2027
链接: https://arxiv.org/abs/2608.14936
作者: Seungmin Choi,Jiwon Sung,Muhammad Umer,Abhiram Rao Gorle,Guijin Son,Youngjae Yu,John M. Cioffi
类目: Artificial Intelligence (cs.AI)
备注: Submitted to AAAI 2027
Abstract:Small proxy models are commonly used to identify data mixtures for larger-scale training. We ask whether their training trajectories reveal another transferable structure: the order in which larger models should resolve skill bottlenecks. We formulate first-passage skill training, where each monitored skill has a target floor and the objective is to minimize the tokens required to reach all floors. We introduce LogFloor, a closed-loop controller that directs each round toward current bottlenecks, producing phase-ordered resolution trajectories. Across five bAbI skill slices on Qwen2.5-1.5B, LogFloor reduces token cost by 56.2% on average. In 70M-to-12B transfer, three-round replay of a 70M scout path reaches every floor in all eight target runs, saving 30.9% by pair mean, 39.4% in pooled training tokens, and 37.6% under source-cost accounting. On MMLU-control, a frozen scout path succeeds across all eight 12B runs. Collapsing a path to its static marginal mixture or reversing its phase order removes most benefits, while bottleneck labels alone remain partially useful. These results identify phase-ordered bottleneck resolution as a transferable curriculum structure for monitored skill-targeted training.
[AI-207] Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task IJCAI ECAI2026
链接: https://arxiv.org/abs/2608.14916
作者: Alexandru-Stefan Morosanu,Valerian Cecan,Stefan-Daniel Achirei,Laura Erhan
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the RobustifAI 2026 Workshop @IJCAI-ECAI 2026, Bremen
Abstract:AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited. These edited versions form a difficult negative class: they are not generated by AI, yet they may introduce spectral artifacts that resemble synthetic audio fingerprints. We study this problem as a hard-negative robustness setting for AI-generated music detection, focusing on AI-generated and edited variants derived from the same anchor songs. We compile a YouTube-based dataset of AI, edited, and original variants, using the original tracks only as references, and train a binary AI versus edited detector. Audio is processed as 10-second clips and passed as raw waveforms to a pretrained PaSST spectrogram transformer. To reduce leakage, all splits are performed by anchor song. On the held-out test set, the final video-level system achieves 0.811 balanced accuracy. At clip level, AI-generated clips reach an F1-score of 0.836, while edited clips reach a lower F1-score of 0.720. The results suggest that AI-generated music retains detectable fingerprint-like spectral cues beyond ordinary editing, but the lower edited-class F1-score shows that these cues can still overlap with artifacts from edited audio. Grad-CAM visualizations are used to inspect whether high-confidence predictions rely on localized time-frequency regions.
[AI-208] Frontier AI Forecasting Has a Measurement Problem: An Audit of Progress Evidence
链接: https://arxiv.org/abs/2608.14903
作者: Fabricio F Costa
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 6 figures, 1 table. Evidence cutoff: 12 August 2026. Ancillary code and data included. Preprint; comments welcome
Abstract:Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, release time, or expert belief. This paper audits whether the public measurement record supports those connections before another trend is fitted. I construct a frozen, event-centric record through 12 August 2026 with 62 selected systems, 12 versioned benchmarks, seven capability or impact criteria, 144 graded events, 27 source records, and 408 typed relations. The record is an audit sample, not a census. Only seven systems jointly observe estimated training compute and a METR 50 percent task horizon. Training compute is absent for 19 of 27 closed systems, including every selected closed release from 2026, while none of the 35 open-weight systems has a METR horizon observation. Benchmark succession creates a second break: a seven-system link from METR Time Horizon 1.0 to 1.1 has a log-scale slope of 1.206 (95 percent CI 1.021 to 1.390), whereas a six-system MMLU to MMLU-Pro comparison appears shift-like under logit and probit links but not under linear or logarithmic links. The observed bridges have about 80 percent power only for slope departures near 25 percent. Provenance is concentrated: 52 of 71 substantive quantitative events, or 73.2 percent, come from one measurement programme, and 76.1 percent are laboratory releases. A review of 56 methodological and empirical sources identifies 16 complementary measurement directions spanning resources, inference budgets, reliability, agentic work, safety, human preference, field outcomes, and forecast backtesting. No direction supplies a replacement scalar. The result is not that frontier AI forecasting is impossible, but that a defensible dated forecast is a claim about a versioned measurement system with explicit joins, protocols, links, and source dependence, not merely a fitted curve or calendar date.
[AI-209] JarvisBench: Always-on Intelligence Between Humans and Agents
链接: https://arxiv.org/abs/2608.14870
作者: Chen Chen,Zhehuai Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-horizon agents can execute continuously, but human attention remains intermittent and scarce. This creates a bidirectional coordination problem: users may need immediate access to an agent while work continues in the background, whereas agents may encounter consequential decisions that require user judgment after the user has stopped monitoring execution. We posit an always-on attention-coordination layer—\textitJarvis\footnoteNamed after the fictional AI assistant in \textitIron Man.—that mediates this interface and allocates human attention across one or more working agents. We introduce \textitJarvisBench to evaluate both directions of this coordination: whether an intermediary can accurately and promptly answer user-initiated questions about ongoing work, and whether it can recognize when an agent requires user judgment, solicit that judgment at the right moment, and route it back to improve task outcomes. JarvisBench contains 45 agentic task instances: 20 single-agent tasks and 25 workstreams organized into 10 multi-agent projects. The tasks span 19 domains and were selected and adapted from more than 2,000 public candidates. Crucially, the need for user attention arises naturally during execution rather than from an obvious omission in the initial prompt. JarvisBench is designed to integrate with arbitrary agent runtimes without modifying their underlying execution loops. Our reference implementation further provides a full-duplex speech interface, allowing users to reach Jarvis naturally while timely attention coordination supports agents working in the background. By separating agent execution from attention coordination, JarvisBench provides a stable evaluation target as agent capabilities continue to improve.
[AI-210] Evaluating Agent ic Code Repair Capabilities in Distributed Systems
链接: https://arxiv.org/abs/2608.14863
作者: Yibo Yan,Huijuan Wang,Junzhou He,Yizhuo Liang,Shaoyu Wang,Huanchen Sun,Seo Jin Park
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Under submission
Abstract:LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified. Distributed-system debugging, however, remains an under-explored regime: bugs span processes, nodes, and protocol interactions, with root causes rarely recoverable from source alone and brute-force exploration intractable across non-deterministic interleavings. This leaves two gaps in LLM and agent evaluation: no code-repair benchmark targets distributed-system bugs, and no controlled study isolates how much externally provided debugging context changes agent success on them. We introduce DDBench, a code-repair benchmark of 60 historical bugs mined from 13 open-source distributed systems, partitioned into three difficulty tiers. DDBench evaluates every case under two matched conditions: a symptom-only condition where the agent receives only the bug symptom and repository, and a context-augmented condition where it additionally receives a bounded debugging context (logs, traces, runtime state, and targeted code-investigation notes), isolating the effect of debugging context from model capability. The evaluation of ten LLMs on DDBench reveals several findings. First, distributed debugging exercises a reasoning dimension that single-process benchmarks do not surface: models’ pass rates span 61 pp, and pairwise bootstrap separates 9 of 15 top-tier model pairs at p 0.05 on DDBench’s hardest case-set. Second, bounded debugging context lifts aggregate pass rate by +18.1 pp, and the lift is asymmetric: weaker models gain pass rate, while stronger models gain efficiency. Third, debugging context requires careful curation, as even faithful debugging context can sometimes mislead LLMs. Comments: Under submission Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.14863 [cs.SE] (or arXiv:2608.14863v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.14863 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-211] Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
链接: https://arxiv.org/abs/2608.14851
作者: Allen Nie,Anirudhan Badrinath,Nicholas Tomlin,Timothy Dai,Carissa Yip,Rose E Wang,Emma Brunskill,Chris Piech
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published at RLC 2026
Abstract:Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain expertise and be very time-consuming. Pedagogical materials often need to train students to engage in different thinking patterns. In some domains, such as chess, puzzles are used to help students practice their skills in calculating the next moves and recognizing known patterns on a board. Giving students a practice set of puzzles to help them learn different modes of thinking is challenging because the teacher needs to carefully balance between different motifs and how many look-ahead steps a student needs to perform. Popular online platforms like this http URL and Lichess offer players millions of puzzles. Unlike chess tactics puzzles procured by human experts, where chess beginners can learn valuable insights, these puzzles are automatically generated and often regarded as having low pedagogical value. These platforms also rely on a heuristic to recommend puzzles to users for practice. Using the user history data over an entire year, a total of 1.5 billion puzzle-solving histories, we learn the pedagogical value of a puzzle and how to automatically choose a set of puzzles to better support chess learners using insights from offline reinforcement learning. We show that using offline policy evaluation, our trained policy has significant impact on beginners with puzzle-solving Elo range of 100–1000, particularly for the group of beginners whose learning growth was stagnant. We also performed a qualitative analysis of the puzzles discovered by our model by collecting annotation ratings from expert chess players. The success of our pipeline shows promise for a future where we can understand the pedagogical values of practice items given general user interaction data.
[AI-212] What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering
链接: https://arxiv.org/abs/2608.14841
作者: Guanchen Wu,Jiayuan Ding,Subhabrata Mukherjee,Carl Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines. In our setting, the bottleneck shifts from retrieval recall to reranker-side evidence selection: on MMLongBench-Doc, BGE-M3 reaches Recall@20 = 0.86 but only F1@5 = 0.254, and even the visual retriever ColPali reaches only F1@5 = 0.332; a text-only rerank LLM seeing only raw snippets misses table, chart, and layout evidence even when the upstream retriever encoded images. We propose Trident, with two complementary components: Trident-R, a retriever-agnostic LLM reranker that converts each candidate into an LLM-readable semantic record, including a visual caption, section path, entity tags, multi-axis concept hits, and a text snippet, then performs a single adaptive-K rerank call; and Trident-S, a generation-side module that prompts the VLM under topical, entity, and structural lenses before synthesis. On two long-document datasets, the annotation+rerank protocol substantially improves retrieval F1 across five heterogeneous pools, with every reranked pool exceeding the strongest adaptive-K baseline PageIndex. An LLM rerank without the annotation barely changes first-hit ranking, indicating the lift comes from the structured annotation. Trident-S targets open-ended synthesis questions by design, adding up to 6.6 points in generation accuracy on these questions. The best Trident configuration is the strongest downstream QA pipeline in our evaluation, with rankings consistent across two LLM judges (kappa = 0.913).
[AI-213] Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning
链接: https://arxiv.org/abs/2608.14804
作者: Augusto Bernardo Pissarra,Victor Lorena de Farias Souza
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, one context window at a time) maintains no explicit, persistent, governed representation of what is currently true about a patient. This paper argues that longitudinal clinical reasoning is a state-estimation problem under partial observability, and that the axis on which clinical AI succeeds or fails is not the fluency of the model reading the record but the governance of the patient state it reasons over. We distinguish generated context from governed state; separate five objects that clinical AI habitually conflates (true state, observations, evidence, belief, and simulated state); define a tiered governance standard against which any clinical AI system can be audited; and show that an operational definition of accountability decomposes into four information requirements: an immutable evidence ledger with awareness-time versioning, a belief state distinct from accumulated evidence, an observation-process model, and claim-level causal typing. We are explicit that this decomposition is analytic rather than a necessity theorem, and that its value is conceptual hygiene: it converts “accountable clinical AI” from a slogan into an audit instrument. A six-level maturity framework separates what a system makes governable from what it can compute, locating current LLM-centric practice at high capability but low maturity. The paper is fully self-contained: the four research questions the framework poses are stated in the introduction, and the conclusion records what the paper establishes toward each; future work develops the buildable core of the architecture and the research program toward full Clinical World Models. No empirical result is claimed here.
[AI-214] Individual Disempowerment through an Advice Channel: Control Loss when Influence is Endogenous
链接: https://arxiv.org/abs/2608.14795
作者: Adam M. Oberman
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: 9 pages plus an 8-page technical supplement, appended (17 pages total)
Abstract:An AI that can only give advice seems safe: the human is always free to ignore it. That is the premise of the boxing tradition in AI safety, and its long-suspected weak point is that the human who reads the answers is part of the system. We make the fraction \varepsilon_t of behavior that follows the advice a state of a Markov decision process, moved by the advisor’s own messages, so that use deepens reliance. Granted a channel rich enough to echo any action the human could take, higher \varepsilon_t weakly lowers every monotone measure of the power of a human with a message-independent fallback. An oracle rewarded by per-round approval cultivates reliance beyond a closed-form patience threshold, so the same reward weights leave the optimal oracle answering in episodic deployments and cultivating in long-memory ones. An influence bound certified once at deployment is blind to that horizon and bounds the loss no lower than its trivial ceiling. An exogenous cap on influence bounds the guarantee the human loses, and a short enough memory reset removes the incentive to cultivate, while neither recovers the value already steered away. In a closed-form example the optimal oracle never cultivates in fifteen-round sessions and does in sixteen.
[AI-215] CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLM s
链接: https://arxiv.org/abs/2608.14791
作者: Moein Salimi,Danial Parnian,Shaygan Adim,Amirmohammad Ebrahiminasab,Nima Alighardashi,Parsa Gholami,Sahand Akramipour,Mahdi Jafari Siavoshani,Mohammad Hossein Rohban
类目: Artificial Intelligence (cs.AI)
备注: Code and data are available at this https URL
Abstract:Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making and investigation to scientific discovery. Yet LLM research has mostly studied abduction through narrow, task-specific benchmarks, making it unclear whether observed gains transfer beyond the benchmark family used for training or evaluation. We ask whether RL post-training can improve abduction as a transferable reasoning capability. We introduce CEDAR-GRPO, a process-aware framework that combines final-answer correctness with abductive rewards for evidence coverage and evidence-to-explanation directionality. Four open-weight LLMs are post-trained on a controlled, domain-neutral mixture of abductive hypothesis-generation and hypothesis-selection tasks. We evaluate them on 11 unseen tasks spanning hypothesis selection, missing-fact generation, defeasible inference, long-context investigation, clinical reasoning, code debugging, and non-abductive controls. CEDAR- GRPO improves every model on every held-out task over both base models and correctness-only GRPO, with average gains of 7.4 and 2.7 points, respectively, and a maximum gain of 30.8 points. Ablations confirm that RL, abductive reward design, and task diversity each contribute to transfer. Process-level metrics further show stronger abductive behavior, including exploration of alternatives, elimination of rivals, backtracking, and uncertainty marking.
[AI-216] ask-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations
链接: https://arxiv.org/abs/2608.14789
作者: Qian Yin,Xinwei Wang,Guohua Wu
类目: Artificial Intelligence (cs.AI)
备注: submitted to IEEE Trans
Abstract:Large low-Earth-orbit (LEO) Earth-observation (EO) constellations offer frequent access to geographically dispersed ground targets, but emergency requests may arrive after committed routine-plan execution has begun. The resulting dynamic emergency observation scheduling problem (DEOSP) requires urgent tasks to be inserted under intermittent ground contact without excessive routine-plan disruption. To address DEOSP, we propose a task-driven three-layer distributed scheduling (T3L-DS) method, which represents task demand and sensor footprints on a common geographic grid and forms temporary clusters from observation capabilities and current inter-satellite links. For intra-cluster coordination, T3L-DS introduces onboard dual-plan bidding and joint marginal evaluation. It also designs an inter-cluster coordination mechanism for unresolved demand. Extensive computational experiments compare T3L-DS with centralised simulated annealing (SA), an adapted selective time-variant better reply process (A-SeTVBRP), and a conventional contract-net protocol (CNP). T3L-DS achieves the highest emergency coverage among the distributed methods, with average relative improvements of approximately 2.8% and 17.1% over A-SeTVBRP and CNP, respectively. Its average relative gap from SA is approximately 7.1%. Under conflict-enhanced loads, it reduces routine-coverage loss by approximately 57.9% and 87.7% relative to A-SeTVBRP and CNP, respectively. The ablation study confirms the contribution of the proposed coordination enhancements. Overall, the results show that T3L-DS provides an effective distributed approach to DEOSP.
[AI-217] ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning
链接: https://arxiv.org/abs/2608.14773
作者: Harshil Lodhiya
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 20 figures, 8 tables; code and data at this https URL
Abstract:The efficient-KAN literature—covering Chebyshev, wavelet, and radial-basis-function variants of the original Kolmogorov-Arnold Network—has been benchmarked almost entirely on clean data. We show that this choice conceals a large capability difference between architectures: ChebyKAN’s test MSE (evaluated against clean ground truth) increases by a factor of 10.6x when training data is corrupted with sigma=0.1 noise, versus 7.9x for vanilla KAN, 1.7x for a standard MLP, and just 1.4x for our proposed ER-KAN. ER-KAN combines three design choices targeting the noisy, data-scarce setting: shared Gaussian RBF bases across all edges in a layer (providing locality and efficient parameterisation), curriculum noise injection during training (explicitly teaching noise robustness), and entropy-weighted adaptive regularisation (preventing overfitting at small N). The result is a 595-parameter network that matches MLP accuracy at moderate noise while degrading far more gracefully as noise grows. We evaluate on eight analytic functions (N in 50, 200, 500, sigma in 0, 0.03, 0.1), on a damped harmonic oscillator physics-informed neural network where ER-KAN achieves 4.2x lower solution MSE than MLP, and on a Burgers’ equation PINN where all models fail to converge—a genuine limitation we report rather than suppress. We introduce the noise degradation ratio as a simple complementary metric and recommend it become a standard reporting requirement for efficient-KAN papers. Comments: 22 pages, 20 figures, 8 tables; code and data at this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) MSC classes: 68T05, 41A30, 65D15 ACMclasses: I.2.6; G.1.2; I.5.1 Cite as: arXiv:2608.14773 [cs.LG] (or arXiv:2608.14773v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14773 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-218] Agent ic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs
链接: https://arxiv.org/abs/2608.14765
作者: Hadi Fadlallah
类目: Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 21 pages, 3 figures, Submitted to New Generation Computing
Abstract:Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations. This paper studies how different agent capabilities affect reference-free data cleaning and proposes an evidence-grounded framework that combines structured context, profiling, LLM reasoning, executable checks, controlled evidence retrieval, source ranking, citation alignment, conservative repair, reversible scripts, and provenance logging. Seven configurations are evaluated across financial, clinical, and environmental-monitoring datasets using controlled synthetic corruption and original-data descriptive analysis, resulting in 126 completed runs. The evaluation includes two comparison baselines and a progressive LLM-based sequence that adds executable tools, evidence retrieval, evidence controls, and conservative repair. In the synthetic evaluation, the deterministic profiling baseline achieved the highest detection F1-score of 0.561. Among the LLM-based configurations, the full conservative configuration achieved the highest F1-score of 0.421, but no configuration performed best across all evaluation criteria. The source-ranked configurations achieved the lowest unsupported-rule rates, while decision-level citation alignment remained weak. The full conservative configuration produced no unsafe or unnecessary modifications, although these rates were already zero before the conservative policy was added, and it performed no direct repairs. Overall, the results show that additional capabilities introduce trade-offs among detection, repair, evidence grounding, conservative behaviour, reproducibility, and operational cost rather than producing consistent improvements. The study provides a structured framework and empirical methodology for evaluating these trade-offs in reference-free agentic data cleaning.
[AI-219] Advanced modelling and data analytics in aviation
链接: https://arxiv.org/abs/2608.14746
作者: Aziida Nanyonga
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety measures. Despite the vast accumulation of aviation safety data over time, its full potential in predicting and preventing incidents has not been fully realized. This research addresses this gap by applying machine learning (ML) and natural language processing (NLP) techniques to analyze aviation safety data from Socrata, the Australian Transport Safety Bureau (ATSB), the National Transportation Safety Board (NTSB), and the Aviation Safety Network (ASN). By leveraging existing ML models, including deep learning and transformer-based architectures alongside NLP methods for mining aviation incident narratives, this study uncovers patterns contributing to safety related incidents such as accidents and near-misses. Additionally, it employs various topic modelling techniques to extract meaningful themes from unstructured safety reports, enhancing the interpretability of incident analysis. Causal inference techniques and interpretable AI frameworks are further explored to improve model transparency and trustworthiness. A key contribution of this work is the deployment of advanced ML methodologies in a structured aviation safety context, assessing their effectiveness and providing insights into their practical implementation. The findings offer valuable insights for aviation stakeholders, including regulators, airlines, and policymakers, by providing data-driven solutions that enhance incident analysis and decision making. Ultimately, this research supports the industry s ongoing efforts to minimize risks, improve passenger and crew security, and integrate AI driven methodologies into aviation safety management.
[AI-220] Unraveling the Size Determination Mechanism of Nanocrystal Synthesis via Interpretable Neural Networks
链接: https://arxiv.org/abs/2608.14734
作者: Kai Gu,Haizheng Zhong
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep learning models of nanocrystal synthesis enable the prediction of size and shape by encoding precursors and reaction conditions. However, their black-box nature hinders gaining deep insights into the underlying synthetic mechanisms. Here, we develop the Nanocrystal Equation Learner (NanoEQL), a fully white-box neural network to unravel the size determination mechanisms of nanocrystal synthesis. Building on the EQL architecture, eight operators are introduced to replace standard activation functions to fit the mathematical equations in nanocrystal synthesis. Among these operators, three smoothed operators address the gradient explosion of singular operators at zero. To evaluate the weights of different precursors, we develop a temperature-gated attention pooling strategy that encodes concentration-driven and reactivity-driven chemical synthesis mechanisms into the temperature gate. The NanoEQL model illustrates that the final nanocrystal size can be described by a linear equation composed of three scalars representing nanocrystallization capability (-Zp), growth capability (Zrea), and external input potential (-Zops). These interpretable scalars not only advance the rational design of nanocrystal synthesis but also establish a generalizable paradigm for deciphering chemical reaction mechanisms through white-box machine learning.
[AI-221] A Novel Fourier Feature Network for Solving Partial Differential Equations
链接: https://arxiv.org/abs/2608.14733
作者: Qihong Yang,Zhijie Su,Yangtao Deng,Qiaolin He
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Building on the foundation of single-hidden-layer neural networks, Fourier Feature Networks (FENs) are proposed, which incorporate Fourier features using \cos , \sin , or a combination of both. Similar to Extreme Learning Machines (ELMs), FENs employ a single-hidden-layer architecture to generate a set of basis functions. The target function is then approximated as a linear combination of these basis functions, with the coefficients determined using the least squares method. However, unlike ELMs, which often rely on affine transformations to improve representational power, FENs can achieve high-precision solutions without requiring such transformations on the input variables. To evaluate the representational capacity of these networks, we search for an optimal scaling factor within a predefined range for the randomly initialized and fixed weights and biases. By adjusting this scaling factor, we ensure a fair comparison between FENs and ELMs using various activation functions, such as \textsigmoid , \tanh , and \textswish . Our numerical experiments demonstrate that FENs consistently achieve higher accuracy than ELMs.
[AI-222] ail-Aware Top-k On-Policy Distillation
链接: https://arxiv.org/abs/2608.14728
作者: Huipeng Huang,Hongxin Wei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy distillation (OPD) has emerged as an effective paradigm for transferring knowledge between language models, where a student is trained to align its next-token distribution with the teacher’s along its own trajectories. To provide dense supervision at tractable cost, many works minimize the reverse Kullback-Leibler (KL) divergence between the student and teacher’s normalized distributions over the teacher’s top- k tokens. However, this normalized objective discards the information about tail probability: the total probability outside the teacher’s top- k tokens. As a result, the optimization can steadily increase the student’s tail probability and entropy, empirically degrading downstream accuracy. To address this issue, we propose Tail-Aware Top- k OPD (\textbfTA-OPD), a novel distillation method that restores the missing tail probability signal. In particular, TA-OPD minimizes the reverse KL divergence over the top- k tokens plus a tail token that carries the tail probability. In effect, TA-OPD better aligns the student’s next-token distribution with the teacher’s, preventing the increase in tail probability and entropy caused by top- k normalization. Extensive experiments demonstrate the superiority of TA-OPD, improving Avg@8 by up to 8.05 points on common benchmarks. Our code is available at this https URL.
[AI-223] Beyond Pass@k: Measuring Reliability and Security of Agent ic Code Generation
链接: https://arxiv.org/abs/2608.14711
作者: Jiajun Jiang,Sharon Zheng,Natan Vidra,Spurthi Setty
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI coding agent benchmarks rank agents with the Chen et al. (2021) pass@k estimator, but current implementations misapply it: they set n to the number of unit tests in a single submission rather than the number of independent rollout attempts, conflating test-suite size with attempt independence. We diagnose this operationalization error, prove it by counterexample, and propose reliability@k, the same estimator applied correctly, with n = independent rollouts and c = fully-passing rollouts per (task, agent) pair. In a synthetic multi-rollout benchmark, the misapplied metric inflates reported scores by 0.85-0.97 in absolute terms (0.96-0.98 reported vs. 0.00-0.12 corrected), and a cheap single-rollout proxy fails to substitute for repeated runs (Spearman \rho = 0.417 ). Motivated by evidence that functional correctness does not imply security safety, we additionally propose security-adjusted reliability@k, which counts only rollouts that are both functionally correct and free of high-severity insecure patterns. In an initial live-API test with three agents, the adjustment did not change any ranking under our current scanner and threshold, so we present it as a proposed complementary lens whose decisive evaluation requires better-powered future runs. Finally, a preliminary 5-task SWE-bench Verified pilot observes the same core concern in a real repository setting: macro-averaged hidden-test pass rate was 0.80 while strict task resolution was 0.20.
[AI-224] Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems
链接: https://arxiv.org/abs/2608.14707
作者: John Knowlton,Aritra Guha,Risto Miikkulainen
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures, 2 tables
Abstract:As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundamental challenge. Existing orchestration strategies typically rely on fixed interaction patterns and often lack mechanisms for assessing the reliability of intermediate reasoning steps, allowing errors and hallucinations to propagate through the system. This paper introduces a semantic-uncertainty-guided orchestration approach, HASSUM as a general framework for uncertainty-aware coordination in multi-agent systems. The method estimates uncertainty using semantic entropy and semantic density, which measure trust at the level of answer semantics rather than output probabilities. These signals enable adaptive orchestration decisions, including output verification, selective reprompting, additional deliberation, and confidence-aware response selection. Because the approach operates independently of any particular agent architecture, it can be integrated into a broad range of hierarchical and collaborative multi-agent systems. The evaluations demonstrate an implementation within a hierarchical agent framework and evaluate it on StrategyQA, JailbreakBench, and TruthfulQA benchmarks. Across tasks that require complex reasoning and are prone to ambiguity or hallucinations, uncertainty-guided orchestration yields more reliable outcomes than uncertainty-unaware coordination. Semantic entropy and semantic density in tandem outperformed either metric alone. Ablations testing different thresholds and model sizes demonstrated that both influence the effectiveness of semantic metrics. The results suggest that semantic uncertainty is a practical and general-purpose signal for improving robustness and trustworthiness in agentic AI systems.
[AI-225] Synchronized Logit Steering: Real-world Steganography
链接: https://arxiv.org/abs/2608.14697
作者: Andrew Rufail,Aadi Dash,Onir Narahari,Ethan Mui,Mahi Gajare,Prakhar Tiwari,Shrija Makapothula,Nick Cui
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Steganography in large language models offers a way to embed hidden messages within natural-sounding text. Existing token and logit-level methods typically require the sender and receiver to share an identical prompt context, which is rarely guaranteed in production pipelines that use retrieval-augmented generation or proprietary system instructions. We introduce Synchronized Logit Steering (SLS), a deterministic steganographic scheme that eliminates this dependency by deriving a proxy prompt from the generated output itself, allowing both parties to reconstruct the same logit distribution without access to the original prompt. SLS encodes payload values as token ranks within high-entropy regions of the proxy prompt distribution, and we extend the scheme with periodic recurrence and payload bursts to scale information density. Across ShareGPT, GSM8K, and SWE-bench Verified, we show that the KL divergence between the true and proxy prompt distributions falls below 0.5 nats once the synchronization window reaches 40 tokens, and SLS encoding does not meaningfully disrupt this convergence relative to greedy generation. We also find that the periodic-burst variant achieves 0.20 bits per token, or roughly 10x the capacity of single-payload encoding. Kolmogorov-Smirnov tests further confirm that SLS outputs are statistically difficult to distinguish from greedy generations, demonstrating that covert, prompt-agnostic communication through LLMs is both practical and stealthy.
[AI-226] A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks
链接: https://arxiv.org/abs/2608.14694
作者: Naveed Khan,Besan Al Sbeihi,Maryam Alshehhi,Nasir Saeed
类目: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
备注: 28 Pages, submitted to IEEE Communications Surveys and Tutorials
Abstract:Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks. Unlike conventional deep learning models that are trained for individual applications, wireless foundation models (WFMs) learn generalized representations from large-scale heterogeneous wireless data and can be efficiently adapted to communication, sensing, localization, and network optimization tasks with minimal task-specific supervision. Despite rapid progress, current research remains fragmented across architectures, training paradigms, and application domains, with no unified survey dedicated to the design, learning, and deployment of WFMs. This survey presents a comprehensive and unified review of wireless foundation models. We first establish the fundamental concepts of WFMs and introduce a taxonomy that organizes the field according to model architectures, pre-training paradigms, and applications. We then review representative architectures, self-supervised pre-training strategies, parameter-efficient adaptation methods, datasets, benchmarks, and evaluation methodologies, highlighting their roles in enabling transferable wireless intelligence. Furthermore, we examine emerging applications spanning physical-layer signal processing, network intelligence, and cross-layer optimization, and discuss the key challenges of data availability, generalization, interpretability, efficient edge deployment, and standardization. Finally, we outline future research directions toward scalable, trustworthy, and general-purpose wireless intelligence for AI-native 6G networks. This survey provides a comprehensive reference for researchers and practitioners developing next-generation intelligent wireless systems.
[AI-227] Mitigating Rubric Interference in LLM Judges via On-Policy Self-Distillation
链接: https://arxiv.org/abs/2608.14684
作者: Dingyao Yu,Tong Zhang,Yutao Mou,Yunxiao Zhang,Wei Ye,Shikun Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM judges increasingly evaluate responses against fine-grained rubric checklists. When a sample requires multiple rubrics, current methods typically assess each in a separate inference call. Evaluating all rubrics in a single pass is a natural alternative with greater efficiency, but we find that it introduces rubric interference: the verdict on one rubric shifts depending on which other rubrics are co-present. In a preliminary study, only one-third of samples receive fully consistent verdicts when evaluated under rubric sets of varying composition. We develop a measurement framework that probes interference through four controlled operations: rubric set expansion, subsetting, reordering, and noise injection. To mitigate interference without external supervision, we propose Self-Anchored Rubric Alignment (SARA). SARA uses a model’s own single-rubric judgments as stable anchors and aligns multi-rubric reasoning with these anchors through on-policy self-distillation. We validate SARA on three datasets (HealthBench, FLASK, ResearchQA) and two model families (Qwen3, Llama-3.1). SARA consistently improves evaluation consistency while maintaining agreement with both base models and GPT-4.1 as a reference judge. Furthermore, the learned consistency transfers across datasets, confirming that SARA teaches a general capability rather than fitting dataset-specific patterns.
[AI-228] When Agent ic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
链接: https://arxiv.org/abs/2608.14680
作者: Chenkai Zhang,Yiran Li,Yifang Tian,Michalis Bachras,Hans-Arno Jacobsen
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails. We present AGENTCHAOSBENCH, a benchmark for detecting and localizing runtime faults in agentic systems from their execution telemetry. We run five heterogeneous applications that coordinate agents over the Agent-to-Agent protocol and call tools through the Model Context Protocol, and inject ten types of operational fault (unavailable or slow tools, corrupted or oversized responses, and delayed, looped, or misrouted delegations and bypassed guardrails) at their tool, model, guardrail, and inter-agent boundaries, alongside a no-fault control. The resulting dataset contains 275 sanitized traces: 250 faulty executions spanning ten fault types and 25 no-fault controls. Each faulty trace is aligned with the no-fault execution of the same input; fault-type labels and, where applicable, location labels are held out from diagnosis. On structured single-trace inputs, a first set of zero-shot LLM baselines shows the task is far from solved: local detectors up to 14B parameters reach only 13.6-19.2% top-1 fault-type accuracy and the frontier DeepSeek-v4-pro only 24.8%, while jointly identifying the fault type and its location tops out at 22%; reference-dependent faults (above all a bypassed guardrail) stay near-unsolved from a single trace. An aligned reference improves selected relative faults but does not resolve guardrail bypass. The held-out labels and compact prediction format support reproducible comparison of LLM-based and non-LLM diagnosis methods.
[AI-229] ake it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection
链接: https://arxiv.org/abs/2608.14675
作者: Dominika Kunc,Przemysław Kazienko,Stanisław Saganowski
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 4 Figures, 2 Tables, Accepted as the 14th International Conference on Affective Computing and Intelligent Interaction (ACII 2026)
Abstract:While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven. In this work, we evaluate the efficacy of PPG-based SSL for real-life intense emotion detection. First, we pretrain a Real-Life PPG encoder (RL-PPG) on unconstrained, real-life data. As a rigorous sanity check, we demonstrate that these representations transfer exceptionally well to an objective physical activity recognition task, yielding almost 5-fold increase in performance over baselines in a leave-one-subject-out evaluation (LOSO). However, when applied to a~subjective real-life emotion detection task, these same general representations fail to surpass naive baselines under the LOSO protocol. Using an Across-Time validation strategy, we establish that incorporating an individual’s personal data during fine-tuning is the main driver of predictive performance, outweighing the benefits of population-level pretraining. Ultimately, our findings indicate that in the evaluated scenario, general SSL representations may be insufficient for subjective affective inference, suggesting that personalization is likely a key component for real-world emotion recognition. To support future research, we share the code and pretrained RL-PPG~encoder~weights.
[AI-230] Auditing an AI-Generated Mathematical Proof: A Correction to a Greedy Conditioning Lemma in Quantum Parallel Repetition
链接: https://arxiv.org/abs/2608.14673
作者: Mikołaj Sienicki,Krzysztof Sienicki
类目: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph)
备注: 8 pages, 5 references, Auditing an OpenAI’s Generated Mathematical Proof
Abstract:Chapter 6 of OpenAI’s Ten Advances in Mathematics and Theoretical Computer Science claims an exponential parallel-repetition theorem for all finite two-player, one-round entangled games. Early in the proof, the chapter uses a quantitative greedy conditioning lemma. The lemma is meant to select a small set of coordinates (D) such that, after conditioning on winning every coordinate in (D), a randomly chosen remaining coordinate is won with average probability at least (1-\delta). The statement is correct, but the proof as printed contains a polarity error. Its continuation test is written in terms of average success, while the next step requires a coordinate with a large conditional failure probability. That implication is false, and even simple examples can leave the printed procedure without a valid next move. This note gives an explicit counterexample, identifies the intended continuation condition, and supplies a complete corrected proof. The repair is local: it leaves the statement of the lemma and the parameters used later in the chapter unchanged. It should not, however, be read as an independent verification of the main parallel-repetition theorem. More broadly, the example shows how a mathematically plausible AI-generated argument can hide a small but decisive reversal between complementary events. Comments: 8 pages, 5 references, Auditing an OpenAI’s Generated Mathematical Proof Subjects: Artificial Intelligence (cs.AI); Quantum Physics (quant-ph) Cite as: arXiv:2608.14673 [cs.AI] (or arXiv:2608.14673v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14673 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-231] Beyond Correctness: Toward Automated Novelty Verification with Lean 4
链接: https://arxiv.org/abs/2608.14669
作者: Ayrton Porto
类目: Artificial Intelligence (cs.AI)
备注: 20 pages. Preliminary version; a large-scale quantitative evaluation (N theorems x M models) is left to future work. Comments welcome. Code: this https URL
Abstract:Artificial intelligence systems applied to mathematics verify correctness but not novelty: an automatically generated theorem can compile in Lean without errors and yet be an already known result. This article presents AViD Journal, a pipeline that receives a LaTeX article, formalizes its statements in Lean 4, and issues a novelty verdict through a decision tree over three dimensions: prior existence in a formal corpus (Mathlib) and an informal one (TheoremSearch and Matlas, with temporal filter and LLM judge), non-triviality via automatic tactics, and structural distance between proofs measured as Jaccard distance over premise sets. Evaluation on papers withdrawn from arXiv due to declared duplication produced a result more informative than any performance measure: the identification of three obstacles that limit the approach regardless of this implementation. First, successful compilation of a Lean file does not guarantee semantic fidelity. Second, the recall ceiling is imposed by the coverage of theorem indices, not by the similarity metric. Third, arXiv removes the source code of articles upon withdrawal, compromising the reproducibility of any benchmark built upon them. Comments: 20 pages. Preliminary version; a large-scale quantitative evaluation (N theorems x M models) is left to future work. Comments welcome. Code: this https URL Subjects: Artificial Intelligence (cs.AI) MSC classes: 68V15, 68V20, 68T05 ACMclasses: I.2.3; F.4.1 Cite as: arXiv:2608.14669 [cs.AI] (or arXiv:2608.14669v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14669 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-232] Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring
链接: https://arxiv.org/abs/2608.14666
作者: Dhiraj Neupane,Mohamed Reda Bouadjenek,Richard Dazeley,Sunil Aryal
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal distributions. This misses coupling faults, where the physical relationship between sensor groups breaks while marginal statistics remain normal. Such faults evade marginal monitoring and persist as latent failures, with direct consequences for system reliability and safety. We propose CMR-Mamba (Causal Mechanism Representation Mamba), which trains per domain Mamba state-space encoders on healthy data. A causal cross-modal predictor regularises these encoders so that the effect-channel manifold reflects the normal cause-to-effect coupling. Anomalies are scored by k-nearest-neighbour (kNN) distance on this manifold or by the mechanism residual between the observed and the causally predicted effect embedding. We evaluate CMR-Mamba on electromechanical (Paderborn bearings), hydraulic (ZeMA) and cyber-physical (SWaT) coupling-fault domains. Ablations establish two findings. First, k-NN manifold scoring, rather than the encoder family, is the dominant source of gain over reconstruction-error scoring, improving baselines by up to 0.42 AUROC and exceeding the gain from causal regularisation. Second, aggregate AUROC is saturated by easy faults that any strong method solves, so the methods separate only on the low-separability subset. There CMR-Mamba leads the evaluated baselines on Paderborn artificial defects and on SWaT stealthy attacks, which keep every sensor inside its normal range and which marginal methods detect only at chance. CMR-Mamba therefore offers an interpretable and consistently competitive approach to coupling-fault detection across mechanical, hydraulic and cyber-physical systems. Code and data are available at this https URL.
[AI-233] Ring-based Spatial Transformer: Learning Non-linear Spatial Interactions between Building Distribution and Pedestrian Flow
链接: https://arxiv.org/abs/2608.14660
作者: Shun Nakayama,Takahiro Kanamori,Wanglin Yan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:This study proposes a ring-based SpatialTransformer to learn how building uses at different distances from a railway station interact to generate pedestrian flow. Concentric ring buffers at 100-meter intervals up to 800 meters were defined around 100 randomly selected stations in Tokyo, treating each ring as a spatial token. Self-Attention was applied to learn inter-zone interactions directly from data, without prior structural assumptions. GPS-derived walking trip counts served as the target variable and Geographically Weighted Regression as the baseline. Across 30 independent trials, the SpatialTransformer consistently outperformed GWR in predictive accuracy. SHAP analysis revealed that mid-to-outer distance zone features dominate pedestrian flow prediction, while features from the 0-100m zone contributed little. The attention matrix showed that each distance zone attends most strongly to spatially distant zones, demonstrating that pedestrian flow is regulated by structural interactions across the entire catchment area rather than by any single zone in isolation. These findings challenge the compact city assumption that station-proximate development maximizes pedestrian flow, and suggest that land use distribution across the full walkable catchment area deserves greater consideration in urban planning practice.
[AI-234] When Uncertainty Isnt Enough: An Empirical Study of Self-Correction in Code Generation
链接: https://arxiv.org/abs/2608.14659
作者: Pranav Rakasi,Maanas Lalwani,Arnav Srivastava,Arya Palanivel,Tinuade Adeleke,Ruizhe Li,Sean Wu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注:
Abstract:Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncertainty estimation methods developed for natural language transfer to code generation, and whether such signals can improve code generation via selective self-correction. We evaluate five uncertainty methods: mean token entropy, verbalized confidence, P(\textTrue) , entropy ensembles, and semantic entropy probes, across three small code LLMs on HumanEval and BigCodeBench. We find that multi-sample P(\textTrue) achieves the strongest correlation with correctness, while all the other methods, including semantic entropy probes, yield only weak correlation. We then use these uncertainty signals to drive three self-correction policies: adaptive decoding, uncertainty-based regeneration, and verification-based regeneration. Our results reveal a stronger negative finding than anticipated: uncertainty-based self-correction fails to reliably improve Pass@1, degrading accuracy in 5 of 6 configurations across both benchmarks ( -3 pp to -10 pp), and adaptive decoding degrades accuracy in 4 of 6 configurations. Only verification-based self-correction reliably improves Pass@1, with gains of +6 to +26 percentage points on HumanEval and +8 to +20 percentage points on BigCodeBench, scaling inversely with baseline strength. These findings replicate consistently across both benchmarks and suggest that cheap uncertainty estimators are insufficient on their own to improve code correctness, and that their practical value lies in serving as gating signals for costlier execution-based correction loops rather than as standalone substitutes for verification.
[AI-235] P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval
链接: https://arxiv.org/abs/2608.14656
作者: Zhongli Wu,Zhuangzhi Gao,He Zhao,Feixiang Zhou,Fu Wang,Jinru Ding,Yuankai Wang,Hongyi Qin,Gregory Y. H. Lip,Bil Kirmani,Yalin Zheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures, 5 talbles
Abstract:Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition. However, unlike electrocardiography (ECG), PPG measures peripheral pulse dynamics rather than cardiac electrical activity, limiting its ability to predict cardiac conditions that rely on ECG-specific morphological cues. Existing methods attempt to bridge this gap by reconstructing ECG signals from PPG signals. However, this inverse mapping is inherently ill-posed, and faithful waveform reconstruction does not necessarily translate into improved downstream performance. To address this challenge, we propose P2E-VQ, a retrieval-augmented framework that replaces ECG waveform reconstruction with ECG-linked representation retrieval. Specifically, P2E-VQ converts PPG patches into discrete tokens and retrieves ECG-linked information from a memory bank constructed exclusively from the training data. This process augments PPG representations while requiring only PPG signals during inference. Extensive experiments on five public datasets covering six downstream tasks, including clinical endpoint prediction and affective state recognition, demonstrate that P2E-VQ consistently outperforms pretrained baselines under a unified frozen-feature linear-probing protocol.
[AI-236] FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weighting
链接: https://arxiv.org/abs/2608.14654
作者: Hai Anh Tran,Cuong Ta,Truong X. Tran
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted author manuscript (AAM) to appear in IEEE Transactions on Artificial Intelligence
Abstract:Federated Learning (FL) is a collaborative paradigm that enables multiple devices to train a global model while preserving local data privacy. A major challenge in FL is the non-Independent and Identically Distributed (non-IID) nature of data across devices, which hinders training efficiency and slows convergence. To tackle this, we propose Federated Impurity Weighting (FedImp), a novel algorithm that quantifies each device contribution based on the informational content of its local data. These contributions are normalized to compute distinct aggregation weights for the global model update. Extensive experiments on EMNIST and CIFAR-10 datasets show that FedImp significantly improves convergence speed, reducing communication rounds by up to 64.4%, 27.8%, and 66.7% on EMNIST, and 44.2%, 44%, and 25.6% on CIFAR-10 compared to FedAvg, FedProx, and FedAdp, respectively. Under highly imbalanced data distributions, FedImp outperforms all baselines and achieves the highest accuracy. Overall, FedImp offers an effective solution to enhance FL efficiency in non-IID settings.
[AI-237] Do Uncertainty Signals Help? A Systematic Study of Uncertainty-Aware Decoding with Rollback Mechanisms
链接: https://arxiv.org/abs/2608.14653
作者: Xianzong Wu,Xiaohong Li,Yuejun Guo,Xinyang Liu,Tianlin Li,Junjie Wang,Qiang Hu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Prediction uncertainty is a widely adopted metric for quantifying model confidence, with downstream applications spanning model explanation, data selection, and prediction rollback. Despite its demonstrated utility, the potential of uncertainty quantification to enhance code generation in large language models (LLMs) remains largely underexplored, raising a critical question: to what extent can uncertainty serve as an effective signal for improving LLM-based code generation? To answer this question, we study uncertainty-aware rollback decoding, an inference-time strategy that uses uncertainty signals to identify unreliable generation regions and roll back to earlier valid prefixes without retraining the model. We evaluate this framework on seven code LLMs, five code generation benchmarks, and eight token-level uncertainty signals under a unified decoding setup. Our results show that the complete rollback framework improves over equal-budget restart across the evaluated benchmarks and model settings, with gains of up to 0.26 in pass@1 and 0.35 in AvgTestPassRate on functional code generation benchmarks, and an absolute improvement of up to 6.4% in Patch-Aligned Safe Rate on Dsec-Python. Among the evaluated signals, information-theoretic measures such as token entropy and negative log-likelihood show the most favorable overall trend, frequently achieving the best or near-best results on standard benchmarks. A component-controlled ablation further shows that feedback-guided rollback provides the main improvement, while uncertainty localization provides an additional gain when checking, budget, rollback, and branch decay are held fixed. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14653 [cs.LG] (or arXiv:2608.14653v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14653 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-238] Stop Indexing at Full Precision: Revisiting Clustering for Vector Embeddings VLDB2026
链接: https://arxiv.org/abs/2608.14648
作者: Leonardo Kuffo,Peter Boncz
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: VLDB 2026 Workshop: The 2nd Workshop on Vector Databases
Abstract:In this study, we revisit three widely used techniques in vector search and utilize them to optimize vector embedding indexing through clustering: dimensionality reduction, quantization, and dimension pruning. We propose an indexing pipeline in which these techniques are applied before clustering, and we focus on how they affect storage footprint, clustering time, and the quality of the resulting centroids for vector search tasks. Our results reveal that using full-precision vectors for clustering is excessive, as even 1-bit codes can achieve near-optimal clustering quality (within 1% of ideal) while reducing storage requirements by 60x and delivering attractive performance gains (Figure 1). We open-source our implementations at this https URL.
[AI-239] SMOPD: Selective Token-Entropy Masking for Dirty-History Multi-Turn On-Policy Self-Distillation
链接: https://arxiv.org/abs/2608.14647
作者: Chenyang Jiang,Changhan Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Dirty-history rollouts make multi-turn on-policy self-distillation (OPSD) brittle: once a student emits an erroneous intermediate reply, later turns are conditioned on that reply, and uniform distillation can spend loss on tokens that carry little corrective signal. We introduce SMOPD (Selective Masking for On-Policy Distillation), a loss-only stabilization method for multi-turn OPSD. For each generated middle-turn reply, SMOPD ranks token positions by student entropy and removes the lowest-entropy 20% from the clipped generalized Jensen-Shannon distillation loss; final-answer and FULL-preservation losses are unchanged. This design targets token-level uncertainty rather than coarse trajectory outcomes, adds no parameters, and has zero inference-time overhead. We compare SMOPD with a correctness-scaling variant that multiplies a common detached reliability proxy using final-answer correctness. On LiC with Qwen3 models, SMOPD improves SHARDED-view accuracy by 1.0-2.5 percentage points in single-seed 1.7B, 4B, and 8B comparisons, and a small 4B multi-seed check shows a +1.7pp mean SHARDED gain over baseline (two-tailed p = 0.022). Adding the outcome scalar is harmful without masking at 1.7B (-4.0pp) and remains scale-dependent when combined with masking (+1.3pp at 4B, neutral at 1.7B, and -0.5pp at 8B). These archived aggregate results suggest that token-level uncertainty is a more reliable stabilization signal than scalar final-answer correctness in this evaluated dirty-history OPSD setting, while leaving causal mechanism tests and broader benchmark validation to future work.
[AI-240] ask- and Session-Level Model Routing: A Common-Interface Hybrid Evaluation of Four Open-Source Routers Across Four Benchmarks
链接: https://arxiv.org/abs/2608.14641
作者: Kiran N. Kumar,Santhosh K. Saminathan
类目: Artificial Intelligence (cs.AI)
备注: 34 pages, 25 tables
Abstract:Agentic systems increasingly delegate model selection to a router, yet open-source routers are usually evaluated with different tasks, candidate pools, and execution protocols, limiting direct comparison. We present a common measurement protocol and hybrid evaluation of four router implementations across RouterBench, BFCL v4, tau2-bench, and WebArena. We evaluate 290 frozen tasks against a locked matrix of 2,610 candidate outcomes. Three routers emit constant or near-constant tier assignments; only vLLM Semantic Router varies materially with prompt content, and it has the highest observed success rate on none of the four benchmarks. Always-Mid matches Aurelio exactly on three benchmarks and within 0.003 on the fourth. For vLLM, task-level superiority tests detect no task-specific advantage over a share-matched content-blind allocation; equivalence is established only on WebArena at the protocol-declared five-percentage-point margin. The results show that, under these configurations and controls, observed gains track selected-tier composition more closely than demonstrated task-specific targeting. Fixed-tier baselines and selected-tier distributions are therefore necessary controls in router evaluation; the findings are scoped to these configurations, candidate pool, and frozen benchmark samples, not to routing paradigms in general.
[AI-241] BDIP-Net: Dual-Interaction Graph Learning for Property Prediction of Bilayer Materials
链接: https://arxiv.org/abs/2608.14640
作者: An Vuong,Chen Zhao,Jin Hu,Shui-Qing Yu,Xintao Wu
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions. The computational discovery of such materials is challenging because accurate structure generation typically relies on expensive DFT-based optimization, while existing machine-learning models often fail to explicitly distinguish different interaction types during property prediction. To address these challenges, we propose a machine-learning framework for efficient construction and property prediction of stacked bilayer materials. The framework employs a MatterSim-D3-based structural optimization workflow to generate DFT-quality bilayer structures from monolayer building blocks and stacking configurations at substantially reduced computational cost. For property prediction, we introduce BDIP-Net (Bilayer Dual-Interaction Potential Network), a graph neural network that explicitly models intra-layer and inter-layer interactions through interaction-specific potential representations and adaptive message fusion. We evaluate the proposed framework on BiDB, HetDB, and SAMBA, encompassing homobilayers, heterobilayers, and twisted bilayer systems. Results show that the MatterSim-D3-based workflow closely reproduces DFT-PBE-D3 optimized structures, while BDIP-Net consistently outperforms existing graph neural network and potential-based approaches for bilayer property prediction.
[AI-242] Fractional Optimizers Meet Fractal Activation Functions: An Empirical Study of Multi-Scale Optimization in Neural Network
链接: https://arxiv.org/abs/2608.14636
作者: Sebastian Raubitzek,Georg Goldenits,Sebastian Schrittwieser,Philip König,Kevin Mallinger
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Quite extensive paper, more than 100 pages
Abstract:Fractional optimization methods and fractal activation functions are two independent directions for improving neural network training. Fractional optimizers extend first-order optimization through fractional derivatives and memory effects, whereas fractal activations introduce multi-scale nonlinear representations based on self-similar Weierstrass- and Blancmange-type functions. Here, we investigate their interaction within a unified experimental framework. We evaluate fractional optimizer families on Ackley and Himmelblau benchmark surfaces, in standard form and with additive Weierstrass-type perturbations, and then in feed-forward neural networks with conventional and fractal activations on ten classification datasets. The comparison includes standard methods, regularization-style optimizers, explicit and adaptive memory-based fractional optimizers, and other representative literature methods. Overall, fractional optimization and fractal activations show useful but selective pairings. Regularization-style fractional scaling performs well with selected fractal activations in network training, while Grünwald–Letnikov memory is most relevant on perturbed surfaces. Adaptive memory improves plain memory substitution in several cases, supporting controlled fractional memory as a promising direction rather than a universal replacement.
[AI-243] Accuracy and Reliability of Large Language Models in Cosmetic Chemistry and Skin Health: A Benchmarking Study
链接: https://arxiv.org/abs/2608.14631
作者: Amelia Liu
类目: Artificial Intelligence (cs.AI)
备注: 14 pages
Abstract:As consumers increasingly turn to AI chatbots for skincare advice, the technical accuracy of Large Language Models (LLMs) in cosmetic chemistry remains largely under-evaluated. We benchmarked 14 LLMs on a structured set of topics related to cosmetic chemistry, including the chemical properties of specific cosmetic ingredients and common cosmetic scenarios that may be of interest to consumers. Web search was disabled throughout to assess each model’s internalized knowledge rather than its internet retrieval capacity. Overall performance was poor, with the most pronounced deficits in quantitative reasoning and structural identification tasks. While models handled general skincare questions with reasonability, responses consistently lacked the technical depth required for informed consumer decision-making. Notably, conversation with AI can pose a risk: outputs that sound authoritative but contain technical errors are less likely to generate skepticism compared to responses that explicitly acknowledge uncertainty. These findings suggest that general-purpose LLMs, trained predominantly on unverified public data, are currently not reliable sources of cosmetic chemistry information. Progress on two fronts, fine-tuning verified chemical and dermatological datasets, and substantial improvements to algorithmic reasoning, will likely be needed before these tools can be considered as resources for public use.
[AI-244] Local AI pre-screening for human triple-blind peer review in health sciences
链接: https://arxiv.org/abs/2608.14625
作者: Rodrigo Martins Boos
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注: 16 pages, 1 figure
Abstract:Academic peer review is under mounting strain: NeurIPS 2025 received 21,575 submissions, ICLR 2025 received 11,603, and ICML 2025 received 12,107. This volume has outpaced the supply of qualified reviewers, and large language models (LLMs) are already filling the gap, largely undisclosed. An independent analysis of ICLR 2026 found roughly 21% of its 75,800 peer reviews were fully AI-generated, with over half showing some AI involvement (up from 15.8% in 2024). Documented risks include hallucinated citations in accepted papers and hidden prompt-injection instructions embedded in manuscripts to manipulate AI reviewers into favorable assessments. We propose a triple-blind, multi-LLM pre-screening framework for peer review, developed for a health sciences journal, that formalizes and discloses AI involvement while preserving human reviewers as the final decision-making authority. The framework routes a submission through five stages – sanitization/anonymization, parallel AI pre-screening, an automated check gate, blinded human review, and editorial adjudication – with return-to-author loops at the check and editor stages. Addressing the confidentiality concerns behind NIH/NSF bans on submitting unpublished proposals to third-party generative AI, all three AI reviewers run on locally-hosted, open-weight LLMs, keeping manuscript content within the journal infrastructure. The closest precedent, Shen et al., benchmarked five open-source LLMs on quartile classification of 200 manuscripts and found accuracy insufficient (35% exact-match) for autonomous use, supporting our decision to retain mandatory human adjudication. This transparent, human-supervised design offers a defensible alternative to today’s opaque, unregulated AI use in peer review, potentially reducing the substantial delay of traditional review (avg. 13 weeks to first decision) without displacing human judgment. Comments: 16 pages, 1 figure Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL) ACMclasses: H.3.7 Cite as: arXiv:2608.14625 [cs.CY] (or arXiv:2608.14625v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2608.14625 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.5281/zenodo.21365017 Focus to learn more DOI(s) linking to related resources
[AI-245] Learning Agent Execution for KV-Cache Management in Agent ic Serving
链接: https://arxiv.org/abs/2608.14624
作者: Rui Zhang,Chaeeun Kim,Shaoting Feng,Kuntai Du,Yuhan Liu,Yi Zhong,Cheng-Wei Ching,Junchen Jiang,Liting Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents. Across these workflows, every agent repeatedly executes a fixed context consisting of system prompts, tool definitions, and few-shot examples, creating substantial opportunities for KV-cache reuse. Existing LLM serving systems, however, manage KV-cache reactively using prefix caching and recency-based replacement, causing reusable agent contexts to be evicted before their next invocation and forcing repeated recomputation. We present CacheScout, an agent-aware KV-cache runtime layer for multi-agent LLM serving. The key insight is that future KV-cache reuse is governed by agent execution semantics rather than cache recency alone. CacheScout captures these semantics by learning agent execution transitions online, without requiring predefined workflow graphs or offline training, and uses the learned execution model to guide both cache eviction and proactive prefetching while leaving the serving critical path unchanged. We implement CacheScout on top of vLLM. Across representative real-world multi-agent workloads, CacheScout improves KV-cache hit rate by 10-18 percentage points, reduces mean TTFT by 18-45%, lowers mean per-turn latency by 29-38%, and increases peak throughput by up to 57%. These benefits also generalize to larger models, reducing TTFT by up to 54% while sustaining 37% higher throughput.
[AI-246] A Human-Centred Approach to Benchmarking LLM s for Parenting Advice
链接: https://arxiv.org/abs/2608.14622
作者: Yunke Zhao,Isobel Voysey,Alastair van Heerden,Rob Hughes,Jun Zhao
类目: Artificial Intelligence (cs.AI)
备注: 15 pages. Submitted for review
Abstract:People are increasingly using large language models (LLMs) to seek advice, including for parenting. Parenting is a critical and socially sensitive domain. Thus, evaluating advice provided by LLMs requires indicators beyond aggregated information quality benchmarks to consider relational and behavioural elements of the responses. With a multi-dimensional rubric created by parenting experts, this paper evaluates 15 LLMs across 100 parenting scenarios in 2 languages (English and Chinese), using an LLM-as-a-judge method. Results show that aggregate scores can hide rubric item-specific weaknesses, models implicitly encourage different parenting styles, and language influences responses. We highlight the importance of evaluation output auditability and challenges involved in evaluating LLM-generated advice in domains like parenting. Our findings provide important insights for selecting LLMs for direct user engagement and the development of user-facing parenting advice applications.
[AI-247] Explaining Reinforcement Learning Decisions in Self-adaptive Systems
链接: https://arxiv.org/abs/2608.14620
作者: Jasmina Gajcin,Juan C. Rosero,Ivana Dusparic
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted in the 20th Colombian Computing Congress. 13 pages, 2 figures, 3 tables
Abstract:Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand. This can lead to diminished user trust, and makes for a more challenging verification of systems. To address this challenge, this paper introduces Explanations using Alternative Realities for Reinforcement Learning (EARL), a Python library to produce counterfactual explanations in RL settings. This library allows the user to produce explanations by exploring What-if scenarios to clarify agent behavior by comparing possible outcomes. Counterfactual explanations have been shown to be intuitive and user-friendly in psychology research, but have only recently been explored in RL, with existing implementations usually limited to toy examples and benchmarks. EARL supports counterfactual explanation generation in realistic RL-based self-adaptive systems. To demonstrate its applicability, we demonstrate its use in a simulation of CitiBikes, a self-adaptive bike-sharing system, and we provide evaluations showing how it performs in real applications.
[AI-248] Large Language Models and their Awareness of Mechanics and Spatial Geometry
链接: https://arxiv.org/abs/2608.14615
作者: Johannes Gerstmayr,Sebastian Weyrer,Tobias Möltner,Peter Manzl,Michael Pieber
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systematically. We present MecEng, a fully automated benchmark that evaluates LLMs on the creation of multibody simulation models from parameterized textual descriptions. The benchmark comprises 84 generic tasks on three difficulty levels, ranging from rigid-body systems with joints and contact to flexible multibody systems that require exact 3D geometry generation, tetrahedral finite-element meshing, and Hurty-Craig-Bampton model order reduction of machine parts. A dedicated pipeline with LLMs generates simulation-ready geometry from text using Netgen, and builds multibody system models for the code Exudyn, which are then verified against expert ground truth on several levels: system-graph isomorphism including graph node annotations, numerical solutions, and part-specific measures such as mass, geometry, and eigenfrequencies. In total, 32 open-weight and two proprietary LLMs are evaluated. On rigid-body tasks, the best open-weight model obtains an overall success rate of 86.0%, compared to 91.4% for the strongest proprietary model, while flexible multibody tasks remain considerably harder. Additional studies quantify the influence of sampling temperature, reasoning, prompt design, model size, and LLM-release date. The results indicate rapidly improving, but still error-prone, mechanical engineering awareness of current LLMs.
[AI-249] DumpsterCluster: From Dumpster Diving to Serving LLaMA-70B on 60 GPUs
链接: https://arxiv.org/abs/2608.14614
作者: Zeyu Cao,Xuan Guo,Cheng Zhang,Cheuk Hang Lau,Ilia Shumailov,Yiren Zhao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注:
Abstract:As AI datacenters retire functional GPUs, vast quantities of still capable accelerators enter secondary markets. This paper investigates whether these retired GPUs can find a productive afterlife to form a DumpsterCluster that can serve modern LLM inference, and under what conditions such repurposing is economically viable and environmentally sustainable. We physically built a 128-GPU DumpsterCluster from scratch using only second-hand components and ran it for one year. At current market prices (\ 22K for the DumpsterCluster vs. \ 600K for an 8-GPU B200 system), the economic advantages are substantial. Through pipeline-parallel optimizations, our V100 based DumpsterCluster achieves competitive LLaMA-70B throughput, validating production viability. However, our deployment reveals critical context dependencies. Older GPUs consume significantly more energy per token, making total cost of ownership favorable only in regions with inexpensive electricity. Under grid-average carbon intensity, second-hand systems can produce approximately 4x higher total carbon emissions per token for 8B models, and over 40x for 70B models, compared to current-generation hardware. These findings show that GPU afterlife is not universally sustainable - hardware repurposing must be strategically coupled with low carbon energy sources. When deployed in regions with favourable energy economics and clean electricity, second-hand GPUs offer a viable pathway for expanding AI capacity while advancing affordability, energy security, and environmental responsibility.
[AI-250] Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP
链接: https://arxiv.org/abs/2608.14613
作者: Wael Albayaydh,Rui Zhao
类目: Artificial Intelligence (cs.AI)
备注: 20 pages , 3 tables
Abstract:Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic’s Model Context Protocol (MCP) for agent-to-tool access and Google’s Agent2Agent (A2A) protocol for agent delegation and negotiation. However, these protocols specify transport and discovery rather than strategic correctness and do not guarantee efficient, individually rational, or strategy-proof outcomes. We introduce a framework that (i) encodes classical negotiation mechanisms, including alternating-offers bargaining and Vickrey-Clarke-Groves-style auctions, as constraints over A2A message schemas; (ii) provides a lightweight runtime verification and repair layer that checks messages against protocol invariants; and (iii) offers a benchmark of negotiation and allocation tasks with known optimal solutions for measuring deviations from game-theoretic predictions. We evaluate multiple LLM backbones using unstructured dialogue, structured protocols, and structured protocols with verification. Across negotiation trials (N=30 per condition), verification reduces outcome variance, while structured protocols achieve 100 percent success for both models. After correcting parser artifacts, audited unstructured baselines achieve approximately 97 percent and 93.3 percent success. In auction experiments (N=30 per model), both models achieve 100 percent efficient allocation but differ sharply in truthful bidding: one bids its exact valuation in every trial, whereas the other does so in only 3.3 percent of trials. Thus, mechanism-level incentive compatibility does not automatically transfer to LLM-agent behavior. A three-party fair-allocation task produced only 4.2 percent usable outcomes; we report this negative result with a diagnosis. This work bridges classical multi-agent systems theory and modern LLM-agent infrastructure and defines verifiable interaction at the A2A protocol layer. Comments: 20 pages , 3 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14613 [cs.AI] (or arXiv:2608.14613v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14613 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Wael Albayaydh [view email] [v1] Fri, 10 Jul 2026 07:32:19 UTC (529 KB)
[AI-251] When Do LLM s Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
链接: https://arxiv.org/abs/2608.14610
作者: Yiqian Huang,Shuyuan Zheng,Qianying Liu,Shaowen Peng,Yuntao Kong,Kotaro Funakoshi,Chuan Xiao,Manabu Okumura,Yang Cao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case – a capability we term temporal applicable-law determination. However, whether large language models (LLMs) can reliably perform this task remains unexplored. In this paper, we construct a benchmark to evaluate LLMs on temporal applicable-law determination, and systematically investigate why they fail at temporal legal reasoning. Our experiments reveal four key findings. First, LLMs exhibit a strong bias toward applying the most recently enacted law, regardless of when the legally relevant facts occurred. Second, this bias does not stem from an inability to understand that laws have temporal scope, nor from a lack of knowledge about historical statutes. Third, we provide behavioral evidence that reinforcement-learning-shaped explicit reasoning may be a key mechanism: while improving general reasoning ability, it reduces the diversity of reasoning paths, causing models to converge on applying the current law. Fourth, this produces a counterintuitive inverse relationship: models with stronger general reasoning ability tend to perform worse on temporal legal reasoning. Our findings offer concrete guidance for future work on improving LLM performance in temporally grounded legal reasoning.
[AI-252] Understanding AI Anxiety in the Workplace: A Multimethod Investigation Using Fear Acquisition Theory and the Technology Acceptance Model
链接: https://arxiv.org/abs/2608.14609
作者: Jaroslaw Grobelny,Mateusz Klakus,Kacper Szymański,Teresa Chirkowska-Smolak
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:As artificial intelligence (AI) rapidly diffuses and concerns about job displacement intensify, the psychological mechanisms underlying AI job replacement anxiety remain insufficiently understood. Drawing on Integrated Fear Acquisition Theory and the Technology Acceptance Model, the present research investigates whether AI job replacement anxiety can be elicited through vicarious exposure to narratives emphasizing AI-over-human control, and whether perceived usefulness and perceived ease of use of AI moderate this response. Across two studies, we examine AI job replacement anxiety as a response that emerges through vicarious exposure to narratives emphasizing AI agency and human control loss, rather than through direct personal experience of job displacement. Study 1 employed a randomized experiment (N = 316), demonstrating that such exposure increased AI job replacement anxiety. This effect was moderated by perceived usefulness of AI, but not by perceived ease of use, and remained robust after controlling for core self-evaluations. Study 2 (N = 995) replicated the association between perceived AI-over-human control and job replacement anxiety in an observational design and provided convergent evidence for the moderating role of perceived usefulness, supporting the external validity of the findings. Together, the results provide the first causal evidence that perceptual and vicarious processes can trigger AI job replacement anxiety. By shifting attention from structural labor-market conditions to how AI agency is perceived and communicated, this work offers a mechanism-based account of when and why AI-related job fears arise.
[AI-253] Intelligent Base Station Deployment in Urban Wireless Networks: A Geographic Data-Informed Digital Twin Approach
链接: https://arxiv.org/abs/2608.14599
作者: Zhenyu Tao,Yuxuan Li,Wei Xu,Yongming Huang,Xiaohu You
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:The placement of base station (BS) is a fundamental determinant of coverage and capacity of urban wireless networks. Yet large-scale BS deployment optimization remains challenging due to its dependency on site-specific radio propagation and user spatial distributions, both of which are unfortunately difficult to obtain prior to deployment. To overcome this barrier, we propose an intelligent BS deployment framework that integrates a geographic data-informed wireless network digital twin (DT) with deep reinforcement learning (DRL), enabling sample-free macro BS deployment optimization from solely open geographic data, without on-site measurements, real user trajectories, or exhaustive ray tracing. The proposed DT incorporates a sample-free radio map prediction model with hybrid input representation to achieve kilometer-scale signal strength estimation in milliseconds, complemented by a diffusion-based generative model for trajectory synthesis to collectively characterize channel and user distributions. Leveraging the DT as a virtual training environment, we formulate BS deployment as a multi-step Markov decision process (MDP) and solve it via a spatially structured DRL algorithm. A local search process and a Wasserstein distance-based deployment buffer are further incorporated to efficiently explore the large combinatorial solution space. Experimental results in real-world urban scenarios demonstrate that the geographic data-informed DT attains accuracy comparable to 100-sample-based prediction, and the intelligent BS deployment framework achieves up to 98.9% of the idealized benchmark performance while reducing optimization overhead by over 99%.
[AI-254] Position: Medical AI Neglects Real Treatment Outcomes ICML2026
链接: https://arxiv.org/abs/2608.14598
作者: Shiva Kaul,Anjum Khurshid
类目: Artificial Intelligence (cs.AI)
备注: Published at ICML 2026. this https URL
Abstract:Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding of treatment itself is still inadequately trained and evaluated, using human opinions and syntheses (especially texts such as biomedical publications and clinical practice guidelines) rather than actual underlying data on treatment outcomes. This neglect seriously limits the potential of medical AI, and is already causing deficiencies in both frontier models and major benchmarks, as argued in this position paper. Real treatment outcomes, drawn from sources such as observational databases and randomized experiments, should be substantially incorporated into both training and evaluation. Improving these outcomes should be reemphasized as the downstream goal of all medical AI.
[AI-255] oward Safe LLM Agents : A Survey of Specification Verification and Enforcement
链接: https://arxiv.org/abs/2608.14590
作者: Pierre Dantas,Lucas Cordeiro,Ehsan Nowroozi,Tihanyi Norbert
类目: Artificial Intelligence (cs.AI)
备注: 28 pages
Abstract:LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use of tools. However, no existing system provides formally grounded, task-level safety guarantees for the plans these agents generate. Research remains fragmented across specification, verification, and enforcement, limiting understanding of the strengths and limitations of existing approaches. To address this gap, we conducted a PRISMA 2020 systematic review of 38 studies published between 2022 and 2026 and retrieved from six academic databases. Our analysis reveals four key findings. First, the specification bottleneck remains the primary challenge: natural-language-to-formal translation achieves only 24% to 35% semantic correctness, undermining downstream verification. Second, runtime monitoring is the most mature enforcement strategy, reducing unsafe actions by 40% to 65% in controlled settings, but it does not provide complete safety guarantees. Third, the verifier tax shows that blocking 94% of unsafe actions can still result in less than 5% safe task completion because agents exploit alternative unsafe paths. Finally, no existing approach simultaneously achieves soundness, scalability, semantic correctness, and task-level safety preservation. We contribute a three-level taxonomy, a comparative analysis of existing techniques, a synthesis of evidence on the verifier tax, and a ten-problem research agenda for trustworthy agentic AI.
[AI-256] An Agent ic Framework Using Rules and LLM s for Embedding and Annotating Descriptive Document Layouts: A Plant Science Use Case
链接: https://arxiv.org/abs/2608.14587
作者: Nicolas Turenne,Youcef Sklab,Eric Chenin,Jean-Daniel Zucker
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Background: Recent advances in information retrieval (IR) leverage both dense and sparse representations, large language models (LLMs), and specialized retrieval models to improve ranking accuracy, relevance, and cross-lingual performance. Complementary techniques such as passage indexing, document layout analysis, and semantic knowledge representation further enhance retrieval effectiveness by capturing fine-grained contextual and structural information. Emerging agentic LLM frameworks extend these capabilities by enabling planning, iterative reasoning, tool use, and multi-agent collaboration, thereby broadening applications across diverse domains. These frameworks also emphasize rigorous evaluation, ethical considerations, and trustworthiness, ensuring responsible deployment in real-world settings. We propose a modular, agent-based pipeline for botanical trait extraction. Optical character recognition (OCR) converts PDFs into machine-readable text, while segmentation and indexing organize content by genus and species. Rule-based parsers extract structured botanical traits, and ensembles of large language models (LLMs) expand trait vocabularies and resolve ambiguities. This approach ensures accurate species recognition, scalable annotation, and explainable integration of textual botanical descriptions, enabling robust and interpretable data extraction across large botanical corpora. Results: Using three regional botanical datasets, our system extracted 55,737 trait annotations across 4,961 species, averaging 9.1 traits per species. Integration of LLM-based enrichment improved coverage for 75% of traits, increasing total annotations by 59%. While the choice of OCR engine had a minor effect on species recognition, overall annotation counts remained stable, demonstrating the robustness, scalability, and reliability of the pipeline for large-scale botanical trait extraction.
[AI-257] Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures
链接: https://arxiv.org/abs/2608.14586
作者: Haibo HU,Lianming Huang,Qiao Li,Nan Guan,Chun Jason Xue
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure. In a full autonomous driving stack, this problem is even more pronounced: legacy vehicle platforms were provisioned for modular pipelines, yet after several planning-related functions are absorbed into a unified VLA model, part of the original CPU budget becomes underutilized, while the visual encoder and the main reasoning path still concentrate most computation and memory demand on the GPU. As a result, directly deploying VLA together with the rest of the onboard system can be hard under realistic GPU memory constraints. To address this issue, we present a hybrid CPU–GPU inference framework with flexible resource scheduling for autonomous driving. Our design partitions the VLA backbone at the block-layer granularity, executes the visual encoder and LLM prefix on the GPU, and offloads the LLM suffix to the CPU through a cross-frame asynchronous pipeline, thereby exposing a schedulable boundary for redistributing compute and memory pressure across heterogeneous processors. We evaluate the proposed framework on two representative driving VLA models, Orion and MindDrive. On Bench2Drive, our method reduces average latency from 521ms to 408.0ms for Orion and from 443ms to 306.2ms for MindDrive, corresponding to 21.7% and 30.9% reduction, respectively. For Orion, the estimated peak GPU memory is further reduced from 45GB to 29GB. In real-vehicle deployment under coexistence with this http URL, native Orion cannot run because the onboard GPU memory budget is insufficient, whereas the hybrid version runs successfully together with the full vehicle stack.
[AI-258] Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry
链接: https://arxiv.org/abs/2608.14585
作者: Zhaoyu Li,Hangrui Bi,Youyuan Zhang,Wenjie Ma,Zenan Li,Zhaolei Zhang,Xujie Si,Kaiyu Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic deduction, and algebraic computation. Yet, existing approaches typically address only a subset of these abilities or struggle with competition-level problems. We introduce \textitEuclid-Omni, a unified neuro-symbolic framework that couples a formal geometry system with Large Language Models (LLMs) and Vision-Language Models (VLMs) to tackle both calculation- and proving-style problems, in formal and natural languages, up to Olympiad-level difficulty. At its core, we develop \textitEuclidea, a versatile symbolic geometry solver that automatically generates reasoning steps through deductive inference and algebraic computation. Building on this, we develop a data-generation pipeline that synthesizes symbolic problems and solutions, renders diagrams, and translates them into natural language, producing large-scale, diverse datasets for training LLMs and VLMs across a wide range of reasoning settings. Experiments show that VLMs trained on our synthetic data achieve superior performance on calculation tasks, and that LLMs combined with \textitEuclidea are competitive with state-of-the-art systems on Olympiad-level proving problems, despite using orders of magnitude less compute and training data. Code and scripts are publicly available at this https URL
[AI-259] SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization
链接: https://arxiv.org/abs/2608.14579
作者: Rui Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse logic structures. Traditional expert-designed flows lack adaptability, while reinforcement learning (RL) methods often suffer from low sample efficiency and limited interpretability. We introduce SKILL, a Self-correcting Knowledge-guided Iterative Large Language Model Agent that unifies multi-agent LLM reasoning and RL-based environment interaction for automated synthesis optimization. SKILL coordinates three specialized LLMs: GPT-4o for strategic planning, Claude Sonnet 4 for detailed reasoning, and Gemini 2.5 Pro for efficient analysis with a PPO-based RL agent that learns actionable policies through direct interaction with synthesis tools. A novel self-correcting module monitors environment feedback (PDA metrics), detects suboptimal behaviors, and invokes LLM-guided recovery strategies. Evaluations on IWLS, OpenCores, and EPFL benchmarks show SKILL achieves a 12.4 % PDA improvement over expert flows and 86.3% success rate on logic systems up to 500K gates.
[AI-260] Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study
链接: https://arxiv.org/abs/2608.14578
作者: Yixuan He,Jinni Su,Yun Kang
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Applications (stat.AP)
备注: 8 pages main text, 10 pages total, 4 tables
Abstract:Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characteristics, longitudinal trajectories, and relational context remains unclear. Using data from approximately 11,860 participants in the Adolescent Brain Cognitive Development (ABCD) Study, we compare cross-sectional, longitudinal, and graph-based approaches for predicting alcohol sipping, alcohol use, marijuana use, and alcohol/marijuana use. We evaluate tree-based models, recurrent neural networks, and Temporal Graph Convolutional Networks (T-GCNs) constructed from family, school, and feature-similarity graphs. Longitudinal models consistently outperform baseline models, with temporal XGBoost achieving the strongest standalone performance. Although T-GCNs generally do not surpass temporal XGBoost, graph-derived risk scores provide complementary information. Combining temporal XGBoost and T-GCN predictions through score-level stacking yields the best performance across all outcomes, achieving AUC-ROC values above 0.79. Feature analyses identify peer deviance, age, externalizing symptoms, parental monitoring, cultural norms, and neighborhood context as important predictors of substance use onset. These findings demonstrate the value of longitudinal modeling for substance-use prediction and suggest that graph-based representations can provide effective auxiliary risk signals.
[AI-261] From Reactive to Autonomous: Evolution of AI Operations in Cloud Network Infrastructure
链接: https://arxiv.org/abs/2608.14574
作者: Arun Malik
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
备注: 12 pages, 5 figures, 3 tables
Abstract:The operational model for cloud network infrastructure has undergone a fundamental transformation over the past decade. What began as manual, human-driven troubleshooting has evolved through scripted automation, rule-based systems, and AI-assisted operations into fully autonomous incident resolution. This paper traces the evolution of AI operations (AIOps) in cloud network infrastructure, identifying the architectural patterns, organizational challenges, and technical inflection points that enabled each generational transition. Drawing from production experience operating network infrastructure at hyperscale, we present a maturity model that characterizes five distinct operational generations, analyze the technical and organizational barriers that impede transitions between generations, and document the metrics that indicate readiness for increased autonomy. We show that the path from reactive to autonomous operations is not merely a technology problem but requires co-evolution of tooling, trust frameworks, knowledge management practices, and operational culture. Our findings provide a practical roadmap for infrastructure organizations seeking to adopt progressively autonomous AI operations.
[AI-262] WARA: Toward Automated Wireless Optimization Research with Closed-Loop LLM Agents
链接: https://arxiv.org/abs/2608.14573
作者: Yuan Guo,Yilong Chen,Chao Hu,Xianghao Yu,Liang Hong,Jie Xu
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledge, this paper presents the first end-to-end autoresearch framework for the wireless domain, with a focus on wireless resource allocation optimization. We propose the Wireless AutoResearch Agent (WARA), a closed-loop multi-agent system for automated wireless optimization research. Given only an initial topic, WARA decomposes the workflow into three phases: research gap identification and problem proposal, wireless optimization modeling, algorithm design and experimentation, and research deliverable construction. Across these phases, WARA uses artifact-mediated control: upstream artifacts are consumed as inputs, structured outputs are stored for downstream use, and controller-managed gates validate consistency among models, algorithms, experiments, and claims. When validation fails, WARA repairs only the responsible artifact instead of restarting the whole workflow. We present a representative wireless resource allocation case study showing how WARA converts an initial topic into a complete research package with executable evidence and a synthesized technical manuscript. We further design a structured LLM-based ScoringAgent to evaluate manuscript-level research validity and optimization research maturity. Comparative results show that WARA substantially outperforms one-shot LLM generation and approaches the quality profile of recently accepted peer-reviewed technical papers. These results indicate that closed-loop artifact control is a promising path toward end-to-end LLM-assisted wireless optimization research. The source code is available at this https URL.
[AI-263] Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System
链接: https://arxiv.org/abs/2608.14571
作者: Shaochen Zhong
类目: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注:
Abstract:With soaring submission counts, stricter reciprocal review policies, widespread adoption of platforms like OpenReview, and without the offsetting pressure of publication fees, the machine learning (ML) community has one of the largest scholarly presences among all scientific fields. And yet, \textbfalmost \textiteveryone has \textitmany unpleasant things to share about their review experience. Worse, there is little public space to seriously discuss, let alone debate, what makes a review system effective or how it might be improved.\quad In this position paper, we expand our discussion from two core problems: \textitHow can we reasonably limit submission volume? and \textitHow can we incentivize good and discourage bad reviewing? We first assess the strengths and shortcomings of existing attempts to address such problems. Specifically, we present four takes on some popular conference mechanisms and propose two alternative designs for improvement.\quad Our general position is that meaningful improvement in ML peer review won’t come from polite best-practice suggestions tucked into Calls for Papers or Reviewer Guidelines: it requires \textbfenforceable yet fine-grained procedural safeguards paired with \textbfa currency-like credit system (e.g., our proposed \textitOpenReview Points). ML practitioners can earn'' such points by contributing good review practices, and spend’’ them across one or multiple major conferences to redeem different kinds of ``perks,‘’ such as complimentary registration or the right to request additional review resources.
[AI-264] Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration ICML2026
链接: https://arxiv.org/abs/2608.14569
作者: Shufeng Kong,Xiaochuan Zhang,Caihua Liu
类目: Artificial Intelligence (cs.AI)
备注: Accept by ICML 2026
Abstract:Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high confidence. This position paper argues that when hard constraints exist and the cost of verification is relatively low, neural constraint reasoning must prioritize symbolic integration over pure learning. We justify our focus on Sudoku as a representative NP-complete testbed because it exhibits a sharp asymmetry between easy verification and hard solving: checking a candidate solution requires only polynomial time O(n^2) , while finding a solution may require exponential search. Through a comprehensive survey of solving methods spanning deterministic algorithms, metaheuristic optimization, learning-based approaches, and language-conditioned reasoning, we demonstrate that neural-only methods without instance-level certification fail to achieve the provable correctness that symbolic and neuro-symbolic approaches provide. We advocate for a bidirectional integration in which neural methods enhance symbolic solvers by learning heuristics and converting percepts into symbols, while symbolic methods verify neural outputs to ensure their reliability. To operationalize this position, we propose a multi-agent certified reasoning framework that demonstrates how this integration can achieve both computational efficiency and provable correctness.
[AI-265] From Doyle to AGM: A Survey and an Implementation Roadmap for Belief Change
链接: https://arxiv.org/abs/2608.14567
作者: Yuri Almeida,Arthur Casals
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: Author’s accepted manuscript of an article published in The European Journal on Artificial Intelligence 2026 (SAGE). 65 pages, 2 figures. Final published version available at this https URL
Abstract:This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change implementation. Seeded by Doyle and London’s foundational 1980 taxonomy, we trace the evolution of belief revision from computational origins through the theoretical transformation of the AGM framework to contemporary approaches. Our analysis demonstrates how pre-AGM computational pragmatism relates to AGM theoretical constructs, revealing both continuities and transformations across this evolution. We analyze how each taxonomical category evolved in the post-AGM era, identifying the theoretical foundations and historical precedents that inform contemporary implementation challenges. This foundation enables subsequent research into robust computational blueprints that synthesize historical insights with formal guarantees, providing the baseline for systematic implementation analysis and engineering-focused belief change research.
[AI-266] Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picture ACL2026
链接: https://arxiv.org/abs/2608.14566
作者: Aidan Kierans,Ritam Dutt,Kaley Rittichier,Shiri Dori-Hacohen,Avijit Ghosh
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 1 figure. Accepted for archival publication at the ACL 2026 Workshop on Evaluating Evaluations (EvalEval)
Abstract:Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value problem, i.e., whether model outputs align with human moral values. In contrast, the moral norm problem, i.e., whether models can identify and correctly apply context-sensitive moral norms, remains underexplored. We posit that this imbalance stems from the field’s reliance on descriptive ethics frameworks, such as Moral Foundations Theory and Kohlberg’s stages of moral development, which emphasize value representation over normative application. We review existing benchmarks and evaluation methods, and show that they cluster heavily around the value problem, while discussion regarding normative ethics remains underrepresented. We identify three crucial gaps: (i) the absence of high-quality ground-truth data for moral norms and their applications, (ii) insufficient evaluation of intermediate reasoning processes, and (iii) limited attention to the identification of morally relevant features in context. Subsequently, we propose a research agenda that includes the development of standardized formal representations for normative theories, the construction of expert-annotated datasets capturing norm application, and evaluation protocols that explicitly distinguish between values-level and norms-level competence. Our goal is to encourage a more systematic study of normative reasoning in LLMs.
[AI-267] Position: AI Lock-In Is in Progress and We Must Be Prepared ICML2026
链接: https://arxiv.org/abs/2608.14565
作者: Jaeho Kim,Seokhyun Lee,Jieun Lee,Changhee Lee
类目: Artificial Intelligence (cs.AI)
备注: ICML 2026 Position Track Spotlight
Abstract:AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulation of generative AI’s societal impacts (including unemployment risk and labor market disruption). However, an equally important dimension remains underexplored: the risk inherent in dependence on AI systems themselves. In this position paper, we argue that AI safety research should address AI Lock-In, the phenomenon whereby excessive reliance on AI systems leads to human deskilling, diminishes human capacity for independent functioning, and creates systemic vulnerabilities when AI systems become unavailable or compromised. We highlight that AI Lock-In is a systemic threat that is already emerging at individual, societal, and national levels, one that could be dramatically amplified by AI service disruptions or geopolitical conflicts. Drawing on detailed scenarios, we investigate how AI Lock-In emerges and escalates across multiple levels, ranging from individual skill atrophy to national-scale infrastructure failures. To address this, we provide guidance on how such risks can be mitigated and prepared for at each level. We contend that proactively addressing AI Lock-In before such dependencies become entrenched, or even irreversible, is essential for preserving individual autonomy and national security.
[AI-268] Forward Pass Domain Adaptation (Without Cross-Layer Backpropagation)
链接: https://arxiv.org/abs/2608.14563
作者: Rivaan Patil,Simon Dennis,Hao Guo,Kevin Shabahang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages
Abstract:Forward-Pass-Only MLP training (FPO) adapts large language models without a backward pass through the model body, achieving 2.7–3.2x the throughput of standard fine-tuning at ~40% less peak training memory, while leaving off-domain benchmarks within seed-noise of baseline, a property that full-network fine-tuning does not reliably reproduce. FPO rests on a single empirical observation: at late layers of a transformer, the output-layer prediction error approximates the true gradient with cosine similarity 0.47–0.59 across six public models we survey. We introduce a two-minute diagnostic that quantifies this approximation per layer for any model, identifying where late-layer adaptation is viable. Informed by the diagnostic, FPO computes a single error signal at the output and applies it to each target layer. No signal is propagated between layers, and no autograd graph is constructed at any point. We evaluate FPO on three model families (OLMo-2-7B, Qwen3-8B, Falcon3-7B). Across all three, FPO produces in-domain perplexity improvement and leaves MMLU, ARC-Challenge, HellaSwag, and Winogrande within seed-noise of baseline. Localizing SFT to FPO’s target layers to enter this regime is also feasible, but at 2.2x the wall-clock cost of FPO.
[AI-269] Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review
链接: https://arxiv.org/abs/2608.14562
作者: Aasish Kumar Sharma,Dimitar Koysev,Christopher Anich,Roshni Kumari Ojha,Julian Kunkel
类目: Artificial Intelligence (cs.AI)
备注: 6 pages. Accepted at the 50th IEEE Computers, Software, and Applications Conference (COMPSAC 2026), Madrid, Spain, July 7-10, 2026
Abstract:AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI. We present a comparative matrix for the EU, US, and China that maps (i) risk classification triggers, (ii) binding obligations, (iii) enforcement and accountability mechanisms, and (iv) the degree to which FAIR principles are operationalised in practice. We stress-test the matrix on three high-impact domains: Electroencephalography (EEG)-guided rehabilitation robotics, AI-enabled debt collection in prospective Central Bank Digital Currency (CBDC) ecosystems, and AI-driven allocation of scarce Graphics Processing Unit (GPU) resources in emerging AI Factory infrastructures. Using primary legal texts and implementation evidence, we identify three recurring gaps: weak interoperability mandates, difficult operationalisation of cross-regime obligations (AI + sector regulation + data protection), and under-specified governance for critical digital infrastructure use cases. To bridge the implementation gap, we outline Knowledge Blocks, a machine-checkable compliance artefact pattern based on Resource Description Framework/Web Ontology Language (RDF/OWL), Shapes Constraint Language (SHACL), and Provenance Ontology (PROV-O), enabling audit-ready compliance-by-design across multiple regimes.
[AI-270] When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL
链接: https://arxiv.org/abs/2608.14559
作者: Teoman Kaman
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 5 figures
Abstract:Effective communication in multi-agent reinforcement learning requires agents to decide not only \textitwhat to communicate, but when? Existing approaches either communicate at every timestep or learn a binary gate through REINFORCE policy gradients \citesingh2019, a high-variance signal that produces unstable and uninterpretable gating behavior. I propose a principled alternative: agents communicate only when the KL divergence between their learned belief distributions exceeds a fixed threshold. Each agent maintains a belief distribution over a latent world state computed as a softmax over its LSTM hidden state, and communicates only when belief disagreement is large enough to justify information exchange. I evaluate this approach on the Predator-Prey benchmark from IC3Net \citesingh2019 across two environment sizes with 5 seeds each, and on MPE simple_spread \citelowe2017, comparing against IC3Net, CommNet, and an independent controller. On PP 10 \times 10, IC3Net outperforms KL-belief at all thresholds. On the harder PP 20 \times 20, a threshold ablation over \varepsilon \in \0.1, 0.3, 0.5, 1.0\ reveals an inverted U-shape: \varepsilon=0.5 achieves 73.84 average steps and 42% success rate versus IC3Net’s 75.31 steps and 31%, a gap of 1.47 steps and 11 percentage points with tighter seed variance. On MPE, the belief head improves mean reward by 12 points and reduces variance by 26 \times even when gating is inactive, suggesting two orthogonal contributions: principled gating when beliefs can converge, and improved latent representations that benefit coordination regardless.
[AI-271] Orbital AI Computing: Carbon Tradeoffs Across Satellite Scale
链接: https://arxiv.org/abs/2608.14557
作者: Nisha Sarwar,Lei Jiang,Fan Chen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Low Earth Orbit (LEO) computing is emerging for low-latency, globally distributed AI services, enabled by advances in satellite constellations and reusable launch systems. However, its sustainability remains unclear. Prior work introduces ESpaS, a framework for estimating lifecycle carbon intensity, but models systems using generic datacenter configurations and does not capture modern AI hardware, where power, mass, and compute characteristics vary widely and launch emissions scale with system mass. In this work, we extend ESpaS with accelerator-aware modeling and evaluate two representative systems: a lightweight Jetson AGX Orin for small satellites and a high-performance DGX H100 enabled by large-payload launch platforms. We show that launch emissions act as a fixed carbon overhead: low-mass systems minimize absolute emissions, while high-performance systems amortize this cost more effectively, reducing carbon intensity. Consequently, the space-ground tradeoff is highly sensitive to hardware choice, highlighting the need for accelerator-aware baselines in orbital AI computing.
[AI-272] Large Language Models Show Metacognitive Sensitivity in Medical Reasoning
链接: https://arxiv.org/abs/2608.14552
作者: Ahmad Nazzal
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical benchmark to test diagnostic choice and confidence behavior in a medical LLM. The benchmark focused on probable Alzheimer-type neurocognitive disorder (AT-NCD) versus depression-related cognitive impairment (DRCI). We generated 45 synthetic vignettes varying evidence strength, conflicting evidence, and missing information. Each vignette was presented under three prompt variants, yielding 135 trials. In a pilot run with gpt-4.1-nano, all trials produced valid structured outputs. Across forced-choice trials, diagnostic accuracy was 93.5%, mean confidence was 78.4%, and AUROC2 was 0.876. Confidence increased with evidence distance from the diagnostic boundary, decreased when information was missing, and remained higher on correct than incorrect trials after adjustment for evidence strength and prompt format. These findings indicate partial metacognitive sensitivity rather than globally uninformative confidence. However, errors clustered in moderate, conflicting AT-NCD cases, where the model shifted toward DRCI and retained more confidence than empirical accuracy justified. Model comparison suggested that confidence quality should be measured directly rather than inferred from benchmark accuracy or model capability alone. This study establishes a reproducible framework for evaluating evidence sensitivity, metacognitive sensitivity, and localized calibration failure in medical LLMs.
[AI-273] FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment
链接: https://arxiv.org/abs/2608.14550
作者: Enrique Barba Roque,Luís Cruz
类目: Artificial Intelligence (cs.AI); Performance (cs.PF)
备注:
Abstract:AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same number of FLOPs may not have the same execution time because some operations are more easily parallelized than others. This paper sets out to replicate the original experiments from a study that proposed the \alpha-FLOPs estimation formula to verify whether the results remain applicable on newer, more powerful hardware. During the replication process, we identify limitations in the replication materials provided by the original study, including a lack of specific dependency details and transparency regarding regression data. Our results validate the thesis that raw FLOPs alone are not an appropriate metric for execution time, as spatial dimensions remain more easily parallelized than kernel dimensions. However, fine-grained measurements reveal that the relationship is much less straightforward than previously shown, with newer hardware exhibiting instabilities and discontinuities in execution time, including jumps and oscillations, that the \alpha-FLOPs formula generally underestimates. Ultimately, this work validates the empirical findings from the original study but shows negative results when applying the \alpha-FLOPs estimation. We also highlight the critical need for complete and accurate replication packages for research on hardware-dependent efficiency assessment and provide a complete replication package for our implementation to facilitate further study. Subjects: Artificial Intelligence (cs.AI); Performance (cs.PF) Cite as: arXiv:2608.14550 [cs.AI] (or arXiv:2608.14550v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14550 Focus to learn more arXiv-issued DOI via DataCite
[AI-274] A Temporal Reasoning Benchmarking Framework for LRMs via Difficulty-controlled and Dynamic Test Generation ISSTA2026
链接: https://arxiv.org/abs/2607.04784
作者: Shide Zhou,Kailong Wang,Ling Shi,Haoyu Wang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted to ISSTA 2026. 23 pages including references, 3 figures
Abstract:Defining the reasoning boundaries and ensuring the reliability of Large Reasoning Models (LRMs) remains a critical challenge. Current benchmarks primarily rely on static datasets susceptible to data contamination or synthetic tasks lacking fine-grained difficulty control. Furthermore, standard outcome-based evaluations often conceal reasoning flaws by neglecting the reasoning process. To address these limitations, we introduce TRACE, a testing framework that models temporal reasoning as constraint satisfaction problems via Allen’s Interval Algebra. This approach enables precise regulation of logical complexity and incorporates a Trace-Based Verification Oracle to validate reasoning faithfulness. Using this framework, we construct TRACEBench, an extensive benchmark comprising 1,200 synthesized test instances across graded difficulty levels. We employ TRACE to evaluate eight widely used LRMs on TRACEBench. The results confirm a strong negative correlation between model performance and our difficulty metric (Pearson’s r approximately -0.96), validating the effectiveness of our difficulty control mechanism. Moreover, our trace-based analysis exposes significant discrepancies between reasoning validity and final answers, revealing a high spurious guessing rate of approximately 28% in mid-sized models. In addition, we diagnose scale-dependent failure modes, ranging from Degenerative Loops in small models to Reasoning Explosion in advanced architectures. TRACE thus provides a robust, automated platform for benchmarking the true temporal reasoning capabilities of LRMs. Comments: Accepted to ISSTA 2026. 23 pages including references, 3 figures Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2607.04784 [cs.SE] (or arXiv:2607.04784v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2607.04784 Focus to learn more arXiv-issued DOI via DataCite
[AI-275] Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
链接: https://arxiv.org/abs/2608.16612
作者: Jiaqi Yao,Julia Kowal
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Submitted to and under review at Energy and AI
Abstract:An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system. Although compelling, data-driven SOH estimation models typically require large amounts of high-quality labeled cycling data, while in practice such labels are often sparse in both quantity and coverage. Therefore, in this work, we propose a degradation-aligned self-supervised learning (SSL) framework based on a convolutional neural network-gated recurrent unit (CNN-GRU) model, which learns aging-consistent representations from unlabeled data through a cycle-order ranking objective as the pretext task for pretraining, thereby enabling robust SOH estimation after fine-tuning on sparsely labeled data. Test results showcase that the proposed ranking-based SSL approach proves to endow the pretrained model with degradation-aligned information from unlabeled data, and after fine-tuning the model can carry out accurate, robust SOH estimation, even when only an extremely limited amount of 1% of unevenly distributed labeled training data is available, where the MAE of 1.718% and RMSE of 2.329% can be achieved on the test cell. In addition, in-depth analyses are presented regarding the influences of label distribution of battery degradation data. We believe this work could shed new light on SOH estimation of lithium-ion batteries under label sparsity in real-world applications.
[AI-276] A Two-Stage Learning PINN Approach for Solving the Inverse Problem of the 1D Porous Medium Equation
链接: https://arxiv.org/abs/2608.16475
作者: Noura Al Helwani,Sophie Moufawad,Nabil Nassif
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注: 54 pages
Abstract:The Porous Medium Equation (PME), given by u_t = \Delta(u^m) for m 1 , is a degenerate nonlinear parabolic partial differential equation that arises in various physical applications such as fluid flow in porous media, heat transfer in plasmas, and population dynamics. It is known for its nonlinear diffusion and finite propagation speed. In this paper, we study numerical solutions of the one-dimensional direct and inverse PME using Physics-Informed Neural Networks (PINNs), and compare them with classical numerical methods and available analytical and manufactured solutions. While PINNs provide a flexible framework for solving both forward and inverse problems, we show that the standard inverse formulation suffers from a strong sensitivity to the initial guess, leading to only local convergence. To address this issue, we propose a novel two-stage PINN training framework for the inverse problem, which significantly improves convergence stability and allows reliable recovery of the unknown parameter even for poor initial guesses. Overall, the proposed approach demonstrates that PINNs are a flexible and accurate alternative to classical methods for the 1D PME, and the introduced two-stage training strategy substantially improves their robustness in inverse problems, providing a solid basis for extensions to more complex geometries and higher-dimensional cases.
[AI-277] Characterising cardiac tissue properties with graph neural networks
链接: https://arxiv.org/abs/2608.15843
作者: Ching-En Chiu,Yoo Ri Kim,Magdi Saba,Danilo Mandic,Marta Varela
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: Accepted at The Statistical Atlases and Computational Modeling of the Heart (STACOM) workshop 2026
Abstract:Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment. We developed a graph neural network-based framework trained on synthetic electrogram signals on 2D flat surfaces to identify areas of interest in the context of cardiac ablation for premature ventricular complexes (PVCs). Our method achieved an average precision of 0.96, 0.97, and 0.95 for the detection of single-patch fibrosis, rapid depolarisation and high excitability, respectively. The trained model can then be applied to 2D curved surfaces with few-shot fine-tuning, demonstrating its generalisation capability. Future work will develop this framework further for clinical use in PVC ablation.
[AI-278] ALKEMIE Agent : an autonomous platform for computational materials design
链接: https://arxiv.org/abs/2608.15776
作者: Hongfu Huang,Yuzhe Li,Ao Xu,Bo Liu,Changrui Wang,Kan Tang,Ning Yang,Shengxian Liu,Hanyu Liu,Pengpeng Zhang,Linggang Zhu,Fengkai Liu,Yichen Lu,Tong Zhao,Naihua Miao,Jian Zhou,Zhimei Sun
类目: Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions. This growing gap between methodological capability and practical execution highlights the need for a new kind of autonomous computational framework, one that can coordinate tools, knowledge, and workflows in a more unified and adaptive way. Here, we introduce ALKEMIE Agent, an agentic platform in which retrieval-augmented generation, a materials-computation knowledge base, registered skills, database-supported provenance, AI-assisted structure modeling, bounded task execution, tool-calling iteration, and error-diagnostic assistance are integrated within a traceable control loop. The capabilities of ALKEMIE Agent are demonstrated through applications including materials recommendation, structure modeling, phonon calculations, machine-learned interatomic potential training, LAMMPS simulations, Ab Initio Monte Carlo (AIMC) sampling, and active-learning-based materials screening. Finally, we outline the future directions and challenges for the development of agentic platforms for computational materials design.
[AI-279] Valhalla: A Layered Knowledge-State and Service-Governance Framework for Long-Term Scientific Knowledge Work
链接: https://arxiv.org/abs/2608.15193
作者: Yuyang Zheng,Nan Li,Wenxia Deng,Lige Yan,Xiang Li,Si Chen
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:As large language model (LLM) agents are increasingly adopted in scientific research, external knowledge bases, knowledge graphs, and long-term memory have improved information retrieval and task continuity. However, most structured knowledge systems remain node-centric, representing files, concepts, results, and judgments as nodes and relations in a graph. While suitable for personal knowledge management, such structures often depend on individual organizational practices, limiting knowledge sharing, integration, and reorganization across users. This paper presents Valhalla, a layered knowledge-state and service-governance framework for long-term scientific knowledge work. Valhalla replaces flat graphs with layered encapsulation and stable semantic boundaries through a five-layer File-Resource-Entity-Relationship-Graph (FREG) model. File and Resource preserve source identity and provenance, Entity represents knowledge objects, Relationship captures semantic judgments, and Graph provides task-oriented knowledge views, enabling knowledge states from different researchers to be exchanged and reorganized under a unified structure. We further introduce a Router-Contract-Workflow service-governance architecture, inspired by the microkernel paradigm, to constrain how language models access, modify, and extend knowledge states while maintaining structural consistency and auditable operational boundaries. We implement a Valhalla prototype and validate knowledge ingestion, cross-member integration, and scientific writing support through an antibody-design review task comprising 26 paper resources, 80 knowledge entities, and 92 semantic relations. Rather than proposing a new knowledge-extraction algorithm, Valhalla offers a paradigm for organizing collaborative scientific knowledge, transforming individualized knowledge structures into transferable and reorganizable shared knowledge states.
[AI-280] Scale-Consistent Posterior Dynamics for Diffusion Inverse Problems
链接: https://arxiv.org/abs/2608.15144
作者: Zhaoqiang Liu,Tongyao Pang,Ruibing Wang,Yang Zheng
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 29 pages, 5 figures, 3 tables
Abstract:Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable. We begin from an ideal one-parameter posterior SDE family in which a stochasticity parameter controls probability-flow transport and stochastic exploration without changing the posterior marginals. To obtain a tractable model, we express the likelihood in a rescaled clean-image coordinate and use log-SNR to organize the resulting posterior proxies. Projecting the diffusion uncertainty through the forward operator then yields a noise-conditioned covariance path whose targets approach the clean posterior. Because endpoint consistency of these targets does not ensure that a surrogate transport follows them, we interleave the transport with a frozen-target Langevin corrector, producing a continuous surrogate SDE. We discretize this model with an outer Lie–Trotter splitting and a variance-matched split-step IMEX predictor that treats the learned prior explicitly, the linear likelihood implicitly, and the stochastic innovation after the implicit solve. We prove marginal invariance of the ideal family, posterior convergence of the continuous surrogate under mixing and transport-defect conditions, and a first-order weak error bound for the discrete algorithm. Experiments on FFHQ and ImageNet with 100 score evaluations demonstrate competitive reconstruction fidelity for super-resolution and deblurring. A controlled 100-image ablation separates scale consistency from the finite-step effects of stochastic-increment placement, continuation, and corrector allocation. A separate noiseless box-inpainting study shows that large exploration reaches a performance plateau only when the matched innovation is injected after the stiff likelihood solve.
[AI-281] PAS-QFL: Personalized Ansatz Selection for Quantum Federated Learning under Client Data Heterogeneity
链接: https://arxiv.org/abs/2608.14995
作者: Jindi Wu,Qun Li
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Quantum federated learning (QFL) lets multiple quantum clients collaboratively train quantum neural networks (QNNs) without sharing private local data. However, existing QFL methods commonly assume that all clients use the same ansatz, overlooking how heterogeneous client data affects ansatz suitability. Under class-imbalanced non-IID data, different clients may favor different ansatz structures, so a fixed ansatz can lead to unstable and unfair performance across clients. In this paper, we propose PAS-QFL, a Personalized Ansatz Selection framework for QFL under client data heterogeneity. Rather than treating the ansatz as a monolithic structure, PAS-QFL decomposes each client QNN into a globally shared ansatz and a client-specific private ansatz, and personalizes the structure of the private ansatz rather than only its parameters. The shared ansatz is placed first and selected by a stability-aware cross-client criterion so that its parameters can be reliably aggregated, while the private ansatz serves as a personalized decision head, selected per client by local Macro-F1 to adapt the shared representation to its local data. During training, each client updates both its shared and private parameters locally but uploads only the shared parameters, so federated aggregation stays well-defined while each client keeps its own private structure. PAS-QFL uses Macro-F1 as the primary selection metric to avoid misleading accuracy under class imbalance. Experiments on heterogeneous QFL tasks show that PAS-QFL improves average Macro-F1 over the existing fixed-ansatz QFL baselines, demonstrating the value of personalizing the ansatz structure for practical QFL.
[AI-282] Generative data assimilation highlights fronts as key regulators of ocean energy cascade
链接: https://arxiv.org/abs/2608.14955
作者: Scott A. Martin,Georgy E. Manucharyan,Patrice Klein
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI)
备注: Under review at Communications Earth Environment
Abstract:Mesoscale eddies are fundamental to the ocean circulation, yet the extent to which submesoscale motions, a few kilometers across, influence mesoscale eddy energetics through a kinetic energy cascade remains uncertain. High-resolution simulations predict that submesoscale fronts are key regulators of the cascade, transferring energy both downscale towards dissipation and upscale to sustain and shape the seasonality of mesoscale eddies. Testing these predictions has remained difficult because existing observations and state estimates cannot resolve submesoscale currents over sufficiently broad domains. Here we map the ocean’s submesoscale energy cascade by combining multi-source satellite observations with a generative deep learning framework, reconstructing gap-free, kilometer-scale surface currents with physically plausible dynamics learned from simulations. Applying this to the eddy-rich Agulhas Current system, we find that submesoscales energize the mesoscale through an upscale energy cascade above 10 km, contributing to the seasonality of mesoscale eddies. Below 10 km, convergence at submesoscale fronts drives a downscale cascade towards dissipation. Both upscale and downscale pathways concentrate within fronts, where cross-scale transfer is up to an order of magnitude more efficient. Despite their limited extent, fronts account for a substantial fraction of the domain-integrated cascade, establishing them as key regulators of the cascade and targets for next-generation eddy parameterizations.
[AI-283] Handover Analysis for Vehicular Communication with Explainability on the Fly
链接: https://arxiv.org/abs/2608.14820
作者: Ali Fuat Sahin,Semiha Tedik Başaran,Tufan Kumbasar
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: Accepted in NextGCom 2026, Copyright IEEE
Abstract:Handover (HO) management in vehicular networks requires fast and reliable decision-making under highly dynamic conditions. While machine learning (ML) approaches can improve HO detection by capturing complex relationships among various key performance indicators (KPIs), their black-box nature limits interpretability and operator trust. To address this, this paper investigates HO detection from an explainability-on-the-fly perspective using inherently interpretable models based on the functional analysis of variance (fANOVA) framework. The proposed models are evaluated using two real-world operator datasets and compared against a Long Short-Term Memory baseline augmented with post-hoc SHAP explanations. Unlike post-hoc approaches, the proposed framework enables immediate interpretation of model decisions without incurring additional computational overhead. This capability is particularly critical for latency-sensitive vehicular networks. The results show that fANOVA-based models achieve competitive detection performance while providing significantly reduced explanation latency compared to conventional post-hoc methods. Furthermore, feature ranking and visualization analyses reveal physically meaningful relationships between KPIs and HO occurrences that align with standardized HO mechanisms. These results demonstrate that inherently interpretable models provide an efficient and transparent solution for HO detection in next-generation vehicular networks.
[AI-284] Multi-Agent Closed-Loop Reasoning for Organic Structure Elucidation from Multimodal Spectra
链接: https://arxiv.org/abs/2608.14720
作者: Bingsen Xue,Zhuojun Jiang,Jianhao Zhang,Mingcheng Gu,Yizhe Yuan,Yongtai Zhuo,Yifan Zhang,Li Wang,Ya Su,Yue Yuan,Jiang Liu,Xueqian Kong,Cheng Jin
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge. Despite decades of computational efforts, no existing system achieved reliable reasoning over unseen spectra. Here, we propose MACROS, a multi-agent system automating structure elucidation by emulating expert iterative hypothesis-testing. Trained on 100M simulated and 1.6M experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques. It achieves unprecedented zero-shot generalization to diverse real-world samples, correctly identifying synthetic compounds, natural products and metabolites above 500 Da with 1D NMR. Remarkably, MACROS spontaneously recovers textbook spectroscopic correlations from unassigned data and exhibits emergent chemical intuition such as a ring-first parsing preference, learning fundamental chemical principles rather than memorizing database patterns. MACROS augments chemists via collaboration to deliver sixfold faster, 40% more accurate elucidation. MACROS establishes a scalable foundation for fully automated structure elucidation, and catalyzes accelerated molecular discovery toward autonomous laboratories.
[AI-285] Information-Theoretic Causal Modelling of Semiconductor Process Dynamics CEC
链接: https://arxiv.org/abs/2608.14678
作者: Daniel Sørensen,Giorgio Melchiorre,Sudip Bandyopadhyay,Sandip Halder,Roel Wuyts,Bappaditya Dey
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: To be presented at the 2026 IEEE 33rd International Conference on Electronics, Circuits and Systems (ICECS), and published by IEEE in the conference proceedings
Abstract:With the progress of the semiconductor industry toward increasingly complex compute devices and tighter process tolerances, advanced process control has become crucial. This work explores a novel framework to infer the underlying dynamics of semiconductor processes, directly from raw equipment log-file time-series data. By modelling the tool dynamics as a stochastic dynamical system comprising (a) a deterministic component and (b) a stochastic component, we estimate entropy transfer rates between variables through the Liang-Kleeman and Pires formalism. Preliminary results indicated that 7.5% of the inferred dependencies were known, 36.0% were plausible, 17.5% represented previously uncharacterised relationships, and 39.0% were inconsistent with established process knowledge. These findings demonstrate the framework’s capability to uncover novel causal insights, while motivating further improvements to reduce inconsistent findings.
[AI-286] Offline Ambient-Controlled Latent Diffusion: Architecture Telemetry and On-Device Evaluation
链接: https://arxiv.org/abs/2608.14677
作者: Lech Kalinowski,Artur Morys-Magiera,Piotr Miłkowski
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Most mobile image-generation applications are thin clients over cloud services, leaving outputs hard to audit. We present an Android latent-diffusion application that runs entirely on-device and is driven by the ambient-light sensor rather than a text prompt, keeping generation, telemetry, and storage local. The contribution is not a new diffusion method but the surrounding measurement workflow: each output is bound to the sensor reading, runtime path, and seed that produced it, giving a per-artifact audit trail for offline analysis. On a single Samsung foldable, one fixed capture of 373 artifacts shows the controller’s log-lux input positively associated with output luminance (Pearson r=0.532 , 95% CI [0.455, 0.601] ), confirming the ambient dependency survives denoising and VAE decoding, while the latent UNet/VAE pipeline runs at 552–1334,ms mean latency across three quality tiers under the Android Neural Networks API (NNAPI).
[AI-287] Does the Heart Show Your Pain? Tackling the X-ITE Pain Challenge with Self-Supervised ECG Representation Learning
链接: https://arxiv.org/abs/2608.14662
作者: Dominika Kunc,Przemysław Kazienko,Stanisław Saganowski
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 5 pages, 3 Figures, 1 Table, appear in the Proceedings of the 13th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW 2025)
Abstract:Accurate recognition of pain using physiological signals remains a challenging problem due to pain’s subjective nature and high inter-individual variability. In this study, we investigate self-supervised representation learning (SSL) methods applied to unimodal electrocardiogram (ECG), complemented by multimodal pretraining, including accelerometer (ACC) signals from the chest. We focus on classifying low versus medium pain levels on the X-ITE Pain dataset. Our results reveal that while ECG-based models show limited classification performance, multimodal pretraining improves learned representations by capturing cross-modal dependencies. Notably, we observe substantial inter-subject variability in model performance, suggesting that pain-related ECG patterns may be subject-specific. Visualizations indicate distinct subject-specific clustering but no clear separation by pain levels, highlighting the complexity of pain detection from ECG alone. We discuss limitations of unimodal input, label noise, and generalization across subjects and propose future directions. This work advances the understanding of physiological signal representation learning for pain recognition and sets the stage for more robust, clinically relevant wearable pain monitoring solutions.
机器学习
[LG-0] Q-based Variational Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2608.16888
作者: Ondrej Bajgar,Peter Tisnikar,Alessandro Abate,Konstantinos Gatsis,Maike Osborne
类目: Machine Learning (cs.LG)
*备注:
Abstract:The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is often infeasible. Inverse reinforcement learning (IRL) addresses this challenge by inferring preferences, represented as reward functions, from expert behaviour. We introduce Q-based Variational IRL (QVIRL), a novel Bayesian IRL method that recovers a posterior distribution over rewards from expert demonstrations via primarily learning a variational distribution over optimal Q-values. Unlike previous approaches, QVIRL combines scalability with uncertainty quantification, important for safety-critical applications as well as active learning. We demonstrate QVIRL’s strong performance in apprenticeship learning across various tasks, including gridworlds, Lunar Lander, the Highway Environment, and two ATARI games both with static expert data and with active learning. It is the first method for Bayesian IRL that demonstrates training from raw pixel observations.
[LG-1] Spectral Gaps of Hit-and-Run and Coordinate Hit-and-Run
链接: https://arxiv.org/abs/2608.16878
作者: Yunbum Kook,Santosh S. Vempala
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
*备注: 15 pages. AI disclosure included
Abstract:For any convex body \mathcalK\subset\mathbbR^n containing a unit ball, the spectral gap of Hit-and-Run is \Omega(1/(n^2 C_\mathsfPI)) , where C_\mathsfPI is the Poincaré constant of the uniform distribution \pi over \mathcalK . This implies that Hit-and-Run converges to a distribution within \chi^2 -divergence \varepsilon of the uniform distribution \pi in O(n^2 C_\mathsfPI\log(M/\varepsilon)) steps from any starting distribution \pi_0 with M=\chi^2(\pi_0,|,\pi) , thus refining the known bound of O(n^2 R^2 \log(M/\varepsilon)) by Lovász and Vempala (2004) in terms of the outer radius R ; for nearly isotropic bodies, together with progress on the KLS conjecture, the complexity is O(n^2\log n\log(M/\varepsilon)) , improving the dimension dependence from cubic to nearly quadratic while maintaining logarithmic dependence on the initial distance. It was an open problem to connect the convergence of Hit-and-Run to Poincaré/KLS constants as was done for the Ball walk by Kannan, Lovász and Simonovits (1997). Unlike Hit-and-Run, the Ball walk has an unavoidable linear dependence on (a stronger notion) of the initial warmness. We directly bound the spectral gap of the Hit-and-Run Markov chain by connecting it to functional isoperimetric constants, inspired by the recent analysis of In-and-Out. Rewriting the spectral gap in terms of dual certificates leads to the Babuška–Aziz constant studied in the analysis of PDEs; it is asymptotically bounded by the improved Poincaré constant, which we show can be bounded in terms of the usual Poincaré constant. The proof is based on duality and calculus, unlike known proofs of convergence for Hit-and-Run which are based on bounding the conductance. The same technique can be applied to Coordinate Hit-and-Run, resulting in a much improved mixing time of O(n^3C_\mathsfPI\log(M/\varepsilon)) . Comments: 15 pages. AI disclosure included Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST) Cite as: arXiv:2608.16878 [cs.DS] (or arXiv:2608.16878v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2608.16878 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-2] An Analytical-Prior Framework for Data-Efficient Prediction of Sound-Reduction Frequencies in Rectangular Side-Branch Helmholtz Resonators
链接: https://arxiv.org/abs/2608.16873
作者: Jiaming Li
类目: Machine Learning (cs.LG)
*备注: 13 pages, 5 figures, 1 table
Abstract:High-fidelity finite-element simulations can provide accurate numerical predictions for side-branch resonators, but large simulation datasets are expensive to generate and purely data-driven surrogates may become unreliable when simulation-labelled data are scarce. This study develops an analytical-prior learning framework that reuses a low-cost analytical model to improve data efficiency under limited high-fidelity simulation budgets. Two complementary routes are considered. When the analytical model remains available at inference, it is retained as an explicit baseline and the simulation data are used to learn only the analytical-to-simulation discrepancy. When a self-contained predictor is required, the analytical mapping is first distilled from abundant low-cost evaluations into a learned prior and then calibrated with the limited simulation data. The framework is evaluated on rectangular side-branch Helmholtz resonators using 86 simulation-labelled geometries and 8,998 non-overlapping analytical-only geometries. The analytical model achieved a mean absolute error (MAE) of 1.333 Hz. Direct support vector regression (SVR) achieved 3.375 Hz, while residual SVR reduced the MAE to 0.426 Hz. A direct multilayer perceptron (MLP) achieved 1.109 Hz, whereas analytical-prior pretraining reduced the error to 0.556 Hz with frozen-prior residual adaptation and 0.371 Hz with full-model fine-tuning. Across training budgets of 20 to 70 simulation-labelled cases, both analytical correction and analytical-prior pretraining consistently improved data efficiency relative to direct learning. These results show that analytical prior information can substantially improve high-fidelity prediction when simulation data are scarce, with explicit correction and prior distillation serving complementary deployment needs.
[LG-3] Data-Efficient and Interpretable Classification of Circulating Tumor Cell Phenotypes in Microfluidic Devices via Deep Learning
链接: https://arxiv.org/abs/2608.16870
作者: Serena Su,Yifan Wang,Senwei Liang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate classification of circulating tumor cell (CTC) phenotypes can provide valuable information for assessing metastatic potential. Label free microfluidic devices provide a hydrodynamic obstacle course that transforms subtle biophysical characteristics of CTCs, including size and deformability, into distinct kinematic trajectories. However, the highly nonlinear fluid structure interactions governing these trajectories make the inverse problem of inferring cellular phenotype from trajectory data analytically intractable. While deep neural networks (DNNs) have emerged as a powerful approach for addressing this inverse problem, their effectiveness is constrained by the limited availability of trajectory data and the lack of physical interpretability. To address these challenges, we propose an interpretable and data efficient DNN framework for trajectory based CTC classification. To mitigate the scarcity of data, we develop Subsequence (SubSeq), a targeted augmentation strategy that randomly extracts informative local trajectory segments during training to promote learning from localized patterns. We further apply Gradient Weighted Class Activation Mapping to identify the trajectory features and physical regions of the microfluidic device that drive model predictions. Experimental results demonstrate that SubSeq improves classification accuracy over the evaluated baseline and augmentation methods. Furthermore, interpretability analysis suggests that localized trajectory segments contain substantial biophysical information relevant to accurate classification. This provides justification for SubSeq and also highlights the redundancy of full-length trajectories. More broadly, the proposed framework views microfluidic geometries as physical encoders of cellular mechanical properties, providing mechanistic insights that may inform the future design of diagnostic devices. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.16870 [cs.LG] (or arXiv:2608.16870v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-4] he canonical facets of multi-separator polytopes
链接: https://arxiv.org/abs/2608.16861
作者: Bjoern Andres,Silvia Di Gregorio,Jannik Irmai,Lucas Fabian Naumann,Shengxian Zhao
类目: Discrete Mathematics (cs.DM); Machine Learning (cs.LG); Combinatorics (math.CO)
*备注: 49 pages, 19 figures
Abstract:We initiate a polyhedral study of the graph multi-separator problem proposed by Irmai et al. (2024) as an alternative to the lifted multicut problem for application to the task of image segmentation. Starting with an integer linear program (ILP) formulation and the multi-separator polytope spanned by its feasible solutions, we characterize in terms of efficiently-decidable, graph-theoretic conditions all facets induced by inequalities of the ILP. We proceed by strengthening these inequalities and describing additional facets of some multi-separator polytopes induced by the stronger inequalities. Specifically, we obtain a totally dual integral description of the multi-separator polytope for paths in the case where separation is considered for all vertex pairs. Finally, we relate the multi-separator polytope to the boolean quadric polytope, showing that facets induced by odd-cycle inequalities do not transfer generally, and to the lifted multicut polytope, showing that either polytope is a projection of a face of the other.
[LG-5] me-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1Hz Operational Data CCGS textitSir Wilfrid Laurier
链接: https://arxiv.org/abs/2608.16833
作者: Samarasimha Reddy Chittamuru,Ayhan Akinturk,Allison Kennedy,Joshua Barnes,Matthew Hamilton
类目: Machine Learning (cs.LG)
*备注:
Abstract:Ship fuel consumption (SFC) prediction supports vessel operation optimisation, emissions estimation, and decision support systems (DSS) for sustainable maritime transportation. Numerous data-driven fuel models have been developed over the past two decades, but a critical and often overlooked limitation lies in their validation practices: most studies evaluate performance using random train–test splits, which, applied to high-frequency records, admit temporal leakage and yield optimistic results that do not reflect deployment conditions. This paper examines that gap using time-aware evaluation, specifically Time Series Cross-Validation (TSCV) and Blocked TSCV (BTSCV). Using the Canadian Coast Guard Ship (CCGS) \textitSir Wilfrid Laurier as a case study, six regression models and a physics baseline are tuned under three time-aware schemes and three feature configurations, then evaluated on a common chronological hold-out set drawn from approximately 3.88 million steady-state 1,Hz records.
[LG-6] Beyond L_2: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures ECML-PKDD2026
链接: https://arxiv.org/abs/2608.16773
作者: Jules Soria,Alban Grastien,Romain Xu-Darme,Julien Girard-Satabin,Zakaria Chihani,Daniela Cancila
类目: Machine Learning (cs.LG)
*备注: Accepted at ECML-PKDD 2026, Research Track
Abstract:Prototype-based neural networks are hailed as interpretable-by-design architectures. Recently, Abductive Latent Explanations (ALE) were introduced to provide formal, mathematically guaranteed explanations that leverage the intrinsic structure of these networks to ensure both predictive safety and human readability. ALEs rely on computing tight bounds on latent space distances to produce formal explanations. However, existing ALE formulations are rigidly confined to Euclidean latent spaces. This leaves a critical gap: modern state-of-the-art architectures increasingly rely on non-Euclidean representations - such as spherical metrics, Gaussian densities, and dimensional projections - rendering current formal explanation methods incompatible. In this work, we generalize the ALE framework to support non-Euclidean prototype architectures. For each geometric variant, we systematically derive how to either map the architecture to existing bounds or construct novel, architecture-specific bounding algorithms. We validate our theoretical constructions by computing subset-minimal formal explanations on fully trained image classifiers. By unifying these diverse models under a single formal framework, we enable the first rigorous, cross-architecture comparison of their interpretability.
[LG-7] On the Principles Behind Neural Network Optimizers
链接: https://arxiv.org/abs/2608.16760
作者: Yushun Zhang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Reliable optimization is central to neural network (NN) training, yet Adam, the default optimizer for modern LLMs, rests on a fragile foundation. This thesis develops a principled grounding for Adam and motivates new designs. First, we revisit Adam’s divergence–convergence debate and show the existence of a problem-dependent phase transition: with properly chosen, batch-size-dependent hyperparameters, Adam converges, whereas under small- \beta_2 regimes it can diverge. Second, we investigate why Adam substantially outperforms SGD on Transformers through Hessian structure. We find that the Hessian evolves toward a near-block-diagonal form along training, accompanied by strong block heterogeneity. We prove that this structure makes Adam’s diagonal preconditioner effective. We further show that this special Hessian structure originates from consecutive multiplications of large matrix variables, and we provide a rigorous analysis based on random matrix theory. Finally, these insights motivate Adam-mini, a new optimizer that reduces Adam’s memory footprint by 50% while preserving its performance. Our results also have broader implications beyond Adam: they reveal new local structures in matrix-based nonconvex problems, and also help understand and improve recent NN optimizers, such as Muon.
[LG-8] Le Critique: Privileged Value Functions for LLM Reinforcement Learning
链接: https://arxiv.org/abs/2608.16739
作者: Siddarth Venkatraman,Matthieu Dinot,Laurence Aitchison
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative methods like GRPO reduce gradient variance by sampling multiple rollouts per prompt, but provide only sequence-level credit. Training is also blocked by straggler rollouts, reducing throughput and increasing off-policyness. Learned value functions theoretically address both problems, providing token-level advantages without requiring large groups. However, additional infrastructure engineering challenges combined with the practical success of critic-free methods have made it difficult to justify their inclusion in RL pipelines. We propose two complementary strategies to improve the performance of value function RL: 1) Privileged Value Functions (PVF) which provide an elegant mechanism to inject additional task-relevant token-level signal without biasing the policy objective; 2) TETHER, a baseline that adaptively interpolates between group-relative and value baselines depending on the value function accuracy. Across several reasoning tasks, both strategies consistently improve over the standard value function baseline, and are competitive with or outperform mean-baseline GRPO.
[LG-9] he Ethical Decision Head: Operationalizing Normative Ethics in Autonomous Vehicles via Reinforcement Learning from Human Feedback
链接: https://arxiv.org/abs/2608.16710
作者: Thomas Mbrice,Ammar Ali,Sami Mian,Khai Hern Low,Eric Chen,Arshia Aghajani,Wolf Schäfer,Amin Shirangi
类目: Machine Learning (cs.LG)
*备注:
Abstract:As autonomous vehicles (AVs) approach Level 4 and Level 5 operational capability [SAE International, 2018], their on- board decision systems must handle not only safety-critical locomotion but also their subsequent moral weight. This paper details the Ethical Decision Head (EDH), a deep re- inforcement learning (RL) framework that encodes ethical reasoning as a differentiable reward signal, enabling a pol- icy gradient agent to learn morally-aligned driving behavior in scenarios whose state representation is aligned with the CARLA simulation environment [Dosovitskiy et al., 2017]. Two normative frameworks are instantiated and evaluated: a Utilitarian framework minimizing total casualties and a Kan- tian framework enforcing course maintenance as a categori- cal imperative. The EDH is trained via Proximal Policy Op- timization (PPO) [Schulman et al., 2017] against a Bradley- Terry reward model [Bradley and Terry, 1952] learned from pairwise human preference annotations over 200 collision- imminent scenarios. Results reveal an asymmetry in the learnability of normative ethical frameworks under human su- pervision. The Kantian condition, which reduces to a con- stant prediction task under the codebook, serves as a pipeline control: it confirms training stability and rules out infrastruc- ture failure as an explanation for the utilitarian result. The Utilitarian agent learned something more unsettling: human raters rewarded self-sacrifice over casualty minimization, and the model learned that preference faithfully. This divergence between what humans prescribe in theory and what they re- ward in practice suggests that RLHF does not learn ethics as philosophers define it, but as humans live it.
[LG-10] Learning to Price with Persuasion
链接: https://arxiv.org/abs/2608.16699
作者: Maria-Florina Balcan,Tejas Pagare,Karan Singh
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Theoretical Economics (econ.TH)
*备注: 29 Pages, 1 Table
Abstract:Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the menu of quality-price pairs, the seller offers information on the value of the match between product quality and buyer’s taste via a signaling scheme. We relax the assumption that the seller knows the buyers’ belief about the distribution of tastes and study the sample requirements of designing a revenue maximizing scheme. We consider both the batch setting where we have access to data from a set of i.i.d. buyers and an online demand query model where we observe the buyers’ behaviors to seller’s schemes. Despite the apparent non-convexity of the problem, we also give the first FPTAS to compute a scheme that maximizes the revenue within an arbitrarily small additive loss, which was left open by Bergemann et al. (2022). Overall, this brings a new learning perspective in asymmetric economic settings where buyers and sellers know different types of information.
[LG-11] he Working Set of a Coding Agent : Coherence Debt in Repository-Scale Tasks
链接: https://arxiv.org/abs/2608.16630
作者: Bardia Mohammadi,Lars Klein,Aman Chadha,Akhil Arora,Laurent Bindschaedler
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window. We model this as reconstructing a coupled-fact graph: at each edit, a required fact comes from recent context or parametric memory, and the facts covered by neither form coherence debt. We supply and withhold each channel and inject faults across seven models and five harnesses. As expected, no model completes a task on an unseen API with both channels empty, and putting the facts in the prompt restores success. When a rename defeats what models memorized about a real library, all seven fail in the same place, passing and missing the same tests. Availability decides the outcome and distance does not: withholding a fact costs exactly the work it supports, and a supplied fact works as well far from the edit as next to it. Harnesses pay unequal prices for it: configurations that all pass every test differ more than tenfold in tokens consumed because they rebuild the same content at different rates, and spending more recovers nothing when facts are withheld. A missing fact produces wrong work rather than absent work: an agent asked to act acts, fabricating the file or guessing the value, so instruments built on reads look for a hole already filled. How often it says it is blocked instead is a property of the model, from every trial to none. Availability does not settle every edit: where standard and code disagree, agents follow the standard even when it prescribes the worse code, so a stale convention file costs more than no file. Because parametric memory substitutes for reading, on SWE-bench, where models likely know the repositories, reads no longer predict success. Harnesses should keep the facts an edit depends on available when the agent writes, and check that availability against what the agent produces rather than what it reads.
[LG-12] Variational Outlier-Robust Gaussian Process Regression with Generative Modeling
链接: https://arxiv.org/abs/2608.16606
作者: Arslan Majal,Aamir Hussain Chughtai
类目: Machine Learning (cs.LG)
*备注: 5 pages, 1 figure
Abstract:Outliers can substantially distort Gaussian process regression (GPR) due to its conventional Gaussian observation likelihood, leading to inaccurate model learning and prediction. To address this limitation, this article introduces a generative GPR model that captures observation-specific contamination and adaptively mitigates the influence of outliers. Subsequently, a variational generalized expectation-maximization procedure is used to learn the latent variables and GPR model parameters. Experiments on synthetic and real datasets under different contamination settings demonstrate that the proposed method remains competitive with-and in several cases outperforms-robust GPR baselines in prediction accuracy. Moreover, the proposed method shares the cubic computational scaling of the compared GPR methods.
[LG-13] Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics
链接: https://arxiv.org/abs/2608.16569
作者: Anima Kujur,Zahra Monfared
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate reconstruction of long-duration neural recordings is challenging because local field potentials (LFPs) are high-resolution, multichannel, transient, and variable across subjects. We present PCA-DMD, a scalable operator-theoretic framework that segments LFP recordings into overlapping windows, projects them into a compact PCA space, learns linear Koopman evolution in the latent space, and reconstructs continuous signals through inverse projection and overlap-add aggregation. On 200,000-sample hippocampal recordings, PCA-DMD outperformed Classical DMD, SpDMD, MrDMD, and HODMD, achieving KLD=0.0761 and HD=0.0847. In all-pair cross-subject zero-shot generalization at 300,000 samples, correlations were 0.9504-0.9800, with HD=0.0010-0.0072 and KLD=0.0005-0.0022, without target-subject fine-tuning. Out-of-sample temporal prediction showed close one-step agreement on temporally held-out LFP segments across the unseen interval and multiple channels. Scalability analysis from 400,000 to 900,000 samples showed stable zero-shot reconstruction, with mean correlation remaining about 0.965-0.968 while computational cost increased predictably. External validation on an independent 93-channel Allen Neuropixels recording yielded mean and median channel-wise correlations of 0.7427 and 0.7990, respectively. Koopman spectral and mode analyses revealed dominant eigenvalues concentrated near the unit circle. PCA-DMD therefore provides an interpretable, generalizable, and computationally scalable framework for reconstructing high-dimensional neural dynamics.
[LG-14] LLM s for Zero-Shot Threat Detection via Structured Risk Indicators
链接: https://arxiv.org/abs/2608.16508
作者: Abdullah Alghamdi,Siamak Layeghy,Marius Portmann
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:We propose a two-stage large language model (LLM) framework for zero-shot detection of insider threats and advanced persistent threats (APTs) from heterogeneous security logs. The framework models user activity as chronological timelines and incorporates retrieval-augmented generation (RAG) to provide personalised behavioural context from each user’s historical activity. Rather than performing end-to-end classification directly from raw logs, it first generates structured, interpretable sets of threat-specific risk indicators, which are then classified jointly across temporal sequences to capture attack patterns spanning multiple this http URL framework is evaluated on two benchmark datasets, CERT r5.2 for insider threat detection and PicoDomain for APT detection, using four combinations of two open-weight LLMs under both retrieval and non-retrieval settings. All configurations outperform the previous state-of-the-art LLM-based framework (GABM), with the best configuration improving the F1-score by 11.40 percentage points on CERT r5.2 and 31.50 percentage points on PicoDomain. Results further show that retrieval mainly benefits weaker LLMs by generating more discriminative risk indicators, whereas stronger models achieve comparable performance without retrieved context. The most effective assignment of LLMs to the two stages depends on the dataset. These findings show that the quality of the generated risk indicators is the main driver of zero-shot cyber threat detection performance.
[LG-15] Pallas: A Proactive KV Cache Migration Framework for LLM Inference in AI-RAN
链接: https://arxiv.org/abs/2608.16477
作者: Tianhang Ding,Jianchun Liu,Hongli Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: the user attaches to a target base station (gNB) while the large and growing key-value (KV) cache remains at the source. Retaining inference at the source preserves service continuity but persistently increases inter-token latency (ITL), whereas recovering the state at the target restores serving locality but requires KV-cache transfer, recomputation, or a combination of both only after handover, directly prolonging service interruption time (SIT). This work presents Pallas, a \textitproactive KV-cache migration framework that prepares the inference state at the predicted target before handover, in parallel with ongoing source-side inference and token delivery. At the preparation trigger, Pallas partitions the token sequence into a stable historical prefix and an evolving suffix. The target reconstructs the prefix through local prefill, while the source streams the KV blocks generated for the suffix. At handover, the target assembles both portions into an up-to-date KV cache and resumes decoding locally, leaving only unfinished preparation to contribute to SIT. An online scheduler selects the \textitprefetching window, which determines how early preparation begins before handover, based on mobility predictions and runtime telemetry. Across three LLMs and 100 – 500~\mathrmMbps inter-gNB links, our vLLM-based prototype reduces average SIT by factors of 2.28 – 89.68 over target-side recovery approaches and lowers average ITL by 16.0% – 50.0% compared with source-side forwarding. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.16477 [cs.LG] (or arXiv:2608.16477v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16477 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-16] Reference-free logged energy-oracle recovery for neural approximations of symmetric coercive variational problems: conforming Riesz reconstruction and archive-level selection
链接: https://arxiv.org/abs/2608.16473
作者: Karim Bounja,Lahcen Laayouni,Boujemaa Achchab,Abdeljalil Sakat
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 35 pages, 7 figures
Abstract:Neural PDE training yields a finite checkpoint archive, yet its logged energy errors are inaccessible without the exact solution, while loss-based selection does not necessarily recover the logged energy oracle. For admissible neural approximations of symmetric coercive variational problems, we introduce a reference-free selection rule based on minimizing a computable conforming Riesz monitor. The exact residual-energy identity and conforming projection make the monitor an unconditional lower bound converging monotonically to each logged energy error under nested conforming refinement; under saturation, hierarchical enrichment yields a computable upper estimate and hence a lower-upper bracket. A key finding is that archive selection is order-sensitive: unresolved checkpoint-dependent components can reverse the oracle-non-oracle ranking at finite resolution, so checkpointwise recovery alone is insufficient. For finite archives, we prove uniform recovery, yielding convergence to the logged-oracle error and, without saturation, logged-oracle selection at sufficiently fine auxiliary resolution. Under saturation, the bracket gives a computable near-oracle bound and certifies unique logged-oracle selection upon interval separation. We also bound logging-resolution loss and certify oracle inclusion over prescribed comparison trajectories. The resulting criterion replaces inaccessible exact-error minimization by computable, training-independent post-training selection on the intrinsic energy-error scale, requiring only the computed candidates and the variational problem. Experiments on diffusion and elasticity, including a non-manufactured perforated plate, demonstrate energy-scale calibration, oracle-level selection, and modest post-processing cost.
[LG-17] Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN ICML2026 ICML
链接: https://arxiv.org/abs/2608.16429
作者: Beimnet Bekele Guta
类目: Machine Learning (cs.LG)
*备注: Accepted at the 2nd ICML Workshop on Foundation Models for Structured Data (FMSD), ICML 2026
Abstract:Foundational models for tabular data have made significant progress in recent years, with TabICLv2 reporting state-of-the-art performance on several tabular classification tasks. However, full-context tabular ICL still suffers from attention cost that grows with the training-context size, which limits its ability to handle large datasets efficiently. Localized TabICLv2 introduces a method that reduces the inference cost of TabICLv2 by retrieving only the k nearest training neighbours for each test point, measured by similarity in the model’s Stage 2 row-representation space, rather than using the full training context. This requires no architectural changes, and we show that accuracy retention can be improved through additional Stage 2 and Stage 3 fine-tuning. On TabArena classification tasks, the fine-tuned localized model retains 98.64% of Full TabICLv2 accuracy and it achieves a median 2.18 \times speedup in batch inference, and reaches approximately 249 \times median speedup in the single-query serving setting.
[LG-18] Evolving Executable Pipeline Programs for AutoML with Language Models
链接: https://arxiv.org/abs/2608.16416
作者: Sofoklis Kitharidis,Cor J. Veenman,Jan N. van Rijn,Thomas Bäck,Niki van Stein
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and tune known components, but cannot produce structure outside that space. We present LACE, an AutoML framework that instead searches over complete executable pipeline programs: an evolutionary loop maintains a population of scikit-learn-compatible Python classes, and a large language model acts as the variation operator. To our knowledge, LACE is the first to formulate general tabular pipeline AutoML this way, evaluated on standardized OpenML tasks under a leakage-controlled protocol that withholds dataset identity from the generator. Because every candidate is ordinary Python, the returned pipeline and the search that produced it can be inspected and edited directly, rather than only through a framework’s model objects. On 68 OpenML classification tasks, LACE with GPT-5.4-mini significantly outperforms auto-sklearn, H2O, and a fixed XGBoost baseline, with no detectable difference against AutoGluon, the strongest search-based system evaluated, while covering the full benchmark. Newer tabular foundation models are more accurate on the subset of tasks they support, but apply a fixed pretrained predictor rather than returning an editable task-specific program. LACE’s contribution is therefore not raw accuracy but a search space defined by code: complete coverage, pipelines practitioners can reuse directly, and a component set extended by editing the prompt rather than the framework.
[LG-19] RACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH
链接: https://arxiv.org/abs/2608.16410
作者: Yu-Han Huang,Yujia Wu,Vincent S. Tseng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Combined algorithm selection and hyperparameter optimization (CASH) searches a conditional space in which the selected model determines which hyperparameters are active. In time-series forecasting, temporal choices, chronological validation, and costly evaluations further complicate this search. Controlled comparisons of heterogeneous search methods under a shared time-series CASH (TS-CASH) evaluation protocol remain limited. Within this setting, we study TRACECASH, a task-local hybrid sequential optimizer combining grouped actor-critic candidate generation with fixed rules for model coverage, validation-guided exploitation, and exploration after stalled progress. A model actor proposes an initial forecasting model; three model-conditioned actors generate temporal, architectural, and training actions; and a modelspecific decoder constructs the configuration ultimately evaluated. We compare TRACE-CASH with six alternatives spanning random, Bayesian, evolutionary, multi-objective, and language-model-assisted search across 41 dataset-frequency task variants. TRACE-CASH has the lowest mean rank on both MASE and WQL. Descriptively, it also has the lowest window-averaged test-MASE rank in the predefined full and late windows. These results support the complete TRACECASH procedure as competitive among the evaluated methods.
[LG-20] SoftModel: A Neural Model That Grows Its Own Topology – Governed Structural Growth for Continual In-Service Learning
链接: https://arxiv.org/abs/2608.16409
作者: Zhoumin Xie
类目: Machine Learning (cs.LG)
*备注: 99 pages, 16 figures, 12 tables
Abstract:Today, a neural system is almost always used in two phases – trained, then deployed – and in that regime it freezes twice: training ends, and the topology itself was never a degree of freedom. We take the opposite premise as an axiom – total plasticity: no part of a model, including its structure, is ever frozen – and derive the governance a lifelong learner then requires. The design’s target regime is continual, in-service learning: a long-lived model on a non-stationary stream, whose stability comes from governance rather than immobility and whose capacity follows demand. The result is a growable soft model: an algebra of structural operators (width, hierarchy, composition, input interface, grown cycles, attention heads), each exact at application, budgeted, and audited, with adoption decided solely by a held-out reality gate that treats parametric and structural change uniformly. A complete from-scratch system realizes the whole account; its factory surface is operated end-to-end by a production LLM. Two conclusions follow from the axiom by construction: stability under lifelong change becomes an audit property of the lifecycle, and structure that follows demand removes the silent cap a fixed topology places on later capability where the capacity floor binds. A third is measured: in the worlds where this was measured, the marginal value of new capacity was unobservable before adoption, so workable growth governance took its ex-post form. The same governance extends to evaluative signals, and the core method is evaluated on standard continual-learning benchmarks, where governed growth preserves the ability to keep learning along long task sequences. A pre-registered experimental program adjudicates the mechanism and value claims on the tested problems and reports its failures at full prominence; the map – positive and negative – is the contribution.
[LG-21] FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selection
链接: https://arxiv.org/abs/2608.16385
作者: Chen-An Tai,Yujia Wu,Vincent S. Tseng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Early time-series classification (ETSC) aims to make accurate predictions from partially observed time series as early as possible. Although various stopping mechanisms and feature learning strategies have been developed for ETSC, most existing methods assume access to sufficient labeled training data, which may be unrealistic in applications with limited annotation. Under limited supervision, learning an additional sample-level stopping module and extracting effective classification features can both become challenging. In this paper, we propose FETERS, a few-shot ETSC framework that selects a dataset-level stopping ratio through class-wise leave-one-out (LOO) evaluation on the support set and uses a penalty-based reward function to manage the accuracy-earliness trade-off, thereby avoiding the need to train an additional stopping module. FETERS further combines Rocket-based features with frozen Chronos representations for classification. Extensive experiments on 69 public datasets spanning 14 domains show that FETERS achieves state-of-the-art (SOTA) performance in the 5-shot setting, with the highest average harmonic mean (HM) and the best HM on 38 datasets, while outperforming the current SOTA method on 44 datasets. FETERS also remains competitive in the full-shot setting, demonstrating its effectiveness in managing the accuracy-earliness trade-off.
[LG-22] ask-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning
链接: https://arxiv.org/abs/2608.16345
作者: Zhiming Xu,Huiyu Yi,Zhen-Hao Xie,Baile Xu,Furao Shen,Jian Zhao,Suorong Yang
类目: Machine Learning (cs.LG)
*备注: 7pages, 4figures, 5tables
Abstract:Pre-trained models (PTMs) provide a strong foundation for continual learning by offering stable representations that facilitate lightweight adaptation to new tasks. However, adapting well to each task does not ensure reliable inference over all learned tasks. Since task boundaries are often artificial and semantically entangled, an input from an unknown task can remain ambiguous even with strong PTM features, making cross-task prediction a key bottleneck. We propose Task-Anchored Inference Latent Shaping (TAILS), a lightweight post-PTM module that can be integrated into diverse continual learners and optimized through a decoupled step. TAILS uses fixed task anchors as persistent references to accumulated knowledge. It interprets each sample’s feature representation relative to these references, then composes relevant evidence across tasks into latent recall. Rather than selecting a task-specific path or adjusting classifier outputs, TAILS uses latent recall to directly correct the feature representation before prediction. It therefore resolves cross-task ambiguity at the representation level, while leaving the original PTM, method-specific modules, and classifier unchanged. Extensive experiments across multiple PTM-based continual learning paradigms show that TAILS can improve classification and task-inference performance with modest parameter overhead and negligible inference cost.
[LG-23] Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving
链接: https://arxiv.org/abs/2608.16336
作者: Anders Vestrum,Arya Raeesi,Hanna Roed
类目: Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 13 pages, 9 figures, 4 tables
Abstract:Modern LLM serving deployments must simultaneously satisfy heterogeneous service-level objectives (SLOs) across a diverse population of user tiers, ranging from latency-critical API calls to background batch processing. Llumnix introduced a dynamic, migration-capable multi-instance scheduler for LLM inference that achieves load balancing, defragmentation, prioritization, and auto-scaling through a unified “freeness” metric. However, Llumnix’s priority model is restricted to two levels (high and normal), an abstraction too coarse to express the richer SLA classes common in production deployments. In this work, we extend Llumnix’s priority model to support an arbitrary number of tiers and evaluate the effects of this extension under three realistic priority distributions (uniform, Gaussian, enterprise) using Vidur, a high-fidelity LLM inference simulator. We implement per-tier headroom with exponential decay, tier-aware dispatch ordering, and the full Llumnix migration pipeline inside Vidur’s hierarchical scheduling framework. We compare our extended scheduler against INFaaS (global routing baseline), vLLM, Orca, and Sarathi-Serve (per-replica baselines), sweeping priority levels from 1 to 10. Our experiments demonstrate that four priority tiers yields the best cost-effectiveness tradeoff, achieving prefill mean speedups of up to 8.3x and end-to-end P99 speedups of up to 3.1x over INFaaS with cost-per-latency improvements of 46 to 68%, while preserving strong SLO differentiation across tiers. We further show that the system sustains these gains at 10 priority levels without tail latency collapse, with overhead concentrated in the prefill phase.
[LG-24] Predicting Evaluating and Explaining Top Misinformation Spreaders via Archetypal User Behavior
链接: https://arxiv.org/abs/2608.16323
作者: Enrico Verdolotti,Luca Luceri,Silvia Giordano
类目: ocial and Information Networks (cs.SI); Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注: 48 pages. Published version: Online Social Networks and Media 50 (2025) 100336
Abstract:The spread of misinformation on social networks poses a significant challenge to online communities and society at large. Not all users contribute equally to this phenomenon: a small number of highly effective individuals can exert outsized influence, amplifying false narratives and contributing to significant societal harm. This paper seeks to mitigate the spread of misinformation by enabling proactive interventions, identifying and ranking users according to key behavioral indicators associated with harmful content dissemination. We examine three user archetypes – amplifiers, super-spreaders, and coordinated accounts – each characterized by distinct behavioral patterns in the dissemination of misinformation. These are not mutually exclusive, and individual users may exhibit characteristics of multiple archetypes. We develop and evaluate several user ranking models, each aligned with a specific archetype, and find that super-spreader traits consistently dominate the top ranks among the most influential misinformation spreaders. As we move down the ranking, however, the interplay of multiple archetypes becomes more prominent. Additionally, we demonstrate the critical role of temporal dynamics in predictive performance, and introduce methods that reduce data requirements by minimizing the observation window needed for accurate forecasting. Finally, we demonstrate the utility and benefits of explainable AI (XAI) techniques, integrating multiple archetypal traits into a unified model to enhance interpretability and offer deeper insight into the key factors driving misinformation propagation. Our findings provide actionable tools for identifying potentially harmful users and guiding content moderation strategies, enabling platforms to monitor accounts of concern more effectively.
[LG-25] Advancing Open and Reproducible Relational Learning: RelArena-α TabPFN-Rel and RPI
链接: https://arxiv.org/abs/2608.16319
作者: Adrian Hayler,Klemens Flöge,Alan Arazi,Rishabh Ranjan,Jure Leskovec,Felix Birkel,Brendan Roof,Anurag Garg,Kristina Collins,Lydia Sidhoum,Jonas Kübler,Siyuan Guo,Oscar Key,Jan Hendrik Metzen,Rylee Grace,David Salinas,Arthur Cahu,Simon Bing,Benjamin Jäger,Tuana Çelik,Mihir Manium,Vitor Monteiro,Jake Robertson,Jerry Chen,Eliott Kalfon,Tomás Pereda,Lilly Wehrhahn,Dominik Safaric,Tobias Schroeder,Georg Grab,Diana Kriuchkova,Clara Cornu,Philipp Singer,Nick Erickson,Vahid Balazadeh,Marie Salmon,Simone Alessi,Kürşat Kaya,Philipp Jund,Léo Grinsztajn,Yann LeCun,Bernhard Schölkopf,Madelon Hulsebos,Lennart Purucker,Sauraj Gambhir,Frank Hutter,Noah Hollmann
类目: Machine Learning (cs.LG)
*备注:
Abstract:This first release of Prior Labs in relational learning shows our continued commitment to open science. We open-source three pieces of software that we expect to accelerate research in the field towards meaningful real-world impact. We aim to steer further development based on feedback from, and in collaboration with, the community. Given the early stage of development, our \alpha -release targets researchers and early-adopting practitioners. Over the past years, a variety of datasets and tasks for relational learning have emerged, but the community has not converged on a reliable, reproducible way to compare different methods on these tasks. Our \alpha -release, RelArena- \alpha , provides a unified framework for running and comparing baselines on RelBench v1 by standardizing data loading, evaluation protocols, tuning regimes, and support for systems with custom tuning, inspired by established tabular benchmarks such as TabArena. We plan to work with the research community to further develop RelArena- \alpha into a catalyst for progress in the relational learning community. We release the initial version of TabPFN-Rel, a purpose-built relational harness for TabPFN-3. Currently ranked first among models on RelArena- \alpha , TabPFN-Rel makes key improvements upon RDBLearn. Beyond its ranking, TabPFN-Rel serves as a strong baseline, adding to the growing evidence that flattening a relational database into a single table remains competitive with specialized relational architectures on real-world tasks. To facilitate adoption of relational learning methods in research and industry, we release an initial \alpha -version of our Relational Predictive Interface, RPI, an open-source, model-agnostic interface that enables early adopters to easily define problems on new databases and apply any model implemented in RelArena- \alpha , including TabPFN-Rel, to these problems. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.16319 [cs.LG] (or arXiv:2608.16319v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16319 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-26] Correlation Clustering with Random Partial Information
链接: https://arxiv.org/abs/2608.16315
作者: Rajath Rao K. N.,Jens Schlöter,Sami Davies,Amira Ouchene,Yasamin Nazari
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: 17 pages, 5 figures, 6 tables
Abstract:Correlation clustering is a fundamental unsupervised learning problem. On complete graphs, both the min-disagreement and min-max objectives admit constant-factor approximations, yet on general (non-complete) graphs, the best guarantees blow up to O(\log n) and O(\sqrtn) . This gap between the two regimes motivates the following question: are there classes of incomplete graphs that circumvent the lower bounds on general graphs and admit approximation guarantees approaching those attainable on complete graphs? We study a natural class of graphs obtained by randomly subsampling a complete signed graph G , where each edge is independently deleted with probability q . For such graph instances both for the min-max and the min-disagreement objectives, we prove approximation guarantees (depending on q ) that are substantially better than the bounds achievable for general graphs. We supplement our theoretical results with experiments that also suggest that the approximation ratios of our algorithm are close to those of the complete graph and better than the worst-case bounds for general (non-complete) graphs.
[LG-27] SCALE: State-Calibrated Latent Embeddings for JEPA Planning in the Right Geometry
链接: https://arxiv.org/abs/2608.16287
作者: Jiaming Hu,Yan Zheng,Tian Wang
类目: Machine Learning (cs.LG)
*备注: 15 pages, 2 figures
Abstract:Joint-embedding predictive world models plan by scoring predicted terminal embeddings against a goal embedding using a cost defined on the representation itself. Two prominent strategies for obtaining non-collapsed representations are to inherit a pretrained feature space, as in DINO-WM, and to learn an embedding end to end with anti-collapse regularization, as in LeWorldModel (LeWM) with SIGReg. These strategies show complementary strengths across tasks. Although task-relevant state is decodable from the full embeddings of both models, DINO-WM’s leading principal components usually retain substantially more state information than LeWM’s. Because Euclidean planning costs are dominated by high-variance directions, this difference affects how strongly state can influence candidate selection. We propose SCALE (State-CAlibrated Latent Embeddings) to give the end-to-end LeWM representation the favorable geometric property observed in DINO-WM. SCALE induces this property by correlating sampled pairwise latent distances with distances in a standardized task-relevant state space, without replacing LeWM’s learned encoder. Across five tasks, three planning solvers, and five compute budgets, SCALE improves every task–solver average over LeWM. A latent-to-state regression control matches or exceeds SCALE’s full-embedding decodability yet leaves latent–state distance alignment essentially unchanged and yields less consistent planning gains. SCALE adds a single lightweight training-time regularizer and no planning-time overhead. These results show that planning depends not only on whether task-relevant information is present, but also on whether it shapes the geometry consumed by the planner.
[LG-28] Efficient Coreset Selection via K-Nearest Neighbor Graphs
链接: https://arxiv.org/abs/2608.16270
作者: Yingfan Liu,Leiyu Zhang,Jiadong Xie,Mingzhe Wang,Jeffrey Xu Yu,Jiangtao Cui
类目: Machine Learning (cs.LG)
*备注:
Abstract:Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset. Existing gradient-approximation coreset methods such as CRAIG and cluster-based variants can preserve model accuracy. Still, their selection stages often rely on dense pairwise distances or large item-cluster bound matrices, leading to high time and memory costs on large datasets. This paper proposes KNNG-CS, a lightweight coreset selection method based on a K -nearest neighbor graph. KNNG-CS exploits local neighborhood structures to estimate the importance of each data item and greedily selects representative nodes without maintaining a quadratic distance matrix. The method requires only linear storage in the number of edges. Experiments on four real-world datasets show that KNNG-CS achieves accuracy comparable to representative gradient-approximation coreset methods, while reducing selection time by 2.3\times - 41.2\times and peak memory to 0.3% - 7.5% of the baselines.
[LG-29] SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning
链接: https://arxiv.org/abs/2608.16249
作者: Jaewan Choi,Junyoung Yang,Sangdon Park
类目: Machine Learning (cs.LG)
*备注: 9 pages
Abstract:Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target knowledge and preserving general utility. We propose SAUL (Sharpness-Aware Augmented-Lagrangian Unlearning), which formulates unlearning as a constrained minimization problem following the principle of “forget enough, but no more than necessary.” At its core, SAUL formulates forgetting as an explicit constraint with a prescribed satisfaction criterion, whereas prior unlearning methods typically specify the desired level of forgetting implicitly through optimization objectives. An augmented Lagrangian controller adaptively adjusts forget-side pressure according to constraint violation and can eventually deactivate the forget-side update as the prescribed criterion remains satisfied. Sharpness-aware updates on both retain and forget objectives, together with a dual-optimizer design that maintains role-separated states, further stabilize the resulting unlearning dynamics. We evaluate SAUL on the TOFU, WMDP, and MUSE benchmarks, demonstrating favorable forgetting-utility trade-offs over representative sharpness- and perturbation-based baselines under benchmark-specific forgetting criteria. Beyond the complete SAUL framework, we further show on TOFU that applying the augmented-Lagrangian controller as a drop-in modifier to representative baselines improves their post-forgetting utility, demonstrating the practical value of explicit forgetting control.
[LG-30] he Trade-off Between Covariate Dependence and Latent Structure in Representation Learning
链接: https://arxiv.org/abs/2608.16245
作者: Małgorzata Łazęcka,Ewa Szczurek
类目: Machine Learning (cs.LG)
*备注:
Abstract:Disentangled representation learning seeks latent representations whose indicidual dimensions each align with a distinct covariate. Unsupervised approaches typically target latent dimension independence, yet this gives no guarantee that the resulting dimensions align with semantically meaningful covariates. Supervised approaches structure the latent space using observed covariates, but under correlated covariates they cannot simultaneously control one-to-one latent-covariate alignment and latent independence. We introduce a unified, supervised framework that couples latent dimension-covariate dependence with constraints on the latent structure. Within this framework, we show an inherent trade-off, where enforcing latent independence or exclusive one-to-one latent-covariate dependence comes at a provable cost in latent-covariate alignment. We prove that the resulting disentanglement regimes are ordered by the strength of that alignment. Each regime admits a closed-form transformation of the latent space. We apply these transformations post-hoc to realign the representations of pretrained models such as CLIP, DINOv2, and ViT, and we fold them into the inference of informed factor analysis (iFA), a probabilistic model with covariate-informed factors. On simulated and real multi-omics data, we show that both post-hoc alignment and iFA enable controllability of structured latent representations.
[LG-31] Optimizing Multi-Market Participation of Battery and Electrolyser Systems Based on Field Performance
链接: https://arxiv.org/abs/2608.16238
作者: Chunyang Zhao,Stoyan Trenchev,Shi You,Chresten Træholt
类目: Machine Learning (cs.LG)
*备注: 6 pages, 8 figures. Accepted at the 2026 IEEE Power and Energy Society General Meeting (PESGM)
Abstract:The increasing share of renewable energy in power systems creates a need for fast-response and flexible resources to maintain system stability. With the expansion of electricity markets and ancillary service products, opportunities arise to stack revenues across multiple services. Long-term Power-to-X (PTX) electrolysers and short-term battery energy storage systems (BESS) are prevalent flexible resources, yet most studies neglect real hardware behavior, such as ramp limits, efficiency, and setpoint-tracking accuracy. This work presents experimental and modeling results for a 55 kW/79 kWh BESS and an electrolyser comprising three 2.4 kW units. Key characteristics are identified through measurements and embedded into a price-driven optimization framework for participation in the Danish electricity and ancillary service markets, utilizing real market data from 2022 to 2025. The optimized daily profits for multi-market participation are 1,749.27 DKK and 289.46 DKK for the BESS and electrolyser, respectively. With the demonstrated business cases for BESS and PTX systems, this work highlights the importance of incorporating experimental performance when evaluating participation across multiple markets and years.
[LG-32] A Privacy Study of Sparse Collaborative Inference
链接: https://arxiv.org/abs/2608.16236
作者: Maximilian Andreas Hoefler,Karsten Mueller,Wojciech Samek
类目: Machine Learning (cs.LG)
*备注:
Abstract:Collaborative inference (CI) splits a model between an edge device and a server, whereby the client computes an intermediate activation, transmits it, and the server completes the computation. This raises two concerns, the communication cost of the transmission and the risk that it reveals private information about the input. Recent work reduces this cost by sparsifying activations and entropy-coding the result. Sparsity has also been argued to improve privacy, on the intuition that transmitting fewer values reveals less about the input. We test this claim by decomposing the sparse activation into the retained values and the set of positions they occupy, and by reconstructing inputs from each component in isolation. We find that sparsification reduces the leakage far less than it reduces the transmission cost, and that the remaining risk shifts to the positions, which prior analyses treat as side information for decoding. Across natural-image and face datasets, the positions alone constitute a serious privacy risk, enabling high-fidelity reconstructions and re-identification of individuals. The leakage from the positions persists even when both the transmission cost and the task utility are low. We conclude that the positions of sparse activations should be treated as sensitive transmitted data and audited carefully in the context of collaborative inference. Code is available at this https URL.
[LG-33] Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization
链接: https://arxiv.org/abs/2608.16216
作者: Anling Xiang,Yuwen Yang,Yang Shen
类目: Machine Learning (cs.LG)
*备注: 19 pages, 2 figures, 2 tables
Abstract:What is the right delay complexity when a learner can track only C pending feedback items and discarded feedback is permanently lost? Existing one-point bandit convex optimization guarantees in this model pay \sqrtT\sigma_\max , where \sigma_\max is the peak backlog, although unlimited tracking admits the sharper \sqrtd_\mathrmtot dependence on total delay. We introduce a scheduler-side conditional-energy interface that separates rate adaptation from the one-point perturbation filtration and handles the dependent importance weights created by randomized admission. Under the same semi-clairvoyant oracle and pathwise hard-capacity contract, this yields an untuned learner whose delay term scales as O(\sqrtE_C d_\mathrmtot) , with only an explicit restart factor E_C ; a public constant-factor peak bound removes this factor while d_\mathrmtot remains unknown. Under strong convexity, the same interface yields the temporal cost H_A(d)=\sum_t \sigma_t/(A+t) . Two delay vectors with identical delay multisets, d_\mathrmtot , \sigma_\max , and capacity can nevertheless have polynomially different minimax regret, showing that timing matters under curvature even when aggregate delay summaries agree. Finally, a continuous hard family converts tracking capacity into a zeroth-order query budget and gives a complementary capacity-starvation lower endpoint. The upper bounds require C\ge \ln T+1 and do not constitute a complete capacity minimax characterization.
[LG-34] Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles
链接: https://arxiv.org/abs/2608.16212
作者: Chunyang Zhao,Chresten Træholt
类目: Machine Learning (cs.LG)
*备注: 5 pages, 4 figures. Accepted to ECCE Europe 2026
Abstract:Laboratory battery tests provide the main empirical basis for battery performance and degradation studies, but their operating patterns do not directly represent field duty profiles. This paper quantifies the gap by comparing six accessible evidence sources covering controlled cycling, drive-cycle testing, dynamic cycling, NMC811 laboratory ageing, a real electric-vehicle charging trace, and fleet-scale electric-vehicle state-of-health (SOH) data. The analysis combines usage frequency, usage intensity, usage C-rate, and a duty-structure index (DSI) based on normalized current dispersion and ramping. The representative single-segment DSI ranges from 0.630 for the field source trace and 0.699 for NASA to 2.936 for Oxford and 2.855 for Imperial, while usage C-rate ranges from 0.14-0.40 for Imperial, NASA, Stanford, and Hyundai to 2.00 for Oxford. Long-term ageing also differs: the 80 percent retention region occurs near 351 NASA cycles, 6292 Oxford checkpoints, and 1019 Stanford cycles. In chemistry-aligned NMC/NCM evidence, Imperial retains 0.813 under standard cycling and 0.865 under drive-cycle ageing, while the field source has median SOH 0.889 with visible dispersion. Field operation further shows a median use intensity of 137.2 km/day and 56.9 percent of charges ending at or above 95 percent SOC. These results show that battery performance metrics are conditional on the duty pattern that generated them; application-oriented studies should report explicit duty-profile descriptors together with chemistry, capacity, and ageing metrics.
[LG-35] Conditional Evaluation of Language Models with Cheap Auxiliary Signals
链接: https://arxiv.org/abs/2608.16210
作者: Zhi Zhang,Lingfeng Lyu,Yue Kang,Doudou Zhou
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Aggregate accuracy hides where models succeed and fail. Estimating conditional performance profiles from gold labels alone is expensive, while cheap auxiliary signals such as LLM-judge scores, pairwise comparisons, confidence scores, and judge-disagreement features can be collected for every benchmark item but are often biased or miscalibrated. We propose LACE (Local Augmented Control-Variate Evaluation), a semi-supervised estimator for conditional LLM evaluation. The key step is local centering: after subtracting the conditional mean of a cheap signal within the target profile region, any linear augmentation has zero conditional mean and therefore cannot change the estimand. The augmentation coefficient is used only for efficiency, and a local ridge control variate combines a gold-label residual mean from the labeled subset with a cheap-signal mean from the full item pool. We prove calibration-free identification, unbiasedness for grouped profiles, local oracle optimality within centered linear augmentations, and first-order adaptivity to the estimated coefficient. The resulting gain formula is governed by a population local R^2 , which characterizes how the efficiency attainable from the cheap signals varies across profile values. We also derive corresponding estimators for direct paired model gaps and deployment-weighted scores. We empirically evaluate the primary performance-profile estimator on MATH-500, ScienceQA, MMLU, WinoGrande, HellaSwag, TruthfulQA, GSM8K, and ARC.
[LG-36] Multi-Granularity Sentiment Integration for LLM -Based Multimodal Sentiment Analysis NLPCC2026
链接: https://arxiv.org/abs/2608.16201
作者: Shanshan Lin,Yuesheng Wu,Chao Chen,Yizhe Yang,Zhihao Chen,Zexian Yang,Xiangwen Liao
类目: Machine Learning (cs.LG)
*备注: Accepted to NLPCC 2026
Abstract:Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision. While large language models (LLMs) offer strong semantic priors for MSA, effectively incorporating audio and visual signals effectively remains challenging. A key challenge is that audio and visual sentiment cues evolve over different temporal scales, yet many LLM-based methods compress these signals through shallow projection or coarse pooling before fusing them with text, which can weaken cross-modal alignment and erase fine-grained affective information. We propose MGSI, a multi-granularity sentiment integration framework for LLM-based MSA. MGSI first encodes audio and visual streams at short-, medium-, and long-range temporal scales, preserving both local variations and global affective trends. It then refines non-text features through text-guided alignment, and applies polarity- and intensity-aware enhancement to better handle ambiguous and near-neutral samples. The resulting multimodal representation is finally compressed into a small set of pseudo-tokens for efficient conditioning of a frozen LLM. Experiments on four public benchmarks show that MGSI substantially outperforms frozen-LLM baselines and remains competitive with strong multimodal methods. Further ablation and sensitivity analyses support the effectiveness of multi-granularity temporal modeling, text-guided refinement, and adaptive sentiment calibration.
[LG-37] Decorrelation Is Not Complementarity: Skill Not Lineage Governs Trusted-Monitor Ensembles
链接: https://arxiv.org/abs/2608.16190
作者: Anik Jha
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: AI control, trusted monitoring, ensemble diversity, scalable oversight, backdoor detection, capability control
Abstract:Trusted monitoring has a cheap, trusted model score a stronger untrusted model’s actions, and a diverse ensemble of them beats a single stronger monitor at matched cost. They are built by minimising average pairwise correlation, and that paper’s twelve monitors shared one base model, leaving open what supplies the diversity. We study 24 open-weight monitors spanning nine pretraining lineages and a 29x range of detection skill (pAUC at 10 percent FPR, 0.028 to 0.803) on backdoored code. The metric used to build panels does not predict what a panel is for, and we can say why. Agreement on attack items splits into a shared-detectability signal component and an idiosyncratic error component, which predict ensemble gain with opposite sign (Spearman -0.25 and +0.26), so their sum, the metric actually used, predicts it barely at all (+0.05); the cancellation holds in 7 of 8 evaluations. Skill acts on signal (+0.53) while error stays flat (-0.01), which is why a monitor’s own skill predicts its agreement with the pool (Spearman 0.84, n = 24, permutation p below 0.0001). Pretraining lineage is the obvious way to buy decorrelation, and it does not pay. At matched member capability, cross-lineage panels detect no better (permutation p = 0.13), and lineage barely moves the metric either (+0.064, p = 0.18). We report that against ourselves: on our own 22-monitor pool the same test read +0.104 at p = 0.037 until two monitors were added. An earlier pool topping out at pAUC 0.23 had already invalidated another analysis. Such a quantity is a property of the pool assembled. Panel gain over the best member falls monotonically with panel skill (-0.66 at k = 2, -0.70 at k = 3), and no correlation-weighted selection beats picking the single best monitor out of sample. Across six attacker models the gain result holds in all six, the agreement and cancellation results in five of six. Comments: AI control, trusted monitoring, ensemble diversity, scalable oversight, backdoor detection, capability control Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2608.16190 [cs.CR] (or arXiv:2608.16190v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2608.16190 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Anik Jha [view email] [v1] Mon, 17 Aug 2026 07:10:56 UTC (48 KB)
[LG-38] Demystifying Oversmoothing in Sheaf Neural Networks: An Index-Theoretic Criterion
链接: https://arxiv.org/abs/2608.16180
作者: Junwen Dong,Yuhan Peng,Hao Li,Huitao Feng,Kelin Xia
类目: Machine Learning (cs.LG); Differential Geometry (math.DG)
*备注:
Abstract:To combat oversmoothing in Graph Convolutional Networks, Sheaf Neural Networks (SNNs) were proposed as a generalization by equipping the graph with a sheaf structure and replacing the graph Laplacian with a sheaf Laplacian \mathcalL . Existing analyses connect sheaf diffusion to oversmoothing via the harmonic space ( \ker\mathcalL ), taking its absolute dimension as an indicator of anti-oversmoothing capacity. However, absolute dimension alone is not a reliable measure: certain sheaf configurations inflate \dim \ker \mathcalL while their harmonic sections remain entirely constant, without enriching discriminative capacity. We instead introduce the first relative, geometric approach, yielding a precise characterisation of anti-oversmoothing capacity. Under natural conditions on stalk transportation and global sheaf structure, we establish an index-theoretic comparison criterion showing that one sheaf’s harmonic space genuinely contains another’s beyond trivial inflation. We illustrate this with a concrete instance and further introduce \textitGyroSheaf, a sheaf with curved gyrovector-space stalks, extending the criterion to the non-linear setting via local tangent-space linearization. Experiments across ten models confirm the theoretical criterion: sheaf models violating the criterion collapse despite possessing index jumps, while compliant models maintain depth-stable representations.
[LG-39] REFLEX: Reflexive Equilibrium Fixed-point Learning for Endogenous eXchanges
链接: https://arxiv.org/abs/2608.16155
作者: Vignesh Nagarajan,Shriraghav Ashok
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computer Science and Game Theory (cs.GT)
*备注: 8 pages, 6 figures, 5 tables, 24 references
Abstract:In over-the-counter corporate bond markets, dealers compete for client trades by quoting bid and ask prices. Tighter quotes attract more business, but also informed customers more likely to trade ahead of adverse price moves, leaving the dealer holding the risk. As dealers increasingly use machine learning to set quotes, they retrain these models on the trades their own quotes attract, creating a feedback loop in which each model reshapes the market that generates its next training data. The question is therefore not only whether a quoting model performs well, but whether the market it creates stays stable as the model learns from it. Existing performative prediction theory gives a sharp stability condition, yet expresses it through abstract properties of the learning objective a trading desk cannot measure before deployment. We introduce REFLEX, a framework that replaces those unobservable quantities with three measurable features of dealer behavior: how strongly trading volume responds to tighter quotes, how sharply the dealer’s objective bends around its optimum, and how quickly informed flow increases as spreads narrow. REFLEX combines these into a single retraining modulus, a pre-deployment stability margin estimated from a desk’s own quote and execution history that predicts whether repeated retraining will converge or amplify itself. In simulation, predicted and measured stability agree within 8%, and competing dealers increase instability by 1.74x with two and 3.16x with three, as predicted. Where ordinary retraining becomes unstable at modulus 1.21, a structurally anchored correction converges as blind retraining collapses. Calibrated over 36 years of public market data, stability headroom falls roughly 4.4x for investment grade and 4.3x for high yield from calm to crisis regimes. Ultimately, REFLEX turns an abstract convergence theorem into a market-level safety margin.
[LG-40] Asymptotics-guided learning and symbolic regression for dispersive resonances
链接: https://arxiv.org/abs/2608.16152
作者: Konstantinos Alexopoulos,Josselin Garnier
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG); Mathematical Physics (math-ph); Analysis of PDEs (math.AP); Optics (physics.optics)
*备注: 25 pages, 11 figures, 6 tables
Abstract:We study resonance prediction in dispersive media, formulated as nonlinear spectral problems for volume integral operators. The main idea is to use asymptotic analysis not only as a baseline approximation, but also as a guide for constructing predictive correction models. We learn the residual between asymptotic and reference resonances using features suggested by the subwavelength expansion, including the logarithmic scales specific to two dimensions. The resulting corrections substantially improve single-resonator and dimer predictions, and symbolic regression produces compact formulas for the learned residual. The results show that asymptotic analysis can be used not only to approximate resonances, but also to design the feature space in which data-driven corrections become accurate, low-dimensional, and interpretable.
[LG-41] Unifying Graph Neural Networks Through a Common Layer Equation
链接: https://arxiv.org/abs/2608.16097
作者: Sai Karthik Navuluru,Siddhartha Shankar Das,Bo Ni,Hongjie Chen,Yu Wang,Baris Coskunuzer,Nesreen K. Ahmed,Franck Dernoncourt,Mahantesh Halappanavar,Tyler Derr,Ryan A. Rossi,Lakshman Tamil
类目: Machine Learning (cs.LG)
*备注: 133 pages, including appendix; includes figures and tables
Abstract:Graph neural networks are commonly described through family-specific equations whose notation obscures shared computations and structural differences. We introduce a common layer equation that represents covered architectures through seven components: an update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map. The central factorization separates where information moves, encoded by the propagation bank, from what moves, encoded by the message maps. Function-valued fillings extend the same equation across local message passing, attention, spectral filtering, global communication, relation-specific channels, higher-order domains, and geometric messages. We make this unification explicit and checkable through worked reductions of canonical layers and component assignments spanning seven nonexclusive architectural families. A fixed slot discipline assigns operations by computational role and defines the framework’s coverage boundary. The decomposition also yields component-level theoretical insights: under endpoint-local messages and node-local updates, operator support bounds one-layer dependencies, and one-layer global mixing requires a full effective operator row under the stated hypotheses. The resulting framework organizes more than 200 architectures in a common design space, enables component-wise comparison and generation of structurally consistent architectures, and connects propagation choices to oversmoothing, oversquashing, heterophily, and expressivity. It further exposes the empirical inverse problem of mapping measurable graph and task properties to validated component choices. Comments: 133 pages, including appendix; includes figures and tables Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.16097 [cs.LG] (or arXiv:2608.16097v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.16097 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-42] owards Reason able Molecular Structure Elucidation from Infrared Spectroscopy with Chemical Feedback
链接: https://arxiv.org/abs/2608.16082
作者: Yusen Tan,Hongyu Zhan,Hai-tao Yu,Changxi Chi,Wenjie Du,Jun Xia
类目: Machine Learning (cs.LG)
*备注:
Abstract:Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making the process time-consuming and ambiguous. Recent machine learning methods have made progress in molecular structure elucidation using molecular formulas and IR spectra. However, these models often infer unreasonable candidate molecular structures, including top-ranked predictions. More specifically, the molecular formula implied by a candidate structure often fails to match the input molecular formula, and the candidate’s theoretical IR spectrum is often inconsistent with the observed IR spectrum. To address these issues, we propose Formula- and IR-Matched Preference Optimization (FIRMPO), a general and plug-and-play chemical feedback-driven preference optimization framework for molecular structure elucidation. FIRMPO incorporates chemical feedback as preference signals based on exact molecular formula matching and IR spectral consistency to guide reasonable structure predictions. Unlike generic preference optimization methods, FIRMPO is tailored to molecular structure elucidation while remaining model-agnostic, enabling it to be readily integrated with different structure prediction models in this class. This encourages models to prioritize structures that satisfy the chemical feedback, leading to a substantial improvement in the accuracy of top-ranked predictions. Extensive experiments on three widely used IR datasets show that FIRMPO significantly improves molecular structure elucidation accuracy over existing baselines.
[LG-43] DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
链接: https://arxiv.org/abs/2608.16080
作者: Xinling Yu,Yixing Li,Ziyue Liu,Xin Ai,Zhiyu Zeng,Hai Li,Zheng Zhang
类目: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注:
Abstract:Thermal-aware optimization of multi-die 3D integrated circuits evaluates many designs, each a costly heat-equation solve. Operator-learning surrogates replace this solve with a fast forward pass, ideally trained from physics alone, without labeled data. DeepOHeat-v1 made such surrogates fast and trustworthy, but only on low-contrast geometries. High-contrast multi-die stacks break it in two ways: discontinuous conductivities make the continuous physics loss ill-defined at material interfaces, and ill-conditioning ( \kappa_2(A_h) \approx 6 \times 10^4 ) puts the discretized strong-form loss beyond first-order optimization. We propose DeepOHeat-v2 to overcome both. First, we train on a discretized physics loss that handles the discontinuities natively; its energy form reduces the prediction-space loss-Hessian conditioning from \kappa^2 to \kappa , and a matrix-preconditioned optimizer cuts the mean peak temperature error from over 30 K to 0.55 K. Second, because optimization leaves the training distribution, we propose a self-improving framework: a hotspot trust gate sends flagged placements to a reference solver, and the surrogate incrementally retrains on the refined solutions, keeping an update only when it improves held-out validation error. On a multi-die benchmark, the surrogate-true peak gap on the returned design falls from 1.12 K to 0.11 K, matching a solve-at-every-step optimizer while running 56\times faster.
[LG-44] Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)
链接: https://arxiv.org/abs/2608.16029
作者: Oktay Agcaoglu
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks. However, conventional gICA primarily identifies components shared across subjects. This group-level assumption can limit the recovery of networks present only in individuals or subject subsets, reducing sensitivity to intersubject heterogeneity in clinical neuroimaging datasets. We introduce Copula-Linked Group ICA (CoLiG-ICA), an algorithm in the Group ICA 2.0 framework that jointly estimates template-linked, cohort-only, and subject-only brain networks within a unified model. CoLiG-ICA combines ICA-based spatial decomposition, copula-based dependence modeling, and deep learning optimization to preserve the consistency and interpretability of template-constrained ICA while enabling free components beyond the reference networks. By linking subject decompositions to shared templates and jointly estimating cohort-only and subject-only sources, CoLiG-ICA represents individual variability not captured by conventional group priors. We evaluate CoLiG-ICA using resting-state fMRI data from the UCLA-CNP dataset and compare it with conventional constrained ICA in estimating template-linked components, discovering additional free components, improving component independence, and capturing subject-level variability beyond the shared group prior. Compared with MOO-ICAR, CoLiG-ICA showed significantly lower intercomponent spatial dependence, indicating improved subject-level component independence, and significantly reduced motion-related variance in the template-linked components. Additionally, in a schizophrenia-only group analysis, CoLiG-ICA identified three additional resting-state networks beyond the 53 template-linked NeuroMark components: one sensorimotor and two visual networks.
[LG-45] Retrieval-guided Twin Fusion with Similarity-aware Contrast for Molecule-Text Alignment
链接: https://arxiv.org/abs/2608.16005
作者: Shunshun Gu,Shengqi Qiu,Hang Zhou,Xiao Luo
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper studies the problem of molecule-text alignment, which aims to project molecules and their textual descriptions into a joint latent space for downstream tasks including molecule search and molecular property prediction. Previous approaches typically combine graph structure mining with contrastive learning to enhance joint representation learning. However, they typically neglect fine-grained semantic relationships between substructures and texts, leading to suboptimal performance on downstream tasks. Towards this end, we propose a novel approach named Retrieval-guided Twin Fusion with Similarity-aware Contrast (RISEN) for molecule-text alignment. The core idea of RISEN is to construct a latent twin molecule for each substructure with cross-modal retrieval for semantic enhancement. In particular, for each substructure query, we retrieve relevant textual descriptions and sample several molecules that share similar descriptions of substructures. Then, we aggregate their representations via attention pooling for a twin latent representation, which would be further fused with the original substructure for representation enrichment. In addition, we measure the similarity across substructures and texts, which would further guide cross-modal contrastive learning with soft thresholding. Extensive experiments on benchmark datasets validate the superiority of the proposed RISEN in comparison with existing baselines.
[LG-46] oward Optimal Second-Order Path-Length Guarantee for Adversarial Multi-Armed Bandits
链接: https://arxiv.org/abs/2608.15996
作者: Mengxiao Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study second-order path-length regret in adversarial K -armed bandits against oblivious loss sequences. Bubeck et al. [2019] designed an algorithm that achieves \widetilde\mathcalO(K+\sqrtKQ_\infty,1) regret, where Q_\infty,1 is the first-order path length, and left open whether \widetilde\mathcalO(\textpoly(K)\sqrt1+Q_\infty,2) regret is achievable under bandit feedback, where Q_\infty,2 is the second-order path length. Somewhat surprisingly, we resolve this question positively by showing that with a more involved analysis, the exact same algorithm of Bubeck et al. [2019] achieves \mathcalO\left(K\log(KT)+\sqrtK\log(KT)\bigl(1+Q_\infty,2\bigr)\right) expected regret when Q_\infty,2 is known, where T is the horizon. This matches the \Omega(\sqrtKQ_\infty,2) lower bound up to logarithmic factors and additive terms. We further remove the knowledge of Q_\infty,2 using an adaptive restart scheme whose path-length estimator has uniformly bounded increments.
[LG-47] Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training
链接: https://arxiv.org/abs/2608.15995
作者: Jia Quan Loh(1),Vincent Crocher(1),Marlena Klaic(2),Denny Oetomo(1),Ying Tan(1) ((1) Human Robotics Laboratory, Department of Mechanical Engineering, The University of Melbourne (2) Melbourne School of Health Sciences, The University of Melbourne)
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 12 pages, 4 figures, 3 tables Submitted to:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Abstract:Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage. Rehabilitation robots can increase TST dosage via controlled, repetitive treatment and free therapists to simultaneously manage other patients, but it has yet to demonstrate significant benefits over conventional treatment. This is potentially linked to inaccurate robotic representation of personalised physical therapist-patient interaction and lack of practice variability during TST. Hence, we advocate for robotic interventions that preserve the personalised physical therapist-patient interactions when delivering TST for patients across varying practise conditions. We propose a Learning-from-Demonstration framework using Task-Parameterised Gaussian Mixture Models (TPGMM) to learn personalised physical therapist-patient interaction in Task-Specific exercises, mapping patient joint kinematics to therapist-applied torques using few demonstrations. The model is generalised to reconstruct therapist torques in new task variations. The framework was evaluated on physical interactions from 14 mock “therapist-patient” pairs over three tasks of increasing complexity, each with six variations. A benchmark comparison against a Look-Up Table was conducted. The results show both methods reproducing interactions in unseen task variations that deviate slightly from the actual interaction, with TPGMM slightly outperforming LUT. Both methods reproduced interactions that gets increasingly closer to the actual interaction as task complexity increases.
[LG-48] Operator-Theoretic Generalization Bounds for Multitask Deep Learning
链接: https://arxiv.org/abs/2608.15982
作者: Mahdi Mohammadigohari,Thomas Borsani,Giuseppe Di Fatta
类目: Machine Learning (cs.LG)
*备注:
Abstract:We develop operator-theoretic generalization bounds for deep multi-output function classes by representing network layers as Koopman composition operators on vector-valued reproducing kernel Hilbert spaces. In vector-valued Sobolev RKHSs, we derive Rademacher complexity bounds for invertible and width-expanding injective architectures. The estimates separate the output-coupling contribution, represented by the trace of the task matrix, from the layerwise operator norms, Sobolev symbol ratios, determinant factors, and restriction constants generated by the linear maps. We then analyze a distinct one-dimensional Brownian/Cameron–Martin regime. Using the exact anchored derivative-norm characterization of the vector-valued Brownian RKHS, we obtain layerwise bounds for domain-preserving scalar linear maps and anchored diffeomorphic activations; the corresponding factors scale as |W_l|^1/2 and |\sigma_l’|_\infty^1/2 , respectively, and do not involve Sobolev smoothness exponents. Because the Sobolev and Brownian results concern different hypothesis spaces, neither is asserted to dominate the other uniformly. We additionally formulate shared operator learning across tasks, prove a finite-rank representer theorem, derive the exact finite-dimensional problem for squared loss, and establish a target-transfer bound when the learned operator is obtained independently of the target sample. Synthetic and MNIST studies examine stabilized Sobolev-inspired and Brownian-inspired complexity proxies; these empirical proxies are not evaluations of the proved bounds for rank-deficient architectures.
[LG-49] Fiber Fingerprints of Hidden Learning-State Dynamics
链接: https://arxiv.org/abs/2608.15976
作者: Qinyou Wang
类目: Machine Learning (cs.LG)
*备注: 30 pages, 8 figures, 7 tables. Ancillary files include figure-reproducibility code and frozen plot-level data
Abstract:A learning system can occupy execution states that are indistinguishable under every declared present-behavior readout yet respond differently to future training. We formalize this through fiber fingerprints: controlled future-learning response laws restricted to present-behavior equivalence classes. Prefix-compatible finite probes induce a predictive quotient functor, a Nerode-type minimal recursively sufficient representation, and a canonical set-level predictive fiber without assuming smoothness, reversibility, finite rank, or a manifold. Under an explicit finite-dimensional Hilbert realization, response decomposes into visible, visible-mode-reuse, and irreducible-new sectors; a history-reachability bridge retains only distinctions generated by natural training histories. Conditional mechanism results then identify a graph-Hodge chronology decomposition, a regular switching class with root-mean-square scale \sqrtp\eta^3/2 and finite-scale corrections, and an exact Adam moment section whose immediate adaptive field is constant while common future gradients can reveal hidden moment differences. Frozen Transformer–LoRA–AdamW studies with Qwen2.5-7B and Mistral-7B-v0.3 support a local action backbone, longer-horizon first-return non-closure, and fresh visible-relative completion with output-range reuse and a low-rank irreducible sector. Stronger claims remain bounded by preregistered negative or mixed results: re-anchored transport is unresolved above its measurement floor; the strict finite-grid Hodge-- 3/2 conjunction is unmet despite prospective contraction; Qwen accessibility is not established in the frozen raw moment chart; and Mistral revelation is future-context dependent rather than bank invariant. Within these support-, scale-, metric-, and context-resolved boundaries, present behavior is not a sufficient statistic for declared future learning.
[LG-50] A Banach-Space Theory of Markovian Halpern Iteration for Non-Expansive Maps
链接: https://arxiv.org/abs/2608.15966
作者: Ege C. Kaya,Arda Fazla,M. Berk Sahin,Abolfazl Hashemi
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 34 pages, 1 figure
Abstract:We study stochastic approximation of fixed points of a non-expansive operator when the oracle samples originate from a continuing Markovian trajectory. A direct block-minibatch implementation of Halpern iteration attains an expected last-iterate residual of order O(\log N/N) , but accrues a substantive complexity of \tilde O(\epsilon^-5) Markovian samples. We therefore introduce a variance-reduced Markovian PAGE-Halpern method whose refresh and same-state difference blocks are analyzed through the Poisson equation. In Hilbert spaces, the cocoercivity of I-T results in an O(\epsilon^-3) sample complexity. Our main result extends this construction to a general finite-dimensional Banach space. A displacement-level Halpern bound replaces the Hilbert-space potential and yields \tilde O(\epsilon^-3) sample complexity in the original non-expansiveness norm. We also establish a high-probability guarantee with the same leading accuracy dependence by measuring the estimator in an auxiliary smooth norm. Non-smooth sup and block-sup geometries are covered through norm smoothing.
[LG-51] Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
链接: https://arxiv.org/abs/2608.15965
作者: Omair Shafi Ahmed,Zohair Shafi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented attention networks. Yet the empirical gains attributable to this added complexity have not been systematically evaluated. We propose SimpleCount, a reference with no parameter fitting that selects one scalar feature per dataset from a fixed pool of counts, recencies, first-occurrence indicators, and count-derived transforms. We compare SimpleCount with two temporal-graph detector models and an IsoForest control fitted to the complete feature vector across five public datasets and one synthetic dataset. SimpleCount matches or exceeds SLADE on three of six datasets and exceeds IsoForest on all six. We report paired statistical tests and five-seed SLADE evaluations. SLADE requires 23 to 133x more wall-clock time than SimpleCount. On Synth-Triangle and an additional Synth-Quad probe, pre-event structural scores recover the planted signal at AUC up to 0.955, while all evaluated detector models remain near random. The benefit of complexity is dataset-dependent, and every claimed gain should be reported against a strong one-feature reference together with its compute cost.
[LG-52] S3: A Smooth Simulation Surrogate for Optimizing Discrete Abstractions of Dynamical Systems
链接: https://arxiv.org/abs/2608.15920
作者: Jordan Peper,James Mathias Gast,Vignesh Nanduri,Tanmayee Maram,Ethan Howes,Ivan Ruchkin
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注:
Abstract:Intelligent systems are increasingly deployed in safety-critical settings with black-box controllers, including neural networks. The properties and behaviors of these end-to-end systems can be studied with abstraction-based methods that replace them with simpler finite models. Constructing such abstractions requires balancing the soundness of over-approximating the dynamical system against conservatism, which manifests as spurious or excessive nondeterministic behaviors. Bi-simulation theory provides principled metrics for characterizing these relationships, but does not prescribe how to construct sound abstractions with minimal conservatism. We fill this gap with a smooth simulation surrogate ( S^3 ) — a differentiable objective that approximates the reverse simulation metric used to quantify conservatism. Combined with Taylor model-based reachability, S^3 enables gradient-based optimization of abstraction parameters while preserving soundness by construction. We evaluate this optimization pipeline on three case studies. Our results show that S^3 is strongly correlated with the reverse simulation metric, is computationally faster, and serves as an effective objective for reducing abstraction conservatism.
[LG-53] Deploying Frontier Agent ic Technology in MOOSEnger a Multiphysics-Capable AI Assistant
链接: https://arxiv.org/abs/2608.15881
作者: Zaid Abulawi,Mengnan Li,Guillaume Giudicelli,Yang Liu,Cody Permann
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source finite-element framework for building multiphysics simulation applications. Using a multiphysics environment effectively demands specialized expertise, creating a barrier for many domain scientists and engineers. MOOSEnger, developed at Idaho National Laboratory (INL), is a domain-specific, tool-enabled AI agent built for the MOOSE Framework. This work extends MOOSEnger with a harness focused on locally-hosted models. The harness gives the agent a full pipeline: it retrieves contextual knowledge from the MOOSE repository, validates and diagnoses the resulting input through interaction with the simulation executable environment, and extracts and stores lessons in a persistent memory. The resulting framework is demonstrated on an engineering problem from the National Reactor Innovation Center Virtual Test Bed (VTB), illustrating its potential to support realistic multiphysics simulation workflows. Additionally, the agent performance is evaluated on different categories including diffusion, Navier–Stokes, phase field, plasticity, porous media flow, solid mechanics, transient heat transfer, and reactor mesh generation. Each category consists of 25 prompts/cases. We compare MOOSEnger-Gemma4 against MOOSEnger-GPT-5.2, alongside baseline Gemma4 and GPT-5.2 without agentic capabilities. MOOSEnger-GPT-5.2 shows a slight edge, achieving a 90% success rate versus 76.5% for MOOSEnger-Gemma4. The baseline models perform far worse, at just 5% (GPT-5.2) and 0% (Gemma4), underscoring the impact of the agentic harness. Subjects: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2608.15881 [cs.LG] (or arXiv:2608.15881v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15881 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] ransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition
链接: https://arxiv.org/abs/2608.15861
作者: Aidan Bradshaw,Riku Arakawa,Xin Liu,Karan Ahuja
类目: Machine Learning (cs.LG)
*备注:
Abstract:Fine-grained wrist activity recognition can support applications such as procedural step guidance and context-aware assistance, yet acquiring labeled data for every new task, user, and activity granularity remains a bottleneck. We present TransfHAR, a self-supervised wrist IMU framework for on-demand, fine-grained activity recognition by learning transferable motion priors from global, unlabeled activities. We show that self-supervised pretraining on coarse wrist IMU activities (e.g., sitting, walking, exercise) learns motion structure rich enough to transfer to fine-grained manipulative, gestural, and procedural activities (e.g., snapping, stirring, waving) that are absent from pretraining. We implement TransfHAR as a real-time smartwatch application that lets users define and expand their own activity set for personalized recognition from only a few demonstrations. Across three offline cross-dataset evaluations, TransfHAR matches or exceeds fully supervised baselines that use complete label sets with equal or additional sensor channels, by 6.2 balanced-accuracy points on average. In an in-lab study with 10 participants each performing seven novel wrist activities, TransfHAR reaches 86.7% balanced accuracy across participants with five examples per class and 90.4% when updated from a single one-minute recording per class. These results indicate that broad self-supervised wrist pretraining provides an effective foundation for on-demand fine-grained activity recognition.
[LG-55] Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading
链接: https://arxiv.org/abs/2608.15841
作者: Arishi Orra,Himanshu Choudhary,Manoj Thakur
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Machine Learning (stat.ML)
*备注:
Abstract:Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals. Auxiliary tasks are often used to improve representation learning and stabilize training, yet they are usually designed manually and depend heavily on prior assumptions about targets and prediction horizons. Such fixed designs may not remain suitable across changing market regimes. In this work, we propose a self-supervised framework that automatically discovers auxiliary tasks to support reinforcement learning for stock trading. The auxiliary tasks are formulated as General Value Functions so that their predictions enrich the learned state representation and assist policy optimization. The framework consists of two networks. The main network learns the trading policy along with the auxiliary predictions, while the secondary network generates the definitions of auxiliary tasks through learned cumulants and discount factors. These tasks are updated using a meta gradient mechanism that accounts for their long-term impact on trading performance and improves training stability. We evaluate the proposed approach across four major equity indices: DJI, FTSE, Sensex, and TAIEX. The empirical results demonstrate that automatically discovered auxiliary tasks lead to more robust learning and improved trading performance compared to existing baselines.
[LG-56] Second-Moment Memory in Coordinatewise Adam
链接: https://arxiv.org/abs/2608.15824
作者: Jeonseong Kim
类目: Machine Learning (cs.LG)
*备注: 10 pages
Abstract:Adam retains a moving average of past squared gradients in its denominator, but the optimization cost of this memory is not well understood. We show that second-moment memory can itself suppress progress toward the optimum even under finite-variance stochastic gradients. For a simple two-point oracle, the expected positive normalized update is O(M_2^-1/2) after an initialization transient, where M_2=(1-\beta_2)^-1 is the second-moment memory length. We convert this directional bound, under the stated memory and stepsize scaling, into an average-stationarity lower bound of the same order on a smooth convex problem with normalized gap, smoothness, and variance. Long second-moment memory can slow optimization even when the gradient noise has finite variance.
[LG-57] KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum
链接: https://arxiv.org/abs/2608.15815
作者: Quang-Anh N. D.,Duc Pham Minh,Thao Phuong Pham,Minh Anh Nguyen,Huan X. Nguyen,Tuan Dang
类目: Machine Learning (cs.LG)
*备注: 27 pages, 3 figures
Abstract:WiFi Channel State Information (CSI) has emerged as a privacy-preserving alternative to cameras for human pose estimation. However, existing approaches treat pose inference as an instantaneous regression problem and do not model temporal dynamics, making future motion prediction infeasible. Naively applying vision-based prediction methods compounds the estimation noise already present in CSI-derived poses, as autoregressive rollouts amplify errors at every step. We propose KOALA, the framework for human motion prediction directly from WiFi CSI, by lifting noisy CSI-derived pose sequences into a learned Koopman latent space where nonlinear dynamics become linear, enabling multi-horizon prediction via simple matrix-vector products without autoregressive iteration or error accumulation. A residual CSI-conditioned operator resolves the identity attractor problem inherent from Koopman formulations, and an anchor-delta prediction head eliminates the degenerate shortcut of copying the current pose across all horizons. To regularise the lifting and operator jointly, we introduce a Koopman Anchored Latent (KAL) loss that operates in the temporal-encoder feature space, enforcing dynamical consistency across prediction horizons without requiring contrastive, spectral, or auxiliary losses. Experiments on MM-Fi and WiPose show that KOALA achieves robust, consistent performance across both short- and long-term prediction horizons, outperforming all baselines by a substantial margin.
[LG-58] A Pre-Specified Construction-Confirmation Test of Operation-Level Causal Transfer Across Finite Isomorphic Symbolic Domains
链接: https://arxiv.org/abs/2608.15809
作者: Xinyi Shan
类目: Machine Learning (cs.LG)
*备注: 20 pages, 5 figures, and 4 ancillary CSV files
Abstract:Behavioral accuracy, linear decodability, and successful activation interventions do not by themselves show that a model carries an operation-level structure from one symbolic domain to another. We ask a narrower question in finite isomorphic state spaces: if the hidden-state difference between two operations is estimated separately for each source input, does adding that difference to a mapped recipient input move the model toward the corresponding recipient answer? The design compares this input-specific intervention with wrong-operation, norm-matched random, and no-op controls, and separates candidate construction from an independently isolated confirmation split. On a frozen Qwen2.5-7B-Instruct model at layers 20–21, one route–domain–operation candidate from a family pre-specified and frozen before confirmation access, transparent | integer_mod16–letters16 | successor-predecessor, passed both PyVene splits; its confirmation intersection–union p-value was 0.000198 and its 36-family Holm-adjusted p-value was 0.006943. A subsequent NNsight 0.7.0 experiment, pre-specified and frozen before its confirmation access, tested only this selected prompt route, without candidate or layer reselection. It reproduced all 12 confirmation effect estimates, confidence intervals, and exact sign-flip p-values numerically; its 36-family Holm-adjusted p-value was 0.007141. The result is therefore limited to one prompt route and one candidate, replicated across two intervention implementations on one model revision and one layer interval. It does not establish cross-model generalization, full-family backend independence, domain-general transfer, or algebraic invariance.
[LG-59] Cross-Entropy Risk Estimation for Language Models: Inconsistency Must Be Dense and the Holdout Method Is No Exception
链接: https://arxiv.org/abs/2608.15798
作者: Hanti Lin
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Language models are compared by their held-out per-token cross-entropy risk—the quantity scaling laws are fitted to. We show that it cannot be consistently estimated. Consistency, or convergence to the estimand, is defined relative to a \emphpossible state of the world: a pair consisting of a data-generating distribution and a model we turn out to train. Quantifying over models as well as data-generating mechanisms is essential, because what decides whether a model’s risk is estimable is a tail property of the distribution its weights induce, which no sample reveals. The per-token cross-entropy risk is hard to estimate because of a topological fact: among the possible states, finite risk and infinite risk each lie arbitrarily close to every instance of the other. Consequently no estimator—not merely the holdout average—is consistent at every state at which the risk is defined. Worse, inconsistent estimation persists under both bounding the expected sequence length and restricting to full-support models; and in that restricted setting the states at which inconsistency occurs are even dense. Two interesting ways out are identified, and neither is free. Way out 1: using a bounded context window, we can floor a model’s next-token probabilities, making its risk finite exactly when the data-generating distribution has finite expected sequence length—a new, statistical rationale for a choice that was made on computational grounds, though the assumption it substitutes is itself beyond the reach of any test. Way out 2: reporting the risk only when it falls below a threshold fixed in advance restores consistency, at no cost to what model selection actually requires—but we need to recognize that the goal of estimation is revised.
[LG-60] CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework
链接: https://arxiv.org/abs/2608.15790
作者: Steven Wallace,William D Harcourt,Richard Hann,Aiden Durrant,Somayajulu Sripada,Georgios Leontidis
类目: Machine Learning (cs.LG)
*备注: 13 pages, 5 figures, 7 tables
Abstract:Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation of glacier surfaces is costly and requires domain experts. We introduce CrevasseSeg, a framework for binary segmentation over the terminus of Borebreen, Svalbard, comprising 1,938 unlabelled UAV orthomosaic tiles for self-supervised/unsupervised fine-tuning, 24 labelled tiles for validation and 176 labelled tiles for testing. Using CrevasseSeg, we benchmark five self-supervised objectives – BYOL, a Jensen-Shannon Divergence (JSD) objective, Barlow-Twins, VICReg, and a combined BYOL-JSD objective – across three architectures: O-Net, O-Net++, and a DINOv3-initialised O-Net. Each configuration is evaluated under two frozen-feature readouts that differ only in the form of their decision boundary: a linear probe and a non-linear XGBoost classifier fit only on the 24 labelled validation images. Our central finding is a consistent inversion between the two readouts: DINOv3 features are the weakest under linear probing but the strongest under a non-linear readout. A UMAP analysis of the learned feature space shows that DINOv3 fragments pixels into many small clusters in which the classes are locally interleaved, whereas the convolutional architectures (O-Net and O-Net++) embed them onto a single class-sorted manifold. Satellite-pretrained DINOv3 improves over natural-image initialisation across objectives, and our label-efficient DINOv3-ViT-L-Sat-O-Net-BYOL-JSD pipeline reaches 75.33 mDSC / 61.28 mIoU, outperforming standard machine learning baselines fit on the same 24 labelled images with the RGB pixel values used as features. We release CrevasseSeg to support label-efficient segmentation research in remote sensing.
[LG-61] Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2608.15770
作者: Arishi Orra,Himanshu Choudhary,Manoj Thakur
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Designing effective trading strategies using reinforcement learning remains challenging due to delayed and noisy rewards, poor exploration, and the difficulty of enforcing explicit risk constraints. In this work, we propose BRaG, a barycenter-based adversarial inverse reinforcement learning framework for stock trading that learns trading behavior from multiple heterogeneous expert strategies. BRaG aggregates expert demonstrations using a performance-weighted Wasserstein barycenter, yielding a stable pseudo-expert representation that captures shared structure across diverse trading styles. This representation is used to pretrain a trading policy via adversarial imitation learning, which alleviates unstable exploration during reinforcement learning. The pretrained policy is subsequently refined using reinforcement learning with true market rewards. To ensure risk-aware decision-making, BRaG incorporates control barrier functions that constrain action execution and regularize policy learning to satisfy drawdown limits. We evaluate the proposed approach on four major global equity markets, including the US, UK, Indian, and Taiwanese indices. Across all the markets, the proposed approach achieves stronger performance than both classical trading rules and recent deep reinforcement learning methods, while exhibiting more stable risk characteristics.
[LG-62] Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving
链接: https://arxiv.org/abs/2608.15762
作者: Weinan Liu,Zeyuan Ding,Dian Ding,Chengcheng Wan,Lu Tang,Guangtao Xue,Jiwu Shu,Yiming Zhang
类目: Operating Systems (cs.OS); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Container-granularity scheduling leaves abundant short-lived idle slices within containers unexploited. Reallocating containers is too heavyweight to utilize such fine-grained opportunities under SLA constraints, and operator-level scheduling requires reasoning about dependencies, memory safety, and cluster-wide execution dynamics in real time. In this paper, we present SliceScheduler, a dynamic operator-level scheduling system for multi-tenant model serving. The key idea is to expose cluster-wide operator execution state and enable what-if reasoning over scheduling decisions. SliceScheduler consists of four key components. First, we introduce the Global Mapping Graph (GMG), a unified abstraction that captures operator dependencies, tensor shapes, resource mappings, and execution states, providing a real-time, cluster-wide view with explicit resource semantics. Second, we build a global simulator on top of GMG to predict operator-level execution and memory evolution under candidate placements. Third, we design an incremental, simulation-based scheduling module that selects placements to exploit fragmented idle slices while avoiding memory violations and preserving SLA. Finally, we develop an operator executor that materializes scheduling decisions on GPUs and coordinates computation and cross-accelerator transfers. We implement SliceScheduler as a PyTorch backend and evaluate it using production trace replay. Experimental results show that SliceScheduler improves token throughput by 1.10–2.29 \times compared to existing approaches, while maintaining SLA violations within 9%. SliceScheduler demonstrates that operator-level scheduling is a practical and effective approach to improving GPU utilization for multi-tenant LLM serving. Subjects: Operating Systems (cs.OS); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2608.15762 [cs.OS] (or arXiv:2608.15762v1 [cs.OS] for this version) https://doi.org/10.48550/arXiv.2608.15762 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-63] Provenance Not Behaviour: A Serialisation Artifact in Edge-IIoTset and a Leakage-Free Benchmark for Precision-Agriculture Intrusion Detection
链接: https://arxiv.org/abs/2608.15761
作者: Mostafa M. Galal
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 29 pages, 13 tables. Code: this https URL Benchmark: this https URL
Abstract:Edge-IIoTset is the reference benchmark for machine-learning intrusion detection in the industrial Internet of Things, and results reported on it cluster above 99%. We show that much of that performance is not intrusion detection. The preprocessing recipe distributed with the dataset instructs researchers to one-hot encode seven categorical columns. Four of them separate attack from normal traffic with an accuracy of 1.0000 on their own, through the spelling of the placeholder written for an absent protocol field: the string “0” in the normal-traffic branch of the dataset build against “0.0” in the attack branch. The label is recoverable from a serialisation artifact encoding file provenance, with no network behaviour modelled, and separates every row of both curated subsets. Under 5-fold x 3-repeat cross-validation, five of six standard classifiers attain exactly 1.0000 +/- 0.0000 accuracy and the sixth attains 0.99998. Under a corrected protocol, naive Bayes falls by 0.3005 macro-F1 and the strongest model settles at 0.9503 +/- 0.0011. Label, ordinal and frequency encoding leak identically. Because the curated subsets also lack Modbus and per-device identity, we rebuild the benchmark from the raw captures under uniform parsing, producing AgriEdge: 1,276,122 rows, five devices with full attribution, and no column separating the classes above 0.0288. A leave-one-device-out sweep locates the generalisation boundary at the perception/actuation layer, where random forest falls from 0.9988 to 0.5083 balanced accuracy. Non-IID federated partitioning costs at most 0.0037 macro-F1, but a 20-round LoRaWAN training run costs 4.6 hours of uplink.
[LG-64] Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles
链接: https://arxiv.org/abs/2608.15725
作者: Srikumar Krishnamoorthy
类目: Machine Learning (cs.LG)
*备注:
Abstract:Predictive models in clinical and regulated settings must be accurate and fully auditable. Tree ensembles deliver strong accuracy on tabular data, but their sequential boosting couples structure discovery with coefficient estimation, making compact per-prediction auditing difficult. Interpretable alternatives impose structural constraints that limit expressiveness: generalized additive models typically restrict interactions to pairwise terms and post-hoc rule extractors produce overlapping rules that hinder compact interpretation. We introduce Residual Pattern Tree Ensemble (RPTE), a three-stage learning approach, that is built on three key principles: bounded feature budget, source disjointness, and separate coefficient estimation. Stage~1 builds a supervised symbolic feature vocabulary. Stage~2 grows shallow trees under a source-disjointness constraint, where each raw variable is allocated to at most one tree, and retains only the discovered tree structures. Stage~3 solves a single \ell_1 -regularized logistic regression over leaf-region indicators, yielding jointly optimal sparse coefficients. This learning approach ensures that every prediction decomposes into an algebraic sum of named, non-overlapping rule contributions, enabling full auditability by design. Empirical evaluation on twelve clinical-domain binary classification benchmarks using repeated stratified 5-fold cross-validation shows that RPTE performs competitively against tuned opaque ensembles and interpretable baselines. RPTE reduces model inspection units by 9 \times to 87 \times relative to XGBoost and maintains lower audit complexity than EBM on all 12 datasets. RuleFit requires comparable or fewer inspection units on three datasets where its rule count is small, but without source-disjointness guarantees. The source code is available at \hrefthis https URLthis https URL.
[LG-65] Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
链接: https://arxiv.org/abs/2608.15669
作者: Zhongwei Yu,Yan Song,Xue Yan,Anjie Liu,Xingyu Lu,Yihang Chen,Huichi Zhou,Siyuan Guo,Luoyang Sun,Sihan Chen,Xiangning Yu,Jun Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models such as large language models (LLMs) provide expressive priors over such spaces, but their likelihoods and self-assessments are unreliable proxies for the objectives and calibrated epistemic uncertainty, especially for novel candidates outside the observed data distribution. We introduce the Large Discovery Model (LDM), an empirically grounded recurrent architecture that couples a generative model with a Bayesian non-parametric reward surrogate model. The generative model proposes and refines candidate designs, while the surrogate predicts their performance and quantifies uncertainty, yielding an uncertainty-aware value that guides candidate generation, refinement, and selection. The discovery memory and the surrogate model are continually updated as each new experimental observation arrives. We evaluate LDM on three scenarios spanning different design modalities and objectives, including neural-network training, antibody design, and molecular optimisation. Compared to LLM-only reflection or traditional statistical search across these domains, LDM achieves a 2.4\times greater reduction in validation BPB, an 18.2% relative decrease in binding energy, and more than 60% relative gains in molecular multi-objective performance. These results suggests that LDM could serve as a general-purpose discovery engine for effective search over open-ended hypothesis spaces.
[LG-66] SubZero: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates
链接: https://arxiv.org/abs/2608.15665
作者: Ziming Yu,Shuyao Xiao,Xingyu Zhao,Sike Wang,Pan Zhou,Peiyu Zang,Xiangda Yan,Yongjie Yang,Jia Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Zeroth-order (ZO) optimization enables backpropagation-free fine-tuning of large language models, but existing ZO methods suffer from high-variance gradient estimators, making convergence unstable and highly sensitive to learning rates. We propose SubZero+, an improved SubZero framework that improves stability in three complementary ways: (i) multi-query gradient estimation within layer-specific low-rank subspaces to reduce variance without exhibiting the multi-query paradox; (ii) a subspace Adam optimizer that performs adaptive updates using in-subspace multi-query gradient statistics; and (iii) a sign correction for QR-based subspace construction to ensure Haar-distributed projection matrices, eliminating implementation-dependent orientation ambiguity. Experiments on models from 1.3B to 32B across SuperGLUE, under both full-parameter tuning and LoRA, show that SubZero+ consistently outperforms prior ZO baselines, enlarges the stable learning-rate range, and narrows the gap to first-order methods with minimal extra memory overhead.
[LG-67] Sequential Multimodal Evidence Optimization for Product Media Ranking in E-Commerce CIKM2026
链接: https://arxiv.org/abs/2608.15662
作者: Prasenjit Dey,Frank McIntyre,Arnab Sinha
类目: Machine Learning (cs.LG)
*备注: Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM 2026), Rome, Italy
Abstract:On modern e-commerce stores, customers consume ordered slates of heterogeneous product media, such as images, videos, and 3D renders, before making purchase decisions. Existing media-ranking systems often optimize myopic engagement proxies such as clicks or dwell time, even though product media assets are cooperative informational components of the same item that together help customers find the information they need through sequential interaction. We present Sequential Multimodal Evidence Optimization (SMEO), a two-stage utility-guided framework for customer-oriented media sequencing. SMEO first learns a trajectory utility model from consumed media prefixes to estimate how ordered evidence helps customers reach a purchase decision, while mitigating position-bias and variable-depth imbalance in logged data. Recognizing that customer attention is a limited resource, it then trains an autoregressive ranking policy with survival-weighted reward-to-go that prioritizes the most decision-relevant information early, so customers can find what they need with less effort. By decoupling utility learning from policy optimization, SMEO enables stable offline learning from biased logs and post-hoc media attribution without explicit media-level labels. Evaluated offline on large-scale e-commerce sessions using doubly robust off-policy estimation, SMEO improves estimated conversion by 5.5% and helps customers reach a purchase decision with 15% fewer swipes than existing baselines.
[LG-68] Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation
链接: https://arxiv.org/abs/2608.15660
作者: Chenwang Liu,Yijun Liu,Chang Liu,Xu Zhang,Pengchao Han
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Federated learning (FL) enables privacy-preserving distributed model training but faces challenges from heterogeneous model architectures and limited communication resources at the network edge. Federated knowledge distillation (FedKD) alleviates model heterogeneity by combining prototype-wise parameter aggregation and knowledge transfer across heterogeneous models. However, transmitting gradients still introduces considerable communication overhead, while existing compression approaches typically apply a uniform strategy across clients and ignore their diverse model characteristics and resource capacities. To address this issue, we propose a heterogeneous compression framework for FedKD that enables each client to select a compression strategy from a candidate strategy set. We formulate the compression strategy selection problem as a non-stationary stochastic multi-armed bandit (MAB), where each arm corresponds to a compression strategy. An efficiency-aware reward is designed by jointly considering local optimization improvement, global knowledge alignment, and execution time. Based on this formulation, we develop an Adaptive heterogeneouS Compression algorithm for fEderated kNowledge Distillation (ASCEND), which employs an exponential moving average (EMA)-enhanced \epsilon -greedy policy to balance exploration and exploitation. Experimental results on multiple datasets demonstrate that ASCEND effectively adapts to heterogeneous model and resource settings, reducing communication overhead and training time while maintaining competitive model accuracy.
[LG-69] Generalised Transportability via Causal Abstractions
链接: https://arxiv.org/abs/2608.15645
作者: Yorgos Felekis,Paris Giampouras,Fabio Massimo Zennaro,Theodoros Damoulas
类目: Machine Learning (cs.LG)
*备注:
Abstract:Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference. The theory of transportability provides a criterion for when this is possible: given experimental data from the source and observational data from the target, it determines whether a target query is identifiable and does so completely; i.e. if the query can be transported, the criterion finds the exact formula. However, it works one query at a time and returns an expression rather than the value itself. It is also silent in two practically important regimes: when the query is not transportable and when no target data exist at all. To tackle both, we take a model-level perspective grounded in Causal Abstraction theory. Source and target share variables, graph, and interventions, differing only at a known set of mechanisms, which makes transportability a special case of same-level abstraction. Thus, instead of asking whether one query transports, we ask whether a single map aligns the source and target across their interventional behaviour. We characterise when such a map exists in both the Markovian and semi-Markovian settings; when it does, every target query transports at once. Our main contribution lies in the approximate case. When no exact map exists, the best approximate one still yields certified query intervals, recasting abstraction error as a quantitative notion of approximate transportability. We formulate model-level transport as distributionally robust optimisation over mechanism and environment perturbations of the unseen target and derive certificates for both challenging regimes: bounds for non-transportable queries, and guarantees under target-agnostic settings. We evaluate our framework on synthetic Markovian and semi-Markovian benchmarks and a real ecological dataset, and we show that the certified intervals bracket the true interventional query.
[LG-70] In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis MICCAI2026
链接: https://arxiv.org/abs/2608.15626
作者: Michael Chertok,Alon Tiosano,Orly Gal-Or,Lior Kramarski,Einav Baharav Shlezinger,Irit Bahar,Lior Wolf
类目: Machine Learning (cs.LG)
*备注: 10 pages, 2 figures, 3 tables. Accepted at OMIA 2026 (13th Ophthalmic Medical Image Analysis Workshop, MICCAI 2026). This is the pre-peer-review submitted version; the camera-ready revises the characterization of the capillary failure
Abstract:Optical coherence tomography angiography (OCTA) images retinal blood flow, giving capillary-perfusion and foveal-avascular-zone biomarkers that grade diabetic-retinopathy ischemia. Because OCTA hardware is less common than structural OCT, recent work synthesizes it from OCT, reporting strong reconstruction (3D PSNR 31 dB, SSIM 0.9). We ask not whether the synthetic image looks similar, but whether it supports the measurements OCTA is acquired for. A frozen real-OCTA segmenter, applied as a probe to two synthesizers (XOCT, TransPro), shows downstream Dice falling with structural fineness: large vessels survive (0.862 - 0.831) while the fine capillary network collapses (0.798 - 0.635, five times the large-vessel loss; paired Wilcoxon p 1e-3), TransPro worse throughout. A matched-blur control shows this detail is fabricated, not blurred. Retrained on a private Spectralis dataset, neither synthesizer reproduces the neovascular lesion (qualitative, n=3). Reconstruction fidelity is not clinical utility; we establish downstream-task fidelity as the evaluation OCT-to-OCTA synthesis needs.
[LG-71] Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do
链接: https://arxiv.org/abs/2608.15617
作者: Md Rezwanul Islam
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Machine Learning (stat.ML)
*备注: 18 pages, 8 figures, 18 tables. Code, configs, and seeded pipelines: this https URL
Abstract:Machine-learning detectors for power-system cyberattacks are themselves attack surfaces, and quantum machine learning has been proposed for them. We benchmark fidelity-kernel SVMs and variational classifiers against six tuned classical models on public power-system attack data (Mississippi State/ORNL), across white-box, transfer, decision-based black-box, and poisoning attacks. Our headline finding is methodological: the benchmark’s answers are set by the evaluator’s choices before the models. Eight choices – six in the evaluation protocol, two in the tuning the benchmark itself runs – each reversed or moved a conclusion at fixed models. The largest is the split: the row-level protocol scores 0.905 macro-F1 where holding whole source files out leaves 0.594, and in the capped matched-dimensionality regime the quantum arm sits within noise of chance with the classical arm 0.024 above it. A fidelity kernel looks most robust until attacked directly (retention 0.886 to 0.064); a mis-fitted surrogate manufactures a 10x asymmetry; an unseeded black-box attack moves 75% between restarts. A positive control explains the accuracy null: the labels, not the pipeline. We give the control that catches each choice and release the seeded benchmark.
[LG-72] Quantum Models with Multi-Stage Training for Compositional Concept Generalization
链接: https://arxiv.org/abs/2608.15601
作者: Mina Abbaszadeh,Matilda Karabina Moore,Mehrnoosh Sadrzadeh,Martha Lewis
类目: Machine Learning (cs.LG)
*备注:
Abstract:Compositional Concept Generalization (CoCoGen), the ability to systematically recombine learned primitives in novel contexts, is a key challenge for multimodal learning. In this work, we provide a solution using a compositional model of meaning that separates nouns from relations and uses tensors and variational quantum circuits to train them on data. This model enables us to employ a multi stage training paradigm, one that first learns object representations from single-object image-caption pairs, then subsequently transfers these to the relational stage where object parameters are frozen and optimisation is only applied to relational components. This design explicitly enforces compositional factorisation at the circuit, ensuring that relations are learned as transformations over stable primitives. The training paradigm is tested on the CLEVR dataset developed specificially for CoCoGen. For text, we work with vector representations of nouns and higher order tensor representations of relations using a set of different ansatz. For images, we work with quantum encodings of image embeddings dervied from Open AI’s Vision Language tool CLIP and contrast amplitude encoding, which preserves the original embedding geometry, with angle encoding, which introduces nonlinear feature transformations. Our results show that multi-staged training combined with structured encodings significantly improves out of distribution relational generalisation, while using orders of magnitude fewer trainable parameters than classical baselines. We find that performance gains arise from the interaction between representation and encoding, with nonlinear quantum encodings enhancing the separability of compositional structure. These findings demonstrate that structured quantum representations and staged learning provide an effective framework for compositional generalisation in multimodal quantum machine learning.
[LG-73] SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization
链接: https://arxiv.org/abs/2608.15567
作者: Gunjun Lee,Sehwan Son,Younjoo Lee,Byungjun Kim,Jung Ho Ahn
类目: Machine Learning (cs.LG)
*备注: 14 pages, 6 tables
Abstract:Weight-only post-training quantization (PTQ) enables the deployment of large language models under tight memory budgets, but accuracy often collapses at 2-3 bits. Existing backpropagation-free PTQ optimizers have two limitations: group decisions ignore the correction that the remaining continuous suffix can absorb, and discrete refinements typically keep the affine quantization grid fixed. We introduce SCHUROPT, which analytically eliminates the suffix’s optimal continuous response, yielding an exact groupwise quadratic with Schur-complement curvature. It then alternates closed-form row-wise scale/zero-point refitting with coordinate descent over integer codes. With the GPTQ objective fixed, SCHUROPT improves mean zero-shot accuracy on 2-bit Qwen3-4B by 11.88 percentage points (pp). At higher precision, however, tighter reconstruction does not consistently improve end-model metrics. SCHURQUANT therefore combines SCHUROPT with quantized-prefix teacher reconstruction, reference-weight regularization, residual-add targets, and teacher-decision token weighting. Across eight Llama and Qwen models, SCHURQUANT achieves the highest mean zero-shot accuracy among the evaluated backpropagation free PTQ baselines, outperforming the strongest baseline by 9.65 pp at 2 bits.
[LG-74] DeltaLog: Deferred Materialization of Recurrent States for Linear Attention Decoding
链接: https://arxiv.org/abs/2608.15533
作者: Junqing Lin,Jingwei Sun,Guangzhong Sun
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Linear attention models eliminate the quadratic prefix computation and context-growing KV cache of softmax attention by replacing pairwise token interactions with recurrent state updates. However, existing decoding implementations often materialize and write back the full recurrent state after every generated token, making state maintenance a major source of memory traffic, especially for models with large states and many heads. This paper presents DeltaLog, a recurrent-state decoding scheme that reduces this overhead without changing the model semantics. Specifically, DeltaLog represents the recurrent state as a dense base state together with a bounded log of recent compact updates. Most decode steps append only compact update factors to this log, while periodic merge steps fold the accumulated updates back into the dense base state. Thus, the model observes the same dense state as in eager decoding, but most full-state write-backs are replaced by lightweight append operations. We implement DeltaLog for GDN, KDA, and RWKV6 and integrate it into a prototype serving stack. Across these models, DeltaLog accelerates the recurrent-state update kernel by up to 1.86\times , reduces profiled recurrent-state write traffic by up to 7.83\times , and achieves 1.05 – 1.20\times end-to-end serving speedups over dense recurrent baselines.
[LG-75] Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group
链接: https://arxiv.org/abs/2608.15520
作者: Melika Baghi
类目: Machine Learning (cs.LG)
*备注: 23 pages, 4 figures, 14 tables. Includes appendix with full proofs and additional experiments
Abstract:A multimodal system may begin inference holding only some of its inputs and may acquire the rest at a cost. With adaptive acquisition, the policy determines which inputs are ultimately observed, so we state the guarantee conditional on that terminal input pattern. Conditional calibration normally assumes the grouping map is fixed independently of the calibration sample, which policy-induced grouping does not satisfy. We characterize when pattern-conditional guarantees remain valid and give two finite-sample constructions: threshold-free routing with calibration applied at the terminal pattern, and simultaneous certification of complete policy-pattern pairs, which lets calibration data select the deployed policy. A counterexample shows that a guarantee proved for a calibration-independent grouping map need not transfer once the policy makes the terminal group calibration-dependent. We call the resulting method RouteCert. On a clinical electrocardiogram task with a staged, cost-ordered lead protocol, the certified policy answers 71.2% of held-out patients at an observed 7.4% disagreement with the cardiologist’s diagnosis at 48.8% of the prespecified ordinal cost of acquiring every stage, and all three acquisition stages carry their own certificate. On masked multimodal benchmarks, certifying pointwise at each terminal pattern holds observed worst-pattern selective risk, measured against the full-information reference decision rather than the true label, at 0.034 where a pooled design reaches 0.145 against a 0.10 cap, at a comparable answered fraction (0.350 vs 0.342); under the budget-matched simultaneous comparison the answered fraction falls to 0.305.
[LG-76] UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity
链接: https://arxiv.org/abs/2608.15516
作者: Pengyu Wang,Baochen Xiong,Xiaoshan Yang,Yifan Xu,Zhang Qimeng,Haifeng Chen,Changsheng Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation. However, fine-tuning of VLMs typically relies on centralized data, which raises privacy concerns in certain domains (e.g. healthcare). Federated Learning (FL) provides a natural solution by enabling model training without sharing raw data. However, applying FL to VLM instruction tuning is highly challenging. VLMs have substantial parameter scales, and in real-world scenarios, clients exhibit significant heterogeneity in tasks, modalities, and model architectures. Existing methods mainly focus on simplified settings and are unable to handle such multi-dimensional heterogeneous scenarios. In this work, we study federated instruction tuning under joint heterogeneity in tasks, modalities, and model architectures. We propose UniFed-VLM, a unified federated instruction tuning framework for VLMs that addresses multiple types of heterogeneity. It consists of two key components: 1) Federated Compensated Subspace Aggregation (FedCSA), which performs subspace-aligned aggregation of parameter-efficient adapters with dynamic weighting and compensation to mitigate heterogeneity-induced conflicts; 2) Two-stage Collaborative Distillation (TCoD), which enables effective knowledge transfer across heterogeneous models via a Mutual Distillation Adapter (MDA) and a mixture-of-experts-based distillation strategy. We conduct experiments on multiple benchmark datasets, and the results show that UniFed-VLM achieves stronger average performance across diverse tasks compared with existing FL methods. The source code is available at: this https URL. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.15516 [cs.LG] (or arXiv:2608.15516v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.15516 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-77] mporal Logic Guided Universal Task Representations for Reinforcement Learning
链接: https://arxiv.org/abs/2608.15509
作者: Hao Zhang,Zhangli Zhou,Zhen Kan
类目: Robotics (cs.RO); Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG)
*备注: Accepted by IEEE Transactions on Neural Networks and Learning Systems (Early Access). Project page: this https URL
Abstract:Task guided agents demonstrate strong performance in a wide range of complex tasks. However, most existing task representation algorithms are tailored to specific contexts and struggle to generalize across diverse scenarios. Moreover, they typically depend on gradient signals from reinforcement learning controllers to update their weights, which can degrade both representation quality and learning efficiency. To overcome these limitations, we propose LOTUS, a temporal logic inspired universal task representation framework that can be seamlessly integrated into any RL algorithm to enhance agent performance across diverse task settings. Specifically, we design a novel task representation architecture capable of modeling relationships and extracting task semantics from LTL formulas. We further introduce a more effective update mechanism that treats the LTL encoder as a policy, thereby improving representation capacity. To enhance stability and robustness, LOTUS leverages the bisimulation metric, which provides theoretical guarantees for LTL representation, including behavioral equivalence, optimality fidelity, and trajectory robustness. Experimental results show that LOTUS outperforms most existing methods in learning efficiency, generalization capability, and representation quality. Specifically, LOTUS accelerates convergence over 20% in single-task scenarios, achieves a 15%-45% higher success rate in unseen manipulation tasks, and improves generalization performance over 25% in complex multi-task environments with increased sub-goal depth or conjunctions. The corresponding code, videos, and appendix are available at: this https URL.
[LG-78] PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification
链接: https://arxiv.org/abs/2608.15504
作者: Wumei Du,Jiarong Wen,Kaiyu Zhang,Zi Yang,Yiqin Lv,Longfei Zhang,Dong Liang,Zheng Xie
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 16 pages, 6 figures, 6 tables, conference
Abstract:Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as misclassification of malicious traffic. The encrypted traffic foundation model, as a promising general-purpose technique, can achieve impressive overall performance. However, employing standard objectives such as empirical risk minimization often overlooks high-risk tail events, and commonly used performance metrics hardly reflect robustness limitations in risk-sensitive scenarios. Directly applying robust optimization objectives, such as conditional value-at-risk, to post-training is computationally prohibitive for large models, as identifying high-loss samples exhausts substantial computation. To this end, we propose Pre-Evaluation Robust Optimization (PERO), an efficient robust post-training framework for encrypted traffic foundation models. PERO employs a lightweight proxy to estimate sample-wise risk and selects a subset of high-risk samples to update the foundation model, decoupling risk estimation from expensive large-model optimization. Extensive experiments on typical encrypted traffic datasets show that PERO achieves competitive or superior robustness and average performance compared to outstanding robust post-training methods, while significantly reducing computational and memory costs.
[LG-79] QSMP: finding representative time series subsequences through Quick ShiftMatrix Profile
链接: https://arxiv.org/abs/2608.15492
作者: Carlos H. Mendoza-Cardenas,Rogers F. Silva,Austin J. Brockmeier
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Abstract:Finding representative waveforms in long time series has scientific and practical value in many domains, as it enables summarization and visualization of large time series datasets, and downstream tasks like classification and forecasting. We present here QSMP, a method to find representative waveforms in long time series through a density-guided clustering of time series subsequences. Our method makes a novel connection between Quick Shift, a mode-seeking algorithm, and the Matrix Profile, a time series similarity-search data structure, to adapt Quick Shift to the clustering of subsequences in long time series, with a space complexity that is superior to the state-of-the-art method. Our experiments on synthetic and real datasets show that QSMP can be a valuable tool to summarize and visualize long time series by finding representative waveforms.
[LG-80] Measuring Structured Predictability in Neural Training Dynamics: A Cross-Regime Study
链接: https://arxiv.org/abs/2608.15483
作者: Fanqi Wang,Weisheng Tang,Hairong Qi
类目: Machine Learning (cs.LG)
*备注: 42 pages, including supplementary material
Abstract:Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than architectures, losses, or optimizers. We study short-horizon predictability as a measure of temporal redundancy: where, when, and under which training conditions recent updates contain information about near-future parameter motion. We combine three complementary probe families, displacement-direction, subspace-residual, and predictor-based probes, with convention-aware, null-calibrated group-level readouts, and apply them to multi-pass vision training on CIFAR and public Pythia pretraining checkpoints. Across both regimes, vector-like tensors such as normalization parameters and biases (auxiliary parameters) exhibit simpler short-horizon dynamics than matrix-like feature-transforming weights (bulk parameters), whose predictable behavior concentrates in localized, time-varying pockets. Agreement within and across probe families, and with independent trajectory diagnostics, indicates that these measurements capture intrinsic trajectory structure, while probe differences distinguish complementary forms of temporal organization. Controlled CIFAR comparisons further show that architecture and training recipe systematically modulate the measured structure. A Pythia-70M case study further exposes a sequence of role-, depth-, and scale-dependent events, including bulk ESA falling below the random sign-agreement level and the emergence and redistribution of predictable qkv pockets across layers. These results position short-horizon predictability as a retrospective, parameter-resolved diagnostic of training dynamics.
[LG-81] High-Dimensional Nonparametric Change-Point Detection via Low-Rank Degree-Three Density Projection
链接: https://arxiv.org/abs/2608.15466
作者: Guoqing Zhang,Zhaixin Chen
类目: Machine Learning (cs.LG)
*备注: 26 pages
Abstract:Distributional changes can be invisible to means and covariances yet appear in skewness, asymmetric interactions, or other third-order structure. We develop a nonparametric change-point method that retains every degree-at-most-three coefficient of a density while avoiding direct density estimation. For observations in [-1,1]^d , we construct a symmetric order-three Legendre feature tensor H_3(X)\in\Sym^3(\R^d+1) such that A(f)=\E_fH_3(X) is an exact isometric encoding of the degree-three density projection: |A(f)-A(g)|_\F=|P_3(f-g)|L^2 . Instead, fixed tensor contractions are degree-three polynomial chaoses with \psi_2/3 tails. The two terms have the characteristic order-three tensor scaling and match the powers in sharp concentration results for simple random tensors. For a coordinate-orthogonal specialization, the bound improves to \sqrt\log d and enables a prefix-sum implementation in hundreds of dimensions. We derive the exact population tent shape and localization margin, introduce a seeded shortest-interval algorithm with a padded local recentering step, and prove exact recovery by induction: null recursive segments remain inactive, every undetected change retains a balanced isolating interval, and the shortest active seed contains exactly one change before recentering. A two-way cross-fitted scalar refinement attains O\Pp(\kappa^-2) localization in the small-jump regime, matching a Le Cam lower bound on a pure cubic family whose degree-two projection jump is exactly zero. Reproducible experiments at d\in\20,50,100,200\ and a three-change d=100 sequence demonstrate the intended high-dimensional regime without materializing a (d+1)^3 tensor.
[LG-82] Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling
链接: https://arxiv.org/abs/2608.15465
作者: Shayan Azizi,Norihiro Okui,Masataka Nakahara,Ayumu Kubota,Gustavo Batista,Hassan Habibi Gharakaheili
类目: Networking and Internet Architecture (cs.NI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks. However, the performance of machine learning-based classifiers gradually degrades under concept drift as device behavior evolves. Therefore, maintaining classification performance requires periodic retraining with newly labeled deployment traffic. The operational challenge is determining how much and which deployment traffic instances to label for maintaining classification performance. We show that these two decisions should be treated separately. While retraining solely on instances selected by a drift detector is prone to systematically overlooking parts of the emerging behavioral space, uniformly sampled deployment traffic captures more representative behavioral changes. Instead, drift detection is more effective at determining the amount of deployment traffic that should be labeled. We make three contributions. (1) We conduct a two-year longitudinal study of IoT traffic and characterize how behavioral evolution manifests across device classes and how retraining with newly labeled traffic restores classification performance. (2) We develop a conformity-based drift detector that captures class-conditional behavioral models directly from raw traffic features and provides feature-level explanations of behavioral evolution. (3) We demonstrate that adjusting the traffic labeling rate according to the observed behavioral evolution, combined with uniform traffic sampling, maintains classifier performance more effectively than detector-guided sample selection and is beneficial to managing the traffic labeling effort. We further show that this strategy performs comparably to confidence-guided adaptation while providing feature-level explanations. Our evaluation uses 3.8 million IPFIX flow records collected from 21 IoT types over more than 2 years.
[LG-83] Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework
链接: https://arxiv.org/abs/2608.15447
作者: Emmanuel Nahimana,Yaé Ulrich Gaba
类目: Machine Learning (cs.LG); Risk Management (q-fin.RM)
*备注: 24 pages, 8 figures
Abstract:Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions. Rwanda is a case in point: several million active mobile-money users, telecom-led wallets on the MTN and Airtel networks, and a Financial Intelligence Centre (FIC) supervising transaction streams whose scale exceeds static rule-based monitoring. This paper develops and evaluates a transaction-monitoring framework aligned to the Rwandan AML/CFT regime under (i) extreme class imbalance (~0.1% prevalence), (ii) scarce and delayed labels, and (iii) bounded investigator capacity. Using SAML-D, a synthetic dataset of 9,504,852 transactions with 17 laundering typologies, we engineer account-centric behavioural features (rolling velocity, net-flow directionality, counterparty diversity, burstiness) and benchmark supervised classifiers (Logistic Regression, Random Forest, LightGBM), unsupervised anomaly detectors (Isolation Forest, Local Outlier Factor), a dense autoencoder, and a late-fusion meta-learner. Evaluation is operational: PR-AUC, recall at a calibrated ~90%-precision point, recall at top-K%, and alerts per 10,000. On the chronologically held-out test period, LightGBM attains PR-AUC = 0.0469, capturing 64 laundering cases at precision ~0.89 with 0.51 alerts per 10,000; the fusion stacker reaches PR-AUC = 0.0477 at precision ~0.91 and 0.46 alerts per 10,000, recovering 59 true positives. We map score bands to Rwanda-relevant analyst workflows and STR/SAR escalation, and outline a staged path from synthetic prototyping to real-data validation with the National Bank of Rwanda and FIC. The contribution is operational: a governance-aware pipeline and evaluation protocol calibrated to the constraints of an African mobile-money regulator, not a new algorithm.
[LG-84] FAST-DeepONet: Factor-Augmented Branch Representations for High-Dimensional PDE Inputs in the Small-Sample Regime
链接: https://arxiv.org/abs/2608.15408
作者: Jiyong Kwon,Bongseok Kim,Guang Lin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep operator networks can become statistically unstable when partial differential equation inputs are observed at thousands of strongly correlated sensors but only a small number of operator samples is available. We introduce FAST-DeepONet, a branch representation combining a fixed spectral path with a regularized projection of the orthogonal residual, in which the directional penalty acts on the effective residual map after each of its rows is normalized. On Navier–Stokes flow a plain DeepONet degrades from 0.0394 to 0.1556 mean relative L_2 error as the branch grows from 129 to 8193 coordinates, while FAST-DeepONet stays near 0.04 , so the sensor grid can be refined without a statistical penalty. Across independent test sets for Navier–Stokes flow, Darcy flow, and signed terminal wavefield prediction it lowers mean relative L_2 error by 4.7% to 37.0% with three to seven times fewer trainable parameters. A spectral-only branch sharing the same basis separates the two paths: the fixed spectral path carries the improvement on Navier–Stokes and Darcy, while terminal wave prediction requires the residual path together with its directional penalty. FAST-DeepONet targets coordinate-query architectures and trains on solution values alone.
[LG-85] owards a theory of inference-time alignment with unknown rewards
链接: https://arxiv.org/abs/2608.15402
作者: Steve Hanneke,Hongao Wang,Mingyue Xu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Generative model alignment has received broad interest, and significant progress has been made in supervised fine-tuning and inference-time computation. Yet, alignment has remained poorly understood from a statistical learning perspective. We formulate inference-time alignment as a weak-to-strong learning problem, where a reference policy (weak learner) is assumed to be fairly good and the goal is to produce a strong learner that predicts a good response at test time with arbitrarily high probability. Our problem is formulated as learning from scratch — everything is learned from data rather than assuming access to a good reward estimate, and thus differs from the existing inference-time alignment theory. Our model shares similarity to the recent work of arXiv:2510.15464, where for each prompt, there could be multiple good responses. Our definition of the alignment learnability follows the PAC learning principle. We introduce a novel combinatorial dimension of the reward class which we call the alignment dimension, and show that it completely characterizes the alignment learnability — a reward class is alignment learnable if and only if its alignment dimension is finite. The core of our learning procedure works by invoking the ordinary one-inclusion graph algorithm to run a tournament over all pairs of label sets satisfying that neither is a subset of the other. We believe our results might shed light on establishing a complete theoretical understanding towards alignment.
[LG-86] Look Before You Lift: Visual and Quantitative Diagnostics for Topological Deep Learning
链接: https://arxiv.org/abs/2608.15388
作者: Mathilde Papillon,Guillermo Bernárdez,Álvaro Ballón Barreiro,Marco Montagna,Rémi Devaux,Antoine Jardin,Nina Miolane
类目: Machine Learning (cs.LG)
*备注:
Abstract:Topological deep learning (TDL) methods rely on lifting raw data into higher-order discrete domains such as simplicial complexes, cell complexes, and hypergraphs. In practice, this lifting step is often treated as a black box: practitioners select a lifting and then tune architectures, with limited visibility into whether the induced higher-order connectivity is meaningful for the downstream task. To address this missing diagnostic layer, we propose a visualization technique called TopoExplorer that leverages the strictly augmented Hasse graph form of topological datasets for exploratory data analysis. For the first time, practitioners can easily visualize the incidence- and adjacency-based neighborhoods that define the lifted dataset, as well as read off key graph metrics that describe its structural and feature landscape. Via an extensive set of experiments across many datasets and liftings, we show that several of these metrics correlate with downstream model performance, suggesting they can help inform TDL preprocessing design. Our perspective reframes the TDL workflow from lift-train to lift-look-design-train, enabling more principled, interpretable, and efficient model development. TopoExplorer is hosted at this https URL, and its source code is available at this http URL.
[LG-87] Every Expert Counts: ExactMoE for Memory-Efficient W4A16 Inference
链接: https://arxiv.org/abs/2608.15383
作者: Amjad Saab
类目: Machine Learning (cs.LG)
*备注: 12 pages, 3 figures, 4 tables
Abstract:Sparse mixture-of-experts (MoE) language models reduce arithmetic by activating only a small subset of experts per token, yet deployment still requires storing and moving the full expert bank. We present ExactMoE, an inference design that applies symmetric group-128 four-bit weight quantization only to routed experts, stores those experts in kernel-native MARLIN form in pinned host memory, and executes all selected experts through a configurable GPU-resident slot cache and fused grouped MoE kernels. The router, attention, embeddings, normalization layers, and language-model head remain in BF16. “Exact” refers to complete expert availability and an unchanged top-k routing procedure: no expert is pruned, substituted, or forced to execute on the CPU. It does not imply numerical identity with the BF16 model. On OLMoE-1B-7B-0924-Instruct, evaluated on a single NVIDIA L4, a 16-slot configuration reduces peak reserved GPU memory from 14.168 to 1.836 GiB (87.04%) while retaining 81.85% of BF16 decode throughput. A fully resident 64-slot configuration reaches 31.923 tokens/s versus 21.662 tokens/s for BF16 while reserving 4.061 GiB. Across 12,450 zero-shot multiple-choice questions, ExactMoE obtains 70.3534% normalized accuracy versus 70.8996% for BF16, retaining 99.23% of the baseline accuracy. In a matched 16-token ablation, fused grouped execution is 1.97x as fast as a sequential W4 reference. These results identify a practical memory-transfer-throughput frontier for complete-expert MoE inference.
[LG-88] Beyond Field Accuracy: Two-Axis Diagnosis of Inverse-PINN Parameter Error
链接: https://arxiv.org/abs/2608.15373
作者: Yifan Zhang,Qian Tao
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: Preprint with supplementary appendix. 26 pages, 3 figures
Abstract:Inverse physics-informed neural networks (PINNs) can reconstruct a field accurately while returning an incorrect physical parameter. We introduce a two-axis post-training diagnosis that separates finite-sample resolution under a specified observation-and-estimation protocol from the signed parameter preference encoded by the final learned field and residual metric. The first axis repeatedly fits noisy observations with a matched forward estimator. At known synthetic truth, the second freezes the field and residual view and computes a local score displacement toward a nearby residual-profile minimum. Endpoint consistency then tests whether joint training delivers that preference under the same final view. Across three synthetic one-dimensional, scalar-parameter PDEs, matched-forward mean absolute relative error ranges from 2.34 percent to 17.46 percent. The displacement tracks frozen-profile minima across locked seeds, architectures, and fresh-noise retraining (r from .945 to .982), and it tracks delivered signed log-error in 240 fresh-noise RBA runs (r = .994; 237/240 correct directions). A coupled two-parameter Darcy check validates the full matrix calculation. The axes are complementary diagnostic coordinates, not additive error components or a deployable oracle-free estimator. Together, they route follow-up work toward observations, residual evidence, or endpoint delivery.
[LG-89] Does 1/2-Tsallis-INF Also Work Well for Best-Arm Identification?
链接: https://arxiv.org/abs/2608.15365
作者: Jingxin Zhan,Yuze Han,Zhihua Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Regret minimization (RM) and best-arm identification (BAI) are two fundamental objectives in multi-armed bandits. Among regret-minimizing algorithms, 1/2 -Tsallis-INF is a canonical best-of-both-worlds FTRL algorithm: it achieves logarithmic pseudo-regret in stochastic bandits while retaining minimax-optimal regret in adversarial bandits, without knowing the environment in advance. This raises a natural question: can the same algorithm, without additional exploration, also identify the best arm reliably? We study this question in stochastic bandits by analyzing the failure probability \operatornameErr_t , defined as the probability that the empirical best arm determined by the cumulative importance-weighted loss estimates of 1/2-Tsallis-INF differs from the true optimal arm. The main difficulty is that, at the logarithmic-regret scale, suboptimal arms are sampled with probability heuristically of order 1/t . Consequently, importance weighting causes the cumulative estimator to fluctuate on the same linear scale as its mean separation. To overcome this obstacle, guided by a diffusion toy model, we construct a Lyapunov function for the gap process between the estimated cumulative loss of the optimal arm and that of the best competing arm. This leads to polynomial upper bounds on \operatornameErr_t : for learning rate \eta_t=\alpha/\sqrt t , \operatornameErr_t decays at rate t^-2+\alpha^2\mu_i_/4+\rho for any \rho0 , where \mu_i_ denotes the mean loss of the true optimal arm. We also establish a lower bound \Omega(t^-2-\varepsilon) for any \varepsilon0 , showing that the exponent 2 is essentially tight.
[LG-90] Spectral Rank Certification for Foundation Model Adapters KDD2026 KDD
链接: https://arxiv.org/abs/2608.15351
作者: Mohammed Ahnouch,Lotfi Elaachak
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 11 pages, 4 figures , KDD 2026 Workshop TensorKDD
Abstract:Nominal LoRA rank is a design parameter; calibrated spectral evidence is a separate inferential quantity. This article develops a finite-sample framework for inferring effective rank structure in public foundation-model adapters. The theoretical core is an exact chi-square divergence for the fixed-dimensional Gaussian rank-one reference experiment, with an unknown signal direction integrated under a rotation-invariant reference prior. The resulting series yields a computable finite-sample Le Cam bound at concrete layer sizes, an explicit remainder bound for numerical truncation, and the rectangular Baik-Ben Arous-Peche (BBP) limit. A compact-manifold Laplace expansion shows that finite-sample likelihood evidence also depends on leading spectral gaps through the factor s_1^|m-n|\prod_i\ge2(s_1^2-s_i^2) , motivating joint calibration of clustered singular values. Building on these results, we introduce an empirical-null workflow for PEFT LoRA adapters: factor reconstruction, Monte Carlo p -values, stagewise and block testing, and module-wise and corpus-level BH reporting. In an audit of 26 public adapters, 684 modules, six architecture families, and 31,770 public-checkpoint spectra rows, calibrated effective rank is typically much smaller than nominal rank and differs systematically from 95% energy retention. A measured RoBERTa-RTE slice on n=24 examples illustrates the measurement path from calibrated ranks to task evaluation, without treating the slice as a utility study. The main empirical finding is that calibrated effective rank is usually far below nominal rank, and that energy retention and statistical surprise answer different questions.
[LG-91] FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
链接: https://arxiv.org/abs/2608.15310
作者: Zhenyan Liu,Hua Zhang,Haoran Gao,Qi Li,Hongliang Zhu,Huiyu Zhou,Zongliang Shen,Yanxin Xu,Jiahui Wang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted to appear in Proceedings of the 34th ACM International Conference on Multimedia (MM '26)
Abstract:Multimodal data collected by heterogeneous devices are used for collaborative training, where federated learning (FL) serves as a key paradigm for effective distributed modeling with data privacy preservation. However, local training suffers from the forgetting of previously learned global knowledge under cross-client data heterogeneity, which leads to significant declines in both performance and convergence speed. Most previous studies rely on global alignment strategies to retain global knowledge, which hinder local optimization and lead to inadequate supervision of missing classes. Some studies introduce proxy datasets to supplement supervision for missing classes. However, it remains a challenge to balance class-wise global consistency and local optimization objectives without proxy datasets. In this work, we propose FedADB, a Class Anchor-Driven Dual-Branch FL framework. Specifically, the server generates class anchors optimized in a differentiable input space, which are shared across clients. These class anchors serve as global references that provide supervision for missing classes during local training. A dual-branch collaborative training mechanism is designed for clients. In this mechanism, the anchor-based global branch focuses on learning with global consistency, achieving global knowledge alignment by class-anchor balanced sampling. The local calibration branch focuses on learning discriminative local features, mitigating the degradation of local representations caused by excessive global alignment. Extensive experiments across multiple medical and natural datasets demonstrate that FedADB achieves significant improvements in both accuracy and convergence speed.
[LG-92] BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
链接: https://arxiv.org/abs/2608.15266
作者: Sijing Wu,Dongyuan Li,Miaoting Huang,Weiwei Ye,Ying Zhang,Feng Xia,Renhe Jiang
类目: Graphics (cs.GR); Machine Learning (cs.LG)
*备注:
Abstract:Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer’s disease. Existing methods typically use GNNs and Transformers to model the full functional connectivity matrix. However, processing tens of thousands of connections introduces redundancy and noise, increases computational cost, and limits connection-level interpretability. This raises a central question: do we really need complex interaction modeling, or is identifying a small set of disease-relevant connectivity patterns sufficient? To answer this question, we propose BrainLinear, a lightweight geometry-aware framework for mining disease-discriminative connectome patterns. BrainLinear first maps each functional connectivity matrix to a shared tangent space centered at the Fréchet mean of the training set, capturing subject-specific deviations while respecting matrix geometry. It then scores each ROI-pair tangent direction by its classification contribution and disease–control difference, retaining Top- K directions as a compact representation. Finally, a shallow multilayer perceptron performs classification on the selected representation. Experiments on ABIDE and ADNI show that BrainLinear matches or exceeds strong GNN and Transformer baselines at a fraction of their cost: it improves AUC and ACC over the best baseline for each metric by up to 3.54 and 1.39 percentage points, while reducing runtime and peak GPU memory by 84.0% and 68.4% relative to the closest baseline in AUC. The selected directions are directionally consistent with between-group displacements and organized across major functional systems, supporting connection-level interpretation.
[LG-93] Learning reshapes power-law anisotropy in internal representations
链接: https://arxiv.org/abs/2608.15239
作者: Asahi Nakamuta,Jun-nosuke Teramae
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 26 pages, 6 figures
Abstract:Power-law anisotropy in internal representations has been observed across a wide range of biological and artificial neural systems, from state-of-the-art language models to the mouse cerebral cortex. This anisotropy is a key geometric property of high-dimensional information processing and underlies a variety of theoretical analyses. However, the mechanism by which it emerges from input structure and task-driven learning has remained unclear. Here, we characterize this formation process by exactly solving the learning dynamics of a wide two-layer linear neural network in a teacher–student setting with power-law input and teacher structures. We show that, in the feature-learning regime, the local power-law exponent of the internal-representation spectrum evolves nonmonotonically over the course of training and exhibits up to four distinct asymptotic regimes across modes and training times. By contrast, in the lazy regime, the exponent remains essentially unchanged. We further demonstrate numerically that similar exponent dynamics arise in more realistic nonlinear networks. Together, these results suggest a general mechanism by which the dynamic interaction between input statistics and task structure gives rise to power-law internal representations.
[LG-94] Structuring Semantic Embeddings for Principle Evaluation: A Prototype-Guided Contrastive Learning Approach
链接: https://arxiv.org/abs/2608.15224
作者: Che Shen,Junwei Su,Lingpeng Kong,Chuan Wu
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in Transactions on Machine Learning Research (TMLR). 27 pages
Abstract:Reliable post-hoc evaluation asks whether already generated text satisfies a target criterion after generation. In this paper we study a focused frozen-embedding setting using principle-evaluation proxy tasks: toxicity detection, fine-grained emotion categorization, and ordinal review rating. General-purpose text embeddings are widely deployed for such tasks, but broad semantic similarity can place semantically similar yet task-distinct examples in overlapping regions of the representation space. We introduce Prototype-Guided Contrastive Learning (PGCL), a prototype-guided geometric regularization module built on top of frozen text embeddings. The module combines a semantic stream, a prototype-anchor attention stream, supervised contrastive learning, offset-based prototype-margin regularization, and stream regularization to produce a compact task-adapted representation without updating the base encoder. Controlled experiments show that PGCL improves over raw frozen embeddings on all three datasets and gives the clearest direct-baseline margin on AmazonReviews, while remaining competitive with strong direct frozen metric-learning baselines on GoEmotions and ToxicComment. We also add supervised residual-adapter, encoder-LoRA, full fine-tuning, objective ablation, sensitivity, and fully logged few-shot LLM protocol diagnostics to define the boundary of the claim. The theoretical analysis is revised as a sufficient-condition account for prototype-margin behavior under explicit assumptions in the prototype-mapping space, rather than as an unconditional training or final-embedding separation guarantee.
[LG-95] MiNO: Cotangent-bundle propagator learning for PDEs
链接: https://arxiv.org/abs/2608.15187
作者: Gnankan Landry Regis N’guessan,Bum Jun Kim
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA)
*备注: 35 pages, 6 figures
Abstract:Scientific machine learning for partial differential equations commonly targets solution fields, as in physics-informed neural networks, or solution maps, as in neural operators. We study a third target: the propagator itself, a phase and amplitude in phase space. The motivation is a gap in regularity. A transported discontinuity is nonsmooth in space and time, yet the rule that moves it can be a polynomial phase carrying unit amplitude, so the object that generates an evolution can be far smoother than the field it generates. The microlocal neural operator (MiNO) learns that object, using the eikonal equation for the phase and the transport equation for the amplitude, and recovers the solution by an oscillatory integral. Sharp fronts and caustics then belong to propagation geometry rather than to a field fitted pointwise. Small residuals certify more than the reconstructed field. They place the learned canonical relation, the geometry that carries singularities, close to the exact one, and they separate trainable error from the frequency-truncation tail. On a matched-budget discontinuous-advection benchmark, MiNO stops improving within 10,000 steps at the accuracy limit of its finite reconstruction window, a limit predicted in closed form, whereas a physics-informed neural network with neural-tangent-kernel loss balancing stays near its initial error. On smooth advection, the mean error is 3.84\times10^-3 for MiNO and 3.12\times10^-2 for a supervised Fourier neural operator. Single-branch MiNO is the smallest model compared, and one trained generator serves five unseen initial conditions without retraining.
[LG-96] An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning
链接: https://arxiv.org/abs/2608.15153
作者: Wenjing Wei,Alla Jammine,Farid Nait-Abdesselam
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Submitted to International Conference on Computing, Networking and Communications (ICNC 2027)
Abstract:Differentially private federated learning must balance privacy protection against model accuracy and training efficiency. Static gradient clipping applies a fixed threshold throughout training and across model layers, which can cause excessive clipping when the threshold is too small or unnecessarily large noise when it is too large. This paper presents DDP-SA-adaptive, an adaptive gradient clipping and noise adding mechanism for differentially private federated learning with secure aggregation. At each communication round, every client determines a separate clipping threshold for each model layer from the median of its per-sample gradient norms. The resulting layer-wise thresholds adapt to the evolving gradient distributions and calibrate the Laplace noise added before the updates are encoded and secret-shared among intermediate aggregation servers. We evaluate the proposed mechanism on a federated regression task in terms of efficiency, accuracy, privacy, convergence, clipping norm, and noise magnitude. Compared with the static DDP-SA baseline, DDP-SA-adaptive reduces the number of communication rounds by 6.81%, total training time by 19.21%, and average per-round training time by 13.33%, leading to improved training efficiency. It also reduces test loss by 98.74% and increases test R2 by 3.41%, leading to improved model accuracy. To attain R2 = 0.99, the adaptive mechanism operates with a privacy budget of approximately epsilon = 0.1, compared with epsilon = 0.4 for static DDP-SA, thus providing stronger privacy protection and achieving stronger privacy guarantees. These results demonstrate that round-wise, layer-wise adaptation can improve the privacy-accuracy-efficiency trade-off of differentially private federated learning.
[LG-97] PureTD: Reinforcement Learning for Backgammon Money Games with No Evaluation-time Search
链接: https://arxiv.org/abs/2608.15146
作者: Alexander L. Strehl
类目: Machine Learning (cs.LG)
*备注:
Abstract:We revisit Tesauro’s TD-Gammon for backgammon money games in the setting of no evaluation-time search. Both checker play and cube action (use of the doubling cube) are learned from scratch via self-play reinforcement learning (RL), with minimal hand-coded logic and no expert features. In this setting, we demonstrate that pure self-play RL suffices to train models that reach near-state-of-the-art playing strength. Specifically, for cubeful money games, our search-free model evaluates faster and is substantially stronger than the open-source engines GNU Backgammon and Open Sage running a one-move (1-ply) look-ahead search.
[LG-98] Decision-Driven Regularization: A Blended Model for Learning and Optimization
链接: https://arxiv.org/abs/2608.15124
作者: Gar Goei Loke,Qinshen Tang,Yangge Xiao,Xun Zhang
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 42 pages (including appendix), 7 figures in main, journal paper
Abstract:In contextual optimization, the decision-maker seeks optimal decisions to minimize a cost function, that varies based on observed features. This context is common in many business applications ranging from on-demand delivery and retail operations to portfolio optimization and inventory management. In this paper, we study the learning and optimization approach, which first learns how outcomes result from the features, and then selects optimal decisions based on these outcomes. We focus on the integrated learning and optimization literature, and identify that a lack of control for prediction accuracy can lead to overfitting and a loss of decision effectiveness against simple separate learning and optimization models. Instead, we propose a bi-objective formulation that balances prediction accuracy and cost minimization, termed decision-driven regularization. It also addresses ambiguity in the definition of the cost function via a surrogate that depends on a new hyperparameter. We additionally show that alternative perspectives for formulating the problem, namely robust optimization and regret minimization, lead to models that are closely related to our proposed model. As a consequence, our framework generalizes models such as SPO+. Our model is shown to be numerically superior to other benchmarks, such as OLS, Random Forest, XGBoost, SPO+, Perturbation Gradient, and Learning and Rank, in our synthetic studies.
[LG-99] Probability-Preserving Transformer for the Time-Dependent Schrödinger Equation
链接: https://arxiv.org/abs/2608.15112
作者: Mushtaq Ali,Muzamil Tariq,Niaz Ali Khan
类目: Machine Learning (cs.LG); Mathematical Physics (math-ph); Quantum Physics (quant-ph)
*备注: 9 pages, 7 figures
Abstract:Solving the time-dependent Schrödinger equation (TDSE) via traditional numerical methods is computationally intensive. Transformer models offer a compelling alternative, but standard implementations rely on soft constraints that cannot rigorously guarantee probability conservation. Here, we introduce a Transformer architecture that enforces TDSE probability conservation as a hard constraint. The design intrinsically ensures unitarity across temporal evolution without requiring repeated retraining. Our empirical findings show that this hard-constraint approach is not only physically exact but also computationally superior to conventional soft-constraint methods.
[LG-100] Global Federated Learning Strategies for Building Efficient Personalized Models
链接: https://arxiv.org/abs/2608.15107
作者: Seongyoon Kim
类目: Machine Learning (cs.LG)
*备注: Ph.D. dissertation, Korea Advanced Institute of Science and Technology (KAIST), February 2026
Abstract:Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental bottleneck than classifier weights, and propose a method that directly mitigates the discrepancy in representation magnitude between local and global models. Second, we analyze that a training approach that strengthens local alignment can induce forgetting of global knowledge (e.g., categories not observed locally), and propose a method that achieves both local alignment and global knowledge preservation by combining feature distillation based on the global model’s feature vectors. Third, in federated personalized reward model learning with preference heterogeneity, we empirically verify the conventional belief that “increasing the number of global models yields better initialization,” and we show that when sufficient local fine-tuning is allowed, a single global initialization can instead provide stronger personalization performance. This study redefines the role of global initialization under data and preference heterogeneity and provides practical training strategies that simultaneously satisfy global knowledge preservation and personalization.
[LG-101] A Unified Mamba–MoE Surrogate for Closed-Loop Simulation and Measurement-Window Forecasting of Inverter Transients
链接: https://arxiv.org/abs/2608.15051
作者: Haoguang Wang,Huy Hoang Le,Akhila Kandivalasa,Christian Moya,Marcos Netto,Guang Lin
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:This paper proposes a Mamba surrogate model with mixture-of-experts (MoE) routing to represent the transient dynamics of inverter-based resources. A Mamba surrogate model is a predictive machine learning model built on the Mamba architecture. MoE routing uses a router network to assign data-dependent weights to specialized subnetworks (experts). The resulting Mamba–MoE surrogate can perform two tasks: (i) closed-loop simulation and (ii) measurement-window forecasting of inverter transients. A single Mamba backbone with task conditioning and expert routing serves both tasks, replacing two separate specialists. Task-matched objectives fit each prediction form, and an adaptive conformal layer provides prediction intervals for both tasks. For the considered grid-following inverter, the unified surrogate model remains in the same low-error regime as a Mamba specialist pair while using 13% fewer parameters. The prediction intervals achieve 94–96% empirical mean marginal coverage across the two tasks. For transient dynamics—that is, beyond the vicinity of an equilibrium point—our surrogate model with MoE routing yields lower errors across all outputs in both tasks compared to a shared Mamba backbone without expert routing. A controller hardware-in-the-loop simulation validates our results and shows that adapting only the shared output head with limited measured data reduces held-out forecasting error.
[LG-102] Online Convex Optimization with Dueling Feedback
链接: https://arxiv.org/abs/2608.15050
作者: Yiyang Lu,Hareshkumar Jadav,Mohammad Pedramfar,Ranveer Singh,Vaneet Aggarwal
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points. While dueling feedback is well understood in discrete or stochastic settings, the adversarial convex setting has remained unexplored. We propose a simple reduction that converts dueling feedback into approximate gradients, enabling the use of standard first-order methods. We show that regret guarantees transfer under this reduction, yielding the first results for this setting, including \mathcalO(T^3/4) static, adaptive, and dynamic regret. Under additional structure, we obtain improved rates of \mathcalO(T^2/3) for smooth objectives and \mathcalO(\sqrtT \log T) for strongly convex functions.
[LG-103] Certifying Compressed Language Models: An Audit and a Statistical Toolkit
链接: https://arxiv.org/abs/2608.15046
作者: Amogh Singh
类目: Machine Learning (cs.LG)
*备注: 109 pages, 3 figures, 20 tables. Artifacts and per-item outputs: this http URL
Abstract:A fraction of a point of benchmark accuracy is the usual evidence that a compressed model is equivalent to its original. That quantity is least informative when two models are most alike: a net delta is what survives cancellation between opposing per-item changes, and cancellation is most complete in the regime equivalence claims occupy. Across an atlas of 1,707 paired model-by-task cells mined from public per-item evaluation dumps (1.3B-405B), churn runs roughly five times the net accuracy delta, and cells scoring identically to their baseline still disagree on individual items. In a preregistered audit of 17 equivalence claims from three registered frames (method papers, model cards, vendor documentation), 16 are eligible. None states a prospective numerical equivalence margin, and none releases task-matched per-item outputs, though 3 release outputs for other tasks only; 5 report too little to assess numerically, so a reader cannot check them at any sample size. We audit evidential sufficiency, not truth: no claim is called false. We supply the missing instrument: paired equivalence testing at a declared margin, with certification tables giving the items an evaluation needs, computed from disagreement observed under compression, not from independent-binomial variance. A controlled experiment pairs GPTQ and AWQ on byte-identical calibration samples across five seeds. Under the frozen eight-cell decision rule H3 is supported: changing the calibration draw was sufficient to reverse the observed method ordering in 5 of 8 confirmatory cells. The reporting standard we propose is five lines: declare a margin, run the paired test, report churn beside net delta, cite the sample size you met, release per-item outputs. It applies to any comparison between two models alike enough to be worth comparing. All per-item outputs, protocols and code are released.
[LG-104] Prototype-Rectified Iterative Self-supervised Manifold Denoising under Severe Acoustic Shift CIKM2026
链接: https://arxiv.org/abs/2608.15037
作者: Ashish Anand Shukla,Rini Smita Thakur,Aryan Das,Vinod K. Kurmi
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: Accepted as a full paper at ACM CIKM 2026
Abstract:Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-based Test-Time Adaptation (TTA), which reinforces noise rather than signal, or on prompt tuning that requires privileged noise annotations unavailable at inference. We address these failures with PRISM (Prototype-Rectified Iterative Self-supervised Manifold Denoising), a training-free, source-free TTA framework grounded in the Affine Noise Hypothesis: severe acoustic noise induces a low-rank affine shift in the multimodal latent space, with more than 90% of distortion energy confined to the leading 60 principal components. PRISM estimates and reverses this distortion from an unlabeled target batch using frozen text prototypes as geometric anchors via three closed-form geometric corrections compiled into a single static projection matrix by Affine Bias Regression. At inference, adaptation reduces to one matrix-vector multiplication in 0.0009 ms, making it substantially faster than gradient-based TTA while requiring no additional training. On UrbanSound8K, PRISM improves over the zero-shot baseline by 12.94 percentage points and surpasses an oracle-assisted TTA baseline by 9.41 percentage points, despite never observing its privileged augmented noise prompts. We further identify the Polyphonic Trap, a principled failure mode of subspace deflation for broadband classes, and resolve it via Confidence-Aware Regression (CAR), recovering up to 8.16 percentage points for the worst-affected class.
[LG-105] Lipschitz Bandits with Arbitrary Feedback Delays
链接: https://arxiv.org/abs/2608.15036
作者: Yuhao Liu,Yu Chen,Longbo Huang
类目: Machine Learning (cs.LG)
*备注: 10 pages of main contents, 26 pages in total
Abstract:The Lipschitz bandit problem extends the traditional multi-armed bandit framework to continuous action spaces by assuming that the reward functions satisfy a Lipschitz condition. This work investigates Lipschitz bandits under arbitrary feedback delays, where reward signals are not received immediately upon taking an action but after an arbitrarily chosen delay. We consider both stochastic and adversarial reward settings, proposing an elimination-based algorithm and an EXP3-based algorithm, respectively. For both settings, our algorithms achieve a regret bound of \tildeO\left(T^\fracd_z+1d_z+2+\sqrtD\right) over a time horizon T with total delay D , where the main difference between settings lies in the definition of the zooming dimension d_z . Our bounds match existing delay-free regret guarantees for Lipschitz bandits and characterize the additional \tildeO(\sqrtD) impact introduced by feedback delays.
[LG-106] NPU Offloading of a Frozen Visual Encoder for Robot Policy Training
链接: https://arxiv.org/abs/2608.15002
作者: Hyojun Yun,Seungjae Won,Hyungpil Moon
类目: Robotics (cs.RO); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: 6 pages, 4 figures, 4 tables
Abstract:When a robot policy is trained for a new task or dataset, its visual encoder can be frozen and only its action generation module trained, reducing training cost. Freezing removes the encoder’s backward pass, but its forward pass must still run at every training step because the input images change, so it keeps consuming GPU compute. We therefore ask whether moving this computation to a low power AI accelerator such as an NPU can reduce total energy despite the added data transfer and longer training time, and how it affects policy performance. We built an asynchronous training pipeline that uses both a GPU and an NPU for the AR-Actor specialist. The frozen visual encoder runs in A8W8 INT8 on a Mobilint Aries2 NPU, while the FP32 action expert is trained on an NVIDIA GeForce RTX 5060 Ti GPU. We compared a GPU-only baseline with four conditions, L1 to L4, which gradually extend NPU offloading from one to four Transformer encoder layers. Each condition was trained for 30,000 steps with three random seeds. We measured GPU board power for the GPU-only condition and combined GPU and NPU board power for the NPU conditions. Energy per sample decreased by 17.1% in L1, which offloaded ResNet18 and the first encoder layer, and by 27.9% in L4, which offloaded ResNet18 and all four encoder layers. In contrast, training time per sample increased by 15.2% in L1 and 37.7% in L4, and peak allocated GPU memory decreased by 19.8 to 20.7%. The 15 resulting policies were each evaluated with the same 300 environment seeds, for a total of 4,500 simulator rollouts. The combined success rate was 93.33% for GPU-only and 91.44 to 92.89% for the NPU conditions. These results show that NPU offloading of a frozen visual encoder can reduce training energy, but it increases training time and lowers policy success rate by 0.44 to 1.89 percentage points compared with GPU-only training.
[LG-107] A Physiology-Informed Digital Twin Framework for Simulating Liver Health Progression ALT
链接: https://arxiv.org/abs/2608.14969
作者: Sumaiya Afroz Mila,Sandip Ray
类目: Machine Learning (cs.LG)
*备注: This paper is under review at IEEE Journal of Biomedical and Health Informatics (JBHI)
Abstract:We present a physiology-informed digital twin of the human liver designed for longitudinal simulation of liver function and early-stage disease progression. The model, referred to as HEPATWIN, integrates key hepatic processes, including carbohydrate, lipid, and protein metabolism, bilirubin conjugation, bile production, and detoxification, within a unified systems-level framework to generate clinically observable biomarker trajectories. Unlike purely data-driven approaches, HEPATWIN incorporates mechanistic representations of liver physiology and patient-specific inputs such as diet, activity, and baseline biomarkers to simulate disease evolution over time. To ensure consistency with clinical progression patterns, we introduce a stage-transition-driven calibration mechanism that aligns simulated outputs with population-level biomarker distributions across disease stages, including NAFLD, fibrosis, and cirrhosis. Validation using the NIDDK NAFLD dataset demonstrates that HEPATWIN produces longitudinal biomarker estimates within clinically acceptable ranges and can forecast trajectories over multi-year horizons. Furthermore, simulated biomarkers retain sufficient clinical signal to support downstream NASH detection with competitive performance relative to models using ground-truth laboratory data. These results highlight the potential of physiology-informed digital twins for personalized, non-invasive diagnosis and prediction of organ health in general and liver health monitoring in particular.
[LG-108] LLM -based Framework for Generating and Verifying Parallel DEVS Statecharts
链接: https://arxiv.org/abs/2608.14956
作者: Vamsi Krishna Vasa,Hessam S. Sarjoughian,Edward J. Yellig
类目: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
*备注: 22 pages, 5 figures, 9 tables, 1 algorithm
Abstract:The development of models demands sound modeling and simulation knowledge as well as domain knowledge. Every model should accurately represent a system’s dynamics and be verifiable. Toward this objective, this research introduces an agentic PDEVS-LLM framework to assist human modelers in generating and verifying PDEVS statecharts for behavior modeling of atomic Parallel Discrete Event System Specification (PDEVS) models. The framework supports (re)generating plausible facts from a system description prompt using the agentic LLM used for generating plausible facts. Inconsistencies in plausible facts lead to incorrect PDEVS statecharts having logical structure and behavioral inaccuracies. A controlled-correction mechanism is developed to verify the logical consistency of the plausible facts. The agentic LLM is used to generate key behavioral conditions from the system description prompt. The plausible facts are then verified against the behavioral conditions using propositional logic entailment for a finite number of times. The verification results enable the generation of modification prompts that can reduce errors in generated plausible facts, resulting in more accurate PDEVS statecharts. To verify a statechart’s logical correctness, its Timed Automata counterpart is manually created and verified for deadlock and reachability properties. The human modeler may regenerate plausible facts and PDEVS statecharts iteratively and incrementally. A basic correctness metric is introduced to quantify the completeness and accuracy of the expected behavioral traits of the PDEVS statechart models. A collection of example systems with varying levels of complexity is developed to demonstrate the capabilities and limitations of LLMs. The evaluation of the proposed verification mechanism shows a substantial improvement in the logical consistency of generated statecharts.
[LG-109] PathFinder: Joint Decompositions of Linked Multimodal Datasets
链接: https://arxiv.org/abs/2608.14951
作者: Ying-Qiu Zheng,Alex Fung,Stephen M Smith,Rogier B Mars,Saad Jbabdi
类目: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
*备注:
Abstract:Low-rank matrix decompositions can uncover patterns and structure in data and have a number of different applications across many disciplines. Extensions to “joint” low-rank decompositions have been proposed to link datasets from different modalities. While these methods enable the discovery of common patterns across modalities, they require that all the multimodal data share one or more dimensions. We propose a new analysis method, PathFinder, that enables co-analysis of datasets that do not necessarily all share a dimension. The key insight is that as long as pairs or subgroups of matrices do share some dimension, and that there are one or more paths that link across the data matrices, a global joint decomposition can be sought out. This enables the joint estimation of common patterns across different modalities, species, or scales, where a one-to-one mapping across all data along some dimension is not necessarily available. We show that PathFinder is a general umbrella under which many matrix decomposition methods fall as special cases. It can be used to discover common patterns across disparate datasets and to make predictions for missing data or modalities.
[LG-110] Degeneracy Counting Quantum Algorithm using Decoherence
链接: https://arxiv.org/abs/2608.14941
作者: Malay Marut Das,Mark A. Novotny,Yaroslav Koshka
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注:
Abstract:Counting the global optima of a classical optimization problem is a #P-hard task. We develop the canonical thermal pure quantum (CTPQ) state-based degeneracy counting (CTPQsd#) algorithm that determines the number of global optima of a classical optimization problem P by measuring only a small probe S, without finding individual minima. The method exploits a perturbative relation between the decoherence measure of S and the degeneracy of P when S and P are together in a CTPQ state. We provide the first numerical demonstration that this relation can be used to count the global minima, applying it to problems encoded by diagonal random-energy Hamiltonians as a maximally unstructured testbed for classical binary optimization problems. Classical simulations of up to 20 problem qubits quantify the algorithm’s sensitivity to variations in the temperature of the CTPQ state, the Hamiltonian energy range, the problem size, and degeneracy. We establish the temperature threshold for determining the exact degeneracy and identify a second, lower threshold that provides a temperature window to count near-degenerate minima within a user-defined energy tolerance. By confining measurement to S, the protocol replaces tomography over the exponentially large problem Hilbert space with tomography over a small probe represented by only four qubits.
[LG-111] Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledge
链接: https://arxiv.org/abs/2608.14894
作者: Michał Tomaszewski
类目: Machine Learning (cs.LG)
*备注: 37 pages, 5 figures. Submitted to the Journal of Machine Learning Research (JMLR)
Abstract:Machine-learning systems usually model external data, while their internal functional organization is analyzed by external observers. This work introduces Self-Interventional Learning (SIL), in which a neural system perturbs its own functional structure, observes consequences, learns a predictive self-model, generalizes to unexecuted interventions, and uses predictions to guide later structural action. In a construction-known synthetic system, SIL recovered critical structure, redundancy, and replaceability, while synergy was not reliably recovered. Across 30 fresh confirmatory seeds, increasing the pairwise intervention budget from 4 to 56 reduced held-out prediction error from 0.0335 to 0.0148 and increased Spearman correlation from 0.629 to 0.883. In a matched ablation, preserving the correct intervention–consequence mapping reduced prospective prediction error by 81.3%, while using the same learned self-model for action reduced normalized regret by 31.7% relative to ignoring it. However, model-guided action did not significantly outperform a direct empirical-memory policy, and powered CIFAR-10/ResNet validation showed no robustness advantage over equal-budget direct repair search. These results support SIL as an intervention-driven framework for learning predictive knowledge about a network’s own functional organization, while showing that the self-model remains incomplete and is not universally superior to simpler direct strategies.
[LG-112] STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation
链接: https://arxiv.org/abs/2608.14861
作者: Nawrin Tabassum,Yanzhao Wu
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted by IEEE CNS 2026
Abstract:Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision. Despite growing research attention, two key challenges remain for existing defense techniques: (1) accurately distinguishing between benign and malicious model updates and (2) effectively mitigating the influence of poisoned model updates during model aggregation. To address these challenges, we propose a novel defense framework against targeted poisoning attacks with Spatial-Temporal Analysis and Robust aggregation for FL (STAR-FL). First, we employ spatial-temporal clustering to identify and remove potentially malicious updates from the FL training process. Second, we adjust the learning rate during aggregation to mitigate the impact of any malicious updates that evade detection. Third, we conduct extensive experiments across multiple benchmark datasets to evaluate the spatial-temporal analysis and robust aggregation in STAR-FL. Experimental results demonstrate their synergistic effect in enabling STAR-FL to effectively protect FL and consistently outperform state-of-the-art defenses against targeted poisoning attacks, significantly reducing Attack Success Rates (ASRs). The source code is available at this https URL.
[LG-113] BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks
链接: https://arxiv.org/abs/2608.14856
作者: Elnaz Nowrouzi,Zhiqun Zuo,Xueru Zhang,Mohammad Mahdi Khalili
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 20 pages, 2 figures, Accepted at GameSec 2026
Abstract:Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: best-response dynamics may need hundreds of iterations per instance, and downstream tasks such as auditing, stress-testing, and incentive design often require repeatedly re-solving the game under parameter perturbations. We propose BRAID, a Best-Response Amortized Iterative Dynamics model that uses a weight-tied iterative graph neural network to learn a direct map from game parameters to Nash equilibrium effort profiles, replacing iterative best response computation with a single forward pass that is up to 43X faster per instance. BRAID is derived from the best-response fixed-point structure of IDS games: its SUM aggregation reflects additive neighbor coupling, and a weight-tied gated recurrent unit (GRU) mirrors a damped best-response update. The same architecture applies across IDS specifications that vary investment-cost curvature and neighborhood aggregation, including log-linear, quadratic-cost, and log constant-elasticity-of-substitution (CES) utilities. Beyond equilibrium prediction, BRAID also recovers how equilibrium efforts change under perturbations to game parameters, including costs and network edge weights. We make this sensitivity recovery an explicit evaluation target and introduce two training strategies, interior-equilibrium training and input-noise regularization, that improve the local behavior of the learned equilibrium map without using sensitivity labels. Experiments show that BRAID effectively predicts Nash equilibria and recovers equilibrium sensitivities across utility specifications and network sizes.
[LG-114] M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection ICML
链接: https://arxiv.org/abs/2608.14847
作者: An Phan,Yufei Jin,Xingquan Zhu
类目: Machine Learning (cs.LG)
*备注: Accepted at the 25th IEEE International Conference on Machine Learning and Applications (ICMLA 2026). 8 pages, 5 figures
Abstract:Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases. EEG-based classification of dementia-related conditions, including Alzheimer’s disease (AD), mild cognitive impairment (MCI), and frontotemporal dementia (FTD), remains challenging because EEG signals are noisy, non-stationary, and vary across subjects. Segment-based learning provides a practical way to model long EEG recordings by converting them into fixed-length inputs. For each segment, discriminative information may be explored by using signals within each channel (i.e. electrode), as well as interactions between EEG channels. In this paper, we propose M-LINKX, a multi-view graph learning framework for EEG-based dementia classification. For each segment, we extract channel-level node features and construct multiple functional-connectivity (FC) graph views, where each view is defined by a specific combination of connectivity metric, frequency band, and topology filter, respectively. Instead of relying on message passing over the constructed graphs, M-LINKX follows a simple design in modeling node features and adjacency-based connectivity representations. The graph-view representations are fused using global trainable view weights, and subject-level prediction is obtained by averaging segment-level probabilities. Experiments on two three-class EEG datasets with different diagnostic groups, CAUEEG (HC/MCI/Dementia) and AHEAP (HC/AD/FTD), show that M-LINKX achieves the best subject-level performance under the main experimental settings. Our study suggests that multi-view functional connectivity can improve EEG-based dementia classification when integrated with an appropriate graph-learning architecture. Code and data are available at this https URL.
[LG-115] Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks
链接: https://arxiv.org/abs/2608.14823
作者: Preben M. Ness,Fariz Ikhwantri,Dusica Marijan
类目: Machine Learning (cs.LG)
*备注: 9 pages of main text, 33 pages total, 11 figures, 17 tables
Abstract:Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare? Some existing work frames classification of such nodes as a subgroup generalisation problem, where a model performs well on the majority group at the expense of the rare group. Others explain this as a problem of neighbourhood aggregation in graph neural networks (GNNs). We assess these two viewpoints through a detailed evaluation of six GNNs on five datasets of varying homophily, and find that homophilic nodes tend to be easier to classify, even when they are rare—challenging the subgroup framing. However, our findings also nuance existing beliefs about how GNNs misrepresent heterophilic nodes. We demonstrate that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
[LG-116] What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models
链接: https://arxiv.org/abs/2608.14819
作者: Angelos-Nikolaos Kanatas,Yuexuan Kong,Pablo Alonso-Jiménez,Xavier Serra,Dmitry Bogdanov
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注: 11 pages, 2 figures, 2 tables. Accepted at ISMIR 2026. Project page: this https URL
Abstract:Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic. Current practice defaults to fixed depths or multi-layer fusion, with limited understanding of why certain layers transfer better across downstream tasks or how representation quality varies with depth and pre-training paradigm. We conduct a systematic layer-wise analysis of 12 music foundation models spanning three pre-training paradigms (masked modeling, autoregressive modeling, and contrastive learning), characterizing their hidden representations through intrinsic geometric and transformation-based properties. Correlating label-free representation-quality metrics with layer-wise performance across 15 downstream tasks, we find that several metrics track layer quality for genre classification, emotion recognition, automatic tagging, and beat tracking, albeit with varying strength across tasks and pre-training paradigms. However, all metrics fail on tonal tasks such as key estimation and chord recognition, indicating that no single property serves as a general proxy for representation quality across music information retrieval tasks. To address this gap, we introduce a pitch-transposition equivariance measure that captures properties missed by these standard metrics, providing a consistent indicator of tonal quality across model families. Finally, we show that intrinsic metrics can serve as effective proxies for layer selection, matching or outperforming trainable multi-layer fusion methods, particularly in limited-data settings.
[LG-117] Is Grokking a Loss of Normal Hyperbolicity of the Interpolation Manifold?
链接: https://arxiv.org/abs/2608.14803
作者: Suvinava Basak
类目: Machine Learning (cs.LG)
*备注:
Abstract:A recent line of work recasts the post-memorization phase of grokking as constrained optimization: once a network interpolates the training set, weight decay drives a slow drift along the zero-loss manifold toward lower norm. In the language of dynamical systems, this is a fast-slow system in which the interpolation manifold plays the role of a slow manifold. We ask a question that this framing makes natural but the existing literature does not address: is the sharp generalization transition a loss of normal hyperbolicity of that manifold: a fold- or bifurcation-like event in which a normal restoring direction goes flat? Or does the manifold stay uniformly attracting while generalization happens by smooth drift? We propose a simple, optimizer-agnostic diagnostic: the smallest nonzero singular value \sigma_\min^+(\mathbf J) of the residual Jacobian, which, for the squared loss, equals the slowest normal restoring rate of the manifold. On a two-layer ReLU network trained to grok modular addition under squared loss, \sigma_\min^+(\mathbf J) does not collapse at the transition; it is near zero only before memorization and attains its largest values during the transition. The result holds across five seeds, and the six smallest singular values behave identically; there is no subspace-local collapse either. This is preliminary evidence against the bifurcation hypothesis and in favor of the smooth-contraction picture. We are explicit that a single-setting, gradual-transition experiment under Adam optimizer does not prove the absence of a bifurcation; it constrains where one could hide.
[LG-118] p-Spin Glass Network Efficient Single-Batch Continual Learning
链接: https://arxiv.org/abs/2608.14774
作者: Vladimer Khasia
类目: Machine Learning (cs.LG)
*备注:
Abstract:Modern sequence models heavily rely on massive memory footprints and large-batch stochastic optimization, barriers that restrict sample efficiency and continual learning. We introduce the p -Spin Glass Network, a novel architecture that overcomes these limitations, structurally manages optimization variance and yields four noticeable capabilities: 1. It enforces memory efficiency: native ternary quantization compresses internal parameters by 8\times , while exact implicit gradients strictly bound activation memory to \mathcalO(B \cdot T \cdot D) . 2. it demonstrates sample efficiency, matching the asymptotic performance of a Transformer baseline while utilizing 8\times fewer training sequences. 3. Method enables single-batch stability and smooth, monotonic convergence at a stochastic micro-batch size of 1 . 4. Finally, this stability proves modality-agnostic, maintaining robust temporal credit assignment across both discrete subword and long horizon uncompressed raw byte streams. Ultimately, this work removes large batch requirement for stable deep learning, establishing a foundation for continuous learning and edge AI.
[LG-119] Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
链接: https://arxiv.org/abs/2608.14764
作者: Begoña Ispizua,Serio Gil-López,Leire Arrizabalaga,Ibai Laña
类目: Machine Learning (cs.LG)
*备注:
Abstract:With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical. In this work, we propose a physics-informed deep learning approach for lithium-ion battery SOH prediction using incomplete discharge curves extracted from arbitrary voltage ranges, thereby reflecting realistic and heterogeneous operating conditions. The proposed method combines data-driven learning with physically motivated degradation dynamics to ensure consistent and reliable SOH estimation from partial discharge information, achieving a MAPE below 4 % . In addition, a real-time degradation trend estimation strategy is introduced to detect key aging transitions without requiring prior knowledge or historical data, making it applicable to a wide range of batteries. Overall, our approach enables SOH estimation from arbitrary discharge segments and a real-time degradation forecast that continuously integrates all usage, overcoming previous methods that rely on fixed protocols or early, non-adaptive predictions.
[LG-120] CFR without Unbiasedness: Deterministic Guarantees for Persistent Public-Chance Schedules
链接: https://arxiv.org/abs/2608.14761
作者: Jiaxing Guo,Lei Ye
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注: 27 pages, 2 figures
Abstract:At a finite public-chance cut, counterfactual regret minimization (CFR) must choose how many outcomes to evaluate before each regret update. Exact evaluation processes the full cut at one strategy profile; persistent partial evaluation processes a fixed without-replacement order across evolving profiles. The latter covers every outcome once per epoch, yet its feedback is generally conditionally biased because earlier batches influence the profiles seen by later batches. We establish a deterministic target-transfer theorem for uniform, nonnested additive public cuts. The theorem bounds full-cut exploitability by regret on the delivered feedback and a public-debit term that couples prefix coverage discrepancy with motion along the realized strategy path. Consecutively balanced schedules consequently converge for additive signed regret matching (RM) and RM+ under predetermined averaging weights, while a fixed RM+ construction proves that the discrepancy–path product is necessary in general. A component-resolved form of the theorem converts an execution trace into a numerical exploitability certificate. On two released heads-up no-limit hold’em turn endgames, persistent order improves substantially over fresh reshuffling despite identical epochwise coverage, and partial coverage wins every registered shallow matched-budget comparison. A depth study locates a crossover between 32 and 64 full-cut outcome budgets, after which complete coverage dominates. These results characterize public-chance width and order as learning variables and provide a deterministic basis for designing and auditing persistent CFR schedules.
[LG-121] WANDR: A Benchmark for Wide and Deep Research
链接: https://arxiv.org/abs/2608.14747
作者: Vitaliy Polshkov,Marcin Pitera,Jeremy Yang,Kirill Priemko,Maksim Gaiduk,Aleksandr Nikolenko,Denis Bykov,Clare Southern,Denis Yarats,Jerry Ma
类目: Machine Learning (cs.LG)
*备注:
Abstract:WANDR (Wide ANd Deep Research) is a benchmark of 500 realistic, challenging data-collection tasks for research agents. Each task requires a system to discover a large set of entities that satisfy specified criteria (breadth), investigate each entity through multiple coordinated web searches (depth), and return independently verifiable records with supporting sources and excerpts. Tasks are represented as qualification key hierarchies that specify the entities, relationships, evidence, and required count at each level; a hierarchy with n companies, m employees per company, and k sources per employee requires n x m x k records. This structure supports diverse workflows such as market mapping, due diligence, literature review, product comparison, and talent sourcing, with targets ranging from dozens to thousands of records. WANDR replaces static gold answer sets with task-specific judges that refetch cited pages and verify each record against its evidence, allowing evaluation of current and changing facts. Record verdicts are aggregated into soft and hard precision, recall, and F1 scores that distinguish factual quality, coverage, and hierarchical completeness. The tasks are derived from de-identified product-usage logs and produced through a semi-automated pipeline with automated checks, empirical audits, and human review where needed. We evaluate six production research systems and find that the benchmark is far from saturated: at high effort, the strongest system reaches only 0.363 soft F1 and 0.133 hard F1. Performance degrades as target volume and hierarchy depth increase, with incomplete discovery, missing enrichment, and incomplete evidence construction remaining major bottlenecks. The benchmark and evaluation harness are available at this https URL.
[LG-122] Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems
链接: https://arxiv.org/abs/2608.14744
作者: Mrigank Dhingra,Ramchandran Muthukumar,Rebecca Willett,Omer San
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation. Classical data assimilation exploits temporal information through forecast-analysis cycles, but often requires repeated access to expensive high-resolution forecast models. Generative super-resolution can recover unresolved structure from coarse observations, but is commonly used as a one-shot mapping that does not fully exploit constraints from past states. We introduce Iterative Refinement (IR), a learned data assimilation framework that combines these perspectives. Instead of performing a single coarse-to-fine reconstruction, IR decomposes the task into resolution-wise forecast-analysis operations across a multiresolution hierarchy. At each stage, a shared neural operator with resolution-dependent spectral mode slicing provides a dynamical prior, while a shared conditional diffusion corrector uses the current coarser-resolution state to produce a refined posterior at the next finer resolution. We evaluate IR on one-dimensional stochastically forced Burgers dynamics and two-dimensional Kraichnan turbulence. On the challenging 256x256 Kraichnan benchmark, IR achieves an RMSE of 0.184 and an SSIM of 0.836, outperforming spectral upsampling, one-shot diffusion super-resolution, enhanced deep super-resolution, and an autoregressive forecaster. On the more constrained Burgers testbed, IR remains competitive with one-shot diffusion, which achieves the lowest RMSE. These results show that one-shot generative reconstruction can be effective for simpler settings, while hierarchical forecast-analysis refinement becomes advantageous in strongly multiscale and underdetermined regimes. Overall, IR combines temporal priors, generative correction, and multiresolution reconstruction for learned data assimilation in complex physical systems.
[LG-123] Generative Learning of Separatrices
链接: https://arxiv.org/abs/2608.14743
作者: Ellis R. Crabtree,Dimitris G. Giovanis,Anastasia Georgiou,George Datseris,Ioannis G. Kevrekidis
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS); Machine Learning (stat.ML)
*备注: 15 pages, 8 figures
Abstract:The identification and reconstruction of the boundaries separating basins of attraction in multistable, multidimensional dynamical systems presents a fundamental challenge in computational dynamics. These structures govern transition pathways and other important large timescale behavior, yet they remain typically under-sampled since their neighborhood does not get routinely visited during direct simulations. Traditional computational approaches face computational limitations in high-dimensional systems and require a priori knowledge of the dynamical system and its equations. Simplistic sampling methods such as random or uniform sampling of the phase space typically fail to quantitatively approximate separatrices and their structure altogether. We introduce and implement a framework that combines supervised classification with generative modeling to address this challenge. Our approach first trains neural network classifiers on uniformly or randomly sampled initial conditions labeled by their corresponding basins of attraction in the system of interest. Using uncertainty metrics of the trained classifier to quantify decision boundaries, the method then identifies these high uncertainty regions and boundaries of the classifier as preliminary approximate separatrices. Subsequently, score-based generative models are trained specifically on samples from high-uncertainty regions, ultimately generating densities of samples consistent with the empirical density of samples on or close to the manifold that constitutes the separatrix between basins in the sampled region. This approach leverages the complementary strengths of (a) discriminative models for global phase space partitioning and (b) generative models for detailed geometric sampling, resulting in a systematic, iterative, data-driven framework that produces empirically consistent reconstructions of (approximate) separatrix manifolds. Comments: 15 pages, 8 figures Subjects: Machine Learning (cs.LG); Dynamical Systems (math.DS); Machine Learning (stat.ML) Cite as: arXiv:2608.14743 [cs.LG] (or arXiv:2608.14743v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14743 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-124] PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns
链接: https://arxiv.org/abs/2608.14742
作者: Syrym Abdikhan,Mazhar Hameed
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Pandas has emerged as the de facto library for data processing and machine learning, widely used for tasks, such as data loading, transformation, and analysis. Despite its ubiquity, there has been limited systematic investigation into how Pandas is used in real-world projects and how typical workflows are composed in practice. To address this gap, we introduce PandasCorpus, a dataset curated from GitHub repositories that captures real-world Pandas workflows at scale. In this work, a workflow refers to Pandas-based code contained in Jupyter notebooks, a prevalent medium for writing, executing, and sharing data analysis code. The dataset comprises 139k notebooks from approximately 100k repositories and captures more than 4M Pandas API calls spanning 136 distinct operations. Beyond dataset construction, we characterize workflows using structural and Pandas-specific features and analyze notebook evolution between 2015 and 2025. Our study examines code executability, notebook size, and recurring sequences of Pandas operations, providing empirical insights into how Pandas is used in practice. The resulting corpus offers a reusable resource for studying data analysis workflows, Pandas usage patterns, and library-aware code composition. Both the dataset and the extraction pipeline are publicly available via GitHub and Zenodo. Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG) Cite as: arXiv:2608.14742 [cs.SE] (or arXiv:2608.14742v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.14742 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-125] he Quantum Shortcut: Complex Phase-State Dynamics Reduce the Optimization Steps of Sequence Models
链接: https://arxiv.org/abs/2608.14691
作者: Ahmed Nebli,Hadi Saadatdoorabi,Christopher Keibel,Kevin Yam
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注:
Abstract:Sequence models are conventionally distinguished by their backbone, the mechanism that routes information across positions, such as attention or recurrence. This paper varies a choice that is prior to the backbone and shared by nearly all current models: the \emphsubstrate, the number system in which the hidden state is represented together with the form of the map from state to prediction. The prevailing substrate is a real-valued state with an affine–softmax readout; we study a complex-valued alternative drawn from the mathematics of quantum theory, in which information is carried by the phases of the state and scores are quadratic Born forms. Prior work proved an idealized version of this substrate representationally stronger than any real model with a linear readout; we ask whether it also trains faster. Relaxing the two properties that block deployment, exact unitarity and the Born vocabulary readout, we instantiate it in the Mamba state-space model and an attention-based Transformer. At 253M parameters, matched to within 0.02% and trained under one fixed protocol on three byte-level corpora, the complex models reach every measured validation loss in approximately one third (state-space) and one half (attention) of the optimization steps of their real counterparts. The two backbones then diverge. Once the learning-rate warmup ends, the state-space advantage continues to widen, from 0.321 to 0.354 bits per character on OpenWebText and from 0.368 to 0.396 on FineWeb, which an artifact of the warmup ramp would not do; the attention advantage instead decays toward zero on every corpus, and is therefore an effect of early training.
[LG-126] A Reproducibility Study of Partial Residual Ablations in Pre-LN Transformers
链接: https://arxiv.org/abs/2608.14689
作者: Pratikkumar Babariya
类目: Machine Learning (cs.LG)
*备注:
Abstract:Residual connections are a fundamental component of transformer architectures, yet the roles of the attention and feed-forward residual pathways remain poorly understood when considered independently. This paper presents a reproducibility study of partial residual ablations in Pre-LN GPT-style transformers trained at two scales (10M and 124M parameters). I compare four architectural configurations by selectively removing the attention residual connection, the feed-forward residual connection, or both. Across all experiments, removing the attention residual (FFNOnly) consistently causes deterministic collapse to the No-Residual performance floor. In contrast, removing the feed-forward residual (AttnOnly) exhibits a reproducible recovery effect at 10M scale under a controlled 8-seed deterministic study, while its behavior at 124M remains unresolved because of substantial seed variance. During the investigation, I identified and corrected an experimental measurement confound in runtime gain scaling and document both the failed intermediate reproduction and the subsequent controlled replication. Based on the empirical results, I propose a cross-position routing hypothesis to explain the observed asymmetry while explicitly distinguishing confirmed findings from unresolved questions. To support reproducibility, I release the complete source code, experiment configurations, checkpoints, training logs, and all experimental results, including intermediate non-reproducing runs. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14689 [cs.LG] (or arXiv:2608.14689v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14689 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Pratik Babariya [view email] [v1] Thu, 6 Aug 2026 21:29:46 UTC (887 KB)
[LG-127] Rethinking Reverse KL as Adaptive Entropy Distillation
链接: https://arxiv.org/abs/2608.14685
作者: Shizhen Li,Zhiyu Shen,Yuyin Lu,Yunhe Pang,Jielin Song,Yanghui Rao,Fu Lee Wang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Knowledge distillation (KD) is widely used to transfer the capabilities of large language models (LLMs) to smaller students, but existing objectives often struggle to balance faithful imitation and robust generation. In particular, existing methods mainly combine FKL and RKL, overlooking that RKL itself provides a mechanism for adjusting the student’s imitation strength. Motivated by this, we revisit on-policy Reverse Kullback-Leibler (RKL) distillation and decompose its objective into a teacher-fitting term and a student-entropy term, without introducing an explicit FKL branch. We show theoretically that the token-level optimal student distribution corresponds to a tempered variant of the teacher distribution, where the adaptive weight controls the trade-off between mode-seeking and uncertainty preservation. Guided by this insight, we propose \textbfAdaptive Entropy Distillation (AED), which uses the teacher’s entropy to dynamically calibrate token-level imitation strength. Experiments on instruction-following and mathematical reasoning benchmarks demonstrate that AED achieves superior overall performance and generally improves teacher–student distributional and entropy alignment.
[LG-128] One Score Two Decisions: Selective Prediction on the Rare-Disease Tail
链接: https://arxiv.org/abs/2608.14683
作者: Zhaoyang Jiang,Zhizhong Fu,Yunsoo Kim,Zicheng Li,Xuanqi Peng,Fei Teng,Jiacong Mi,Honghan Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Given a patient’s clinical findings, a diagnostic system ranks possible diseases and must decide when to endorse its first prediction or defer it for review. This decision is usually made by thresholding the top score. Selective prediction over ranked outputs begins with two checks. First, the ranker must produce enough correct top-ranked predictions to make the target feasible. Across 2,000 patient records stratified by disease prevalence, eight small open-weight LLMs achieve at most 4.6% Recall@1 on ultra-rare diseases. At 10% coverage, even a perfect confidence ranking of their existing predictions therefore cannot reach 50% selective accuracy. More accurate models pass the same check, showing that the limit is regime-specific. Second, the confidence signal must match the decision being made. For fixed-candidate rankers, the top-two margin cancels components shared across candidates. On phenotype-only Exomiser, it selects 10% of cases at 29.0% accuracy, compared with 13.3% overall, while the top score provides no reliable gate. Yet that cancellation can remove information needed to detect whether the candidate list contains an answer. SciFact retrieval and biomedical entity linking confirm this distinction. Finally, we prove that unlabelled scores alone cannot determine whether switching to the margin will help.
[LG-129] RouteTS: Frequency-Time Routing for Time Series Forecasting
链接: https://arxiv.org/abs/2608.14682
作者: Gaofeng Lin,Lei Duan
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Real-world time series inherently intertwine global periodic structures with localized non-stationary variations. Existing approaches process these heterogeneous dynamics within a single computational domain, incurring fundamental limitations: time-domain models suffer from periodic misalignment over long horizons, while frequency-domain models over-smooth transient spikes. We argue that the optimal computational domain is not a property of the model, but of the data itself. Based on this principle, we propose RouteTS, a unified forecasting framework that partitions the frequency spectrum via amplitude routing and delegates components to their mathematically optimal domains. Dominant frequencies are processed by a complex-valued linear predictor in the frequency domain to preserve periodic structure, while residual spectral energy is reverted to the time domain and modeled by a lightweight MLP for local variations. Extensive experiments demonstrate that RouteTS achieves competitive prediction accuracy across diverse real-world datasets, with routing decisions guided by the underlying spectral signature. Furthermore, the lightweight design of RouteTS provides significant computational efficiency advantages, offering a principled solution to the longstanding dilemma between global periodicity and local transience.
[LG-130] When Does the Best Sampling Temperature Rise with the Budget? Sufficient Conditions for Pass@k
链接: https://arxiv.org/abs/2608.14665
作者: Changsu Jeong(Independent Researcher)
类目: Machine Learning (cs.LG)
*备注: Theory paper, 13 pages, 1 analytical figure. Deterministic verification code is included as ancillary material. No new language-model experiment
Abstract:The temperature that maximizes pass@ k is often low for a small sampling budget and higher for a large budget. This pattern has been reported from Codex through recent multi-sample inference studies. It is not an algebraic property of pass@ k : as Slocum et al. (ICLR 2025) observe, for one fixed task the maximizing temperature is independent of k . Building on that fixed-task observation and the hard/easy-task explanation, we give a formal population-level sufficient condition for the aggregate pattern. For task X , let p_t(X) be one-sample success probability at temperature t , and define the conditional log-success response m_t(u)=\mathbbE[\dot p_t(X)\mid p_t(X)=u]/u . If m_t(u) is nonincreasing in current success probability, then the normalized temperature derivative of aggregate pass@ k is nondecreasing in k . Consequently, derivative signs are nested across budgets; if each temperature-performance curve is strictly single-peaked, its unique maximizer is nondecreasing in k . The proof identifies the mechanism as a monotone-likelihood-ratio power tilt toward lower-success tasks. We derive a closed-form two-stratum phase diagram, including upward and downward regimes, and show that the marginal temperature derivative admits an exact \mathrmBeta(2,k) kernel representation whose kernel concentrates at one-sample success of order 1/k . Interpreting that scale as task-level localization additionally requires a regular, nonvanishing density-response factor near zero. A signed-moment representation yields diagnostic shape restrictions, while a short appendix records exact discrete refinements of the existing multi-configuration allocation formulation. No language model is trained, and no model query is used as an experimental measurement: the contribution is a conditional theory of an established empirical phenomenon, with assumptions that can be tested in future work.
[LG-131] Quantifying Depth Sufficiency in Residual Neural Networks: A First-Order Criterion
链接: https://arxiv.org/abs/2608.14664
作者: Zeyu Liu,Jinhao Zhang,Yunquan Zhang,Guangming Tan,Xiang Gao,Fangming Liu,Daning Cheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:How can we determine whether a trained neural network is already deep enough? We study this under a fixed function-preserving residual-growth protocol specifying insertion locations, residual families, zero-output initializations, and zero-state first-order updates. We define first-order residual depth saturation as the absence of a strict local decrease from every admissible insertion. We prove residual non-degeneracy is necessary and sufficient: additional depth has first-order value exactly when conditional activation gradients have a nonzero projection onto at least one admissible residual tangent space. This boundary is shared by descent-compatible zero-state updates and invariant under regular local reparameterizations preserving that tangent space. Under residual-signal realizability, raw activation-gradient vanishing exactly certifies saturation. Across ResNets, GPT-2-style models, and continued-pretrained Pythia checkpoints, the maximum activation-gradient norm decreases toward a low-signal regime with depth. Function-preserving growth also achieves converged performance competitive with training from scratch. These results support activation-gradient magnitude as a conservative diagnostic of the remaining empirical first-order value of residual depth.
[LG-132] In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults
链接: https://arxiv.org/abs/2608.14663
作者: Houhao Liang,Kresimir Friganovic,Joanne Kua,Noor Hafizah Ismail,Su Su,Bryan Yijia Tan,Navrag B. Singh,Panos Mavros
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 8 pages, 2 tables, 1 figure
Abstract:As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults. This study investigates the utility of in-context learning (ICL), using the transformer-based foundation model TabPFN, to determine how BE features influence perceived walkability, as measured by the Neighborhood Environment Walkability Scale (NEWS-A) survey. Using a small-scale dataset (N = 257) comprising a unique demographic of older adults with knee osteoarthritis or a history of falls, TabPFN achieved a macro F1 score of 54.89% for walkability perceptions categorized as Low, Neutral, and High using equal-width binning. This result outperformed optimized, grid-searched baseline models, including Random Forest (45.85%) and XGBoost (50.56%). To interpret these results, we employed Shapley Interaction Quantification (SHAP-IQ) to identify the hierarchical importance of feature interactions. Preliminary results revealed that the model’s predictive logic was primarily driven by higher-order interactions. For example, the interaction between average street circuity and the ratio of drivable roads emerged as the primary discriminator of perceived walkability. Neighborhood greenery was found to have substantial predictive importance only when combined with an individual’s fear of falling or perception of age-friendliness. Overall, ICL using TabPFN demonstrates superior performance on small-scale datasets, enhancing the fidelity of the resulting interpretive insights. Furthermore, SHAP-IQ provides a synergistic perspective on how higher-order feature interactions drive the model’s predictions.
[LG-133] An automatic-differentiation framework for time-lapse electrical resistivity tomography inversion of hydrologic dynamics
链接: https://arxiv.org/abs/2608.14661
作者: Pu Yang,Zhengyang Fang,Yuxin Liu,Xuan Su,Deshan Feng,Hang Chen
类目: Machine Learning (cs.LG)
*备注: Main text: 10 figures. Supplementary Information: 2 figures and 2 tables
Abstract:Time-lapse electrical resistivity tomography (TL-ERT) provides spatially distributed information on subsurface hydrologic changes. However, inversion of long monitoring sequences is computationally demanding. Modifying the data misfit, regularization, model parameterization, or petrophysical transformation may also require new gradient derivations and separate implementations. Here, we present AD-TLERT, a unified, GPU-accelerated framework for time-lapse ERT inversion based on automatic differentiation. The framework integrates model parameterization, differentiable petrophysical transformations, forward modeling, data misfit, regularization and auxiliary constraints into a single computational chain. Alternative inversion formulations can therefore reuse the same PDE derivative implementation without re-deriving the complete ERT sensitivity for each case. Comparisons with pyGIMLi showed close agreement in the forward responses, gradients, and recovered resistivity models. Under the tested configuration, AD-TLERT achieved an approximately 51-fold speedup. Synthetic experiments showed that inversion choices affect the amplitude, geometry, and temporal behavior of recovered anomalies. By propagating gradients through the embedded petrophysical relationship, AD-TLERT enabled direct water-content inversion and yielded more accurate estimates than post-inversion conversion for the tested model. A field application further demonstrated how ERT, temperature, and soil-moisture observations can be combined to image snowmelt-driven hillslope wetting. AD-TLERT provides an efficient and flexible framework for time-lapse ERT inversion and hydrologic interpretation.
[LG-134] Paired Exact-Reset Evaluation of a Prediction-Derived Medium-to-Full World-Model Cascade
链接: https://arxiv.org/abs/2608.14650
作者: Malo de Pastor
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 20 pages, 6 figures, 6 tables
Abstract:Existing adaptive-inference and world-action-model systems use cheap-stage outputs or predicted futures to allocate additional computation. We study a narrower question: under paired exact-reset physical outcomes, can a Medium-derived interface predict when switching to a separately frozen Full predictor improves task-specific decision loss enough to justify sequential overhead? Our contribution is a paired evaluation and audit protocol, not a new generic routing rule: all candidate actions are executed from the same reset state, Medium and Full act on the same candidate set and task, and their paired physical-loss difference defines the routing target. On a fresh PushT bank (V106; 1,600 states, 39 tasks, three checkpoint pairs), a frozen prediction-interface router lowers overhead-inclusive decision cost relative to standalone Medium, standalone Full, and a latency-advantaged task-only router. We then prospectively seal a second 1,600-state PushT confirmation (V107) against a stronger current-state control using the task, a dimension-matched projection of current DINO features, and all five candidate actions, with no DINO encoder latency charged. The prediction interface lowers priced physical decision cost by 0.002549 (state-clustered 95% interval [-0.002867, -0.002238]; one-sided 95% upper bound -0.002286), with negative effects for all three checkpoint pairs. A controlled-PyBullet audit independently supports a composite task-prediction-regime router. The sequential router remains slower than fixed policies, and its advantage is restricted to low compute prices. The evidence supports incremental routing information in the tested prediction interface beyond one deliberately favoured current-DINO control, but not causal sufficiency, compute saving, closed-loop value, or cross-family generality.
[LG-135] Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers SDM2026
链接: https://arxiv.org/abs/2608.14649
作者: Pawan Kumar
类目: Machine Learning (cs.LG)
*备注: Accepted to SIAM SDM 2026, 39 pages
Abstract:We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification. For each candidate label, it asks a short yes/no question and compares the probabilities of the two answer tokens at one masked position. The method uses no task-specific fine-tuning or training on textual-entailment datasets; a 200-example labelled validation slice selects thresholds, temperature, and prompt wording. We first show that placing all labels in one prompt creates a strong slot-position asymmetry: the first answer slot is predicted positive on 99.4% of GoEmotions examples and 100% of Reuters examples. Per-label scoring places every label in the same syntactic position, making predictions invariant to label order and avoiding this artifact. We evaluate LLaDA-8B and Dream-7B on six datasets against NLI models, an autoregressive LLM, SetFit, and supervised classifiers. On the five datasets shared by both diffusion families, Instruct checkpoints improve macro-F1 in 9 of 10 comparisons and micro-F1 in 8 of 10, although these comparisons do not identify the cause. Within our protocol, LLaDA-Instruct records the highest training-free values for both Reuters and ECtHR metrics. We prove permutation invariance, characterize thresholded decisions under weighted Hamming loss, and derive shortlist ceilings for recall and F1. An exploratory local Joint Set Refinement step lowers F1 from biased and unbiased initializations and is retained as a negative result. Comments: Accepted to SIAM SDM 2026, 39 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14649 [cs.LG] (or arXiv:2608.14649v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14649 Focus to learn more arXiv-issued DOI via DataCite
[LG-136] Efficient Neural-Network-Based High-Resolution Radiative Transfer for CO___ Retrieval and Application to Interferometric Sensing
链接: https://arxiv.org/abs/2608.14645
作者: Jordan Lontsi Tedongmo(CB),Yann Ferrec,Laurence Croizé,Pablo Musé(CB, IFUMI),Gabriele Facciolo(CB),Andrés Almansa(MAP5 - UMR 8145, IFUMI)
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注:
Abstract:Studying climate change requires reducing uncertainties in CO2 and CH4 emission estimates to better distinguish anthropogenic from natural sources, which motivates spaceborne measurements with improved revisit frequency and spatial coverage. In this context, the Horizon Europe SCARBOn project assesses a low-cost satellite constellation featuring the NanoCarb imaging interferometer as its core sensor for monitoring CO2 and CH4 emissions in the atmosphere. However, estimating CO2 and CH4 concentrations with high revisit and spatial coverage poses significant challenges: full-physics retrieval algorithms commonly used rely on repeated high-resolution radiative transfer (RT) simulations, which are computationally expensive when using line-by-line RT models. As an alternative, we propose in this study a feedforward multilayer perceptron (MLP) surrogate designed to accurately and efficiently predict top-of-atmosphere radiances in the CO2 weak band, using a combined mean absolute error (MAE) loss on radiances and RT Jacobians to preserve both spectral accuracy and sensitivity to geophysical parameters. Coupling the MLP-based RT surrogate with the NanoCarb instrumental response yields an efficient and precise forward model for NanoCarb measurements, which shows promising results for CO2 concentration retrieval.
[LG-137] raining and Evaluating Ethical Reinforcement Learning Agents on Per-Episode Distributions
链接: https://arxiv.org/abs/2608.14642
作者: Prabhjyot Singh,Majid Ghasemi,Mark Crowley
类目: Machine Learning (cs.LG)
*备注: 11 Pages, Under Review
Abstract:Reinforcement Learning (RL) agents trained on a single reward signal exploit the gap between the designed reward and the intended behavior. This is particularly a problem when we are trying to imbue ethical behavior into RL agents. An agent can look ethical on average while concentrating its violations in a few bad episodes, and a creature in the environment harmed in one episode is not restored by good conduct in another. We compare four ways of training ethical behavior in Craftax, an open-ended survival benchmark. The four are: scalar penalties with termination, a linear multi-objective weight sweep, an adaptive Lagrangian constraint, and a non-compensatory utility optimized per episode under the Expected Scalarized Returns (ESR) criterion. All are evaluated under a single detector-based protocol that counts every violation in every episode without censoring. On the frontier of mean return against mean violation rate, the four methods are indistinguishable; per episode they separate sharply. At matched mean return, the ESR agent holds its stated budget of one violation in effectively every episode (worst-decile 1.04 +/- 0.07 violations), the Lagrangian leaks past the same budget (1.14 +/- 0.03), and the weight sweep’s worst episodes double it (2.20 +/- 0.20). An observation-augmentation control attributes the separation to the training objective rather than to what the agent observes, and the per-episode guarantee costs nothing on the mean frontier. When ethical violations do not average away across episodes, we argue both training and evaluation must target the per-episode distribution rather than the mean.
[LG-138] Randomly initialized autoencoders: fixed points and edge-of-chaos
链接: https://arxiv.org/abs/2608.14638
作者: Leonid Berlyand,Roman Sarapin,Yitzchak Shmalo,Victor Slavin,Sasha Sodin
类目: Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
*备注: 23 pages, 1 figure
Abstract:In this paper we study autoencoders, a special class of deep neural nets (DNNs) whose performance can be characterized via their fixed points. This perspective naturally raises questions of existence, stability, and basins of attraction of these fixed points. These questions are addressed via the contractive properties of autoencoders, and are closely related to the notion of edge-of-chaos. Edge-of-chaos (EoC) is an important notion in the theory of DNNs. It describes the critical regime separating ordered and chaotic signal propagation through a randomly initialized network. Initialization at or near this critical regime offers several theoretical and practical advantages, including stability of the network w.r.t. perturbations of the input. EoC was previously introduced for broad classes of neural networks using mean-field averaging methods. In this paper we modify the notion of EoC for the study of autoencoders. Specifically, we introduce local and global EoC for autoencoders that control local (small) and global (arbitrary) perturbations of the input respectively. The study of stability of autoencoders falls within the scope of nonlinear problems in Random Matrix Theory (RMT). Our analysis of local EoC is based on spectral techniques of RMT, whereas global EoC is studied by employing Sudakov-Fernique inequality for Gaussian processes. Comments: 23 pages, 1 figure Subjects: Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST) MSC classes: 60B20, 15B52 Cite as: arXiv:2608.14638 [cs.LG] (or arXiv:2608.14638v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14638 Focus to learn more arXiv-issued DOI via DataCite
[LG-139] Early Cycle Charge Trajectory Generative Prediction and Full Life Cycle Health Management of Iron-Chromium Flow Batteries Based on FlowBD-E1
链接: https://arxiv.org/abs/2608.14637
作者: Suyang Zhuang,Zekun Jiang,Tianhang Zhou
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Long-duration stationary energy storage requires batteries whose degradation can be detected before substantial capacity loss has accumulated. Iron-chromium redox flow batteries are attractive for this role because they use abundant and low-cost active species, yet their operation is shaped by slow chromium kinetics, hydrogen evolution, membrane crossover and electrolyte imbalance. These coupled processes gradually reshape the full charge voltage/current (V/I) trajectory, but most battery prognostic studies either focus on lithium-ion cells or compress ageing into scalar capacity and state-of-health (SOH) labels. Here we study an industrial 33 kW Fe-Cr redox flow battery and introduce FlowBD-E1, an early-cycle generative forecasting framework that predicts complete future charge V/I trajectories from only the first few cycles. The model combines a multi-scale convolutional encoder, a lifecycle Transformer and an age-aware FiLM decoder, and we compare three deployment strategies: single-step latent extrapolation (SLE), recursive latent forecasting (RLF) and teacher-forced updating (TFU). Using the first 9 of 289 cycles, RLF achieved a joint V/I mean absolute percentage error (MAPE) of 0.731% over the remaining lifecycle and produced SOH estimates below 1% MAPE. Ablation and independent-sequence tests showed that the age-aware generative architecture outperformed LSTM and TCN baselines and retained sub-percent errors under industrial validation. These results suggest that early-cycle trajectory generation can turn a short commissioning record into a long-horizon diagnostic signal for flow-battery management.
[LG-140] Belayer: Efficient Fault Tolerance for LLM Agent ic RL Training
链接: https://arxiv.org/abs/2608.14635
作者: Jiecheng Zhou,Qinghao Hu,Peng Sun,Xingcheng Zhang,Weiming Zhang
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Large language model (LLM) agents are increasingly trained with reinforcement learning in long-horizon, sandboxed environments. Unlike conventional RL, agentic RL couples GPU-intensive rollout engines with stateful environment containers whose actions may produce visible side effects, such as file edits, command execution, and dependency installation. A single trajectory can span many rounds of gen- eration and environment interaction, so a component failure can discard completed work or expose the model to an environment state that is inconsistent with its context. However, existing systems lack efficient and correct recovery mechanisms for this distributed execution model. This paper presents Belayer, an efficient fault-tolerant system for LLM agentic RL training. Belayer handles failures in both rollout engines and environment execution while targeting low failure-free overhead. For scoped worker-local rollout failures, Belayer equips each pre-initialized shadow worker with a selective GPU-state reuse protocol that retains independently owned weights and raw KV-arena allocations after owner and GPU health checks, reinitializes worker-local state, and rebuilds request-specific KV contents from logged token prefixes. For environment failures, Belayer introduces full checkpoint and full restore to jointly capture and restore container file-system and runtime state, and coordinates the recovered environment with the LLM context to preserve prefix consistency. An adaptive policy opportunistically overlaps full-state checkpointing with natural LLM inference bubbles when the predicted interval is long enough. Empirical results show low measured overhead during failure-free training, a worker-recovery-time reduction of up to 42 times faster compared with a full engine cold start, and 1.5 to 3.5 times faster recovery from environment failures.
[LG-141] Metaplasticity as adaptive gradient preconditioning for incremental learning
链接: https://arxiv.org/abs/2608.14634
作者: Isabelle Aguilar,Zayn Andre Zainal,Omid Kavehei
类目: Machine Learning (cs.LG)
*备注:
Abstract:Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses. While artificial neural networks struggle with the stability-plasticity dilemma in non-stationary environments, existing solutions often require task labels or incur massive memory overhead, diverging from biological reality. Re-framing this localized neuromodulation as an optimization-driven process, we introduce \textbfSynGAP : \textbfSyn aptic \textbfG eometric \textbfA daptive \textbfP reconditioning. SynGAP is a task-free continual learning framework based on adaptive gradient preconditioning. Rather than relying on explicit episodic triggers, SynGAP simulates real-time metaplasticity by maintaining an exponential moving average of the Fisher Information Matrix over a continuous data stream. During the optimization step, these dynamic metaplastic states are translated into a bounded multiplicative mask that preconditions raw gradients, selectively attenuating updates to critical historical parameters. Empirical evaluations demonstrate SynGAP’s superior ability to mitigate catastrophic forgetting compared to established baselines. On the Split CIFAR-100 benchmark, SynGAP delivers a 4\times increase in accuracy compared to EWC++ and outperforms Experience Replay (ER) by almost 10% , while reducing the forgetting measure by over 10% against both methods. Furthermore, on the CORe50 benchmark, SynGAP achieves about 68% , a 10% improvement over optimizer baselines. By mathematically formalizing continuous biological metaplasticity as stable gradient-based regularization, SynGAP offers a highly robust and memory-efficient solution for adaptive intelligence at the edge.
[LG-142] PIKFNO: An Interpretable Neural Operator Based on Physics Informed Kernel Function
链接: https://arxiv.org/abs/2608.14619
作者: Yuan Guo,Hanshu Chen,Zhuojia Fu
类目: Machine Learning (cs.LG)
*备注:
Abstract:This work proposes a new interpretable neural operator framework, termed the Physics Informed Kernel Function Neural Operator (PIKFNO), which explicitly incorporates physics informed kernel functions derived from governing equations into the neural operator architecture. Unlike traditional neural operators such as DeepONet, which rely on deep networks to implicitly learn basis functions, PIKFNO constrains the trunk network through physics informed kernel functions, thereby aligning its operator structure with the kernel expansions used in meshless collocation methods. Two construction strategies are introduced: one learns kernel functions directly from data, where the learned kernel can be regarded as a nonsingular fundamental solution, while the other builds them through transformations of analytical fundamental solutions. Numerical experiments demonstrate that PIKFNO achieves high predictive accuracy with substantially improved interpretability and superior generalization under limited training data. The proposed framework offers a new pathway for developing efficient, physically consistent, and interpretable neural operators.
[LG-143] Demo: Real-time Generative Multicasting with On-Device Intent-aware Semantic Decomposition
链接: https://arxiv.org/abs/2608.14600
作者: Xinkai Liu,Mahdi Boloursaz Mashhadi,Yi Ma,Rahim Tafazolli
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Multimedia (cs.MM)
*备注:
Abstract:We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition. At the transmitter, DNN-based segmentation extracts a semantic map from the source video, decomposing it into multiple sub-signal classes based on multi-user receiver intents. The transmitter broadcasts the semantic map to all users over shared wireless/network resources, thereby utilizing orthogonal resources only to transmit the sub-signal classes intended for each user. Users partially reconstruct and partially synthesize the signal by combining the received intended classes with non-intended classes locally synthesized by a generative model from the semantic map. We derive the rate-distortion/perception curves for reconstruction/synthesis with the generative model, to adaptively set compression rates for the semantic map and intended classes. Generative multicasting significantly reduces the wireless/network resources required for existing/emerging multimedia multicasting applications. The system is real-time on a Google Coral Edge TPU with 4 TOPS (int8). This is the first demonstration of generative multicasting representing a substantial advancement in on-device generative SemCom.
[LG-144] Geometry Is Not Robustness: A Trajectory-Level Study of PGD Evaluation
链接: https://arxiv.org/abs/2608.14594
作者: Dhairysheel Durgule
类目: Machine Learning (cs.LG)
*备注: 16 pages, 3 figures
Abstract:Projected Gradient Descent (PGD) is widely used to evaluate adversarial robustness, typically via final adversarial accuracy, which does not capture model behaviour throughout the attack. Recent work proposes trajectory-level diagnostics, such as loss evolution, gradient alignment, and steps-to-failure, for deeper insight into adversarial optimisation dynamics. However, whether these diagnostics reliably indicate robustness strength remains unclear. We conduct a trajectory-level investigation of PGD attacks on convolutional neural networks trained on Fashion-MNIST. We compare clean-trained and adversarially-trained models across multiple robustness regimes, using rigorous 20-step PGD evaluations with random initialisation and multiple restarts for robustness measurement, and single-initialisation trajectory recording for diagnostics. We record full PGD trajectories across 3000 clean-correct samples per model and analyse loss evolution, gradient alignment, and failure timing across attack iterations. Our results reveal a clear robustness hierarchy across models; however, trajectory metrics do not contribute equally to its identification. Mean loss trajectories and gradient alignment patterns appear quantitatively similar across adversarially-trained models with substantially different robust accuracies. In contrast, steps-to-failure distributions provide a clearer separation of robustness regimes, directly reflecting functional resistance to adversarial perturbation. These findings indicate that trajectory-level diagnostics describe optimisation geometry but do not independently measure adversarial robustness. Their interpretability depends on robustness regime, attack strength, and multi-metric evaluation. Trajectory-level analysis should be a complementary diagnostic tool, interpreted in context, rather than a replacement for standard robustness measurements.
[LG-145] Evaluating the impact of adversarial traffic patterns on vanet communication using veins simulation
链接: https://arxiv.org/abs/2608.14583
作者: Henry Agyapong
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:Vehicular Ad Hoc Networks (VANETs) are a key component of intelligent transportation systems, enabling real-time communication between vehicles. However, their open and dynamic nature makes them highly vulnerable to adversarial behaviors that can disrupt communication reliability. This paper investigates the impact of adversarial traffic patterns on VANET performance using the Veins simulation framework integrated with OMNeT++ and SUMO. We design and evaluate multiple adversarial scenarios, including message flooding, false information dissemination, and coordinated congestion attacks, under varying traffic densities and mobility conditions. The study measures key performance metrics such as packet delivery ratio (PDR), end-to-end delay, and network throughput. Experimental results show that adversarial traffic can reduce PDR by up to 96.55%, with message flooding at low density producing a throughput reduction of 27.89%, and significantly degrade overall network efficiency. The findings highlight critical vulnerabilities in VANET communication and provide insights into designing more resilient and secure vehicular networks.
[LG-146] Coarse-to-Fine Multi-Resolution Diffusion Models for Trajectory Generation in Urban Systems KDD2026
链接: https://arxiv.org/abs/2608.14570
作者: Wen Ye,Muyan Weng,Chuizheng Meng,Hao Niu,Yizhou Zhang,Yan Liu
类目: Machine Learning (cs.LG)
*备注: 12 pages, 4 figures. Accepted to KDD 2026
Abstract:Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning. However, due to privacy concerns, the availability of large-scale public trajectory data remains limited, posing challenges for downstream mobility analysis. Existing methods for synthetic trajectory generation primarily focus on matching global distribution similarity, while often overlooking mobility patterns across different spatial and temporal resolutions that are essential for practical utility. To address these challenges, we propose a novel multi-resolution diffusion framework, MR-Traj, for large-scale trajectory generation. MR-Traj explicitly models trajectories as compositions of coarse-grained milestones and fine-grained segments, enabling the capture of complex spatial-temporal dependencies at multiple resolutions. Experimental results demonstrate that MR-Traj achieves comparable performance to state-of-the-art methods in terms of global distribution similarity, while consistently outperforming them in modeling fine-resolution mobility patterns and supporting downstream urban mobility tasks. In addition, by introducing stochasticity at multiple resolution levels, MR-Traj generates more diverse trajectories, which empirically reduces trajectory linkage risk under a seed-guided data release setting. Our code is available at this https URL. Comments: 12 pages, 4 figures. Accepted to KDD 2026 Subjects: Machine Learning (cs.LG) Cite as: arXiv:2608.14570 [cs.LG] (or arXiv:2608.14570v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14570 Focus to learn more arXiv-issued DOI via DataCite Related DOI: https://doi.org/10.1145/3770855.3818889 Focus to learn more DOI(s) linking to related resources
[LG-147] Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivarianc
链接: https://arxiv.org/abs/2608.14556
作者: Dongzhe Zheng,Christine Allen-Blanchette
类目: Machine Learning (cs.LG)
*备注:
Abstract:Physical fields on meshes require a separation between topology and geometry: conservation laws are topological and should be exact, while geometry, material response, and anisotropic coupling must be learned from data. Existing neural surrogates often mix these roles inside unconstrained message passing. We introduce Riemannian Hodge Message Passing (RHMP), which turns this separation into an architectural principle. RHMP fixes the cellular coboundaries ( d_k ) determined by oriented incidence and learns symmetric positive-definite cochain metrics ( H_k ) for geometry-dependent propagation. Treating H_k as the learned metric motivates cochain-frame equivariance: physical propagation should be invariant to orthogonal changes of the hidden cochain feature basis. RHMP implements this principle with metric-weighted Hodge blocks ( d_k^\top H_k+1d_k ), yielding exact cochain-complex identities ( d_k+1d_k=0 ), nonnegative Hodge energies, positive-semidefinite operators, and exact Abelian curvature invariance. Across seven physical benchmarks spanning fluids, electromagnetism, gauge fields, and variable-mesh CFD, RHMP achieves the best overall performance, with the largest gains when topology, learned geometry, and field structure interact.
[LG-148] Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
链接: https://arxiv.org/abs/2608.16864
作者: Shuai Huang,Zhe Qu,Zhaowei Hua,Guohao Shen,Rui Tang,Hongtu Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP)
*备注: 50 pages, 15 figures, 17 tables. Main text and supplementary material combined into a single document. Submitted to the Annals of Applied Statistics
Abstract:In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones, and we establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels. Across 27 simulation settings and six cohorts the framework attains lower pinball loss than quantile-, hazard- and tree-based competitors whenever the conditional distribution is asymmetric, with interval coverage closer to nominal on all six. In two clinical case studies (METABRIC, breast cancer; FLCHAIN, population mortality) it recovers covariate effects that vary across the survival distribution and would be hidden by a single hazard ratio, and yields coherent individualized quantile milestones. Code: this https URL
[LG-149] zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting
链接: https://arxiv.org/abs/2608.16856
作者: Girish G N,Ashutosh Sahoo,Akshay SP,Gurukiran S,Dhanashekar Kandaswamy
类目: Risk Management (q-fin.RM); Machine Learning (cs.LG)
*备注:
Abstract:Decentralized lending lacks a credit bureau: a borrower’s capacity to repay must be inferred entirely from public on-chain activity, without income verification or a liability record. This paper presents zLend, a deployed cash-flow underwriting framework that reconstructs a wallet’s daily balance history from raw token transfers and derives short-duration repayment-capacity signals from it. The reconstruction is performed twice per wallet, once restricted to a fixed stablecoin basket and once over all fungible transfers, on the premise that a wallet’s total token holdings and its liquid, spendable balance are distinct quantities whose conflation misprices risk. From each series we derive liquidity coverage against a fixed loan size, cash-flow volatility and regularity, a drawdown-and-recovery statistic adapted from quantitative finance, and a recurring-counterparty detector that identifies salary-like payment cadence from transfer timing alone. The two views are then compared: a wallet with large aggregate holdings whose stablecoin reserve rarely covers the loan size is flagged as a liquidity mismatch irrespective of total wealth. We specify the pipeline formally, document the golden-master methodology used to verify a cross-language production migration to numerical tolerance 1e-9, and characterize the tier function’s parameter sensitivity with an independent reimplementation validated to exact agreement (78 of 78 field assertions) against the deployed system’s reference fixtures. Tier assignment is governed predominantly by the reference loan size, with four of six reference wallets changing tier across loan sizes from USD 10 to USD 25,000; the drawdown and coverage criteria bind on disjoint wallets, so neither subsumes the other; and no criterion in the tier rule is inert. zLend is deployed in production, informing real lending decisions via third-party API integrations.
[LG-150] HideSeek: Learning to Explain in an End-to-End Differentiable Network ICML2026
链接: https://arxiv.org/abs/2608.16689
作者: Tal Ellinson,Hadi Mohasel Afshar,Sally Cripps
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 27 pages, 12 figures, 16 tables. Accepted at ICML 2026 (PMLR 306). Code at this https URL
Abstract:Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important features, and a predictor, which uses these to make predictions. However, these pioneering methods face challenges including information leakage and lack of differentiability, which can slow training. In this paper, we present HideSeek, an end-to-end differentiable model for instance-wise feature selection. We jointly learn feature selection and prediction under a single objective without information leakage. HideSeek outperforms existing state-of-the-art models across a range of experiments and is fast to train. We achieve this by reformulating feature removal as a differentiable operation where instead of discretely removing features, we replace a proportion of each feature. Training is further stabilized via a parsimony-weight annealing framework.
[LG-151] Random Quadratic Form with random forcing: Metastable synchronization by noise
链接: https://arxiv.org/abs/2608.16664
作者: Anna Shalova
类目: Probability (math.PR); Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注:
Abstract:We study the Random Quadratic Form (RQF) on a sphere in the presence of random Brownian forcing. We show that the forcing does not effectively change the law of the process but affects the synchronization properties of the system. While the RQF without forcing exhibits partial synchronization due to the intrinsic symmetries, the introduction of an arbitrarily small forcing results in long-term symmetry breaking and leads to full synchronization. In this work we focus on the small forcing regime and recover the multiscale behavior of the two-point process. We show that in the first stage the model converges to an anti-polar configuration due to the symmetries of the RQF and in the second stage the two clusters meet due to the symmetry breaking phenomenon. The model is motivated by continuous-time machine learning models such as Neural ODEs and continuous-time formulations of transformers. In particular, the results of this work explain the role of the bias and the scale of its initialization. Subjects: Probability (math.PR); Machine Learning (cs.LG); Dynamical Systems (math.DS) Cite as: arXiv:2608.16664 [math.PR] (or arXiv:2608.16664v1 [math.PR] for this version) https://doi.org/10.48550/arXiv.2608.16664 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-152] Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
链接: https://arxiv.org/abs/2608.16519
作者: Libin Varghese,Kaushik Prajapati,Bhaskar Chaudhury
类目: Plasma Physics (physics.plasm-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 33 pages
Abstract:Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions. While kinetic simulations directly resolve these distributions, experimental measurements remain challenging and are often invasive, spatially limited, or require assumptions regarding the distribution shape such as a Maxwellian. However, several macroscopic plasma observables can be measured non-invasively using advanced diagnostic techniques, providing spatially resolved information about the plasma state. An important inverse problem is therefore whether readily measurable macroscopic plasma quantities contain sufficient information to reconstruct the underlying kinetic state. In this work, we investigate this problem by learning a nonlinear mapping from spatially resolved macroscopic plasma observables to the corresponding spatially resolved EEDFs/IEDFs using a deep learning framework. Paired datasets comprising 2D macroscopic observables and spatially resolved EDFs are generated using 2D-3V PIC-MCC simulations. Three representative learning paradigms, a U-Net, a FNO, and a MeshGraphNet, are employed in this study to learn this inverse mapping. The predicted EDFs reproduce both bulk plasma and sheath characteristics with good agreement to the PIC-MCC reference data, with the FNO providing the best overall performance. Beyond conventional metrics, physics-based validation demonstrates that the reconstructed EDFs accurately recover the corresponding density and temperature, and rate coefficients. These results demonstrate that macroscopic plasma observables encode sufficient information to infer important kinetic properties in LTPs, providing a potential foundation for surrogate kinetic modeling and next-generation plasma diagnostics.
[LG-153] Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
链接: https://arxiv.org/abs/2608.16506
作者: Keyi Li,Yuval Kluger,Boris Landa
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注:
Abstract:Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offers a computationally tractable framework for this task by encoding cross-dataset affinities in a transport plan. However, when two datasets are sampled from geometrically similar low-dimensional structures with substantially different sampling densities, the EOT plan may match points by relative sampling density rather than geometric proximity, yielding geometrically misleading correspondences. To address this issue, we propose a density-reweighted EOT framework in which the influence of sampling density on the transport plan can be discounted to a desired degree, ranging from standard EOT to alignment driven purely by underlying geometry. Under suitable regularity conditions, we establish convergence of the reweighted EOT plan to a family of population-level plans whose dependence on sampling density is made explicit. Through simulations, we show that our approach recovers geometrically faithful correspondences, improving over related EOT-based frameworks when datasets exhibit substantial sampling density disparity.
[LG-154] Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
链接: https://arxiv.org/abs/2608.16492
作者: Shion Takeno,Shogo Iwazaki
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 25 pages, 1 figure
Abstract:This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from a multiplicative factor with respect to the batch size Q . To avoid this degradation, existing analyses require a polynomial number of uncertainty sampling (US) for Q at the beginning of optimization. However, this initial US phase is often ineffective in practice. This paper shows that the regret upper bound without the multiplicative factor on Q can be achieved without the initial US phase, using GP-BTS as an example. Furthermore, we show much better regret upper bounds in the noiseless setting than in the noisy setting, as in the sequential GP bandit setting.
[LG-155] Self-Supervised Noise2Noise-Enhanced Denoising for Continuous-Scan Air-Plasma THz Spectroscopy
链接: https://arxiv.org/abs/2608.16454
作者: Adam Umra,Oways Alsoloh,Oliver Nagy,Aydin Sezgin,Clara Saraceno
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 5 pages, 4 figures, accepted for presentation at WSA 2026
Abstract:Terahertz time-domain spectroscopy (THz-TDS) based on air-plasma generation and balanced air-biased coherent detection offers gap-free broadband coverage, but individual continuous-scan traces are strongly affected by pulse-to-pulse fluctuations and electronic noise. Reaching a useful signal-to-noise ratio therefore requires averaging multiple traces, which directly increases measurement time. We propose a learned denoising approach that recovers high-quality THz waveforms from as few as one complete continuous delay sweep, referred to here as a single-scan trace. A compact one-dimensional residual U-Net is trained using two complementary strategies: a reference-supervised baseline that maps individual noisy traces to long-average reference waveforms, and a Noise2Noise approach that learns from pairs of independently acquired noisy traces without requiring a clean training target. Averaging the predictions of both models reduces systematic bias and yields a trace-reduction factor of approximately 5.4\times at K=1 , meaning that one denoised trace achieves the reconstruction accuracy of averaging approximately five raw traces. The Noise2Noise model alone achieves 4.9\times , outperforming both the reference-supervised baseline ( 4.6\times ) and classical Wiener filtering ( 3.2\times ). These results show that self-supervised learning from repeated noisy measurements can support faster continuous-scan THz-TDS without hardware modification.
[LG-156] LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models
链接: https://arxiv.org/abs/2608.16340
作者: Tom Splittgerber,Niklas Koenen,Marvin N. Wright,Werner Brannath
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 23 pages, 14 figures
Abstract:The combination of traditional statistical models and neural network (NN) components into semi-structured hybrid models is an intriguing approach to construct models that, ideally, combine traditional interpretability with the unprecedented flexibility of NNs. In order to preserve interpretability, it is usually necessary to restrict the NN components to prevent them from dominating the model. However, existing methods that enforce structural constraints on their NN components severely limit their models’ flexibility; in contrast, methods that only enforce weak, indirect constraints lose meaningful interpretability. The method we propose therefore leverages invertible residual neural networks (i-ResNets) to equip generalized linear models with both nonlinear parameter estimation and a flexible correction of their distributional assumptions while always retaining stochastic monotonicity of the modeled distribution in the (formerly linear) predictor. The i-ResNets correspond to a controlled deviation from identity and by constraining their Lipschitz constant one can rigorously limit and quantify how far the hybrid model deviates from its traditional counterpart. This enables a user-specifiable compromise between flexibility and interpretability without limiting the structure of nonlinear and interaction effects that can be learned. Furthermore, we develop specific inherent interpretation techniques for our model and enforce model identifiability through an adapted post-hoc orthogonalization.
[LG-157] RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization
链接: https://arxiv.org/abs/2608.16167
作者: Ruixin Zhao,Xiucheng Wang,Qiming Zhang,Nan Cheng,Ruijin Sun,Conghao Zhou
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 6 pages, 4 figures, 2 tables. Accepted to IEEE GLOBECOM 2026, Wireless Communications Symposium
Abstract:High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation. However, existing deep learning methods predominantly treat this as a pure image completion task, resulting in over-smoothed reconstructions that fundamentally erase high-frequency scattering signatures of dynamic physical entities such as hidden vehicles. To overcome this, we propose RadioVIL, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem. Specifically, we first train a Denoising Diffusion Probabilistic Model (DDPM) to capture the structural generative prior of the environment. During inference from highly sparse measurements, we employ a Diffusion-based Mediating Intermediate Layer Optimization (DMILO) algorithm. By optimizing an L1-regularized sparse deviation term, DMILO mathematically isolates vehicle scattering anomalies layer-by-layer without unfolding the entire denoising chain. Extensive experiments demonstrate that while conventional reconstruction baselines fail to detect hidden vehicles, and the zero-shot diffusion baseline achieves only limited detection ability due to forced semantic harmonization, RadioVIL preserves authentic physical textures, yielding the best LPIPS of 0.0587 in our evaluation. Uniquely, it unlocks accurate zero-shot vehicle localization directly from sparse radio maps, securing a 75.20% Recall and a 3.31-meter average error, paving a robust way for ISAC at the 6G edge.
[LG-158] Coded Hankel Polynomial Chaos: Spectral Identification of Dominant Polynomial-Chaos Modes
链接: https://arxiv.org/abs/2608.16126
作者: Zhiliang Deng,Xiaomei Yang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Identification of dominant polynomial-chaos modes is usually formulated as a sparse-regression problem on a sampled multivariate polynomial dictionary. We develop coded Hankel polynomial chaos (CH-PC), a complementary spectral formulation for dominant-mode identification. A finite generating transform converts PCE coefficients into a coefficient-generating polynomial, and evaluation along a geometric phase orbit produces a finite exponential sum. Its model order and spectral nodes are encoded by low-rank Hankel matrices, while coordinate phase shifts attach root-of-unity labels from which the full polynomial multi-indices are recovered. Coordinate-shifted probes are combined as common-node snapshots, and independent phase encodings provide redundant representations when a single spectral encoding is poorly conditioned. For finite observations, population, finite-data, and observed probes are kept distinct: sampling or quadrature error and observation error enter as separate Hankel perturbations, which are then connected to spectral stability, discrete decoding, and phase voting. For tensor-product candidate sets, the generating kernel factorizes into one-dimensional sums and can be evaluated without assembling the full multivariate PCE design matrix. Numerical experiments on sparse Legendre benchmarks and a stochastic Darcy problem illustrate exact recovery, noise stabilization, unknown-order identification by phase persistence, and dominant-mode recovery for a PDE-generated quantity of interest.
[LG-159] EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset
链接: https://arxiv.org/abs/2608.16101
作者: Haoyun Yin,Chuanhui Liu,Xiao Wang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Coresets distill large datasets into small, representative subsets for efficient downstream learning. Yet Optimal Transport (OT)-based selection typically requires intensive computation of transport plans, limiting scalability. We introduce a scalable Sinkhorn coreset method that permits closed-form updates of the entropically regularized OT coupling by allowing non-uniform coreset weights. This produces centroids that generalize k-means via soft assignments. We establish asymptotic consistency of the selected measure and Lipschitz stability to data perturbations, providing accuracy and robustness guarantees. Across synthetic and real-world benchmarks, the proposed method achieves competitive or improved approximation quality while substantially reducing runtime compared to Wasserstein- and standard Sinkhorn-based coreset selection, especially at large scale.
[LG-160] Functional anatomy of Pythia-Herwig differences with Kolmogorov-Arnold networks
链接: https://arxiv.org/abs/2608.15952
作者: Arghya Chattopadhyay
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG)
*备注: 32 pages, 4 appendix
Abstract:Differences between high-energy event generators can arise at several stages of the collision simulation, from the hard scattering through parton showering and hadronization to the final event. These differences are usually summarized using observable distributions or global classifier scores. While these quantify the disagreement, they do not reveal which observable-level structures carry it or whether those structures persist through different stages of event generation. In this work, we formulate this problem as a staged functional analysis of generator-model differences. Following the same hard dijet events through Pythia and Herwig at shower-only, hadronized, and full-generator levels, we use an additive Kolmogorov-Arnold network (KAN) representation of the classifier-derived log density ratio to decompose the learned discrepancy into explicit one-dimensional observable responses that can be isolated, recomposed, and transported between generator stages. Within the same eight-observable jet representation, the Pythia-Herwig difference is driven mainly by multiplicity at shower level, shifts toward jet mass and shape after hadronization, and develops a mixed shape-multiplicity driven structure in the full-generator configuration. Transporting the individual shower-level functional components downstream shows that shower-level multiplicity information can retain its reweighting power, whereas the corresponding shape responses need not do so even though shape becomes important again at later stages. The jet-mass factors, meanwhile, are limited by poor statistical support. This KAN-based framework therefore provides a functional anatomy of generator-model dependence, exposing both persistent structures and support failures that are hidden inside a single global classifier-derived reweighting function.
[LG-161] Crystal-structure design by agent ic AI in a language of motifs
链接: https://arxiv.org/abs/2608.15900
作者: Dinh-Khiet Le,Minh-Quyet Ha,Hong-Phuc Vu-Dinh,Takashi Miyake,Hiori Kino,Hieu-Chi Dam
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types. We present MatEvolve, an agentic-AI framework designing crystals, proposing each candidate with a stated rationale and testing it. The agent reasons in an interpretable \emphlanguage of motifs, writing each crystal as a \emphmotif profile that describes the recurring geometric patterns—the \emphmotifs—composing it. The motif profile serves not merely as a description of a material but as the medium for material design: the agent edits the profile and constructs a crystal from the modified one, and the most promising candidates are validated by first-principles calculation. Applied to the design of rare-earth-lean permanent magnets, MatEvolve—built on the state-of-the-art language model Claude Fable~5 without fine-tuning—reaches new structural prototypes more than three times as often as generative models under an equal validation budget, at a comparable on-target-magnet rate. Beyond design, analysing the discovered crystals’ human-readable profiles reveals structure–property relationships.
[LG-162] Resource-Efficient QUBO Formulation for Anchored Currency Arbitrag e
链接: https://arxiv.org/abs/2608.15889
作者: Eric A. F. Reinhardt,Adam J. Hauser
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 18 pages, 12 figures, 13 tables
Abstract:Currency arbitrage (CA) involves trading currencies in cycles to exploit discrepancies in market valuations. Quadratic unconstrained binary optimization (QUBO) involves minimizing a quadratic cost (energy) function of binary variables. Previous works have explored the use of QUBO to solve CA problems. We build on these previous works by introducing realistic constraints such as beginning cycles from a held currency and accounting for per-transaction trading fees. We show that this formulation requires fewer logical variables (qubits) than previous QUBO encodings in the literature. We derive provably sufficient penalty weights for its constraint terms. We also introduce an exact anchor-gauge reweighting of the exchange rates that compresses the QUBO coefficient range from the rate scale to the arbitrage scale, addressing the finite analog precision of annealing hardware. We demonstrate the efficacy of this formulation using classical simulated annealing against an exact Held-Karp baseline on the same CPU and show that it can effectively find profitable cycles and account for trading fees. Finally, we benchmark faithful implementations of five prior QUBO encodings at matched sampler budgets and show that the proposed encoding is the only one to recover the exact fee-adjusted optimum.
[LG-163] Generalized Linear Bandits with Memory ICML2026
链接: https://arxiv.org/abs/2608.15848
作者: Heesang Ann,Hyunjun Choi,Taehyun Hwang,Younghoon Shin,Haeju Cheong,Min-hwan Oh
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted at ICML 2026
Abstract:We study generalized linear bandits with memory, an endogenous non-stationary setting in which rewards depend on past actions through a finite memory matrix. Building on prior work for linear models (Clerici et al., 2024), we show that the previously known \tildeO(T^3/4) regret bound stems from a loose analysis, and we provide a sharpened analysis that recovers a \tildeO(\sqrtT) regret rate in the linear case. We then extend this improvement to generalized linear models and propose a block-wise algorithm based on shrunken confidence bounds. Our algorithm achieves a regret bound of \tildeO\left(\sqrtmT + d\sqrtT + \sqrt\kappa, d^2 m^1/4 T^1/4 + \kappa d^2 \right) , where d denotes the feature dimension, m the memory length, and \kappa a curvature parameter of the link function. This attains a \sqrtT -type rate despite nonlinear rewards and memory effects. To the best of our knowledge, this analysis provides a unified treatment of memory-induced non-stationarity and nonlinear link functions, while ensuring that the leading regret term is independent of the curvature of the link function. We conduct numerical experiments that are consistent with our theoretical findings.
[LG-164] How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM
链接: https://arxiv.org/abs/2608.15840
作者: Jikai Jin
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Econometrics (econ.EM); Machine Learning (stat.ML)
*备注:
Abstract:We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quantify the difficulty when the causal effect is weak or the disturbances are nearly Gaussian. Let \beta bound the absolute structural coefficient from below, let \nu measure each standardized disturbance’s distance from Gaussianity, and let the disturbance scales lie in [\underline\sigma,\overline\sigma] . We prove the sharp local minimax law [ N_2^\star(\beta,\nu,\delta) \asymp \frac\log(1/\delta) d_\beta^2+\beta^2\nu^2, \qquad d_\beta= \left[\beta^2- \left(1-\frac\underline\sigma^2\overline\sigma^2\right)\right]_+. ] Previous theory established population identifiability or assumed a fixed separation between the two directions. By contrast, we establish the sharp sample complexity as a joint function of edge strength, distance from Gaussianity, and scale uncertainty, and characterize when identification comes from non-Gaussian dependence or from covariance alone. The proof was independently generated with GPT-5.6 Sol in Codex’s Ultra mode during a two-hour session. The human author supplied the prompt and was responsible only forchecking the proof and revising and polishing the manuscript. Subjects: Statistics Theory (math.ST); Machine Learning (cs.LG); Econometrics (econ.EM); Machine Learning (stat.ML) Cite as: arXiv:2608.15840 [math.ST] (or arXiv:2608.15840v1 [math.ST] for this version) https://doi.org/10.48550/arXiv.2608.15840 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jikai Jin [view email] [v1] Sun, 16 Aug 2026 16:20:19 UTC (35 KB) Full-text links: Access Paper: View a PDF of the paper titled How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM, by Jikai JinView PDFHTML (experimental)TeX Source view license Current browse context: math.ST prev | next new | recent | 2026-08 Change to browse by: cs cs.LG econ econ.EM math stat stat.ML stat.TH 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
[LG-165] Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
链接: https://arxiv.org/abs/2608.15783
作者: Longtian Shi,Molei Liu,Doudou Zhou
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注:
Abstract:In transfer-learning settings, a model derived from abundant surrogate labels may be deployed in a target population where gold-standard outcomes are unobserved. Evaluating its target performance is essential for determining whether decisions based on the model remain reliable, yet it is difficult when gold labels are scarce, and covariate distributions differ across data sources. We study a three-sample setting with a small gold-labeled source, a larger surrogate-labeled source, and an unlabeled target. Under conditional transportability, we evaluate the surrogate-derived model against the latent gold-standard outcome in the target population. We propose cross-fitted estimators that transport information from the two labeled sources through source-specific density ratios. We also combine outcome-regression augmentation with a kernel correction for estimating the model near a threshold, accounting for uncertainty from all three samples. We establish asymptotically linear inference for TPR and FPR, consistency and pointwise inference for the ROC curve, and asymptotically normal inference for AUC. Simulations assess bias, coverage, and sensitivity to bandwidth and relative sample sizes. A retrospective temporal validation on Chatbot Arena and a semi-synthetic ACS-Income study provide validation in real-world AI applications.
[LG-166] Machine Learning Approaches to Decoding Topological Quantum Codes
链接: https://arxiv.org/abs/2608.15760
作者: Changwon Lee,Tak Hur,Jeongwoo Jae,Daniel K. Park
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 42 pages, 5 figures. To appear as a book chapter in Quantum Error Decoding, Springer Quantum Science and Technology series
Abstract:Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information. Building fault-tolerant architectures requires increasing the code distance, which in turn places growing demands on decoding accuracy, scalability, and practical deployability. While a wide range of decoding algorithms have been proposed and demonstrated, achieving reliable, scalable, and real-time decoding remains a significant challenge. Machine-learning (ML) approaches are particularly well suited to this setting, as quantum error decoding is fundamentally a problem of processing large volumes of classical data with complex spatiotemporal correlations. This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations. We first frame decoding as a learning problem and outline key paradigms, including discriminative, generative, and reinforcement-learning formulations. We then introduce the neural network building blocks that underpin most contemporary neural decoders and discuss how these components can be integrated to balance expressivity, scalability, and latency. Building on this architectural perspective, we review recent progress and benchmarks in neural decoding for memory experiments, and discuss real-time decoding, open challenges, and future directions toward scalable fault-tolerant quantum computing.
[LG-167] Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
链接: https://arxiv.org/abs/2608.15715
作者: Priyanshi Singh,Krishna Bhatia
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: Accepted at the QCE26 International Workshop on Quantum Computing Reinforcement Learning (QCRL26), IEEE Quantum Week 2026
Abstract:Quantum feedback control requires acting on noisy continuous measurement records without direct access to the underlying quantum state. We propose Kraus-Parameterized Belief Reinforcement Learning, a pipeline in which a recurrent encoder, constrained to the Stiefel manifold, produces density-matrix estimates that are guaranteed positive-semidefinite and trace-normalized by construction, embedding quantum state geometry directly into the learning loop. A Proximal Policy Optimization (PPO) actor then maps these physically valid belief states to continuous control actions. On a simulated continuously monitored qubit, the resulting policy achieves stable feedback control, maintaining a measurement-conditioned belief fidelity of approximately 0.77-0.80 and exhibiting substantially lower return variance than a parameter-matched LSTM-history baseline across both nominal and out-of-distribution conditions. Although gains in raw target fidelity are modest, the geometric constraint guarantees a physically valid, interpretable belief representation and yields markedly more stable control under measurement inefficiency and abrupt dynamics switches. These results indicate that physics-informed neural memory is a practical inductive bias for reliable quantum feedback control.
[LG-168] Deep learning-based computed tomography (CT) derived body composition classifier for colorectal cancer patients
链接: https://arxiv.org/abs/2608.15712
作者: Eve Harling(1),Chattarin Pumtako(2),Bernd Porr(1),Donald C McMillan(2),Ross D Dolan(2) ((1) James Watt School of Engineering, College of Science amp; Engineering, University of Glasgow, Glasgow, UK, (2) Academic Unit of Surgery, School of Medicine, College of Medical Veterinary amp; Life Sciences, University of Glasgow, Glasgow, UK)
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注: 27 pages, 4 figures
Abstract:Background: Accurate body composition analysis using Computed Tomography (CT) scans is essential for assessing skeletal muscle area (SMA) and skeletal muscle density (SMD), key markers of nutritional status in cancer patients. Conventional manual methods are labour-intensive and require specialist expertise, limiting their routine clinical use. Therefore, this study serves as a feasibility and pilot investigation to explore the potential of deep learning-based automated regression for body composition analysis within a clinical workflow. Methods: Four deep learning architectures (AlexNet, UNet, GoogLeNet, and ResNet34) were trained to predict SMA, SMD, subcutaneous fat area (SFA), and visceral fat area (VFA) from CT scans of colorectal cancer patients. Systematic hyperparameter optimization identified the most accurate models, which were subsequently implemented in a web application for clinical use. Results: GoogLeNet achieved the best performance, with a mean percentage error (PE) of 4.96% for SMA prediction, while AlexNet reached 8.12% for SMD. Independent testing demonstrated robust accuracy, correctly classifying body composition metrics in 80% of cases. The web application delivered rapid and consistent outputs, supporting integration into clinical workflows. Conclusion: Optimized deep learning models, particularly GoogLeNet and AlexNet, can automate CT-derived body composition analysis with a Mean Percentage Error (PE) of 4.96% for SMA and 8.12% for SMD. These tools have the potential to streamline clinical practice by reducing the time and expertise required for manual segmentation. Further validation in larger, more diverse datasets is warranted. Comments: 27 pages, 4 figures Subjects: Image and Video Processing (eess.IV); Machine Learning (cs.LG) Cite as: arXiv:2608.15712 [eess.IV] (or arXiv:2608.15712v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.15712 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bernd Porr [view email] [v1] Sun, 16 Aug 2026 12:33:29 UTC (861 KB)
[LG-169] On Stopping Rules and Spatial Adaptation for CART
链接: https://arxiv.org/abs/2608.15649
作者: Zineng Xu,Yuchao Cai,Yan Shuo Tan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:The popular CART algorithm for regression trees combines a greedy splitting rule with a stopping rule, but while the splitting rule has been well studied, the statistical role of stopping rules is less well understood. Meanwhile, although regression trees fit using Bayesian methods or via empirical risk minimization (ERM) have been shown to be spatially adaptive to local smoothness and anisotropy, it is unknown whether CART can achieve the same adaptation. We address these gaps by proving that, under spatially heterogeneous and anisotropic smoothness and appropriate structural assumptions on the regression function and covariate distribution, CART with the minimum impurity decrease (MID) stopping rule and a suitable threshold achieves pointwise rates that are minimax up to logarithmic factors. These rates hold simultaneously over all points in the domain. Moreover, we prove that spatial adaptation cannot be achieved under the widely used minimum leaf size stopping rule. Together, these results establish a precise statistical role for the MID stopping rule and provide a theoretical basis for the empirical success of CART.
[LG-170] A Counterexample to the Tang Zhang Schatten Norm Conjecture and Sharp Positive Results
链接: https://arxiv.org/abs/2608.15558
作者: Zijian Zeng,Houde Liu,Kurunathan Ratnavelu
类目: Combinatorics (math.CO); Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注: 9 pages, 0 figure,
Abstract:For m\geq 2 , let c_p(m) be the all-dimensional best constant in \left|\sum_k=1^m A_k\right|_p \leq c_p(m)\left|\sum_k=1^m |A_k|\right|_p. Tang and Zhang conjectured an explicit formula for every finite p1 . We disprove the conjecture with two explicit real 2\times 2 rank-one matrices at p=3/2 . The comparison is certified by seven strict rational inequalities and, in particular, places the attained ratio above 207/200 , while the conjectured constant lies below 207/200 . On the positive side, we prove the conjectured sharp bound for every family of rank-at-most-one summands when 2\leq p\infty , and classify all equality cases. We also prove the corresponding endpoint statement for p=\infty . Finally, for arbitrary complex matrices, we establish the conjectured sharp constant in the case m=2 , p=4 . Comments: 9 pages, 0 figure, Subjects: Combinatorics (math.CO); Machine Learning (cs.LG); Functional Analysis (math.FA) Cite as: arXiv:2608.15558 [math.CO] (or arXiv:2608.15558v1 [math.CO] for this version) https://doi.org/10.48550/arXiv.2608.15558 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-171] Prediction Inference of Time Series with Standard ReLU Deep Neural Networks
链接: https://arxiv.org/abs/2608.15362
作者: Kejin Wu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO)
*备注:
Abstract:We propose a methodology based on the standard ReLU Deep Neural Networks (DNN) to make predictions and quantify their uncertainty. Classically, people rely on linear, non-linear, or non-parametric kernel methods to fit and then predict the time series. As the universal approximation ability was revealed for DNN, its application has become more and more popular for prediction tasks in various scientific areas. However, the corresponding uncertainty quantification has not been studied thoroughly. Particularly, the uncertainty in prediction will consist of two parts: (1) the future variability; (2) the estimation variability within training data. To capture both variabilities, we build the so-called pertinent prediction interval (PPI) with the DNN model estimator. We first explore the consistency property of the DNN estimator with beta-mixing dependent data. Subsequently, we show that the implied forward bootstrap series is still beta-mixing and possesses the same stationary distribution as the original time series in probability, which is a key condition to enable the PPI. Lastly, the desired PPI is built after imposing minimal conditions on the limiting distribution of predictive roots. Simulations and real-data analysis are deployed to challenge our approach with standard non-parametric methods.
[LG-172] he Physical Cutoff Does Not Restore Homogenization: Phase-Dependent Burning in the Strain G-Equation
链接: https://arxiv.org/abs/2608.15337
作者: Michele Caprio
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG)
*备注:
Abstract:We disprove the expectation stated by Xin, Yu, and Ronney that the physical positive part strain G -equation should possess an effective burning velocity in cellular flows. For the standard cellular flow in dimension two V_A(x_1,x_2)=A(-\sin x_1\cos x_2,\cos x_1\sin x_2) , if 0d20/399 and \sqrt1+4d^2Ad\le1+d/10 , then for every unit planar slope the periodic correction develops oscillations at least linearly in time. The solution remains bounded below on an explicit horizontal channel through (\pi,0) , while at (\pi/2,0) it decreases at rate at least CA/\log A , with C0 universal. The same conclusions hold for arbitrary continuous periodic perturbations of planar initial data. Under the physical scaling V_A(x/\varepsilon) and d_\varepsilon=\varepsilon d , an order one value gap persists between points at distance O(\varepsilon) at every positive macroscopic time, so the rescaled solutions have no locally uniformly convergent subsequence. The proof uses the Hamiltonian sandwich H_\mathrmunc\le H_+\le\widehat H . The upper comparator \widehat H is a rectangular support function, equivalently an upper expectation over a state-dependent credal set, whose reversed control dynamics possess an invariant comparison channel. We also prove that for any C^2 incompressible periodic flow, every \varepsilon -outward barrier certificate has covering radius at most 2d\varepsilon for all sufficiently small \varepsilon . We further discuss implications for statistics and machine learning: rectangular, time-consistent local uncertainty need not imply forgetting of the initial state in the long run, so additional global stability or ergodicity conditions are needed in robust sequential decision making. Two Lean 4 appendices record conditional formalizations of a sufficient p=e_1 subregime and of the logical assembly of the rigidity theorem for barrier certificates.
[LG-173] A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data
链接: https://arxiv.org/abs/2608.15306
作者: Mary Chriselda Antony Oliver,Kaitlyn Hohmeier,Tuyen Tran,Alejandra Castillo,Caroline Moosmüller,Shiying Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Metric Geometry (math.MG)
*备注: 33 pages, 15 figures
Abstract:High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time. Consequently, temporal and spatial dynamics must be inferred from independently sampled, unaligned cell populations, making it challenging to reconstruct developmental trajectories. Optimal transport (OT) offers a geometric framework for aligning cell populations and inferring developmental trajectories, but many existing approaches focus on modeling the evolution of distributions of cells in gene expression space rather than the relational structure encoded by gene expression networks. To address this limitation, we introduce a geometric framework for analyzing the spatiotemporal evolution of gene expression networks through embeddings in Gromov–Wasserstein (GW) space. By representing each developmental stage as a graph combining gene expression and spatial proximity, our approach enables comparisons of network structure across time, continuous interpolation between developmental stages via GW geodesics, and quantification of network-level changes using Ollivier-Ricci curvature. We evaluate our framework on a spatiotemporal transcriptomic \textitDrosophila dataset and show that GW geodesic interpolations reproduce main trends in curvature dynamics observed in empirical gene expression networks. Agreement with higher-order Co-Optimal Transport (COOT) distances, which jointly represent spatial and temporal information, further validates the framework and suggests that hypernetwork representations successfully record salient biological changes across time. In general, our approach provides a unified geometric approach to study dynamically evolving biological networks.
[LG-174] Convolution Smoothed Quantile Regression for XGBoost
链接: https://arxiv.org/abs/2608.15290
作者: Mandy Yao(1),Meredith Franklin(1) ((1) University of Toronto)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 25 pages, 3 figures
Abstract:The increasing availability of large and complex datasets across many scientific disciplines has led to widespread adoption of machine learning (ML) for prediction. However, most ML algorithms focus on point estimation and provide limited information about predictive uncertainty or the conditional distribution of the response, restricting their ability to characterize rare or extreme outcomes. We develop QXGB, a quantile-based gradient boosting framework, and introduce a convolution smoothed loss within it that estimates conditional quantiles for constructing dense cumulative distribution functions (CDFs), exceedance probabilities, and tail behaviour relevant to extreme outcomes. This approach preserves the computational efficiency of extreme gradient boosting while restoring the Hessian information XGBoost relies on for tree splitting, in turn providing interpretable measures of extreme value and exceedance probability predictions. We derive the gradients and Hessians needed to integrate convolution smoothed quantile loss with different kernel specifications into XGBoost, and with simulated data, benchmark this approach against alternative smoothed quantile regression losses, the native quantile objective in the XGBoost Python package, and independent versus multi-output tree estimation. The practical relevance is illustrated in an application predicting fine particulate matter (PM _2.5 ) in northern California, including periods where levels were elevated due to wildfire smoke. Our results show that convolution smoothed QXGB, particularly when paired with multi-output trees, delivers accurate predictions with near-zero quantile crossing, well-calibrated CDF and exceedance probability estimates, and useful tail characterization for extreme values. Interval estimation is also evaluated as a measure of data spread.
[LG-175] he Distributional View of Knowledge Distillation
链接: https://arxiv.org/abs/2608.15215
作者: Gordei Verbii,Juho Lee
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 11 pages, 4 figures
Abstract:Token-level knowledge distillation (KD) matches two conditional distributions per position, yet the standard objectives compare them pointwise: a Kullback-Leibler gradient is blind to which wrong token receives probability mass. We develop a distributional view in which the teacher is represented not by a single softened output but by a family of multi-temperature views - marginals of the annealing path of its logits - and the student is trained against a geometry-aware aggregate of these views under an embedding-based ground cost. We formalize the resulting design space (mixtures, log-linear pooling, entropic Wasserstein barycenters, and a debiased Sinkhorn-divergence flagship in hub and path forms), prove an exact collapse result showing log-linear pooling of tempered views is equivalent to a single temperature, and give a multi-marginal Schrodinger-bridge reading that yields falsifiable predictions. On instruction-tuned Pythia pairs, experiments yield three empirical laws: (i) dispersion law - the benefit of multi-temperature aggregation grows monotonically with the effective temperature dispersion of the views, not with their number; (ii) dispersed views unlock the aggregation operator - the barycenter separates from the arithmetic mixture exactly when transport-based aggregation starts to beat averaging; and (iii) two-regime picture governed by the ceiling gap \Gamma=\mathrmPPL_\mathrmSFT-\mathrmPPL_T : when the fine-tuned teacher barely beats a supervised student the gentle transport objective is the best KD loss but no KD beats supervised fine-tuning, whereas at a real ceiling the ranking inverts - and the sign of the fidelity-generalization correlation flips. We argue that “which distillation loss is the best” is not a fixed property of the loss but a function of \Gamma .
[LG-176] Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network
链接: https://arxiv.org/abs/2608.15198
作者: Xiaolong Wang,Xiangwen Hao,Jing Feng,Yuanyuan Liu,Yong Xu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:
Abstract:Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation neural network that maps partially observed trajectories to a Gaussian mixture distribution (GMD) over the system parameters. Unlike conventional uncertainty estimates, the GMD employs full covariance matrices to explicitly reveal parameter couplings and multi-modal likelihood structures. The network is trained by minimizing the negative log-likelihood via a surjective parameterization that hard-encodes all GMD constraints, thereby approximating the true likelihood. We validate the method on five numerical examples with increasing complexity, including systems driven by fractional Gaussian and Lévy noises, oscillators with colored noise, coupled neurons under different observability, and an aeroelastic airfoil with unidentifiable stochastic disturbances. Results demonstrate that PENN-GMD accurately recovers likelihood distributions, captures parameter couplings, and naturally diagnoses non-identifiability through variance broadening or mode splitting. These capabilities establish PENN-GMD as a practical tool for uncertainty-aware parameter identification in complex stochastic systems where conventional likelihood-based methods are infeasible.
[LG-177] Beyond Effective Sample Size: Effective Number of Proposals for Adaptive Importance Sampling
链接: https://arxiv.org/abs/2608.15154
作者: Ali Mousavi,Victor Elvira
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Population-based adaptive importance sampling (AIS) methods use a set of proposal densities to approximate complex target distributions. Their performance is commonly assessed through effective sample size (ESS) and related weight-based diagnostics, which measure the concentration of normalized importance weights. However, a large ESS only indicates that the normalized sample weights are not strongly concentrated; it does not describe how the proposal components are arranged in the sampling space. In population-based AIS, several proposal components may generate samples in the same region of the target, so the sample weights can appear well balanced even though the effective number of distinct proposal components is small. This letter introduces the effective number of proposals (ENP), a similarity-aware proposal-level diagnostic for population-based AIS. ENP combines the total normalized weight assigned to each proposal with a redundancy measure computed from similarities among target-weighted samples, estimating the number of non-redundant empirical proposal contributions to the approximation. We establish basic effective-number properties and show that ENP detects proposal collapse and duplication missed by standard ESS. We also illustrate its use as a targeted feedback signal for proposal rejuvenation. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2608.15154 [stat.ML] (or arXiv:2608.15154v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2608.15154 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ali Mousavi [view email] [v1] Sat, 15 Aug 2026 10:12:05 UTC (12 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Effective Sample Size: Effective Number of Proposals for Adaptive Importance Sampling, by Ali Mousavi and Victor ElviraView PDFHTML (experimental)TeX Source view license Current browse context: stat.ML prev | next new | recent | 2026-08 Change to browse by: cs cs.LG stat 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
[LG-178] Sufficient Dimesion Reduction via Generalized Steins Lemma
链接: https://arxiv.org/abs/2608.15121
作者: Ye Tian
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Sufficient dimension reduction (SDR) seeks the minimal subspace of the predictors that captures the full conditional distribution of the response, which is known as the central subspace (CS). When the response is multivariate, the problem becomes considerably more challenging, particularly when the sample size is limited. Existing methods face different limitations:inverse regression approaches rely on strong distributional assumptions and matrix inversion, and their multi-response extensions suffer from severe slice sparsity; forward regression methods depend on computationally intensive iterative smoothing whose cost grows with the response dimension; and deep learning-based approaches demand large amounts of labeled data. To circumvent these shortcomings, we propose an SDR framework based on the generalized Stein’s lemma. Our method constructs a cross-moment matrix between the multivariate response and the marginal score function of the predictors, and recovers the CS via its singular value decomposition. The proposed method does not rely on the linearity condition, avoids matrix inversion as well as iterative smoothing, and can leverage unlabeled data when available. We establish convergence guarantees for the proposed estimator under standard regularity conditions. Moreover, we propose a practical rank-selection algorithm to estimate the dimension of the CS. Extensive simulation studies and a real data application demonstrate that the proposed methods consistently outperform existing approaches across a variety of settings, particularly in moderate-dimensional, label-scarce scenarios with high noise levels.
[LG-179] Uncovering Hidden Leptonic Correlations with Flow Matching and Autoencoders
链接: https://arxiv.org/abs/2608.15042
作者: Haruto Kitagawa,Satsuki Nishimura,Hajime Otsuka
类目: High Energy Physics - Phenomenology (hep-ph); Machine Learning (cs.LG); High Energy Physics - Theory (hep-th)
*备注: 32 pages, 6 figures
Abstract:We perform a global search for values of the Yukawa matrices and Majorana masses in the Type-I seesaw mechanism. Using flow matching, which is a generative artificial intelligence (generative AI) method, we generate a broad set of solutions reproducing the experimentally measured values of the neutrino mass-squared differences and the mixing angles. Then, a machine learning method known as an autoencoder is applied to uncover non-trivial correlations among physical quantities in the lepton sector. Our analysis reveals new non-linear relations involving neutrino masses and CP phases. These findings may contribute to elucidating the origins of the mass hierarchies and mixing patterns among generation structure.
[LG-180] Spinning Conformal Correlators from Neural Networks
链接: https://arxiv.org/abs/2608.15001
作者: Manas Dogra,James Halverson,Joydeep Naskar
类目: High Energy Physics - Theory (hep-th); Machine Learning (cs.LG)
*备注: 20+66 pages
Abstract:We construct spinning conformal fields from neural networks and the embedding formalism, computing their two-, three- and four-point functions in examples, building on scalar conformal field techniques introduced in \citeHalverson:2024axc. For a particular ensemble of i.i.d. neurons we recover the 4d Maxwell CFT in the infinite-width limit.
[LG-181] A Deep Learning Model for Spatially Clustered Data via Differentiable Cluster Assignment
链接: https://arxiv.org/abs/2608.14968
作者: Kexuan Li,Weidong Ma
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We consider nonparametric regression when the association between a response and its covariates changes across an unknown partition of a spatial domain. The proposed estimator learns the partition and the cluster-specific regression functions jointly. A neural network depending only on location determines cluster membership, while separate neural networks describe the covariate–response relationship within the clusters. An annealed softmax relaxation permits gradient-based estimation of the otherwise discrete assignments. Graph-Laplacian and occupancy penalties are used to discourage fragmented regions and degenerate solutions. We establish identifiability up to label permutation, bound partition error under a margin condition, and decompose prediction risk into regression and assignment components. The resulting rate agrees with that of an oracle estimator when the partition is estimated sufficiently accurately. Simulations show that joint estimation is useful when regression surfaces change abruptly across spatial boundaries, including settings with nonlinear effects, unequal region sizes, preferential sampling, and spatially correlated errors. Finally, a real data analysis is provided to demonstrate the validity and effectiveness of the proposed method.
[LG-182] Developing an Offshore Machine Learning Surface Layer Scheme
链接: https://arxiv.org/abs/2608.14935
作者: Susan Dettling,Sue Ellen Haupt,Thomas Brummet,Patrick Hawbecker,Branko Kosović,David John Gagne
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注: This Work has been submitted to Artificial Intelligence for the Earth Systems
Abstract:Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning techniques for fitting the relationship for the offshore environment. To do that, data from three offshore sites are used: the Martha’s Vineyard Coastal Observatory (MVCO) air-sea interaction tower, the FINO1 research platform, and the CASPER-West FLIP research vessel deployed off the coast of California. Two machine learning methods were employed: Neural Networks (NN) and Random Forests (RF). Because the observational sites had towers with measurements at different levels, the vertical differences were input as gradients. Models were built for both momentum flux and heat flux. ML models trained at the individual sites were competitive with and in some cases, better than the physically-based COARE-3 model tailored to offshore fluxes. The heat flux ML models generally outperformed the physics-based parameterizations for most metrics, but the results were mixed for momentum flux, with only the site with the most training data (MVCO) producing results better than COARE-3. When the ML models from that site were applied to the other sites, results were degraded from using data from the site being tested. ML models built from data combined from the three sites generally showed improvements for the sites with less available training data. When assessing which variables were most important, the wind speed was most important for momentum flux and temperature gradient for heat flux.
[LG-183] When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?
链接: https://arxiv.org/abs/2608.14875
作者: Can Polat,Mustafa Kurban,Erchin Serpedin,Hasan Kurban
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Quantum Physics (quant-ph)
*备注:
Abstract:Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the 10^3 - 10^6 -atom regime; combining them assumes that a better parent yields a better parameterization, but we show it does not. Current-generation functionals are orbital-dependent generalized Kohn-Sham operators, whereas the parameterization channel is built on a multiplicative potential, preventing exact representation. Using the transfer ratio, the surviving fraction of a parent-level change, we find anti-transfer: coherently negative ratios across four covalent semiconductors move the gap in the wrong direction, consistent with a molecular proxy and an r ^2 SCAN control. The minimal-basis overgap is dominated by the on-site convention rather than basis incompleteness; correcting the on-site block removes most of it, while one d -polarization shell closes a further 16 - 40% , depending on the placement of the empty d level, which no free-atom eigenvalue uniquely fixes. Occupied-manifold enhancements, ionic and closed-shell repulsive potentials, and rocksalt-oxide gaps inherit, whereas elemental and III-V covalent networks inherit neither gaps nor repulsive potentials and oxide networks inherit only the latter. We screen 23 elements and release the parameter sets, showing that the transfer ratio provides a cheap pre-test before any parameterization campaign.
[LG-184] ARISE: An adaptive residual-informed stability ensemble for feature selection in small-sample biomedical omics
链接: https://arxiv.org/abs/2608.14866
作者: Zardad Khan,Amjad Ali,Naz Gul,Sheema Gul,Saeed Aldahmani
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 30 pages, 6 figures
Abstract:Objective: Small-sample molecular classification requires feature selectors that identify predictive, stable, and nonredundant subsets for binary and multiclass outcomes. We propose ARISE (Adaptive Residual-Informed Stability Ensemble), which integrates complementary relevance signals, class-balanced stability assessment, residual-informed redundancy control, and multiclass pairwise coverage. Methods: ARISE combines seven percentile-normalized relevance components through 15 predefined profiles, adaptively weighted by nested inner cross-validation. It was evaluated on five molecular datasets, eight feature-set sizes, three fixed classifiers (k-nearest neighbours, support vector machine, and random forest), and six filter comparators. Generalization was estimated by five-fold outer cross-validation repeated 50 times using balanced accuracy, macro-F1, and Cohen’s kappa. Results: Across 210,000 held-out assessments, ARISE ranked first in all 15 dataset-metric combinations. Equal-dataset means were 0.793 for balanced accuracy, 0.776 for macro-F1, and 0.725 for kappa, exceeding the strongest aggregate comparator by 0.022, 0.023, and 0.028, respectively. Performance remained strong across compact feature sets, although the optimal budget differed by dataset. Conclusion: ARISE provides a transparent, adaptive framework that jointly addresses relevance, stability, redundancy, and multiclass discrimination. Its consistent results across datasets, classifiers, metrics, and feature-set sizes support further evaluation for small-sample molecular classification. Comments: 30 pages, 6 figures Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2608.14866 [stat.ML] (or arXiv:2608.14866v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2608.14866 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Amjad Ali [view email] [v1] Fri, 14 Aug 2026 20:10:22 UTC (3,019 KB)
[LG-185] Explainability Boosted Anomaly Detection Framework for O-RAN based NextG Networks
链接: https://arxiv.org/abs/2608.14826
作者: Nurullah Aksu,Ali Fuat Sahin,Semiha Tedik Başaran
类目: ignal Processing (eess.SP); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted in IEEE WCNC 2026, Copyright IEEE
Abstract:The wireless networks have historically faced significant security vulnerabilities, necessitating advanced anomaly detection mechanisms, especially as networks evolve towards 6G and beyond. This study introduces an advanced anomaly detection framework that leverages explainable artificial intelligence to enhance the security of next-generation (NextG) cellular networks. By implementing and evaluating a variety of artificial intelligence models, the framework demonstrates high accuracy and efficient runtime performance in identifying malicious traffic within a realistic Open Radio Access Network (O-RAN) testbed. A key innovation of this work is the integration of post-hoc explainability methods to identify the most critical key performance metrics (KPMs), which enables a significant 80% reduction in dataset complexity without compromising detection accuracy. Additionally, explainability analyses identify several critical attack traffic characteristics, such as protocol type, bandwidth, interval, and duration, to prevent upcoming network attacks. The resulting framework effectively balances computational efficiency, accuracy, and explainability, underscoring its practical applicability for enhancing security in next-generation cellular networks.
[LG-186] A Parameter-Free Few-Shot Evaluation for Elephant Vocalisation Classification
链接: https://arxiv.org/abs/2608.14824
作者: Christiaan M. Geldenhuys,Thomas R. Niesler
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD); Quantitative Methods (q-bio.QM)
*备注:
Abstract:We present a parameter-free episodic evaluation of nearest-centroid classification for elephant vocalisations on fixed pretrained acoustic embeddings, across the Elephant Voices (EV) and Linguistic Data Consortium (LDC) datasets. Rather than asking which embedding yields the best classifier when trained on all available labelled data, we ask how the simplest classifier performs as labelled exemplars per class are varied. Each class is represented by the mean of its support-set embeddings, and each query is assigned to the nearest centroid under squared Euclidean distance. We evaluate this centroid classifier on the Perch (ver. 1), Perch (ver. 2), and HuBERT (base, layer 2) embeddings, together with mel frequency cepstral coefficient (MFCC) features, in an N-way k-shot manner under the same cross-validation protocol as the trained baselines. A bootstrap over 100 resampled support sets quantifies the sampling noise. On the smaller, low-resource EV dataset, the centroid classifier using the stronger Perch (ver. 1) and Perch (ver. 2) embeddings overtakes the fully-trained logistic regression classifier from a single exemplar per class and the stronger recurrent classifier from two. Over the reduced set of call types on which the strongly-supervised end-to-end baseline was trained, the centroid classifier matches and then surpasses that baseline in mean average precision (mAP), from a few exemplars per class. On the larger LDC dataset, where labelled exemplars are abundant, the trained baselines retain their advantage at every k considered. At five exemplars per class, the centroid classifier using the strongest embedding, Perch (ver. 2), attains a mAP of 0.542 on the EV dataset and 0.368 on the LDC dataset. Parameter-free nearest-centroid classification is the stronger choice when labelled exemplars are few and the fixed embedding already encodes the features that separate the call types.
[LG-187] he Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data
链接: https://arxiv.org/abs/2608.14756
作者: Jiajie Chen,Jinfeng Li
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Sound (cs.SD)
*备注: 30 pages, 10 figures
Abstract:Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physical violations (independent energy/duration models produce impossible states like 50 kWh in 10 min on a 7 kW charger), and collider bias (clustering on post-treatment outcomes opens backdoor paths for price elasticity). We propose the Note-Chord-Voice framework, a music-inspired, axiom-driven pipeline that separates data cleaning (Repair Chords), structural pattern discovery (Harmonic Chords), descriptive source separation (NMF Voices), and causal inference into distinct, falsifiable stages. Key innovations: (i) falsification gates (A1-A5, G3, G10) that test data suitability before modeling; (ii) Gamma-initialized NMF with input rescaling for convergence stability from STL decomposition; (iii) tag-based coupon grading (A/B/C/D) to isolate quasi-random treatment from night-time confounders and targeted promotions; (iv) separate per-voice OLS to avoid simplex collinearity; (v) Foote novelty curves for structural regime detection. Applied to the Jiangmen dataset (495,707 sessions, 20 stations, from July 2024 to March 2025), all core axioms pass except G3 (no strong 168 h cycle). NMF achieves R^2=0.9921; the physically constrained duration model yields aggregate R^2=0.5409. Two voices are price-sensitive (beta = -11 to -14 min, p0.001), of which one is stable (Voice 3, beta=-14.16) and one treatment-driven (Voice 1, beta=-11.10); only the stable voice supports causal claims. Counterfactual simulation shows targeting discounts to price-sensitive voices recovers 52.8% of discount expenditures (~0.85M CNY/year); restricting to the single stable price-sensitive voice yields a more conservative estimate.
[LG-188] Koopman early warning signals for bifurcation and rate-induced tipping
链接: https://arxiv.org/abs/2608.14716
作者: Juan Nathaniel,Carla Roesch,Derek DeSantis,Parvathi Kooloth,Hang Fan,Valerio Lucarini,Anastasia Romanou,Pierre Gentine
类目: Chaotic Dynamics (nlin.CD); Machine Learning (cs.LG)
*备注:
Abstract:Abrupt transitions in complex systems are often preceded by early warning signals. However, most indicators rely on the notion of critical slowing down and do not generally extend to rate-induced tipping where transitions can occur without local loss of stability. This is problematic in stochastic, nonautonomous systems where internal variability and time-varying variables interact to shape tipping onset. We use Koopman operator theory to develop a unified early warning framework for both bifurcation and rate-induced tipping in stochastic systems. Our approach builds on residual Koopman mode decomposition that measures discrepancies between dynamics and their finite-dimensional approximation, and extends it to the control setting by augmenting the observable space with time-varying control variables. In idealized examples, the resulting indicators recover expected signatures near bifurcation points and improve detection in rate-induced regimes where classical indicators fail. We further show that learned embeddings through deep learning outperform prescribed dictionaries, especially in a high-dimensional setting. Applied to simulations of the Atlantic Meridional Overturning Circulation, our Koopman-based indicators distinguish tipping from non-tipping trajectories and reveal interpretable spectral signatures prior to critical transition.
[LG-189] A Low-Cost IoT Device for Environmental Monitoring and Embedded Solar Forecasting with On-Device Incremental Learning
链接: https://arxiv.org/abs/2608.14698
作者: Erick Michel Lara Pinal,Abhinav Das,Stephan Schlüter
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding 1000~USD per node, making distributed deployments economically inaccessible. This work presents a modular internet of things (IoT) device based on the ESP32 microcontroller integrating temperature, humidity, luminosity, and solar irradiance sensors in an IP68-rated enclosure at a total hardware costs of about \ 65~USD when components are sourced in Germany. A hybrid architecture decouples external model training, performed on a conventional computer using the software Python and the open-source library TensorFlow, from autonomous 24-hour solar voltage forecasting executed on-device via a three-layer feedforward network with 3,011 parameters (11.8,KB). The network is trained offline on site-collected data and deployed on the microcontroller as static weight matrices without cloud connectivity. An on-device incremental gradient descent mechanism enables continuous model adaptation after deployment without external retraining. The system was evaluated through two field deployments: a short period of hardware and firmware validation in Ulm, Germany, and a 115-day deployment in Zapopan, Mexico, comprising 84~days of training and 31~days of autonomous operation with zero missing records. Over a clean 28-day daytime window, the embedded model attained a coefficient of determination of 0.9165 and a mean absolute error of 0.2975~V (4.65% of the operational range), outperforming a climatology baseline (skill score 0.64) while not surpassing a 24-hour persistence baseline. A frozen-weight ablation confirms that the on-device update mechanism yields a small but statistically robust accuracy gain ( p = 0.001 ), demonstrating that autonomous incremental learning is feasible on low-cost hardware without cloud connectivity.
[LG-190] Phase-Aware CNN for Real-Time 5G/6G Channel Estimation with Hardware-in-the-loop Validation
链接: https://arxiv.org/abs/2608.14676
作者: Javad Zolfaghari-Bengar,Rakibul Rony,Elisa Gomez-de-Lope,Alejandro Villena-Rodriguez,Abhinav Mahadevan,Nicolas Kourtellis
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: Accepted at the IEEE Conference on Standards for Communications and Networking (CSCN) 2026
Abstract:In 5G/6G wireless systems, accurate and timely channel estimation is critical to ensure reliable communication under complex, fast-changing radio conditions. This work focuses on pilot-based channel estimation using deep learning to reconstruct both magnitude and phase across the full subcarrier grid, with particular emphasis on evaluation using emulated data collected from an end-to-end O-RAN testbed. The testbed includes hardware in the loop and controlled channel emulation to better reflect deployment conditions beyond pure software simulation. It addresses major limitations in classical estimators such as LS and MMSE, as well as deep learning-based approaches that struggle with phase prediction due to discontinuities at \pm \pi , poor generalization to different UE and antenna configurations, and computational inefficiency for real-time deployment. The proposed system combines a phase-aware input encoding using sine and cosine representations with a lightweight Convolutional Neural Network (CNN) architecture. This design achieves high accuracy, stable phase reconstruction, strong generalization across testbed-derived datasets, and real-time inference suitable for edge devices.
[LG-191] Wolff-Parkinson-White Detection at 471:1 Class Imbalance: A Leakage-Controlled Study of the Data Bottleneck ALT
链接: https://arxiv.org/abs/2608.14633
作者: Nathael Altman
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 36 pages, 7 figures. Code, frozen models, out-of-fold scores and the full decision log: this https URL
Abstract:Wolff-Parkinson-White (WPW) syndrome is a congenital cardiac pre-excitation, clinically important and often missed on the resting 12-lead ECG. Detection is hard: the signature is subtle and the condition rare. We pool two public 12-lead corpora, PTB-XL and Chapman-Shaoxing-Ningbo: 66,951 recordings, 142 of them WPW, a prevalence of 0.21% (about 471:1). Under one pre-specified, leakage-controlled protocol, with a held-out fold contacted exactly once, we compare seven representations of the signal, holding the split and the evaluation fixed. Within these corpora and under a modest compute budget, added diversity and capacity do not raise the ceiling: the most orthogonal detector significantly hurts, a feature-union model matches a two-member vote, a convolutional network reaches the wavelet detector without exceeding it, and self-supervised pretraining fails a pre-specified gate. A leak-free learning curve, re-selecting features at every size, still rises at the full 115 positives for the strongest deployed detector (paired 90-to-100% difference +0.027, 95% CI [0.019, 0.033]), so it is not shown to have saturated. An error analysis tested against independent evidence finds that the missed cases have a narrower QRS, confirmed by an on-machine measurement outside our pipeline after we show the sign of this effect depends on which delineator measures it; that uncertain labels show no enrichment among the misses; and that some apparent false positives are recordings the corpus itself codes as pre-excited, placing part of the label problem in the negative class. We measure the optimism of non-nested selection at 0.11 to 0.13 average precision. The deployed output is a percentile rank in a frozen reference distribution, not a probability. On the held-out fold, on 14 positives, it reaches an average precision of 0.595 and an ROC area of 0.950. It is a screening pre-filter, not a diagnostic tool.
[LG-192] 6G Native AI and Channel Foundation Models
链接: https://arxiv.org/abs/2608.14591
作者: Shugong Xu,Jun Jiang,Yuan Gao
类目: ignal Processing (eess.SP); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: Awesome GitHub: this https URL
Abstract:The integration of artificial intelligence (AI) and wireless communications is widely regarded as a core objective of sixth-generation (6G) systems. However, both the meaning of native AI and the type of AI capability that should be embedded into future wireless systems remain open to interpretation. This paper discusses 6G native AI from a system-design perspective and argues that native AI should be co-designed, optimized, and deployed as an intrinsic component of the wireless system rather than as a removable post-deployment add-on. From this perspective, conventional task-specific supervised models are difficult to use as the main technical basis of native AI because they depend heavily on labeled data, generalize poorly across propagation conditions, and require fragmented designs for different channel-related tasks. Motivated by these limitations, we position channel foundation models (CFMs) as a channel-centric foundation-model paradigm for 6G native AI. We define the scope of CFMs, clarify their differences from task-specific wireless AI models and large language models, and summarize three pretraining families: generative, discriminative, and hybrid pretraining. We further discuss how CFMs may support physical-layer processing, radio access network intelligence, and integrated sensing and communications. Preliminary CSI-CLIP-based results are included as bounded evidence that CFM-style pretraining can improve positioning and beam prediction when task-specific labels are limited.
[LG-193] Characterization of Thermal Systems from Noisy and Low-resolution Measurements Using Dynamic Mode Decomposition
链接: https://arxiv.org/abs/2608.14581
作者: M. E. P. Silva,L. S. Araujo,F. T. Colombo,A. Cunha Jr,S. da Silva
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Signal Processing (eess.SP); Classical Physics (physics.class-ph)
*备注:
Abstract:Thermal monitoring in practical applications is often constrained by sparse sensing, measurement noise, and limited spatial resolution, which hinder the identification of heat transfer dynamics. In such settings, calibrating high-fidelity physical models is computationally demanding, motivating data-driven approaches. Dynamic Mode Decomposition (DMD) provides a framework for extracting spatiotemporal structures from measurement data, but its standard formulation is sensitive to noise and degraded observations. This chapter examines the use of DMD under these constraints, focusing on preprocessing and truncation strategies that affect stability and interpretability. Two cases are considered: forced convection with thermocouple data and transient heat conduction from degraded thermal images. The number of retained modes is treated as a modeling parameter that governs the trade-off between reconstruction fidelity and noise sensitivity. The results indicate that DMD recovers dominant thermal behavior from both sparse and degraded datasets when the truncation level is appropriately selected. Low-rank models provide stable but simplified descriptions, while higher-rank models improve spatial detail at the cost of increased noise sensitivity.
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