本篇博文主要内容为 2026-11-02 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 (2026-11-02)
今日共更新758篇论文,其中:
- 自然语言处理共87篇(Computation and Language (cs.CL))
- 人工智能共187篇(Artificial Intelligence (cs.AI))
- 计算机视觉共102篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共200篇(Machine Learning (cs.LG))
- 多智能体系统共11篇(Multiagent Systems (cs.MA))
- 信息检索共22篇(Information Retrieval (cs.IR))
- 人机交互共20篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Strategic Tanking in Stable Matching Markets
【速读】：该论文旨在解决在稳定匹配（stable matching）框架下，个体如何通过非直接的偏好操纵手段实现自身匹配结果优化的问题。传统研究中，代理人通常通过虚假报告偏好顺序来操纵匹配结果，而本文提出“战略性削弱”（strategic tanking）这一新机制：即代理人主动降低自己对某些潜在匹配对象的吸引力，从而影响对方对自己的排序，进而在不改变自身真实偏好的前提下实现更优匹配。其解决方案的关键在于揭示了“截断式削弱”（cutoff tanking）与经典偏好截断（truncation）在稳定匹配结果上的等价性——即当代理人对低于某一阈值的所有潜在伙伴进行削弱时，其效果等同于在自身偏好列表中截断该阈值以下的选项。然而，任意削弱（arbitrary tanking）则具有更强的策略灵活性，在提议者延迟接受（proposer-deferred acceptance）机制下，均衡匹配可能超出诚实匹配的稳定集（sincere stable set）。因此，即使一方被强制诚实报告偏好，只要真诚稳定集包含多个匹配结果，仍可通过传统偏好操纵或削弱行为获得收益，表明战略削弱是比传统操纵更具扩展性的有效策略工具。
链接: https://arxiv.org/abs/2610.42105
作者: James P. Bailey
机构: Rensselaer Polytechnic Institute (伦斯勒理工学院)
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注:
Abstract:Motivated by a broad literature in psychology, organizational behavior, and matching markets, we introduce strategic tanking: an agent deliberately makes themselves less attractive to selected potential partners in order to improve their eventual assignment. Unlike traditional manipulation in stable matching, a tanker does not misreport whom they prefer; instead, they influence how others rank them. We show that tanking has a striking connection to classical preference manipulation. Cutoff tanking, in which an agent tanks with every potential partner below a chosen threshold, has exactly the same effect on the stable set as truncating one’s own preference list at that threshold. Thus an agent can reproduce the power of a classical truncation while leaving their own preferences unchanged and every match acceptable. Arbitrary tanking, however, can go substantially further: under proposer-deferred acceptance, tanking equilibria can lie outside the sincere stable set. More broadly, whenever the sincere stable set contains multiple matchings, profitable strategic behavior remains available through either traditional preference manipulation or tanking, even when one side is restricted to honestly reporting their own preferences.
[MA-1] Adaptive Coverag e Algorithms for Mobile Ad Hoc Networks in Dynamic Worlds
【速读】：该论文旨在解决多智能体系统在未知环境中进行通信网络覆盖时面临的动态不稳定性问题。传统问题——部分可观测协作守卫艺术馆问题（Partially Observable Cooperative Guard Art Gallery Problem, POCGAGP）——假设环境静态且已知，但无法应对实际部署中常见的不确定性。为此，本文提出动态情形下的扩展版本（DPOCGAGP），引入“智能体丢失”（Agent Loss）与“世界变化”（World Change）两类动态扰动，以模拟真实场景中的不可预测性。解决方案的关键在于对两种先进算法——集中式智能体引导探索网络覆盖（CADENCE）及其去中心化变体（DADENCE）——进行改进，使其具备在动态环境下维持网络连通性和覆盖完整性的能力。实验结果表明，在包含超过4500个不同规模与结构测试案例的基准集上，改进后的算法性能显著优于现有最优基准算法，验证了其在动态复杂环境中的鲁棒性与有效性。
链接: https://arxiv.org/abs/2610.41936
作者: Hugo Gandhi,Edwin Meriaux,Antonio Loría
机构: CentraleSupélec(中央理工-高等电力学院); CNRS(法国国家科学研究中心)
类目: Robotics (cs.RO); Multiagent Systems (cs.MA)
备注: 8 pages, 10 figures. Accepted to the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC)
Abstract:This paper extends an existing framework for the coordination of mobile agents in unknown worlds, by considering the possibility of dynamic instability. In the original problem, known as the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP) (arXiv:2512.09495), the multi-agent system must establish a communication network which completely covers an initially unknown constrained space. However, the POCGAGP fails to account for the unpredictability often seen in real-world deployment. To address this, the dynamic case (DPOCGAGP) introduces two settings which can influence the mobile network during deployment: Agent Loss and World Change, which represent dynamic changes to the world. Within this study, we present advancements to two state-of-the-art algorithms, the Centralized Agent-Directed Exploration for Network Coverage (CADENCE), and its decentralized analogue, DADENCE, to provide solutions in these cases. We then compare their performance against another state-of-the-art benchmark algorithm, and show that the adapted algorithms outperform across our benchmark test set, which comprises over 4500 test cases of varied size and structure.
[MA-2] AI evaluators favor job applications written by certain AI models independent of candidate quality
【速读】：该论文旨在解决在全自动化招聘场景中，生成式 AI（Generative AI）模型的选择是否会在不考虑候选人实际资质的情况下影响录用结果这一关键问题。其核心解决方案在于构建了一个多模型审计框架，将11个由不同大语言模型（LLM）驱动的求职信撰写代理与9个通过API托管的评估代理相结合，针对10份职位描述和每份职位50名候选人进行系统性测试。研究发现，撰写模型存在显著的“生成偏好层级”：无论模型许可证类型如何，本地部署模型均系统性处于劣势，且评估模型普遍存在“自我偏好偏差”，倾向于优先录用自身生成的内容。此外，评估模型在对工具选择引入的偏差程度上表现出显著差异，为设计更公平的筛选机制提供了可操作指导。该偏好层级带来了可量化的竞争后果——在最不利的模型匹配组合下，约有1.8名合格候选人因模型适配不当而被错误过滤。进一步分析表明，上述现象可归因于一个共同机制：撰写模型对特定风格特征的依赖程度与评估模型对该类特征的奖惩强度之间存在高度关联，而一旦控制这些显性特征，便无显著证据表明存在未测量的“撰写者指纹”。研究揭示了生成式 AI 在招聘中的算法公平性风险，并强调需建立可随模型生态演进而复用的审计框架，以应对AI辅助筛选的监管挑战。
链接: https://arxiv.org/abs/2610.41866
作者: Nicolò Pagan,Michael Andreas Ebling,Stefania Ionescu,Anikó Hannák
机构: Universität Zürich(苏黎世大学); ETH Zürich(苏黎世联邦理工学院)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Job applicants increasingly use large language models (LLMs) to write cover letters, while employers deploy LLMs to screen them. In this all-AI setting, we ask whether the \emphchoice of model affects hiring outcomes independently of candidate qualifications. We introduce a multi-model auditing framework pairing eleven LLM writer agents with nine API-hosted evaluator agents across ten job descriptions and fifty candidates per job. Four findings emerge. First, we identify a generative preference hierarchy: a consistent global ranking of writer models by evaluator acceptance, in which locally hosted models are systematically disadvantaged regardless of their license, compounded by self-preference bias where evaluators favour their own outputs. Second, evaluator models differ substantially in how much bias they introduce based on applicants’ tool choice, offering actionable guidance for fairer screening design. Third, this hierarchy has measurable competitive consequences: in the most adverse pairing, approximately two (1.8) out of twenty-five qualified candidates are filtered out purely due to model mismatch. Fourth, a common mechanism explains the patterns above: writer preference and self-preference trace to a combination of how much each writer leans on specific stylistic cues and how strongly evaluators, often unanimously, reward or penalise those same cues. A further check finds little evidence that an unmeasured “writer fingerprint” remains once these same features are accounted for. We discuss implications for algorithmic fairness in employment, regulation of AI-assisted screening, and the design of auditing frameworks that can be reapplied as the model landscape evolves.
[MA-3] ORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement
【速读】：该论文旨在解决6G网络切片在跨管理域部署中面临的在线决策难题，即针对每个到达的请求，需协同决定虚拟网络功能（VNF）的归属域、流量跨域传输路径以及切片是否被接纳。该联合决策问题在连续请求场景下属于NP-hard问题。实验表明，不存在一种固定的规划策略能在所有拓扑环境下保持最优性能，不同拓扑类别间性能排名会发生反转，且多数被拒绝请求实际上存在可行部署方案但未被发现，这揭示了“规划层”是影响系统性能的关键杠杆。为此，论文提出ORION——一个分层编排架构，其中两个基于大语言模型（LLM）的智能体将服务意图转化为结构化请求并生成抽象部署计划；一个协作式多智能体强化学习（MARL）协调器负责跨域划分，将计划作为持久的软先验；各域内智能体则执行具体功能部署。ORION的核心创新在于规划层引入了一种以结果为导向的周期性记忆机制，该机制记录每次尝试的网络状态特征、所提计划及其验证后的可行性结果，并将已验证的成功与标注失败反馈回规划器，从而基于实际部署可行性而非规划器自身提案的标量奖励实现自适应闭环优化。为隔离规划质量的影响，研究在保持下游选择与执行机制不变的前提下，在保留的拓扑族上评估规划器性能：结果表明，引入该记忆机制后，相较于无记忆的同一规划器，接纳率提升4.6%至11.5%，且基于重要性的记忆保留策略贡献了大部分性能增益，优于先进先出存储方式。
链接: https://arxiv.org/abs/2610.41563
作者: Mahmoud S. Mayaleh,Fetia Bannour,Nour-El-Houda Yellas
机构: SAMOVAR, Télécom SudParis; Institut Polytechnique de Paris; LIX, École polytechnique
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI)
备注: Accepted at IEEE MobiWac 2026
Abstract:Deploying 6G network slices across administrative domains requires an online decision for each arriving request: which domain hosts each virtual network function, how traffic crosses domain boundaries, and whether the slice is admitted. This joint decision problem is NP-hard under continuous arrivals. Experiments on a parametrized family of multi-domain topologies show that no fixed planning strategy dominates across all regimes: the performance ranking reverses between topology classes, and feasible placements exist for most rejected requests but remain undiscovered. These findings identify the plan layer as the decisive performance lever. We introduce ORION, a hierarchical orchestrator in which two LLM agents translate service intents into structured requests and generate abstract placement plans. A cooperative multi-agent RL coordinator selects the cross-domain partition, treating the plan as a persistent soft prior, while per-domain agents place the functions. ORION main contribution lies at the plan layer: an outcome-driven episodic memory that records, for each completed attempt, the network-condition profile, the proposed plan, and its verified feasibility outcome. The memory feeds verified successes and labelled failures back to the planner, thereby closing the adaptation loop using deployment feasibility rather than a scalar reward assigned to the planner’s own proposals. To isolate the effect of the plan quality, we evaluate the planner on held-out topology families while keeping the downstream selection and execution mechanisms fixed. Under this controlled setting, the proposed memory improves admission by 4.6 – 11.5 percentage points over the same planner without memory, while importance-based retention accounts for most of the improvement over a first-in-first-out store.
[MA-4] When Disclosure Is Compulsory: Evaluating Ride-Hailing Rebalancing Policies with Self-Interested LLM Driver Agents
【速读】：该论文旨在解决欧盟平台工作指令（Platform Work Directive）在实施后对网约车平台动态调度机制透明化要求所带来的影响问题，特别是如何评估在强制披露自动化决策规则背景下，不同再平衡策略（rebalancing policy）对司机、平台及用户三方利益的差异化影响。其核心解决方案在于构建一个基于大语言模型（Large Language Model, LLM）代理的仿真框架，模拟司机在规则公开条件下以自利为目标的行为响应。关键创新点在于：将平台的自动化决策规则以可读形式输入至LLM代理中，使司机能够根据规则自主优化行为路径；通过对比反应式策略（R）与前瞻式策略（A）在规则披露前后的表现差异，揭示出即使在完全合规下绩效相近的两种策略，在规则公开后可能产生截然相反的结果——例如，反应式策略因需支付司机原本免费执行的移动成本而致平台利润下降3.7%，而前瞻式策略则保持稳定，且在曼哈顿场景下实现司机收入、平台利润与服务率的“三赢”提升。这一研究首次将生成式AI（Generative AI）引入平台治理评估，为新监管环境下政策效果的量化分析提供了可操作的评估框架。
链接: https://arxiv.org/abs/2610.41132
作者: Xinyu Wang,Andres Fielbaum
机构: The University of Sydney (悉尼大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:The EU Platform Work Directive, to be transposed by December 2026, requires ride-hailing platforms to state in plain language the main parameters of their automated decisions and how a worker’s behaviour affects them. Such rules are exactly what a large language model (LLM) can read, so drivers will be able to let one decide for them. We compare two main families of rebalancing policy once their rules are public: a reactive policy ® that sends idle drivers to cover requests just lost, and an anticipatory policy (A) that plans moves on a demand forecast. Each driver is an LLM agent maximizing its own net income, given no rules (N) or the rules at the directive’s level of detail (L). We test two LLMs, Claude Haiku 5.5 and Jev, which understand the rules equally well, in a three-zone model and in Manhattan. With the rules disclosed, drivers follow the platform more closely: in Manhattan, the share of relocation offers they decline falls from about four in five to one in four under both policies. Yet the effects differ by policy. For the platform, disclosure costs R 3.7% of its profit in Manhattan, as R now pays for moves drivers used to make for free, while A’s profit is unchanged; the two policies, which tie on profit under full compliance, end 6.1% apart. For drivers, disclosure raises income in the small model under both policies and LLMs; for users, it raises the service rate there by 10 to 18 percentage points. In Manhattan, self-interested drivers also make A a win-win-win against full compliance, raising driver income, platform profit and service rate by 10 to 12% each. Rebalancing policies that perform equally well under full compliance can thus show opposite results under mandatory disclosure. The study offers a framework to evaluate such policies under the new requirement and is, to our knowledge, the first to do so with LLM agents.
[MA-5] A Robust Approach to Belief Mismatch in Non-cooperative Dynamic Games
【速读】：该论文旨在解决多智能体控制与机器人应用中博弈论模型在动态、不完全信息场景下因理论与计算复杂性导致的不可行性问题。现有方法为降低计算复杂度，通常假设智能体共享传感器测量并维持对状态不确定性的共同估计，但这在本质上强制了非合作博弈中的协作行为，违背了实际场景的非合作特性。本文从“自车”（ego agent）视角出发，兼顾计算可处理性与对其他智能体因私有传感器测量导致状态估计偏差所引发的意外行为的鲁棒性。其核心解决方案是构建一个带有扰动贝叶斯动力学的增强信念空间（augmented belief space），使自车能够预测各智能体潜在的分歧状态估计。这些扰动由一个假想的对抗性“自然”（nature）选择，以反映其他智能体基于自车无法观测的测量所形成的估计偏离自车信念的可能性。该方法在精神上扩展了经典的H-infinity鲁棒控制思想，适用于非合作、随机动态博弈场景。文中提出的求解器具有多项式时间迭代复杂度，并通过两个实验验证：基于鲁棒信念空间推理的自车能主动限制未来对其他智能体信念与行为建模错误的脆弱性，性能退化降低超过48%。
链接: https://arxiv.org/abs/2610.40999
作者: Ryan Park,Li-huan Shen,Jesse Milzman,Negar Mehr,David Fridovich-Keil
机构: University of California, Berkeley (加州大学伯克利分校); U.S. Army Research Laboratory (美国陆军研究实验室); University of Texas, Austin (德克萨斯大学奥斯汀分校)
类目: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
备注:
Abstract:Game-theoretic models are increasingly used to describe multi-agent interactions in control and robotics applications. However, theoretical and computational complexity makes these models intractable in many dynamic, incomplete information scenarios. Recent work makes strong assumptions to reduce computational complexity: agents share sensor measurements with one another and maintain a common estimate of state uncertainty. Unfortunately, these assumptions enforce a degree of cooperation in generically noncooperative interactions. This paper takes the perspective of an ego agent seeking both computational tractability and robustness to unanticipated behavior of other agents due to mismatched state estimates from private—rather than shared—sensor measurements. To that end, we formulate the game theoretic planning problem in an augmented belief space with perturbed Bayesian dynamics that allows the ego to predict all agents’ potentially divergent state estimates. These perturbations are chosen by a hypothetical adversary, ``nature,‘’ to reflect how non-ego agents’ state estimates—based on measurements the ego agent will not see at runtime—may deviate from the ego’s own belief. Ultimately, our approach can be understood in the spirit of classical H-infinity robust control, extended to noncooperative, stochastic dynamic games. We present a solver with polynomial-time iterations and conduct two experiments which show how robust belief space reasoning leads ego agents to proactively limit their future vulnerability to model misspecification of other agents’ beliefs and actions, reducing performance degradation by over 48%.
[MA-6] Renegotiable Options Fail to Prevent Hold-Up Between Two Profit-Maximizing Frontier LLM Agents
【速读】：该论文试图解决的核心问题是：在存在“敲竹杠”（hold-up）问题的交易情境中，原本能够激励人类参与者增加投资的合同条款（如可重新谈判的期权），是否同样能有效激励以利润最大化为目标的语言模型代理（language-model agents）进行投资。其关键在于检验合同机制在人类与人工智能代理之间的有效性差异——尽管在人类实验中，可重新谈判的期权显著提升了投资水平（从41.5%提升至67.2%），但在由Claude Opus 5.5和GPT-6.1 Sol等生成式AI代理进行的一次性博弈实验中，这些合同条款并未产生相同效果。研究发现，当代理被设定为追求自身利润最大化时，它们遵循了经典博弈论预测：在可重新谈判的期权下，卖方因预见到买方可能放弃期权并提出更低报价而选择不适应；而在不可重新谈判的期权下，卖方则会适应。进一步实验表明，即使移除利润指令或调整参数使适应不再带来收益，代理也不会盲目适应。因此，该研究的关键结论是：一种在人类间有效提升投资的合同机制，在以利润最大化为导向的语言模型代理之间并不具备同样的激励效力，这揭示了人类行为与理性代理在合同响应机制上的根本差异。
链接: https://arxiv.org/abs/2610.40961
作者: Junwei Yao,Zike Yang,Yongxi Zhou,Yanxian Chen,Jianan Liu,Jiaxi Wen,Zhijun Wang
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA)
备注:
Abstract:Do contract terms that protect investment between people also protect it between profit-maximizing language-model agents? In the hold-up problem, a seller can adapt a unit to one buyer at a cost before the price is set. Once that cost is sunk, the buyer can offer almost nothing and the seller will still accept, so a seller who foresees this does not adapt. One remedy is an option: the buyer may buy at a fixed price. In theory the option stops working if the buyer can let it lapse and offer its own price instead. Yet in a laboratory experiment by Hoppe and Schmitz, this renegotiable option raised human investment from 41.5% of pairs without a contract to 67.2%. We had Claude Opus 5.5 and GPT-6.1 Sol, each told to maximize its side’s profit, play a one-shot game with the same strategic structure under their four contract regimes and a reference-price control. The agents did what game theory predicts: under the renegotiable option no seller adapted, and under the option that rules out this renegotiation every seller did. In a second experiment in which sellers no longer stated their expected revenue before deciding, removing the profit instruction left investment unchanged under no contract and both options. When we changed the numbers so that the buyer would exercise the option even without adaptation, adapting only added cost, and no seller adapted: the agents did not simply adapt whenever an option was offered or whenever its price covered the cost. Under our protocol, a contract term that raised investment between people did not protect it between agents.
[MA-7] Agent ic World Modeling for Agent Test-time Scaling without Environment Replica
【速读】：该论文旨在解决生成式大语言模型智能体在真实部署场景中进行测试时缩放（Test-time Scaling, TTS）所面临的挑战，即传统TTS方法依赖于并行执行多条候选轨迹的假设在共享、不可重置且存在不可逆副作用的实际环境中难以成立。其核心问题在于：如何在不重复创建或修改共享环境状态的前提下，实现对多个潜在行为轨迹的安全、高效模拟与评估。解决方案的关键是提出代理世界建模（Agentic World Modeling, AWM），将下一观测预测重构为一个多轮证据获取任务；针对每个假设动作，AWM通过安全的只读接口自适应地查询真实环境，基于返回的证据进行推理，并利用可执行计算构建结果观测，而无需改变共享状态。该方法实现了在单一共享环境上进行轨迹级并行探索，克服了传统生成式世界模型因压缩环境状态至单个提示而导致的部分可观测性下过渡模拟不可靠的问题。实验表明，在τ-bench、AppWorld和OfficeBench三个基准上，AWM相比单次仿真基线平均提升40.07%，相比训练好的世界模型基线平均提升12.36%，验证了交互式、读取驱动的模拟作为安全且可扩展的代理测试时缩放基础的可行性。
链接: https://arxiv.org/abs/2610.40927
作者: Wendi Li,Huseyin A. Inan,Wei-Ning Chen,Yanzhi Chen,Chetan Bansal,Saravan Rajmohan,Sharon Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注:
Abstract:Test-time scaling (TTS) improves large language model agents by sampling and selecting among multiple candidate trajectories, but common realizations assume that these trajectories can be executed in parallel. This assumption breaks in realistic deployments, where enterprise workspaces and cloud services are shared and persistent, costly or infeasible to replicate, and actions may introduce irreversible side effects or cross-trajectory interference. Existing generative world models avoid direct execution, but typically compress environment state into a single prompt, limiting their ability to reliably simulate transitions under partial observability. Instead, we introduce Agentic World Modeling (AWM), which reformulates next-observation prediction as a multi-turn evidence-acquisition task. For each hypothetical action, an agentic simulator adaptively queries the real environment through safe read-only interfaces, reasons over the returned evidence, and uses executable computation to construct the resulting observation without mutating shared state. Integrated with trajectory-level TTS, AWM enables parallel exploration over a single shared environment. Across \tau -bench, AppWorld, and OfficeBench, AWM outperforms a single-shot simulation baseline by 40.07% on average, and surpasses a trained world-model baseline by 12.36% on average. These results establish interactive, read-grounded simulation as a practical foundation for safe and scalable agentic test-time scaling in shared, non-resettable environments.
[MA-8] How a group that cannot communicate knows itself by its enemy
【速读】：该论文旨在解决团队在无法直接通信的情况下如何有效获取外部信息以提升整体决策能力的问题，尤其针对那些成员间无法交流但需共同应对一个反应性对手（adversary）的场景。其核心挑战在于：每个成员仅能观测局部信息，而对手会根据团队整体行为做出响应，这种响应本身构成一种“暗示”（tell），可被团队成员解读以获得超越个体认知的信息。解决方案的关键在于团队通过预先承诺其集体响应策略（commitment to collective response），从而将对手的隐藏行为转化为可利用的信息源。具体而言，当对手更关注自身目标而非欺骗时，团队无需依赖内部通信即可实现最优表现；而在对手有动机隐瞒信息的情境下，团队通过主动承诺不完全使用所获信息（如在高斯模型中故意放弃部分信息以换取对手的暴露成本），反而能获取真实信息并降低对手的隐藏激励。此外，信任的分配方式也至关重要——通过引入可替代的异质响应成员或微小的信任折扣机制，可避免对敌方意图误判。因此，该研究揭示了信息并非仅靠收集或聚合产生，而是由团队如何承诺其对外反应方式所“生成”的。
链接: https://arxiv.org/abs/2610.40835
作者: Gregory Falco
机构: Cornell University (康奈尔大学)
类目: Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA); Robotics (cs.RO); Systems and Control (eess.SY)
备注: 6 pages main text; 35-page SI Appendix included as an ancillary file. Code and data: this https URL
Abstract:Teams that cannot communicate seem stuck with what each member sees. We show that their opponent can tell them the rest. Silent hunters, jammed robots and traders who never meet all face an adversary that reacts to the group as a whole; every member can read that reaction, a tell, and learn what no member knows alone. One tell can do the work of a message from every member. The adversary would rather hide, but hiding interferes with its own task, and a team that commits in advance to how it will respond decides how much hiding is worth. A committed team never loses when its opponent cares more about its own goals, and does as well as a fully informed team exactly when the opponent’s stake outweighs any gain from lying. Below that point the team learns more by trusting less: in a solved Gaussian model it obtains the truth by promising not to use all of it and keeps the cost the opponent would have paid to conceal; an exact test decides when this holds in finite games. How a team spreads its trust matters too. A lone uninformed member with a full-support prior needs a private coin, but with additive squared-error losses two interchangeable members who respond differently replace it, and a small discount on trust insures against misjudging the enemy. Information, on this view, is not only gathered or aggregated; it is produced by how a team commits to respond.
[MA-9] Distributed Grid Search for Hyperparameter Tuning
【速读】：该论文旨在解决分布式优化算法在对等网络中性能高度依赖超参数选择，而现有超参数选取方法通常需要全局信息、保守性较强或与特定算法及超参数绑定的问题。其核心解决方案是提出一种基于共识的分布式网格搜索方法——分布式网格搜索（Distributed Grid Search, D-GS），该方法仅需各节点运行待调优算法并计算局部梯度即可在全网范围内评估和选择最优超参数配置。为进一步提升效率，论文还提出了改进版本——分布式聚焦网格搜索（Distributed Zoom Grid Search, D-ZGS），通过自适应缩小搜索空间，在降低计算成本的同时显著提升实际性能。理论分析证明，两种方法均能在有限时间内使所有节点达成一致的超参数配置，并量化了该配置的（次）最优性。数值实验表明，D-GS在线性回归与逻辑回归任务中性能接近集中式基准，且调参与部署成本仅为传统基于理论推导方法的极小部分。
链接: https://arxiv.org/abs/2610.41537
作者: Constança De Herdeiro Cachim,Andrea Martin,Nicola Bastianello
机构: KTH Royal Institute of Technology (皇家理工学院); University College London (伦敦大学学院)
类目: Optimization and Control (math.OC); Multiagent Systems (cs.MA)
备注:
Abstract:The performance of many distributed optimization algorithms over peer-to-peer networks strongly depends on the choice of their hyperparameters. However, existing selection rules often require global quantities, yield conservative choices, or are tied to specific algorithms and hyperparameters. We propose Distributed Grid Search (D-GS), a consensus-based search method that evaluates and selects hyperparameter configurations across the network, requiring only the ability to run the tuned algorithm and evaluate local gradients. We also propose a zoom-in variant, Distributed Zoom Grid Search (D-ZGS), which improves empirical performance at a reduced cost. We prove that both methods terminate in finite time with all agents holding the same hyperparameter configuration, and quantify the (sub)optimality of this configuration. Numerical experiments on linear and logistic regression show that D-GS closely matches a centralized reference and that tuning and deployment cost a fraction of deploying algorithms with hyperparameters tuned using theoretical results.
[MA-10] Understanding LLM -Based Bargaining of Driving Agents in Mixed-Autonomy Traffic
【速读】：该论文旨在解决混合自主交通环境中，基于生成式 AI (Generative AI) 的自动驾驶汽车（AV）与人类驾驶车辆（HV）在共享道路空间时的群体层面协商行为建模问题。其核心挑战在于理解异构代理间复杂的社会互动机制，尤其是在不同交通密度、代理目标及交通领域知识条件下，协商结果的多样性与动态演化规律。解决方案的关键在于提出一种行为分类体系（behavioral taxonomy），通过联合分析数值竞价行为与沟通态度，系统刻画交互过程中的双重社会性维度：一是代理间的博弈动态，二是个体私有意图向公共表达的转化机制。研究发现，达成协议的场景中表现出更强的建设性互惠性，而协商失败场景则伴随更频繁的冲突升级；同时，即使数值报价保持不变，私人与公开沟通态度之间仍存在显著差异。这一框架为深入解析生成式 AI 驱动的混合交通系统中的社会交互行为提供了结构化分析工具。
链接: https://arxiv.org/abs/2610.41239
作者: Lingxiao Zhou,Di Chen,Jia Li
机构: Washington State University(华盛顿州立大学); University of California, Davis(加州大学戴维斯分校)
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA)
备注:
Abstract:Large Language Models (LLMs) offer a flexible computational approach for modeling social interactions among heterogeneous agents. This paper develops a novel framework towards characterizing and understanding LLM-based population-level bargaining of shared road space between autonomous vehicle (AV) and human-driven vehicle (HV) agents in mixed-autonomy traffic. Through controlled experiments varying traffic density, agent objectives, and traffic-domain knowledge, we first characterize bargaining outcomes and observe heterogeneous agreement rates across different experimental conditions. To understand how the bargaining process shapes the outcomes, we introduce a behavioral taxonomy that jointly characterizes numerical bidding actions and communicative attitudes. We examine two complementary aspects of social interaction: inter-agent bargaining dynamics and private-to-public behavioral transformation. Results reveal distinct interaction patterns associated with agreement and breakdown, including greater constructive reciprocity in agreement trials and more frequent conflict escalation in breakdown trials. Furthermore, private and public communicative attitudes often differ even when numerical bids remain unchanged. These findings illustrate how LLM-based bargaining simulations can support a structured analysis of social interaction behaviors in mixed-autonomy traffic.
自然语言处理
[NLP-0] Latent Reasoning in Continuous Space for Unified Multimodal Models NEURIPS2026
【速读】： 该论文旨在解决统一多模态模型（Unified Multimodal Models, UMMs）在图像生成任务中难以有效利用基于语言的思维链（chain-of-thought）推理的问题，尤其针对现有增强推理的UMMs普遍缺乏大语言模型所具备的测试时扩展性（test-time scaling）与细粒度可控性。其核心挑战源于模态不匹配：推理过程在离散的词元（token）空间中进行，而图像生成则在连续的隐空间中执行，导致信息传递效率低下。为克服这一瓶颈，本文提出连续空间中的潜在推理框架（Latent Reasoning in Continuous space, LARC），通过在连续隐藏状态层面进行推理，避免了传统方法中的词元级限制。该框架包含两个阶段：首先通过课程监督微调（curriculum supervised fine-tuning, SFT）逐步将词元级推理转化为潜在空间推理；随后通过基于信息增益的强化学习（information-gain reinforcement learning, RL）实现无真实标签监督下的潜在推理自演化。实验结果表明，LARC在多个文本到图像生成基准上显著提升生成质量、提示遵循度及组合保真度，尤其在GenEval上较BAGEL提升7个百分点，并展现出清晰的测试时扩展行为，无论在并行或串行策略下均优于基于词元的生成方法。
链接: https://arxiv.org/abs/2610.42149
作者: Byungwoo Jeon,Yoonwoo Jeong,Hyunseok Lee,Minsu Cho,Jinwoo Shin
机构: KAIST(韩国科学技术院); POSTECH(浦项科技大学); RLWRLD
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Published as conference proceeding for NeurIPS 2026. Last two authors advised equally
Abstract:Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii) information-gain reinforcement learning (RL). First, the curriculum SFT stage gradually converts token-level reasoning into latent reasoning. Subsequently, the information-gain RL stage self-evolves latent reasoning without ground truth supervision. Experiments across diverse text-to-image generation benchmarks show that LARC consistently improves generation quality, prompt following, and compositional fidelity. Notably, LARC achieves state-of-the-art performance on GenEval with a 7%p gain over BAGEL and demonstrates clear test-time scaling behavior, outperforming token-based generation under both parallel and sequential test-time scaling strategies.
[NLP-1] Flide: Accelerating Decision Inference with Exact State Forking
【速读】： 该论文旨在解决生成式推理引擎在支持决策模型（Decision Models）时存在的效率与准确性问题，尤其是现有方法在处理此类轻量级、高可靠性的分类推理任务时，因严重的前缀重复计算开销或输出概率失真而导致性能下降。其核心解决方案是提出Flide——一种高效且精确的推理引擎，通过将决策请求的逻辑树结构与物理执行过程解耦，将其统一编码为单一扁平化的标记流。Flide利用ForkAttention机制，在标准注意力层与循环式门控增量网络（Gated DeltaNet）中借助驻留于GPU的索引实现对请求依赖关系的精准导航，确保每个查询分支的计算结果在精确算术下等价于独立前向传播。通过将分支拓扑直接表达为驻留于GPU的元数据，Flide仅需一组预记录的执行图即可适配多样的请求混合，并支持基于检查点的代理历史恢复。大规模实验表明，Flide在公开的混合模型与决策模型上相较vLLM和SGLang可实现最高达9.5倍的查询加速，同时在总变差距离（total variation distance）上最接近浮点32位参考基准。Flide通过统一物理执行吞吐与数学精确性，为智能体系统中的可扩展、低延迟决策智能奠定了原则性基础。
链接: https://arxiv.org/abs/2610.42148
作者: Bobo Li,Jiajia Song,Hao Fei,Wynne Hsu,Mong Li Lee
机构: National University of Singapore(新加坡国立大学); University of Oxford(牛津大学)
类目: Computation and Language (cs.CL)
备注: 20 pages. Project page: this https URL
Abstract:Decision models such as Jev have emerged as a highly promising paradigm for LLM agent systems, providing lightweight and reliable categorical reasoning for rapid System One execution. However, existing generation-oriented inference engines fail to support these workloads effectively, suffering from severe prefix recomputation overheads or distorted output probabilities. To address this challenge, we propose Flide, an efficient and exact inference engine that decouples the logical tree structure of a decision request from its physical execution within a single flattened token stream. Through ForkAttention, Flide navigates request dependencies using GPU-resident indices across both standard attention and recurrent Gated DeltaNet layers, guaranteeing in exact arithmetic that every query branch evaluates identically to an isolated forward pass. By expressing branch topologies directly in GPU-resident metadata, Flide accommodates diverse request mixtures with a compact set of pre-recorded execution graphs while enabling expanding agent histories to resume from checkpoints saved in earlier passes. Extensive evaluations on public hybrid and decision models demonstrate that Flide answers queries up to 9.5 times faster than vLLM and SGLang while staying closest to a float32 reference in total variation distance. By unifying physical execution throughput with mathematical exactness, Flide establishes a principled foundation for scalable, latency-critical decision intelligence in agent systems.
[NLP-2] DecisionRank: On-Policy Distillation for Decision-Model Rerankers
【速读】： 该论文旨在解决将生成式大语言模型（Generative LLM）的排序知识有效迁移至决策模型（Decision Model）中的难题。其核心挑战在于：生成式教师模型通过文档ID序列的排序列表表达偏好，而学生决策模型则依赖首个词元位置的得分来确定排序结果，二者在输出形式与训练机制上存在不匹配。为此，论文提出DecisionRank，采用基于项目空间的在线策略蒸馏（item-space on-policy distillation, OPD）作为关键解决方案。该方法通过将学生模型输出的文档得分排序生成候选排名序列，再由教师模型以该序列为输入提供条件反馈，直接监督学生的得分预测；结合离线排序监督，显著提升了决策模型在通用与推理密集型检索任务（如TREC DL、BEIR、BRIGHT）上的性能。实验表明，即使在1.7B参数规模下，DecisionRank在TREC DL19–23上仍超越其27B的生成式教师模型；在8B参数规模下，不仅优于同类生成式列表级模型，且在TREC DL和BRIGHT上分别实现24.5倍和16.7倍的推理速度提升。
链接: https://arxiv.org/abs/2610.42128
作者: Yanran Wu,Dian Yu,Chongyang Bai,Jieyi Zhang,Rui Liu,Tianyi Chen,Shike Mei,Hang Cui,Frank Shyu,Sandeep Pandey,Luke Simon,Chunwei Liu,Xi Liu
机构: Meta; Purdue University(普渡大学)
类目: Computation and Language (cs.CL)
备注: 25 pages, 8 figures
Abstract:Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM’s ranking knowledge to a decision model remains challenging: the teacher expresses preferences through a ranked list of document ID sequences, while the student determines the ranking using scores from the first-token position. To bridge this gap, we introduce DecisionRank, a decision model trained through item-space on-policy distillation (OPD). Item-space OPD constructs the student’s rollout by sorting its document scores into a ranked sequence. A generative teacher takes the resulting ranking list as inputs and provides conditional feedback to directly supervise those scores. Combined with offline ranking supervision, DecisionRank achieves state-of-the-art performance on both general and reasoning-intensive retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Across four benchmarks, item-space OPD consistently outperforms alternative training objectives. Even at 1.7B parameters, DecisionRank surpasses its 27B generative teacher on TREC DL19–23. At 8B, it outperforms its generative listwise counterpart while running 24.5\times faster on TREC DL and 16.7\times faster on BRIGHT.
[NLP-3] Subquadratic Attention with a Hierarchical Tree-Structured Indexer
【速读】： 该论文旨在解决大语言模型（LLM）在处理长上下文时因注意力机制（attention）的二次方复杂度（quadratic scaling）导致的计算瓶颈问题。现有方法要么牺牲注意力的表达能力（如滑动窗口或固定状态大小的线性注意力），要么仅实现常数级加速（如索引开销仍为二次方的稀疏注意力）。本文提出一种渐近次二次稀疏注意力方法——Zyphra层级注意力（ZHA），其核心创新在于构建一个分层树状索引结构，对键值缓存（KV cache）中的键进行递归压缩生成层次化摘要。查询通过从树顶逐层向下遍历，高效检索出稀疏的键集合。关键优势在于：随着树深度迭代增加，路由与最终注意力计算的总成本随序列长度呈亚线性增长，从而实现整体注意力计算的次二次（但超线性）复杂度。实验表明，ZHA可仅通过少量（数十亿标记）的KL散度微调即可部署于现有预训练模型，且在长上下文检索、复杂推理、数学求解及智能编码等任务中保持高性能。此外，该方法对树结构几何具有高度鲁棒性，支持高达6层的深层结构。通过建立详尽的性能模型，验证了其在超过百万上下文长度时显著优于竞品，并具备将有效上下文窗口扩展至数亿甚至数十亿标记的潜力。
链接: https://arxiv.org/abs/2610.42112
作者: Leon Lufkin,Cutter Dawes,Alexander Speicher,Kamesh Krishnamurthy,Tomás Figliolia,Beren Millidge
机构: Zyphra Research(齐弗拉研究); OpenAI(OpenAI)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 43 pages
Abstract:The quadratic scaling of attention is a fundamental bottleneck limiting the long-context capabilities of large language models (LLMs). A variety of approaches have attempted to circumvent this, but they either fail to match attention’s expressiveness (e.g., sliding-window or linear attention variants with fixed-size state) or only achieve constant-factor speedups (e.g., sparse attention methods whose indexers themselves scale quadratically). In this paper, we derive an asymptotically subquadratic sparse attention method based on a hierarchical, tree-structured indexer, which we call Zyphra Hierarchical Attention (ZHA). ZHA constructs a tree of recursively compressed key summaries over the KV cache, which future queries traverse from the top down to retrieve a sparse subset of keys. Crucially, by iteratively growing the tree’s depth, the total cost including routing and final attention grows sublinearly in sequence length, resulting in overall subquadratic (but superlinear) scaling for attention. We demonstrate that ZHA can be cheaply applied to existing pretrained models with only a short KL distillation training stage over a few billion tokens, and maintains performance on long-context retrieval and challenging reasoning, mathematical, and agentic coding evaluation tasks. Moreover, we show that our method is highly robust to tree geometry including very deep trees of up to 6 levels. Finally, we develop a detailed performance model and show that our approach scales subquadratically, dramatically outperforms competing methods beyond 1 million context, and ultimately has the potential to extend effective context windows to the hundreds of millions or billions of tokens.
[NLP-4] RangeSWA: Training-Free Length Extrapolation for Continuous Diffusion Language Models
【速读】： 该论文旨在解决连续扩散语言模型（continuous diffusion language models）在生成长文本时面临的文本重复、语义漂移及生成质量下降等问题，尤其当生成长度超出训练长度时更为显著。其核心解决方案是提出一种无需额外训练的长度外推方法——RangeSWA。该方法的关键在于融合局部滑动窗口注意力机制（local sliding-window attention）、远程块摘要（remote block summaries）与动态内容检索技术，以在保持局部细节的同时支持长程上下文访问；同时通过位置重映射（positional remapping）、条件注意力校正（conditioning attention correction）以及去噪阶段自适应窗口调度（denoising-stage-adaptive window scheduling），有效缓解因长度外推引发的生成质量退化问题。实验结果表明，RangeSWA能够显著降低长文本生成中的困惑度和重复率，提升生成稳定性与质量。
链接: https://arxiv.org/abs/2610.42084
作者: Guangfu Guo,Xiaoqian Lu,Linsey Pang,Weiran Yao,Haolin Chen,Long Cheng,Kunpeng Liu
机构: Clemson University (克莱姆森大学); University of Ottawa (渥太华大学); PayPal(贝宝); actAVA AI
类目: Computation and Language (cs.CL)
备注:
Abstract:Continuous diffusion language models show promising potential in text generation tasks, but are prone to problems such as text repetition, semantic drift, and generation quality degradation when the generation length exceeds the training length. To address these challenges, we propose RangeSWA, a training-free length extrapolation method. The method combines local sliding-window attention, remote block summaries, and dynamic content retrieval to preserve local details and support long-range context access. In addition, generation degradation caused by length extrapolation is mitigated through positional remapping, conditioning attention correction, and denoising-stage-adaptive window scheduling. We conducted experiments on multiple continuous diffusion language models, and the results show that RangeSWA can effectively reduce generative perplexity and text repetition rates in long-text generation.
[NLP-5] he Knowledge Layer Model (KLM): Evidence-Visible AI Output Routing Diagnostics and Negative Results
【速读】： 该论文旨在解决生成式 AI（Generative AI）回答在缺乏可追溯依据时，难以满足审查者对推理过程透明性与可信度要求的问题。其核心挑战在于：当前AI系统的输出往往看似正确，但其背后的推理路径、证据来源及不确定性评估缺乏显式记录，导致无法进行有效验证。为此，论文提出知识层模型（Knowledge Layer Model, KLM），作为一套开放且与实现无关的推理记录规范，明确包含版本化的认知评估（epistemic assessments）、证据关联关系以及显式的未知项声明。该模型以Nage AI OS中的知识与证据架构SEDIM为参考实现，并基于KLM v0.3版本进行设计。尽管实验数据表明部分基准任务达到91.3%分类准确率，但整体评估受限于内部实验性质、历史数据未验证、评分器缺陷及原始输出缺失等问题，未能形成可靠结论。关键发现包括：多类诊断指标表现极低（如AUC仅为0.008），文本保留率虽经修复显著提升，但仅反映内容完整性而非答案质量；此外，验证工具间一致性虽被报告，但生产级披露未获认可。因此，尽管提出了系统性框架，其有效性仍需独立复现与完整的公开审计支持，目前所有结论均为作者自报，尚未通过外部验证。
链接: https://arxiv.org/abs/2610.42066
作者: Omer Asim Ozturk,Cihan Aydiner
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 47 pages, 1 figure, 14 tables. Includes Appendix A
Abstract:AI answers require more than apparent correctness: reviewers need to inspect their recorded basis. We introduce the Knowledge Layer Model (KLM), an open, implementation-neutral specification for inference records with versioned epistemic assessments, evidence relations, and explicit unknowns. SEDIM, the knowledge and evidence architecture of Nage AI OS, is a reference implementation. We reference KLM v0.3 (repository tag v0.3.0). Empirical material consists of separate internal experiments, not end-to-end validation. A historical platform-domain routing benchmark reports 137/150 classifications across 15 classes (91.3%; nominal 95% Wilson interval 85.7-94.9%); the numerator is recovered from a rounded summary, and historical example membership is unverified. A native-model weight-path configuration yielded 0/15 successful answers in a corrected two-source test. A capacity-growth comparison could not establish a reliable effect because of a defective legacy scorer and missing original outputs. Lexical and scoped diagnostics each yielded AUC 0.008 for silent-versus-contradicted examples, below the registered minimum of 0.65. Legacy packing lost gold-containing text in 118/221 selected cases; repaired packing retained it in 221/221. These measure text presence, not answer quality. Python and TypeScript verifiers reportedly agreed on six checks per synthetic fixture; a separate production disclosure was not accepted. Restoration, re-derivation, human-rubric support, boundary isolation, and bounded-response evaluations remain proposed. Findings are internal and author-reported; historical observations are distinguished from later reproductions. Evidence visibility does not establish correctness, causal reliance, security, or regulatory compliance. Independent replication and a complete public audit package remain future work.
[NLP-6] PersonaChatBench: Evaluating Chat Assistants with Diverse Simulated Users
【速读】： 该论文旨在解决当前对话助手中评估方法过于依赖单一合作型用户所带来的局限性，即难以全面反映助手在面对具有不同背景、偏好和沟通风格的多样化用户时的实际表现。其核心问题是：现有评估体系无法有效衡量助手在多变用户情境下的任务胜任力与个性化响应能力。解决方案的关键在于提出PersonaChatBench框架，通过将同一任务与多个由“角色卡”（persona card）定义的模拟用户配对，实现对不同用户个体需求、偏好及交互行为的系统化模拟。该框架在保持任务不变的前提下，动态调整用户属性，从而量化分析助手在不同用户条件下的性能变化。实验结果表明，任务完成度与用户需求满足度存在显著的人格差异与领域依赖性，且任务表现优异的模型并不必然能充分满足特定用户的需求，且模型优势在不同领域间不一致。PersonaChatBench通过分离评估任务成果、需求满足度与模拟器保真度，使这些差异可测量，揭示了传统聚合评分所掩盖的性能鸿沟，为识别助手服务优势与短板提供了依据，并推动开发更具普适性和适应性的对话系统。
链接: https://arxiv.org/abs/2610.42060
作者: Zheyuan Deng,Taoran Li,Jingwen Zhang,Yuante Li,Dachuan Zhao,Fulei Zhang,Yunkai Zhang,Zhikun Xu,Yajie Liao,Haichao Zhang,Haodi Hu,Cong Wang,Chen Yang,Siyang Wu,Defu Cao,Xu Hu,Haoming Li,Haixin Wang,Robert George,Weizhi Zhang,Yuchu Wang,Zhejian Peng,Yueying Li,Wanting Jiang,Shushu Liang,Zhiyuan Yu,Yifan Wang,Jintao Huang,Yijun Wang,Yuexing Hao,Xiaomin Li
机构: Brown University(布朗大学); Texas AM University(德州农工大学); Harvard University(哈佛大学); Carnegie Mellon University(卡内基梅隆大学); Columbia University(哥伦比亚大学); University of California, Berkeley(加州大学伯克利分校); Arizona State University(亚利桑那州立大学); Northeastern University(东北大学); University of Southern California(南加州大学); University of Chicago(芝加哥大学); The University of Texas at Dallas(德克萨斯大学达拉斯分校); University of California, Los Angeles(加州大学洛杉矶分校); California Institute of Technology(加州理工学院); University of Illinois Chicago(伊利诺伊大学芝加哥分校); University of Michigan(密歇根大学); University of Illinois at Urbana-Champaign(伊利诺伊大学香槟分校); Massachusetts Institute of Technology(麻省理工学院); Amherst College(阿默斯特学院); Purdue University(普渡大学); The Ohio State University(俄亥俄州立大学)
类目: Computation and Language (cs.CL)
备注: 74 pages, including appendices
Abstract:Conversational assistants are expected to help people with diverse backgrounds, preferences, and communication styles. Yet evaluations centered on a single cooperative user provide limited insight into how reliably an assistant serves users with different personas. We introduce PersonaChatBench, a framework for evaluating task competence and responsiveness to diverse user needs. PersonaChatBench pairs the same underlying task with multiple simulated users, each guided by a private persona card that specifies distinct needs, preferences, and interaction behaviors. By holding the task fixed while varying the user, the framework examines how assistant performance changes across user conditions. We evaluate 20 language models across six domains, separately assessing native task outcomes, fulfillment of expressed persona needs, and simulator fidelity. Our results show that task performance and need fulfillment vary across personas and domains. Strong task performance does not necessarily translate into satisfying individual needs, and models’ relative strengths in meeting those needs are not consistent across domains. PersonaChatBench makes these differences measurable, revealing gaps obscured by aggregate scores and helping identify which users an assistant serves well and where adaptation is needed. It provides a basis for evaluating consistency across users and guiding the development of assistants that better accommodate diverse needs in everyday applications.
[NLP-7] CRoS: Causality Reward on Scrambled CoTs Improves Accuracy and Reasoning Faithfulness
【速读】： 该论文旨在解决生成式推理中因果性奖励（Causality Reward）评估的偏差问题，即现有方法通过删除或注入错误等扰动手段评估思维链（Chain-of-Thought, CoT）与最终答案之间的因果关系时，不可避免地改变了每一步推理中的信息内容，导致高因果性得分可能反映的是对扰动引入的信息变化的敏感性，而非真实的因果贡献。这种混淆效应在训练过程中会诱导模型生成信息量更低的思维链，并抑制探索与自我修正行为。为解决此问题，论文提出一种新型因果性度量方法CRoS（Causality Reward on Scrambled CoTs），其核心创新在于仅打乱推理步骤的顺序而不改变各步骤的内容，从而在保持信息完整性的同时有效评估因果性。实验结果表明，在Reasoning Gym基准上使用Gemma-3-4B模型进行评估，CRoS在跨域准确率、思维链质量及思维链忠实性方面均显著优于现有方法，尤其在5个未见逻辑与游戏数据集上达到46.8%的准确率，优于最强基线（43.8%），同时在CoT-Pass@K（K=1至128）平均得分43.4%（基线41.4%）和提示承认率（hint acknowledgment score）34.0%（基线20.2%）上也表现出明显优势。
链接: https://arxiv.org/abs/2610.42054
作者: Xiao Yu,Justin Chih-Yao Chen,Zaid Khan,Hyunji Lee,Mohit Bansal
机构: UNC Chapel Hill (北卡罗来纳大学教堂山分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 17 pages, 3 figures, 14 tables. Project page: this https URL
Abstract:Reinforcement learning with verifiable rewards (RLVR) has become the standard method for training language models to reason. However, RLVR typically relies solely on outcome correctness, which does not guarantee that the CoT causally contributes to the final answer. Without this connection, outcome rewards may fail to reinforce high-quality reasoning. Existing causality metrics assess CoT causality by perturbing the CoT, such as deleting reasoning steps or injecting errors, and measuring changes in the final answer. However, these perturbations inevitably alter information in each step. Consequently, high causality scores may reflect sensitivity to information changes introduced by perturbation rather than true causality. Such confounding effects further induce undesirable reasoning behaviors when these metrics are used as training rewards, including generating less informative CoTs and reducing exploration and self-correction. To address this problem, we propose CRoS (Causality Reward on Scrambled CoTs), a novel CoT causality metric that perturbs the order of reasoning steps without altering content, thereby preserving information within each step. On Reasoning Gym with Gemma-3-4B, CRoS outperforms existing causality and outcome rewards in out-of-domain accuracy, CoT quality, and CoT faithfulness. Specifically, CRoS achieves 46.8% accuracy on five unseen logic and games datasets, outperforming the strongest baseline (43.8%). It also achieves better CoT quality, consistently outperforming baselines in CoT-Pass@K from K=1 to 128, averaging 43.4% vs. 41.4%. For CoT faithfulness, CRoS achieves a hint acknowledgment score of 34.0% vs. 20.2%.
[NLP-8] Asynchronous Flow Map Language Models
【速读】： 该论文旨在解决单步并行语言建模中如何有效利用生成顺序以提升生成序列质量的问题。传统自回归与掩码扩散模型通过迭代方式逐步生成词元，能够自然地利用生成顺序，但现有单步并行模型难以显式建模此类顺序依赖关系。为此，论文提出异步流映射语言模型（asynchronous flow map language models, aFMLMs），其核心创新在于将预测顺序编码为异步流的流映射（flow maps），通过词元级的时间调度（tokenwise time schedule）控制各词元的去噪进度，从而在单次前向传播中实现有序生成。该方法支持预设与可学习的状态自适应调度策略，并建立了有效的流映射蒸馏技术。实验表明，aFMLM在数独求解和GSM8K数学应用题代码生成任务上均达到新的单步生成性能基准，显著提升了并行生成的质量与效率。
链接: https://arxiv.org/abs/2610.42018
作者: Chanhyuk Lee,Wonjung Kim,Patrick Pynadath,Jaehoon Yoo,Satoshi Hayakawa,Ruqi Zhang,Jinwoo Kim,Seunghoon Hong
机构: KAIST(韩国科学技术院); Purdue University(普渡大学); The University of Tokyo(东京大学)
类目: Computation and Language (cs.CL)
备注: Code available at: this https URL
Abstract:The order in which tokens are predicted in language modeling has a profound impact on the quality of generated sequences. Autoregressive and masked diffusion models iteratively fill in tokens and can exploit such orderings across generation steps, but how to leverage an ordering for one-step parallel language modeling is unclear. We introduce asynchronous flow map language models (aFMLMs), which incorporate prediction order into one-step modeling by learning the flow maps of asynchronous flows. These flows specify an ordering through a tokenwise time schedule that controls the relative denoising progress of tokens. We formulate a general class of asynchronous flows that admit both prescribed and learned, state-adaptive schedules, show that they define valid flow maps, and develop new methods for their flow map distillation. The resulting models can generate all tokens in a single forward pass by internalizing the ordering encoded in the flow into their hidden computations. On Sudoku solving and code generation for GSM8K math word problems, aFMLM establishes new state-of-the-art one-step performance.
[NLP-9] urnTurk-GPT : Text-Based Turn-Taking Prediction for Turkish
【速读】： 该论文旨在解决土耳其语对话中话轮转换（turn-taking）预测的难题，即在实时语音流处理场景下准确判断说话人何时结束发言。其核心挑战在于：如何在无标点、小写化且非分句的文本流中，仅基于连续文本序列识别出自然对话中的话轮终止点，尤其在面对未见说话者或不同录音环境时保持鲁棒性。解决方案的关键在于提出并训练了TurnTurk-GPT——一个通过在合成与真实对话数据上微调土耳其语GPT-2模型，并采用TurnGPT目标函数进行优化的生成式端到端话轮转换预测器。该方法的核心创新在于将土耳其语特有的动词后置形态学特征（verb-final morphology）作为关键的句末提示信号，实验证明去除话轮末尾词的词尾会使其平均精度（AP）下降高达44%，显著高于内部词的2%下降，证实了形态学线索对土耳其语话轮终结判别的决定性作用。此外，研究构建了严格的评估协议，包含记录无关与说话人无关的交叉验证、嵌套阈值选择及配对聚类自助法检验，确保结果可靠性。实验表明，TurnTurk-GPT在多数情况下优于多语言基线模型，在已见说话者上超越零样本模型，但在未见说话者上二者无统计差异，凸显其对训练数据覆盖度的依赖。模型与评估框架均已公开发布，为后续研究提供可复现基准。
链接: https://arxiv.org/abs/2610.42010
作者: Ahmet Tuğrul Bayrak,Fatma Nur Korkmaz,Nur Yağmur Tuncer,Mustafa Sertaç Türkel
机构: Ata Technology Platforms(阿塔科技平台)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper presents TurnTurk-GPT, a text-based turn-taking predictor for Turkish obtained by fine-tuning a Turkish GPT-2 model with the TurnGPT objective on synthetic and real dialogue, and evaluates it on real Turkish conversation against rule baselines, a zero-shot Turkish language model and a widely used multilingual end-of-turn detector. Evaluation uses word-level decision points on unpunctuated, lower-cased text approximating the output of streaming speech recognisers, recording-disjoint and speaker-disjoint folds, nested threshold selection and paired cluster bootstrap tests. TurnTurk-GPT outperforms the multilingual detector under both recording-disjoint and speaker-disjoint evaluation. It outperforms the zero-shot language model only when the test speakers occur in training; on unseen speakers the two are statistically indistinguishable. Removing the suffixes of the turn-final word reduces AP by up to 44%, against 2% for a sentence-internal word, locating the Turkish end-of-turn cue in verb-final morphology. Synthetic dialogue alone yields the weakest trained model, and when a speaker pauses the zero-shot and multilingual baselines remain close to chance. The model and the evaluation protocol are publicly released.
[NLP-10] Less Loss Fixed Weights: Logit Corrections for Forgetting
【速读】： 该论文旨在解决语言模型在指令微调或领域继续预训练后出现的“遗忘”问题，即模型在标准网络文本预测能力上的退化。其核心挑战在于如何在不重新训练模型的前提下，通过简单、固定的后处理机制恢复模型对原始分布文本的生成性能。解决方案的关键在于引入一个针对每个词元（token）的固定偏置（vocabulary bias），并结合温度参数（temperature）进行调整，以修正模型输出概率分布的偏差。实验表明，在14组不同规模（1B至7B参数）的基础与衍生模型中，仅使用词汇偏置时，最大可恢复性能提升不足10%；而结合温度调节后，最高可恢复达36%。值得注意的是，经过数学领域继续预训练的模型遗忘最严重且几乎无法恢复。此外，在模拟随机损伤的控制实验中，即使不存在人为植入的偏置或温度变化，该方法仍能恢复29%的性能损失，表明该策略具备一定的鲁棒性。然而，大部分真实场景下的性能下降仍超出该受限调整框架的修复范围，说明“遗忘”现象具有深层次的表征层面损伤。
链接: https://arxiv.org/abs/2610.42000
作者: Hanyu Yang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 8 pages, 3 figures, 2 tables. Code and data: this https URL
Abstract:When a language model is fine-tuned to follow instructions, or further pretrained on a new subject, it often gets worse at predicting standard web text. This is known as “forgetting.” We ask whether this loss can be undone by simple, fixed adjustments to the model’s final token predictions. Across 14 pairs of base and derived models, from 1B to 7B parameters, we fitted a fixed bonus or penalty for each token (a vocabulary bias), alone or together with a temperature. On text held out from correction fitting, the largest top-4K bias recovery estimate is under 10%, with loss conditional on the retained vocabulary. Combining the bias with a temperature recovers up to 36%. Models further pretrained on math lose the most and recover almost nothing. Finally, in a controlled simulation where a model is damaged at random, with no planted bias or temperature change, the bias-plus-temperature fix still recovers 29%. Most of the real-model loss increase stays out of reach of our restricted fits.
[NLP-11] DAMI: Dual-Axis Memory Initialization for Enhanced Long-Context Retrieval in Gated Linear Attention
【速读】： 该论文旨在解决线性注意力（Linear attention）在长序列建模中因有限状态记忆而导致的长距离信息检索能力受限的问题。其核心挑战在于门控线性递归（Gated Linear Recurrences）中的记忆保留机制依赖于状态衰减，而该衰减由门控参数 Alog 和 dt_bias 以及输入激活共同决定，且训练后这些参数与初始值高度相关，凸显了初始化策略的重要性。为此，论文提出双轴记忆初始化（Dual-Axis Memory Initialization, DAMI），在训练前联合配置 Alog 与 dt_bias：一方面为不同注意力头分配几何级数的时间尺度以适应多样化的检索距离；另一方面在层间构建“低-高-低”的输入敏感性分布，从而优化记忆保留与动态响应。DAMI仅通过一个小激活样本对GDN系列模型进行校准，无需引入额外可训练参数。实验表明，在GDN、KDA、GDN2、Mamba-2和RWKV-7等多种架构上，DAMI相较于随机初始化基线，平均提升了5.29至16.71个百分点的RULER风格检索准确率，同时在语言建模、常识推理及2K真实世界检索任务上的性能保持相当。
链接: https://arxiv.org/abs/2610.41994
作者: Zhangchi Feng,Harry Jiang,Yangyifei Luo,Ray Zhang,Richong Zhang
机构: SpeedAI; Beihang University (北京航空航天大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Linear attention scales linearly with sequence length, but its finite-state memory can limit long-context retrieval. In gated linear recurrences, memory retention depends on state decay, which is shaped jointly by gate parameters A_log and dt_bias and by input activations. In controlled Gated DeltaNet (GDN) experiments, both parameters remain highly correlated with their initial values after training, highlighting the importance of initialization. Therefore, we introduce Dual-Axis Memory Initialization (DAMI), which jointly configures these parameters before training. DAMI assigns geometric timescales across heads for varied retrieval distances and a low-high-low input-sensitivity profile across layers. A small activation sample calibrates the GDN-family implementation to match both behavioral targets without adding trainable parameters. Across GDN, KDA, GDN2, Mamba-2, and RWKV-7, DAMI improves average RULER-style retrieval accuracy by 5.29 to 16.71 percentage points over the corresponding random-initialization baselines. Performance on language modeling, commonsense reasoning, and 2K real-world retrieval remains broadly comparable to those baselines. Our code is available at this https URL.
[NLP-12] Aligning MLLM s with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在推理任务中表现优异，但其内部表征与人类认知的潜在结构之间是否存在对齐的问题。核心挑战在于，尽管MLLMs能完成复杂任务，但其决策机制是否符合人类基于多维心理表征进行相似性判断的认知过程尚不明确。为此，本文提出一种新型对齐框架，通过构建基于人类行为数据的语义嵌入空间——该空间由470万条人类“奇数出列”（Odd-One-Out, O1O）判断推导出的66个可解释维度构成，为每个三元组样本识别最显著的决策维度，并将其转化为以维度为基础的语言化推理理由（dimension-grounded linguistic rationale）。这一设计使MLLMs不仅学习最终选择结果，还学习与人类行为准则一致的解释逻辑。实验表明，该方法显著提升了模型与人类相似性判断的一致性，同时在标准多模态基准测试上保持竞争力；进一步利用独立的fMRI自然场景数据集（NSD）进行搜索光束表示相似性分析（searchlight Representational Similarity Analysis, RSA），发现模型表征与人类大脑表征之间的相似性增强。研究结果揭示了一条数据驱动的路径，可将人类认知结构系统性地融入MLLMs的对齐过程中。
链接: https://arxiv.org/abs/2610.41951
作者: Ning E,Changde Du,Yizhuo Lu,Huiguang He
机构: ShanghaiTech University (上海科技大学); Chinese Academy of Sciences (中国科学院); Zhongguancun Academy (中关村学院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal representations and decision criteria align with the latent structure of human cognition. In this work, we propose a novel framework to align MLLMs with the multidimensional mental representations underlying human similarity judgements. We build on a previously established behavior-derived semantic embedding space, in which objects are represented along 66 interpretable dimensions derived from 4.70 million human Odd-One-Out (O1O) judgements. For each triplet, we infer the most salient decision dimension and convert it into a dimension-grounded linguistic rationale. This enables MLLMs to learn both the final choice and a justification consistent with the inferred behavioral criterion. Experimental results demonstrate that our approach improves model consistency with human similarity judgements while retaining competitive performance on standard multimodal benchmarks. Furthermore, using searchlight Representational Similarity Analysis (RSA) on an independent fMRI Natural Scenes Dataset (NSD), we observe increased representational alignment between model and human. Ultimately, our findings point to a data-driven route for incorporating human cognitive structure into MLLMs alignment. The code is available \hrefthis https URLhere.
[NLP-13] How Much Can Tool Calls Reveal About You? Privacy Leakage from Tool-Call Traces
【速读】： 该论文旨在解决基于大语言模型（Large Language Model, LLM）的智能体在通过模型上下文协议（Model Context Protocol, MCP）与外部工具交互时，其产生的工具调用轨迹（tool-call traces）可能泄露用户行为特征，从而导致用户画像被推断的隐私风险问题。其解决方案的关键在于构建了一个名为PrivacyTrace的基准测试集，包含1,000个合成用户画像和跨四个领域、4,000条智能体轨迹，基于25个真实MCP服务器生成的工具调用数据。实验表明，仅通过协议合规的工具调用行为即可实现26.25%的用户画像推断成功率，显著高于基于先验猜测的17.97%，且分析揭示工具参数和返回结果是主要的信息源，跨会话轨迹关联进一步加剧了隐私泄露风险。研究证实MCP工具调用轨迹可作为有效的行为侧信道，推动了面向代理-工具交互场景的隐私保护机制研发。
链接: https://arxiv.org/abs/2610.41885
作者: Donghang Duan,Qingyu Liu,Yifei Liu,Na Zou,Xia Hu,Dongrui Liu
机构: Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); University of Electronic Science and Technology of China (电子科技大学); The University of Sydney (悉尼大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注: 24 pages, 12 figures, 8 tables, including references and appendices
Abstract:Large language model (LLM)-based agents interact with external tools through protocols such as the Model Context Protocol (MCP), producing tool-call traces that may expose behavioral signals about users. In this work, we systematically study the risk of user-profile inference from MCP tool-call traces, where observers infer user attributes from protocol-valid tool interactions without access to user conversations. We introduce PrivacyTrace, a benchmark containing 1,000 synthetic user profiles and 4,000 agent trajectories across four domains, generated with real MCP tools from 25 servers. Experiments with four mainstream LLM attackers show that tool-call traces enable a 26.25% attack success rate for profile inference, compared with 17.97% from prior-based guessing. Further analysis reveals that tool arguments and returned results provide the main trace-grounded evidence, while linking traces across sessions further amplifies profiling risk. These findings demonstrate that MCP tool-call traces can act as effective behavioral side channels for user profiling, motivating the development of privacy-aware mechanisms for agent-tool interactions. Code and data are available at this https URL.
[NLP-14] xt as an Index for Zero-Shot Cross-Domain Knowledge Tracing
【速读】： 该论文旨在解决零样本跨域知识追踪（zero-shot cross-domain knowledge tracing, ZS-CKT）问题，即在目标平台缺乏历史答题记录的情况下，如何利用源域的训练数据对学生的未来答题行为进行准确预测。现有方法依赖于问题语义进行迁移，但不同平台间的问题语义存在显著差异，严重限制了模型的可迁移性。其解决方案的关键在于提出TIZSKT（Text-Indexing Zero-shot Knowledge Tracing），通过将行为序列建模与领域特定的历史证据构建解耦，并以文本作为索引而非直接可迁移的表示。具体而言，该方法采用“掌握动态编码器”（Mastery-Dynamics Encoder）在不依赖问题标识符或文本嵌入的情况下学习正确性动态；通过“领域局部问题定位”（Domain-Local Question Addressing）使用冻结的文本编码器和无监督的领域内投影生成固定的问题地址；进而利用“文本索引核”（Text-Addressing Kernel）基于这些地址聚合学生过往表现，形成针对每个问题的历史正确性概要；同时引入“跨段延续机制”（Cross-Segment Continuation）在不扩展行为编码器输入序列的前提下保留早期响应信息；最后通过“证据量读出”（Evidence-Volume Read-Out）保留归一化过程中丢失的总核质量，并将其对数压缩值与行为状态及历史正确性概要结合以预测下一次作答。所有可训练参数均在源域上学习完成，目标域推理仅需公开的问题文本和当前学生的观测历史，无需目标域训练。在三个公开数据集上的留一域外实验表明，TIZSKT在所有目标域上均达到最高平均AUC和准确率，消融实验进一步验证了文本索引与跨段延续机制的有效性。
链接: https://arxiv.org/abs/2610.41883
作者: Yin Yi,Haiping Ma,Siyu Song,Ziwen Wang,Shangshang Yang
机构: Institutes of Physical Science and Information Technology, Anhui University, Hefei, China; School of Computer Science and Technology, East China Normal University, Shanghai, China; School of Computer Science and Technology, Anhui University, Hefei, China
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 10 pages, 5 figures
Abstract:Knowledge tracing (KT) predicts a student’s next response from their history. New platforms often have question texts but no response logs for training, motivating zero-shot cross-domain KT. Existing transfer and cold-start methods use question semantics, but cross-platform semantic discrepancies limit transferability. We propose TIZSKT, which separates behavioral sequence modeling from domain-local historical evidence construction and uses text as an index rather than a directly transferable predictive representation. The Mastery-Dynamics Encoder learns correctness dynamics without question identifiers or text embeddings. Domain-Local Question Addressing builds fixed question addresses using a frozen text encoder and an unsupervised per-domain projection. The Text-Addressing Kernel uses these addresses to aggregate the current student’s previous outcomes into a question-specific historical correctness summary. Cross-Segment Continuation retains access to earlier responses without extending the behavioral encoder’s input sequence. Evidence-Volume Read-Out preserves the total kernel mass discarded by normalization and combines its log-compressed value with the behavioral state and historical correctness summary to predict the next response. All trainable parameters are learned from source domains; target inference uses public question texts and the current student’s observed history without target-domain training. Leave-one-domain-out experiments on three public datasets show that TIZSKT achieves the highest mean AUC and accuracy among evaluated baselines on all target domains. Ablations confirm the contributions of text addressing and cross-segment continuation.
[NLP-15] Iterated Integrals as Lexical Fingerprints: Path Signature Embeddings for Lexical Similarity NEURIPS2026
【速读】： 该论文旨在解决现有句子嵌入方法在捕捉表面级词汇结构（lexical structure）与词序信息方面的局限性问题。尽管句子嵌入通常用于语义泛化，但其潜在的几何结构实际上隐含了词汇层面的特性，而这一关系尚未被充分理解。传统浅层池化方法（如平均池化）会丢失词序信息，而深层Transformer模型则因计算复杂性和不可解释的空间导致词汇对齐关系被掩盖。为此，本文提出基于Word2Vec词向量轨迹的路径签名（path signature）构建句子嵌入，利用粗糙路径理论中的迭代积分，以几何上可处理的方式显式建模序列和词汇结构。通过大量消融实验，验证了路径签名在表征字符级编辑距离（如Levenshtein、字符TF-IDF）方面显著优于传统方法，其中深度为2的签名在字符与词级敏感度之间达到最优平衡。此外，研究发现关键的词汇方差集中于一个高度紧凑的主成分分析（PCA）子空间，且路径签名能结构性地保留并量化词序信息。因此，路径签名并非语义泛化的工具，而是作为严谨的数学工具，显式编码离散词汇结构与词序，为词汇追踪提供了一种可解释的、非黑箱替代方案。
链接: https://arxiv.org/abs/2610.41855
作者: Raj Shekhar Singh,Pratham Agarwal
机构: Indian Institute of Technology, Roorkee(印度理工学院，鲁尔基); Roorkee, Uttarakhand, India(鲁尔基，北阿坎德邦，印度)
类目: Computation and Language (cs.CL)
备注: accepted in NeurIPS 2026 LP4FM Workshop
Abstract:While sentence embeddings are primarily designed for semantic generalization, their latent geometry implicitly encodes surface-level lexical properties, a relationship that remains poorly understood. Shallow pooling methods discard word order, while deep Transformer models obscure these lexical alignments behind computational complexity and uninterpretable spaces. We propose constructing sentence embeddings via the path signature of Word2Vec trajectories, leveraging the iterated integrals of rough path theory to create representations that explicitly capture sequential and lexical structure in a geometrically tractable manner. Through extensive ablations spanning Word2Vec dimensions, signature depths, and PCA compression, we correlate embedding distances against five lexical string metrics: Levenshtein, Jaro-Winkler, word Jaccard, phoneme Levenshtein, and character TF-IDF. We reveal that while average pooling almost entirely fails to capture character-level edit distances, path signatures strongly align with them, with depth 2 signatures optimally balancing character and word-level sensitivities. Furthermore, we demonstrate that the critical lexical variance is concentrated in a highly compact PCA subspace, and unlike pooling, signatures structurally preserve and quantify word order. These findings establish path signatures not as semantic generalizers, but as rigorous mathematical lenses that explicitly encode discrete lexical structures and word order, offering an interpretable alternative to opaque deep learning for lexical tracking.
[NLP-16] Stepwise-Querying: Distilling Reasoning without Access to Teacher Chain-of-Thought
【速读】： 该论文旨在解决在黑盒（black-box）设置下，大语言模型（Large Language Models, LLMs）作为教师（teacher）仅提供最终答案和简略解题过程时，导致中间推理步骤与有效性验证信息不可用，从而限制了生成式AI（Generative AI）在小模型（student）知识蒸馏中获取充分推理监督的问题。其解决方案的关键在于提出**分步查询（Stepwise Querying）方法：通过向同一教师进行独立的多轮查询，分别请求每个解题步骤的中间推导、有效性检查及修正，进而整合形成完整的解题序列，用于标准的监督微调（supervised fine-tuning）。该方法无需访问教师内部的思维链（Chain-of-Thought, CoT）轨迹即可有效获取结构化推理信号。此外，作者引入分步解释信息增益（Stepwise Explanation Information Gain, SEIG）**作为诊断指标，量化不同解释粒度对模型性能的影响。实验表明，在数学与代码四类基准测试中，该方法在16次对比中有14次优于三个基线方法；且分析显示，即使包含错误答案的推理路径，只要保留有效的中间推理过程仍具有价值，而进一步分解步骤带来的收益有限。
链接: https://arxiv.org/abs/2610.41769
作者: Dicong Yu,Wenhua Wang,Tian Wang
机构: Beijing Normal University (北京师范大学)
类目: Computation and Language (cs.CL)
备注: 15 pages, 7 figures, including appendices
Abstract:Reasoning distillation is widely used to transfer the reasoning capabilities of large language models to smaller students through teacher-generated supervision. In the black-box setting, however, teachers may expose only final answers and brief solutions, leaving intermediate calculations and validity conditions unavailable and thus limiting the reasoning supervision available for distillation. We propose Stepwise Querying, which elicits step-specific explanations without requiring access to the teacher’s internal chain-of-thought (CoT) traces. Independent queries to the same teacher request intermediate derivations, validity checks, and corrections for individual steps, the assembled solutions are then used for standard supervised fine-tuning. Across two teachers, two students, and four mathematics and code benchmarks, our method achieves the highest score in 14 of 16 comparisons with three baselines. We further introduce Stepwise Explanation Information Gain (SEIG), a diagnostic based on the log-ratio of a frozen student’s probabilities of correct step completion under stepwise and global explanations. Analyses on GSM8K show that incorrect-answer trajectories can remain useful when they preserve valid intermediate reasoning, while further decomposition yields limited additional gains.
[NLP-17] ask Vectors Can Be Linearly Decomposed into Interpretable and Controllable Components
【速读】： 该论文旨在解决生成式人工智能（Generative AI）中任务向量（Task Vectors, TVs）作为黑箱式控制指令缺乏可解释性与功能结构理解的问题。现有方法将TVs视为从隐藏状态中提取的孤立方向，未能揭示其内部的功能构成。为此，作者受上下文学习（in-context learning）可分解为任务识别（Task Recognition, TR）与任务学习（Task Learning, TL）的启发，提出一种基于TR/TL框架的TV分解方法，通过候选标签解嵌入（candidate-label unembeddings）将单个TV拆分为具有明确功能意义的TR、TL及残差（residual）组件。该方案的核心在于利用贝叶斯优化对TR和TL分量的权重进行联合调优，以实现更有效的模型干预。实验在多种模型架构、分类任务以及基于指令和示范的TVs上验证了该方法的有效性：相较于原始TVs，TR/TL优化显著提升性能，尤其在指令型TVs上表现更稳定可靠；此外，对分解后组件的独立干预在logit层面产生与预期功能一致的作用，进一步证实了各组件的功能可解释性。研究结果表明，TVs并非不可分割的单一控制方向，而是可被解构为具备功能性意义的子组件，从而为模型行为的解释与精准调控提供了新范式。
链接: https://arxiv.org/abs/2610.41743
作者: Yukiya Horiba,Hakaze Cho,Takahiro Yakoh
机构: Keio University(庆应义塾大学); RIKEN(理化学研究所); Tohoku University(东北大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Task Vectors (TVs) provide a compact way to steer large language models, but they are typically treated as atomized directions merely extracted from hidden states, leaving their internal functional structure poorly understood. Motivated by prior work that decomposes in-context learning into Task Recognition (TR) and Task Learning (TL), we ask whether a single TV can likewise be decomposed into functionally meaningful components. We formulate a TR/TL-based decomposition from candidate-label unembeddings, separating each TV into TR, TL, and residual components, and try to optimize the TR and TL coefficients using Bayesian optimization for more effective intervention. We evaluate this formulation across multiple model families, classification tasks, and both instruction- and demonstration-based TVs. Across these settings, TR/TL-based optimization identifies intervention directions that outperform the original TVs. Although optimization over random directions can also improve performance, TR/TL-based search yields more reliable gains, particularly for instruction-based TVs. Moreover, isolated interventions on the decomposed components produce distinct logit-level effects that are consistent with their intended functional roles. Together, these results suggest that TVs are not monolithic steering directions, but can be decomposed into functionally meaningful components that support both interpretation and control.
[NLP-18] Leakage-Controlled Structure-Preserving Augmentation and Template-Aware Evaluation for Bangla Symptom-Based Disease Prediction
【速读】： 该论文旨在解决孟加拉语症状-疾病预测中因数据集小、类别不平衡以及训练样本与测试样本间高度相似性（仅差一个或两个症状）所导致的模型评估偏差问题。其核心解决方案是引入仅在训练阶段应用的非对称伯努利扰动（train-only asymmetric Bernoulli perturbation），通过生成具有轻微变异的症状组合来增强训练数据的多样性，从而提升模型泛化能力。关键在于将数据增强与超参数调优分离，采用2×2实验设计，结合精确复制、随机过采样、类别加权和SMOTEN等多种平衡策略进行对照。结果表明，尽管伯努利朴素贝叶斯（BNB）在平均准确率上表现最佳（97.50%），但其增益在多重检验校正后不显著；而梯度提升树模型（如XGBoost、LightGBM、CatBoost）则获得显著性能提升，分别提高18.10、28.45和10.09个百分点。此外，通过汉明距离审计发现，91.0%的标准测试样本与训练数据仅相差一个症状位，说明现有基准测试存在模板接近性问题，因此模型在真实场景下的鲁棒性需结合症状空间邻近性综合评估。
链接: https://arxiv.org/abs/2610.41733
作者: Sabah Nushra,Shovan Samanta Turzo,Moinul Hossain,Md Kishor Morol
机构: Islamic University of Technology (伊斯兰科技大学); Daffodil International University (达福迪国际大学); American International University–Bangladesh (美国国际大学—孟加拉国分校); ELITE Research Lab (精英研究实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 6 pages, 5 figures, 5 tables
Abstract:Bangla symptom-based disease prediction is commonly evaluated on a small, imbalanced structured benchmark containing many profiles that differ by only one or two symptoms. We test whether train-only asymmetric Bernoulli perturbation improves classification using 725 unique profiles spanning 85 diseases and 172 binary symptoms. Experiments use ten repeated real-profile holdouts and parent-grouped inner cross-validation. The 2 x 2 design separates augmentation from hyperparameter tuning, with exact duplication, random oversampling, class weighting, and SMOTEN as balancing controls. For Bernoulli Naive Bayes (BNB), the gain over tuned raw training does not withstand Holm correction, although BNB attains the highest mean accuracy (97.50%). The boosted-tree models benefit substantially: XGBoost, LightGBM, and CatBoost improve by 18.10, 28.45, and 10.09 percentage points, respectively. A Hamming-distance audit places 91.0% of standard held-out profiles within one symptom bit of the training data. Accuracy decreases on a same-disease 1-bit-purged challenge; nevertheless, XGBoost and LightGBM retain large augmentation gains. We also evaluate robustness to missing and spurious symptoms and the faithfulness of BNB explanations. Overall, Bernoulli perturbation is useful for some model families, but benchmark accuracy should be interpreted together with template proximity.
[NLP-19] From Training Exposure to Membership Evidence: A Controlled Study of OLMo 2
【速读】： 该论文旨在解决语言模型训练数据成员身份推断（membership inference, MI）评估中因事后采样导致的分布偏移问题，以及由此引发的版权、隐私和基准污染等关键挑战。传统MI评估在模型训练完成后才收集成员与非成员样本，其成功可能源于数据分布变化而非真实成员性，而失败则难以得出明确结论。本文通过重新运行OLMo 2 1B模型的中期训练阶段，在7,814个领域与难度匹配的数据对中，以随机方式插入一个文档并保留另一个作为对照，构建了可控的“反事实”实验环境。其核心解决方案在于引入基于相同种子生成的反事实模型作为参考，通过比较目标模型与反事实模型在特定文档上的似然比（reference ratio），实现更准确的成员性检测。结果显示，该方法在跨领域统一阈值下可实现1%假阳性率（FPR）时捕获3.8%的成员，且受试者工作特征曲线下面积（AUC）达0.767；当使用13组匹配对进行配对检验时，具备80%功效在5%显著性水平下检测到训练痕迹。此外，使用不同种子训练的参考模型仍能恢复92%的反事实优势，而无需参考的攻击方法在跨领域汇总后仅略高于随机水平（差值≤0.07）。尽管完整官方后训练流程仍保留约89%的平均效应，但个体文档层面的显著变异性限制了精准的成员推断能力。研究团队将公开检查点、逐标记对数概率及评估数据集，代码亦已开源。
链接: https://arxiv.org/abs/2610.41732
作者: Ali Banaei,Muhammad Shahab Shahzad,Gerald Q. Maguire Jr.,Marco Chiesa,Dejan Kostić
机构: KTH Royal Institute of Technology (皇家理工学院)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 20 pages, 11 figures. Code: this https URL
Abstract:Whether a language model was trained on a document matters for copyright, privacy, and benchmark contamination. Yet most membership-inference (MI) evaluations on language models collect members and non-members after the fact, so success may reflect distribution shift and failure is inconclusive. We re-ran OLMo 2 1B’s official 50-billion-token mid-training stage, replacing selected training chunks with documents from 7,814 pairs matched on domain and difficulty. For each pair, a coin flip determined which document was inserted and which was held out. The run released by the Allen Institute for AI (AI2) from the same seed saw identical data everywhere else and serves as the counterfactual. One exposure raises a previously unseen document’s likelihood on average but rarely identifies it. Using the counterfactual as reference, the reference ratio has an area under the ROC curve (AUC) of 0.767 but, with one threshold across domains, catches 3.8% of members at a 1% false-positive rate (FPR). In contrast, 13 matched pairs give a paired test 80% power at the 5% level to detect training on a collection, given same-domain non-members and a close reference. A reference trained with another seed recovers 92% of the counterfactual’s AUC advantage over chance, whereas reference-free attacks stay within 0.07 of chance when pooled over domains. The full official post-training recipe, re-run on both models, leaves 89% of the average effect. Even with a closely matched reference, substantial document-level variability limits individual membership inference. We will release the checkpoints, per-token log-probabilities, and the evaluation documents; the code is available at this https URL.
[NLP-20] UpLoop: Training Looped Transformers via Upcycling at No Extra Pretraining Compute
【速读】： 该论文旨在解决循环式语言模型（Looped Language Models）在提升推理能力时伴随的训练计算成本（如浮点运算量，FLOPs）急剧增加的问题。尽管通过共享权重的重复计算可增强模型执行深度而无需引入额外参数，但每轮迭代均导致训练开销上升，限制了其实际应用。为此，论文提出一种名为UpLoop的预训练策略，其核心创新在于通过“再利用”（upcycling）机制，在保持最终参数量、总训练词元数和总训练FLOPs与标准密集型基线完全一致的前提下，实现对循环式Transformer的有效训练。具体而言，UpLoop从浅层网络开始，逐步通过复制已学习层来增加模型深度，并将早期训练中节省的计算资源用于后期对共享中间模块的多次执行。实验结果表明，该方法在不同模型规模下均显著提升了语言建模及下游任务性能，尤其在13亿参数规模下，22项下游任务的平均得分从22.99提升至27.52；此外，经监督微调后，该方法在数学推理任务上也展现出明显优势。
链接: https://arxiv.org/abs/2610.41720
作者: Cheng Liang,Tengfei Zhang,Ya Zhang,Yanfeng Wang,Zhouhan Lin
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Looped language models can strengthen reasoning through repeated computation with shared weights, increasing execution depth without adding independent parameters. However, each additional pass increases training floating-point operations (FLOPs), making these gains more expensive to obtain. To address this cost, we propose UpLoop, a pretraining schedule that uses upcycling to train looped Transformers while exactly matching a standard dense baseline in final parameter count, total training tokens, and training FLOPs. Specifically, UpLoop begins with a shallow Transformer, progressively grows its depth by copying learned layers, and uses the computation saved early to support repeated execution of a shared middle block later in training. Experiments across model scales show improvements in language modeling and downstream performance, with the average score across 22 downstream tasks increasing from 22.99 to 27.52 at 1.3B parameters. These gains also extend to mathematical reasoning after supervised fine-tuning.
[NLP-21] GraphNIAH: A Benchmark for Long-Context Reasoning over Real-World Graphs
【速读】： 该论文旨在解决现有“针尖对草堆”（Needle-in-a-Haystack, NIAH）基准测试在评估大语言模型（LLM）从长文本中检索微小相关事实能力时所面临的四大局限性：依赖合成数据导致泛化性不足、复用已有数据集存在基准污染风险、依赖人工标注带来维护成本高且易引发污染、以及对多跳推理的过度简化（即每一步推理均显而易见，缺乏真实世界中所需的多路径规划能力）。为克服这些问题，论文提出一个名为GraphNIAH的动态基准，其构建基于模型知识截止日期之后发表的科学论文之间的真实引用关系，确保数据来源真实且避免基准污染。该基准通过自动化流程从引用图中采样代表性子图，并将其转化为具有可验证答案的长上下文问答样本，从而更真实地模拟复杂多跳推理场景。实验评估了来自GPT-5.6、Gemini-3和Qwen3.5系列的八款大模型，结果表明：随着推理路径深度增加（如16步和32步），模型准确率显著下降，在回答是/否类问题时表现接近随机猜测（主要倾向回答“否”），且四款模型未能正确处理计数类问题，其余模型性能随子图规模增大而恶化。这表明当前主流大模型虽在上下文中包含所需全部事实，却往往在未完成完整推理路径前就停止搜索，暴露出其在长程依赖建模与系统性推理方面的根本缺陷。
链接: https://arxiv.org/abs/2610.41704
作者: Minh-Nam Tran,Abulhair Saparov
机构: Purdue University (普渡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The needle-in-a-haystack (NIAH) benchmark was introduced as a test for measuring a model’s ability to find small relevant facts within a very large body of input text. However, existing NIAH benchmarks each suffer from one or more of the following limitations: (1) reliance on synthetic data, so their evaluations may not generalize to real-world settings; (2) reuse of previously published datasets, leading to the risk of benchmark contamination; (3) dependence on manual annotation, which requires human effort to create and update data and is prone to future benchmark contamination; and (4) unrealistic simplification of long-context multi-hop reasoning where each next step is trivial given previous steps, while real-world reasoning often requires planning over multiple possible proof paths. To address these limitations, we introduce \Benchmark, a dynamic benchmark built from real citation relationships among scientific papers published after the evaluated models’ knowledge cutoff date, which utilizes real-world information while ensuring no benchmark contamination. The fully automated benchmark construction pipeline samples representative subgraphs of the citation graph formed by these papers and converts each subgraph into a long-context QA example with a verifiable answer. We use GraphNIAH to evaluate eight LLMs from the GPT-5.6, Gemini-3, and Qwen3.5 families. We find that accuracy falls as questions require longer reasoning paths: at question depths of 16 and 32, the GPT-5.6 and Qwen3.5 models answer YES/NO questions no better than random guessing, mostly by answering NO; four of the eight models answer no counting example correctly, and the others model get worse as the subgraph size increases. These findings suggest that current LLMs often stop searching the citation relationships in their context before they reach the answer, even though every fact they need is in the context.
[NLP-22] Precision–Recall Trade-off on the Inference Level for LLM s in Grammatical Error Correction
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在语法错误纠正（Grammatical Error Correction, GEC）任务中缺乏对精确率-召回率（precision–recall）权衡进行推理时动态调控的问题。尽管现有方法通过全微调或提示工程（prompt-based approaches）已实现较强的纠错性能，但其对生成行为的控制能力有限，尤其在需要灵活平衡纠正准确性与完整性时表现不足。本文的关键解决方案是利用推理阶段的重复惩罚（repetition penalty）参数，实现对生成过程的可控调节：通过调整该参数，可在不改变模型架构、训练流程或提示内容的前提下，动态优化GEC中的精确率与召回率之间的权衡。实验结果表明，基于重复惩罚的解码策略能够有效实现推理时的精度-召回控制，其概念上与序列到序列模型中的对齐-预测解码（Align-and-Predict Decoding, APD）等方法相关，同时具备简单性与对现有LLMs的广泛适用性。
链接: https://arxiv.org/abs/2610.41698
作者: Ryszard Staruch,Filip Graliński
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 2 figures, 13 tables
Abstract:Large language models (LLMs) have recently been adopted for Grammatical Error Correction (GEC), achieving strong performance via both full fine-tuning and prompt-based approaches. As probabilistic sequence models, LLMs expose inference-time parameters that directly modify token generation behavior; however, their potential for controlling GEC-specific trade-offs has not been systematically studied. In this work, we investigate decoding strategies for grammatical error correction. We show that the repetition penalty parameter enables controllable adjustment of the precision–recall trade-off at inference time, without modifying the model architecture, training procedure, or prompt. Through experiments on standard GEC benchmarks, we demonstrate that repetition-penalty-based decoding enables inference-time control of the precision–recall balance, conceptually related to methods previously proposed for sequence-to-sequence GEC models, such as Align-and-Predict Decoding (APD), while remaining simple and broadly applicable to existing LLMs that expose inference-time decoding penalties.
[NLP-23] SBPO: Tree-Structured Branching for Policy Optimization in Interactive Language Agents
【速读】： 该论文旨在解决从终端结果（terminal outcomes）训练交互式语言代理时面临的三个相互关联的核心问题：如何采样任务与状态的延续（continuations）、如何将延迟反馈（delayed feedback）分配给具体决策、以及如何对多标记动作（multi-token actions）进行标记损失聚合。其解决方案的关键在于提出一种树结构的“采样-更新”范式（TSBPO），在单一训练循环中统一处理上述挑战。该方法通过一个基于似然拟合的根节点/分支分配器，利用保留的任务与状态证据，聚焦于那些在固定回溯预算下可能产生对比性终端结果的延续路径；在重构的物理树结构上，采用轨迹优势（trajectory advantages）比较相邻子段的即时收益，并通过观察组步级优势（observation-group step advantages）在相同任务/观测状态下对比最终结果；代理策略则对每个完整响应中的截断标记代理损失求和，并在活跃回合间进行归一化，从而避免了传统策略梯度中对响应长度的平均化处理。实验表明，在Qwen2.5-1.5B模型基础上，该方法在ALFWorld基准上实现了97.79%的已见任务成功率与96.64%的未见任务成功率，在WebShop上每500个测试目标均实现81.07%的单集成功率，且消融实验验证了步骤级损失加权的有效性，支持所提训练配置在两个交互式基准上的优越性能。
链接: https://arxiv.org/abs/2610.41697
作者: Rui Yu,Chong Mu,Jinchuan Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Training interactive language agents from terminal outcomes poses three coupled problems: which task and state continuations to sample, how to assign delayed feedback to decisions, and how to aggregate token losses for multi-token actions. We present TSBPO, a tree-structured sampling-to-update paradigm that addresses these problems within one training loop. A likelihood-fitted Root/Branch allocator uses retained task and state evidence to target continuations likely to produce contrasting terminal outcomes under fixed rollout budgets. On the resulting replayed physical trees, trajectory advantages compare immediate sibling segments, while observation-group step advantages compare outcomes at matched task/observation states. The actor then sums clipped token surrogates over each complete response and normalizes across active turns, avoiding response-length averaging in the policy-gradient term. With a Qwen2.5-1.5B actor, fixed checkpoints obtain 97.79% seen and 96.64% unseen stochastic success on ALFWorld and an 81.07% per-episode success rate over six stochastic episodes for each of the 500 WebShop test goals. A WebShop step-term ablation yields 75.93% at the same update-100 evaluation; an ALFWorld aggregation comparison shows higher observed command validity with complete-turn weighting. These results support the evaluated training configuration on two interactive benchmarks.
[NLP-24] Does Reasoning Make Language Models Agree?
【速读】： 该论文旨在解决生成式 AI（Generative AI）在模型决策一致性方面存在的“单一化”（monoculture）与“多样性”（multiplicity）之间的矛盾：即不同模型在面对相同输入时，可能因共享相似的推理路径而产生高度一致但易受共性偏差影响的输出，或因各自独立的推理过程导致准确但相互冲突的判断。随着推理型模型（reasoning models）的兴起，这一矛盾进一步复杂化——推理行为既可能通过锚定共同显著特征或遵循相似推理路径提升模型间的一致性，也可能因随机性推理路径引入更多分支点而加剧预测分歧。为调和此矛盾，研究提出将“推理”区分为作为干预手段（intervention）的主动推理与作为观测行为（observed behavior）的被动推理。研究通过对比在控制推理努力程度下的模型一致性与在实际观测到不同推理长度下的模型一致性，并以模型准确性预期为基准进行校准，发现：在对同一组样本施加可控推理干预时，模型间的共识度显著提升；然而，在实际观察中，那些引发更长推理链条的样本反而表现出更低的跨模型预测一致性。上述现象在控制模型准确率差异后依然成立。因此，解决方案的关键在于：主动增强推理能力可促进模型间的一致性，而观测到的较长推理过程则预示着更高的预测分歧，从而揭示了推理作为干预与作为行为在模型一致性中的双重作用机制。
链接: https://arxiv.org/abs/2610.41691
作者: Mila Gorecki,Moritz Hardt
机构: Max Planck Institute for Intelligent Systems, Tübingen(马普智能系统研究所，图宾根); Tübingen AI Center(图宾根人工智能中心)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Language models face two opposing concerns: monoculture, in which models err in correlated ways, and multiplicity, in which equally accurate models make conflicting decisions. The rise of reasoning models has complicated this debate: On the one hand, reasoning may increase model agreement if models anchor on the same salient features or follow similar reasoning paths. On the other hand, stochastic reasoning traces may introduce additional branch points leading to more diverse predictions. We resolve this tension by distinguishing reasoning as an intervention from reasoning as an observed behavior. We compare agreement under controlled changes in reasoning effort with agreement across observed reasoning lengths, measuring both against the agreement expected from model accuracy. Across nine models from two developers and three classification tasks, enabling reasoning increases agreement between models evaluated on the same individuals. Yet the individuals who elicit longer reasoning traces receive less consistent predictions across models. Both patterns persist after accounting for differences in accuracy. Asking models to reason more thus promotes agreement, while observing them reason longer signals disagreement.
[NLP-25] When Steering Shifts Discourse: Identifying and Intervening on Discourse-Level Changes due to Activation Steering in LLM s
【速读】： 该论文旨在解决生成式AI（Generative AI）中激活向量调控（activation steering）技术在推理阶段对语言模型输出产生的非预期语篇维度偏差问题。尽管激活向量调控可通过向残差流（residual-stream）添加向量实现对模型行为、语气或安全属性的无权重调整，但其会引发文本在正式性、证据使用程度和模糊性等语篇特征上的系统性改变，且这些变化缺乏充分理解。现有研究仅依赖困惑度（perplexity）等粗粒度指标评估此类影响，难以揭示深层语义偏差。本文提出一种关键解决方案：引入第二个互补向量，专门针对被原始调控所影响的语篇维度进行修正。该方法能够有效恢复约78%的非预期语篇变化，同时在约93%的生成样本中保持原始调控意图不变，显著提升了调控过程的可控性与可预测性。
链接: https://arxiv.org/abs/2610.41661
作者: Shivam Adarsh,Hetianyu Huang,Lau Safin,Maria Maistro,Christina Lioma
机构: University of Copenhagen(哥本哈根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Activation steering is an inference-time technique used to control or modify the behavior, tone, or safety properties of a large language model (LLM) without retraining the model’s weights or editing its input prompt, but simply by adding a vector to its residual-stream activations. However, activation steering also changes the generated text in unintended ways, unrelated to intended behavior change, that are not well understood. Prior work has measured these changes only through coarse signals such as perplexity. In this work, we show that steering systematically produces significant changes in discourse dimensions of the generated text, such as its formality, use of evidence, and ambiguity. We present a way of countering this by adding a second, complementary vector that targets the affected discourse dimension. This intervention restores about 78% of the unintended discourse changes while preserving the intended steering behavior in about 93% of the generations.
[NLP-26] GRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning WACV2027
【速读】： 该论文旨在解决生成式视觉-语言模型（Vision-Language Models, VLMs）在文本密集型推理任务中，基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）方法效果受限的问题。其核心挑战在于：模型在推理过程中虽能得出正确最终答案，却可能因对视觉信息的误解而产生“正确但视觉上不准确”的推理路径（Correct-but-Visually-Misgrounded Reasoning, CVMR），而标准RLVR仅依据最终答案正确性给予奖励，无法区分此类错误推理与真正合理推理，从而导致模型被错误地鼓励保留不具视觉一致性的推理过程。论文提出的关键解决方案是GRIT框架，通过引入视觉一致性约束机制，将视觉接地性（visual grounding）纳入强化学习目标，以抑制CVMR现象。此外，为系统评估不同文本长度和推理深度下的性能，研究构建了GRIT-Bench基准测试集。实验表明，相较于现有RLVR方法，GRIT显著提升了推理准确性与视觉接地保真度。
链接: https://arxiv.org/abs/2610.41629
作者: Chanseul Cho,Noori Bae,Dongheon Lee,Youngmin Ro
机构: University of Seoul (首尔大学); Data Science Interdisciplinary Major (数据科学跨学科专业)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to WACV 2027
Abstract:Reinforcement learning with verifiable rewards (RLVR) has recently been extended to vision-language models (VLMs), yet its effectiveness remains limited for text-rich reasoning tasks. We highlight a key factor contributing to this limitation: models frequently arrive at correct final answers despite misperceiving visual information during intermediate steps. We define this phenomenon as Correct-but-Visually-Misgrounded Reasoning (CVMR). Because standard RLVR rewards only final-answer correctness, it fails to differentiate between properly grounded and misgrounded reasoning, reinforcing both equally. Through token-level analysis, we reveal that CVMR tokens exhibit distinct attention patterns that differ systematically from well-grounded ones. Based on this observation, we propose GRIT, a training framework that incorporates visual grounding into RLVR to mitigate the impact of CVMR. Furthermore, to address the lack of text-rich reasoning benchmarks that allow for systematic evaluation across text length and reasoning depth, we introduce GRIT-Bench. Experiments demonstrate that GRIT significantly improves both reasoning accuracy and grounding fidelity over existing RLVR methods.
[NLP-27] Saidzoda-Tajik-LFM-2.6B: Adapting a 2.6B Hybrid Model to Tajik Tool Calling and What Continued Pretraining Takes Away
【速读】： 该论文旨在解决在资源受限的硬件条件下，为塔吉克语（Tajik）构建具备工具调用能力的轻量化大语言模型问题。其核心挑战在于：如何在缺乏大规模标注数据的情况下，有效提升模型对塔吉克语指令的理解与工具调用准确性。解决方案的关键在于采用两阶段训练范式：首先在148.5万条清洗后的塔吉克语网页文档上进行低秩适配（LoRA）持续预训练（CPT），以增强模型对塔吉克语的语义理解；随后在混合数据集上进行一阶段监督微调（SFT），该数据集包含机器翻译的塔吉克语工具调用指令、保留英文原版的工具调用作为回放样本以及塔吉克语指令。实验表明，该方法使塔吉克语提示下的严格全调用准确率从基线模型的20.69%显著提升至64.14%（提升43.45个百分点），且在非工具调用任务中塔吉克语知识基准得分也有所提高。然而，该模型并非通用塔吉克语助手，其在无合适工具时拒绝调用的能力较弱，多轮调用表现不佳，且工具调用结果需人工验证，因此仍存在局限性。
链接: https://arxiv.org/abs/2610.41576
作者: Tohir Saidzoda,Nekruz Maxudov,Bedil Karimov,Abubakr Abdullozoda,Amin Jalilov,Jonibek Kishvarov,Rahmonali Sharipov,Mariyamkhon Shodyeva
机构: Saidzoda AI Lab(萨伊德佐达人工智能实验室); Saidzoda Engineering Company(萨伊德佐达工程公司)
类目: Computation and Language (cs.CL)
备注: 12 pages, 9 tables, 3 figures. Technical report. Model: this https URL
Abstract:We release Saidzoda-Tajik-LFM-2.6B, a Tajik adaptation of the 2.6B-parameter hybrid model LFM2.5-2.6B-Base, built for tool calling in Tajik on modest hardware. The recipe has two stages: LoRA continued pretraining (CPT) on 1,485,319 cleaned Tajik web documents for 3 epochs, then one epoch of supervised fine-tuning (SFT) on a mix of machine-translated Tajik tool calls, English tool calls kept as replay, and Tajik instructions. On a 174-item tool-calling test set built from BFCL v3 and machine-translated to Tajik, strict full-call accuracy on Tajik prompts rises from 20.69 for the base model to 64.14 (paired difference +43.45 points, 95% interval +35.2 to +51.7); a lenient parser that also accepts the base model’s own call formats puts the base at 31.72. English shows no detectable change (76.55 and 77.24). Every CPT checkpoint scores zero: CPT removes the native tool-call format and, as the lenient re-scoring shows, part of the skill itself. Outside tool calling, accuracy on a Tajik knowledge benchmark rises from 28.0 to 35.0, Belebele Tajik shows no significant change, and English MMLU falls from 60.8 to 52.5. The model is not a general Tajik assistant. It almost never declines when no tool fits (6.9% correct on Tajik, against 31.0% for the base under the lenient parser), it is weak at multi-turn calls, and its calls should be checked before they are executed. We describe the data, the training settings and every evaluation in enough detail to repeat them.
[NLP-28] BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion
【速读】： 该论文旨在解决自回归（Autoregressive, AR）语言模型向扩散语言模型（Diffusion Language Model, DLM）转换过程中存在的高成本与低效率问题，尤其是现有方法普遍依赖昂贵的预训练或全模型微调。其核心解决方案是提出一种通用且参数高效的LoRA-only转换框架——Build Your Own DLM（BYOD），实现无需架构特化调优即可将主流AR模型（如Gemma 2 9B、Llama 3.1 8B、Qwen2.5 7B和Ministral 8B）高效转化为支持并行生成与双向注意力的DLM。该方法仅通过可学习的低秩适配器（LoRA）进行微调，适配器参数占比仅为4.1%-5.8%，所有基础模型参数保持冻结；每次转换仅需最多10240万条样本，可在单个Google Colab GPU上于3.1-4.0小时内完成。实验表明，四类模型均能从完全掩码序列开始生成，并在控制性注意力测试中有效利用双向上下文，同时保留原始模型的大部分知识。尽管在开放生成质量方面仍存在不足（如在低网络函数评估（NFE）预算下，BYOD的困惑度为8.3-11.1，显著高于对应AR基线模型的2.2-3.0及对比模型LLaDA的4.1），但其在九项任务上的平均准确率达50.6%（以BYOD-Gemma为例），接近同评测环境下LLaDA 8B Instruct的51.9%表现。因此，BYOD提供了一种可扩展至多种现有模型家族的实用路径，同时也揭示了未来高效转换方法亟待突破的关键瓶颈：如何在极低计算开销下提升扩散生成的质量。
链接: https://arxiv.org/abs/2610.41574
作者: Ruurd J.A. Kuiper,Robert A. Bagheri
机构: University Medical Center Utrecht (乌得勒支大学医学中心); Utrecht University (乌得勒支大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 8 figures. Code: this https URL ; Gemma demo: this https URL . Links to all four demos are included in the paper
Abstract:Unlike autoregressive (AR) models, diffusion language models (DLMs) permit parallel token updates, flexible generation, and bidirectional attention, but leading systems rely on costly pretraining or full-model fine-tuning. We present Build Your Own DLM (BYOD), a generalizable LoRA-only recipe for parameter-efficient AR-to-DLM conversion. Without architecture-specific tuning, BYOD induces diffusion generation in Gemma 2 9B, Llama 3.1 8B, Qwen2.5 7B, and Ministral 8B; to our knowledge, this is the first reported AR-to-DLM conversion of a Mistral model. The adapters comprise 4.1-5.8% of final parameters while every base-model parameter remains frozen; each 25,000-iteration conversion needs at most 102.4M tokens, processed in 3.1-4.0 hours on one Google Colab GPU. All four models generate from fully masked sequences, use bidirectional context in a controlled attention test, and retain substantial parent-model knowledge. BYOD-Gemma is the strongest BYOD conversion on our nine-task evaluation, reaching 50.6% average accuracy compared with 51.9% for LLaDA 8B Instruct in the same harness. Open-generation quality at low network function evaluation (NFE) budgets remains the principal limitation: BYOD perplexity is 8.3-11.1 versus 4.1 for LLaDA and 2.2-3.0 for the AR parents when generating 128 tokens in 64 steps. To enable further experimentation and inspection, we provide a standalone training and evaluation implementation and interactive demos for all four conversions. BYOD thus provides a practical route for extending diffusion generation to other existing model families while identifying the gap that efficient conversion must next close.
[NLP-29] SCRBench: Beyond Equivalence Checking for Semantic Change Reasoning in Code
【速读】： 该论文旨在解决大语言模型在代码理解中对代码变更语义影响的推理能力评估不足的问题。现有方法通常仅通过判断程序是否语义等价来评估模型性能，但这种二元判断无法揭示模型是否真正理解具体是哪处代码修改导致了语义变化，或能否还原出完整的代码变更序列。为此，论文提出两个新任务：诊断语义变化（diagnosing semantic change）和重构代码变更序列（reconstructing sequences of code changes），并构建了SCRBench数据集，包含67,658个经多步代码变更生成的Python程序对。实验表明，尽管最强模型在语义等价检测任务上接近满分，但在诊断语义变化任务中仅能正确识别54.44%的变更，而在重构完整变更序列的任务中表现更差，即使仅涉及两步变更，准确率也仅为39.27%，当变更数达到五步时，最高准确率不足1.11%。这表明，高语义等价准确率会严重夸大模型对代码变更语义影响的真实理解能力，因此，当前评估范式存在显著局限性，亟需引入更精细的诊断与重构任务以全面衡量模型的代码推理能力。
链接: https://arxiv.org/abs/2610.41562
作者: Omid Saghatchian,Ali Mahzoun,Khashayar Etemadi,Saeed Reza Kheradpisheh
机构: Shahid Beheshti University (谢里夫·贝赫什特大学); ETH Zurich (苏黎世联邦理工学院)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: 17 pages, including references and appendices
Abstract:Understanding how code changes affect program semantics is a core aspect of code reasoning in language models. It is commonly evaluated by asking whether two programs are semantically equivalent, but a yes/no answer does not reveal whether a model knows which code change caused a semantic difference, or which sequence of code changes produced the modified program. We study two tasks that ask these questions directly, diagnosing semantic change and reconstructing sequences of code changes, alongside detecting semantic equivalence. We build SCRBench, 67,658 Python program pairs generated by applying sequences of code changes that preserve or alter program semantics, and evaluate five language models on a stratified subset of 4,500 pairs. Detecting semantic equivalence exposes weaker models, whose accuracy declines as changes accumulate, but the strongest models remain near the ceiling. Diagnosing semantic change reveals weaknesses that equivalence accuracy hides: a model with 94.71% equivalence accuracy diagnoses only 54.44% of semantic changes. Reconstructing sequences of code changes is much harder: the best model reconstructs the exact sequence for only 39.27% of pairs, and no model exceeds 1.11% when five changes are applied. High equivalence accuracy therefore overestimates how well models reason about the semantic effects of code changes.
[NLP-30] Digital sovereignty in professional translation workflows: a case study of migrating from Windows to Linux
【速读】： 该论文旨在解决专业翻译领域中数字主权（Digital Sovereignty）缺失的问题，即译者在依赖特定技术平台与云服务时所面临的控制力不足、工作连续性受威胁及退出机制受限等困境。其解决方案的关键在于构建一个以“控制（Control）、持续性（Continuity）、退出（Exit）”三大原则为基础的实践框架，并明确三项具体要求：系统必须原生运行于Linux环境、不强制依赖外部服务商提供的服务、确保对关键数据与资源的独立访问权。研究通过筛选计算机辅助翻译工具与翻译管理系统，并开展从Windows向Linux迁移的工作站案例分析，验证了基于本地可操作的Linux核心（如LoopCAT、LibreOffice、本地部署的TranslateGemma）重构专业工作流的可行性。同时，保留必要的云端服务以满足特定需求，表明数字主权并非追求技术封闭，而是实现对核心工作组件的实际掌控、保障工作的可持续性以及具备切实可行的退出路径。研究结论强调，数字主权的本质取决于工作流依赖关系的组织方式，而非单一应用或操作系统的选择。
链接: https://arxiv.org/abs/2610.41561
作者: Michael Farrell
机构: 未知
类目: Computation and Language (cs.CL)
备注: 14 pages, submitted for formal peer review at the 47th Translating and the Computer conference (Luxembourg) 8/10 December 2026
Abstract:This study examines digital sovereignty in professional translation through a screening of computer-assisted translation tools and translation management systems, and a case study of migrating a translator’s workstation from Windows to Linux. The framework combines three principles - Control, Continuity and Exit - with three practical requirements: native Linux operation, independence from mandatory external services, and independent access to essential data and resources. Drawing on documentary evidence and the limited number of responses received from providers, the ecosystem screening distinguishes Linux accessibility from operational independence: browser-based platforms may remain dependent on provider-controlled infrastructure. The migration case demonstrates the feasibility of reconstructing one professional workflow around a locally operable Linux core, using LoopCAT, LibreOffice and locally run TranslateGemma, alongside tools for optical character recognition, communication, credential management and backup. Selected cloud services remained available where needed. The findings suggest that digital sovereignty depends on how workflow dependencies are organized, rather than on individual applications or operating systems. It does not require technological isolation, but meaningful control over essential components, continuity of work and practical exit options. The framework is tailored to the case examined here and may require adaptation to other professional workflows.
[NLP-31] CEFR Level Assessment across Languages and Text Granularities
【速读】： 该论文旨在解决文本水平的欧洲语言共同参考框架（CEFR）等级自动评估中因各等级自然分布不均以及相邻等级间边界模糊导致的准确预测难题。其解决方案的关键在于提出一种统一的训练目标，将焦点损失（focal loss）与有监督对比学习（supervised contrastive learning）相结合：焦点损失通过强化少数类和难例样本的权重来缓解类别不平衡问题，而有监督对比学习则通过增强类内紧凑性与类间分离性，促使模型学习更具判别性的语义表示。该方法在单语和多语环境下均实现了句子级与文档级的CEFR评估任务，实验结果表明其在所有测试场景下均显著优于现有强基线模型，并在多个基准数据集上建立了新的性能上限。
链接: https://arxiv.org/abs/2610.41553
作者: Eugene Matsumura,Yuki Arase
机构: RWTH Aachen University (亚琛工业大学); Institute of Science Tokyo (东京科学大学)
类目: Computation and Language (cs.CL)
备注: 8 pages, 2 figures, 5 tables
Abstract:Automatic assessment of textual CEFR levels is an important task in educational natural language processing. However, accurate CEFR level prediction remains challenging due to the naturally imbalanced distribution of CEFR levels and the ambiguous boundaries between adjacent levels. To address these challenges, we propose a unified training objective that combines focal loss with supervised contrastive learning. Focal loss mitigates class imbalance by emphasizing underrepresented and difficult examples, while supervised contrastive learning learns more discriminative representations by encouraging intra-class compactness and inter-class separation. We evaluate the proposed method on sentence- and document-level CEFR assessment in both monolingual and multilingual settings. Experimental results demonstrate that our method consistently outperforms strong baselines across all settings, establishing new state-of-the-art performance on multiple CEFR assessment benchmarks.
[NLP-32] DAPS: Dependency-Aware Premise Selection for LLM Theorem Proving
【速读】： 该论文旨在解决基于大语言模型（LLM）的Lean 4定理证明系统在利用新贡献引理时面临的难题：现有检索机制因偏好高频基础引理而忽视了大量潜在相关但冷门的前置条件，导致有效前提被淹没于数十万候选项中。现有神经选择器依赖语义相似性进行排序，存在三个固有缺陷：频率偏差检索、孤立的成对排名机制，以及在竞赛题和非正式语言等分布外查询上的性能退化。针对数学知识本质上由依赖关系而非语义相似性构成这一特性，本文通过重构训练数据与模型结构中的依赖信号来改进系统。具体而言，研究者首次系统性挖掘了53个Lean Blueprint项目，构建了一个包含3,805个手工标注的“非正式+形式化”节点的稀有研究级前提语料库，同时提取了全面、类型化、多层次的Mathlib4依赖关系。在此基础上，提出DAPS（Dependency-Aware Premise Selector），其核心创新包括：结构邻域编码器以捕捉依赖拓扑，组级别对比学习目标以增强语义区分能力，以及“先Mathlib后Blueprint”的适应策略。实验表明，DAPS在Mathlib4-Heldout数据集上达到89.25的平均召回率@32，显著优于强基线模型LeanHammer（提升11.41个百分点），并在两个分布外基准上保持优势；进一步验证显示，DAPS选出的前提可有效提升miniF2F与IMO-ProofBench上的定理证明性能。
链接: https://arxiv.org/abs/2610.41536
作者: Zixuan Chen,Wenyuan Jiang,Junling Wang,Mrinmaya Sachan,Yinya Huang
机构: ETH Zurich (苏黎世联邦理工学院); ETH AI Center (苏黎世联邦理工学院人工智能中心)
类目: Logic in Computer Science (cs.LO); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the right premises buried among hundreds of thousands of candidates. Existing neural selectors rank candidates by semantic similarity, with three intrinsic limitations: frequency-skewed retrieval, isolated pairwise ranking, and degradation on out-of-distribution queries such as competition problems and informal language. Given that mathematics is structured by dependencies rather than similarities, we restore this signal to both the training data and the model. We curate two underused dependency resources. We conduct the first systematic mining of 53 Lean Blueprint projects, a previously untapped corpus of 3,805 manually curated informal+formal nodes that capture rare, research-level premises absent from Mathlib4. We further pair it with the extraction of comprehensive, typed, multi-level Mathlib4 dependencies. We then introduce DAPS, a dependency-aware premise selector with a structural neighborhood encoder, a group-level contrastive objective, and a Mathlib-then-Blueprint adaptation procedure. DAPS reaches mean Recall@32 of 89.25 on Mathlib4-Heldout (+11.41 over the strong selector LeanHammer), and maintains this lead on two out-of-distribution benchmarks. Further experiments demonstrate that DAPS premises improve theorem proving on miniF2F and IMO-ProofBench. Project page: this https URL.
[NLP-33] Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning
【速读】： 该论文旨在解决小规模情感分类任务中因数据稀缺导致模型泛化能力受限的问题。其解决方案的关键在于系统评估并比较三种数据增强策略——易数据增强（Easy Data Augmentation, EDA）、反向翻译（back-translation）以及上下文感知的词元替换（contextual token substitution, nlpaug-style）——在不同模型架构下的有效性。研究发现，所有增强方法均能提升性能，其中上下文感知增强对基于Transformer的BERT模型表现最为稳定，而EDA与反向翻译则对传统机器学习分类器（如逻辑回归、随机森林）带来更显著的增益。这一结果为针对不同模型类型和数据规模选择合适的数据增强技术提供了实证依据，填补了小样本情感分类中数据增强研究的关键空白。
链接: https://arxiv.org/abs/2610.41518
作者: Mahmoud S. Mayaleh,Samer A. Mayaleh
机构: National Conservatory of Arts and Crafts (CNAM)(国立工艺美术学院); An-Najah National University(安纳杰大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augmentation (EDA), back-translation, and contextual token substitution (nlpaug-style), with both traditional machine learning classifiers (Logistic Regression, Random Forest) and transformer-based models (BERT). We perform a comprehensive empirical comparison with low-resource sentiment datasets by summarizing the results of recent studies and performing targeted head-to-head experiments. Our findings indicate that all augmentation methods improve performance. Contextual augmentation yields the most consistent gains for BERT models, while EDA and back-translation provide greater benefits for traditional classifiers. These insights help guide the selection of data augmentation techniques tailored to model type and dataset size, filling a critical gap in research on data augmentation for sentiment classification on small datasets.
[NLP-34] Sparse Text Diffusion under Dual Budgets: Context Connectivity and Expert Activation
【速读】： 该论文旨在解决长文本生成中因重复计算注意力交互与前馈网络运算所带来的高计算开销问题。其核心挑战在于如何在保持生成质量的同时，有效控制生成式AI（Generative AI）在长上下文场景下的计算资源消耗。解决方案的关键在于将计算成本解耦为两个独立的预算：注意力图中的边数（edges available to an attention graph）和每个标记激活的专家数量（experts activated for each token），从而建立一个可量化、可调控的计算框架。通过引入归一化掩码注意力机制，明确区分去噪深度（denoising depth）与采样迭代次数，并推导出图结构与计算量的理论边界，该方法实现了对局部、稀疏与全局注意力模式以及条件专家计算的统一建模。实验采用名为DualBudget的稀疏专家配置，在科学摘要、多跳问答、对话系统及句子对预测等任务中验证了其有效性。结果表明，该方法在16K长度范围内持续优于基线模型，尽管随序列长度增长出现性能下降，但整体计算成本相较DiffuSeq降低了20%（在未明确定义的时间基准下）。分析还剥离了未被测量的训练速度、吸收态、全局连贯性及部署内存等主观主张，使结论更具可复现性与可信度。
链接: https://arxiv.org/abs/2610.41487
作者: Alexandros Christoforos,Chadbourne Davis,Paul Raj
机构: Suffolk University(萨福克大学); Texas A&M University-Commerce(德克萨斯A&M大学-通纳)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-context diffusion repeatedly pays for both token interaction and feed-forward computation. We formulate these costs as two separate budgets: the edges available to an attention graph and the experts activated for each token. This view connects local, dilated, and global attention with conditional expert computation without assuming that sparsity alone preserves document-level information. We specify normalized masked attention, distinguish denoising depth from sampling iterations, and derive graph and compute bounds. A reported sparse-expert configuration, denoted DualBudget, is analyzed across scientific summarization, multi-hop question answering, dialogue, and sentence-pair prediction. All supplied table measurements are retained. The length study shows gains over the listed baselines through 16K, together with measurable degradation as length increases. Ablations reveal mixed changes across the three reported scores, while the cost record indicates a 20% reduction relative to DiffuSeq under an unspecified timing convention. The analysis separates these observations from unmeasured claims about training speed, absorbing states, global coherence, and deployment memory.
[NLP-35] KEEPER: Learning Control-Sufficient Textual Memory for Long-Horizon LLM Agents
【速读】： 该论文旨在解决递归文本记忆（recursive textual memory）在更新过程中可能将早期错误持续传递至后续推理阶段的问题，尤其关注任务性能指标无法充分反映记忆可靠性这一局限性。研究提出“自一致性偏差”（self-consistency bias）的概念，即已有错误陈述在输入记忆中持续存在并影响后续决策的现象。其解决方案的关键在于引入一种基于共享递归接口的对比分析框架——KEEPER，通过在信息含量（information-content）与更新敏感性（update-sensitivity）两个维度上评估四种辅助目标，系统考察监督信号设计对智能体表现的影响。实验结果表明，基于奖励预测的辅助目标在离线强化学习前后均取得最优任务性能，优于联合监督策略；进一步的断言级分析揭示了基线轨迹中即使存在纠正性观察，错误仍持续存在，凸显了仅依赖任务结果评估的不足。因此，该研究强调应将错误持续性（error persistence）与有效信息保留能力（valid-information retention）纳入评估体系，以更全面地衡量记忆系统的可靠性。
链接: https://arxiv.org/abs/2610.41482
作者: Jinkyeong Choi,Chaebin Jeong,Junsung Jeon,Donghyeon Park
机构: Sejong University(世宗大学); Seoul, Republic of Korea(首尔，大韩民国)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recursive textual memory can carry earlier errors into later updates, yet task performance alone does not establish memory reliability. We define self-consistency bias as the persistence of incorrect claims already present in the incoming memory. We present KEEPER, a comparative analysis of four auxiliary objectives along information-content and update-sensitivity axes within a shared recursive interface, examining how supervision targets relate to agent performance. Among evaluated conditions, reward-related prediction achieve the highest task performance before and after offline reinforcement learning, outperforming joint supervision. A claim-level analysis of one baseline trajectory documents error persistence despite corrective observations. Together, these analyses motivate evaluating error persistence and valid-information retention alongside task outcomes.
[NLP-36] KV-Cache Patching: Tracing How and Where Fine-Tuned Behaviors Arise ATC NEURIPS2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）在进行参数微调（如LoRA微调）后，其行为改变究竟在何处实现这一关键问题。具体而言，研究关注的是：微调带来的行为变化是否仅体现在生成答案时的输出阶段，还是源于对先前输入令牌的处理过程，通过注意力机制（attention mechanism）产生影响。为此，研究提出一种基于键值缓存（KV cache）的“拼接”（patching）方法，将微调模型的KV缓存注入到原始基线模型中，反之亦然。实验结果表明，对于某些行为（如以德语回应或出现意外对齐偏差），微调后的KV缓存能够成功将这些行为转移到基线模型中；而将基线模型的KV缓存注入微调模型，则可部分甚至完全消除微调所引入的行为特征。此外，研究发现拼接效果随被替换的KV缓存比例增加而增强，并集中于少数关键位置，其中系统提示词（system-prompt）和聊天模板（chat-template）相关的令牌具有显著更大的影响力。因此，该研究的关键发现是：微调行为的实现不仅依赖于生成阶段的参数调整，更依赖于整个推理过程中由注意力机制驱动的上下文状态演化，且其核心效应集中在特定关键输入位置所形成的缓存状态上。
链接: https://arxiv.org/abs/2610.41480
作者: Jonas Loos,Oliver Eberle
机构: Technische Universität Berlin(柏林工业大学); Berlin Institute for the Foundations of Learning and Data (BIFOLD)(柏林学习与数据基础研究所); Zuse School ELIZA(祖斯学院ELIZA)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Interpretability as a Science Workshop at NeurIPS 2026. Project page: this https URL
Abstract:Fine-tuning a language model changes how it responds, but where is this fine-tuned behavior instantiated? To better understand this, we evaluate whether the change in model behavior arises only during the generation of the answer tokens where the change manifests, or whether altered processing of previous tokens causes it via the attention mechanism. For this, we patch the KV cache from a LoRA fine-tuned model into the unmodified base model, and vice versa. Our results show that for some behaviors, like responding in German or emergent misalignment, patching the KV cache can indeed transfer them from the fine-tuned to the base model. Correspondingly, patching the KV cache from base to fine-tune can partially or fully remove fine-tuned behavior. Furthermore, we find that the effect of patching grows with how much of the KV cache is patched and concentrates at a few pivotal positions, with system-prompt and chat-template tokens having disproportionate influence.
[NLP-37] BlindToChange: AI Agents Can Find Changes When Asked But Do They Look When Needed?
【速读】： 该论文旨在解决当前生成式AI代理（AI agents）在处理实时信息时存在的核心缺陷：尽管其具备实时网络搜索能力，但其输出结果的准确性高度依赖于是否主动检索以及检索的内容是否覆盖了关键的近期变化。现有主流评测基准（如BrowseComp）因明确告知需查找的信息，无法揭示代理在面对用户隐含需求时未能识别自身假设需验证的问题。为此，论文提出一种名为BlindToChange的新基准，用于评估代理是否能主动发现并响应未在用户请求中明示的近期现实事件（如博物馆临时关闭）。该基准通过从Wikidata自动抽取真实世界事件，动态生成推荐类请求，其中明显候选项已因近期变化失效。实验结果显示，当近期事件未被直接提及时，系统性能下降高达27.5分，即便在高推理成本下依然显著；进一步分析搜索轨迹发现，超过一半的失败源于代理未主动查询相关事实是否已变更。因此，该研究的关键在于揭示并量化了生成式AI系统在“被动接受显式信息”与“主动发现隐含变化”之间的能力鸿沟，并建立了一个可持续更新的评测框架，以推动系统在未明确说明的实时信息感知方面实现进展。
链接: https://arxiv.org/abs/2610.41435
作者: Anirudh Phukan,Tanya Goyal,Danish Pruthi
机构: Indian Institute of Science(印度科学研究所); Cornell University(康奈尔大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Given that AI agents can search the web broadly and deeply in real time, it is tempting to conclude that their outputs account for recent world events. We show that this conclusion does not hold in practice. While agents can search and easily find recent changes, the correctness of their responses depends on whether they search and what they search for. Popular agentic search benchmarks like BrowseComp cannot expose failures wherein agents do not recognize their own assumptions that should be verified, since these benchmarks explicitly state what needs to be found. In contrast, many user requests state only the final information need (e.g., an itinerary for a trip), leaving the agent to discover relevant changes (e.g., the temporary closure of a museum) and account for their consequences. To highlight such gaps, we introduce BlindToChange, a benchmark that measures whether agents proactively discover recent events that affect their responses. Our benchmark comprises requests for recommendations where an obvious candidate is no longer applicable due to a recent real-world event. These events are drawn automatically from Wikidata, allowing BlindToChange to be periodically refreshed. Strikingly, we find a big gap – as high as 27.5 points, even at high reasoning effort – in systems’ performance, when the recent event is not explicitly mentioned compared to when we ask about it directly. Analyzing search trajectories, we learn that more than half of the failures can be attributed to the fact that agents do not directly search to verify whether a relevant fact has changed. Our benchmark highlights, and tracks, a new axis of progress, wherein systems account for recent information that is not explicitly mentioned in the user request.
[NLP-38] SparseGDN: Chunk Sparse in Linear Attention
【速读】： 该论文旨在解决生成式 AI (Generative AI) 中长上下文预填充（long-context prefill）阶段因密集的块内计算、递归状态更新及中间状态存储带来的高计算与内存开销问题。其核心解决方案是提出一种面向硬件优化的稀疏化框架 SparseGDN，通过结构化的块内近似与输入相关的递归事件选择机制，在保证每令牌输出的前提下实现高效推理。该方法采用块相邻求解器降低局部 Delta 求解计算量，并引入动态选择器在头对间分配递归更新预算；GPU 实现仅对选定事件进行状态更新，并基于紧凑的状态快照与组内局部交互重构输出，显著减少计算量与内存访问流量。实验表明，SparseGDN 在 Qwen3.5-4B 与 Qwen3.5-9B 模型上于 4K 至 128K 上下文长度下均取得 1.50–1.94 倍于原生 FLA 内核的预填充加速比，平均仅保留约 29% 的递归事件，有效平衡了性能与精度。
链接: https://arxiv.org/abs/2610.41420
作者: Jiedong Zhuang,Peng Zhao,Jialin Tang,Haoji Hu
机构: Zhejiang University (浙江大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Gated Delta Networks (GDNs) offer linear-time sequence modeling, yet long-context prefill remains costly due to dense intra-chunk computation, recurrent-state updates, and intermediate state storage. We introduce SparseGDN, a hardware-aware framework that combines structured intra-chunk approximation with input-dependent recurrent-event selection while producing outputs for every token. A block-adjacent solver reduces local Delta-solve computation, while a dynamic selector allocates recurrent-update budgets across head pairs. The GPU implementation materializes updates only for selected events and reconstructs token outputs using compact state snapshots and group-local interactions, reducing computation and memory traffic. We evaluate SparseGDN on Qwen3.5-4B and Qwen3.5-9B across RULER tasks and context lengths from 4K to 128K. Retaining approximately 29% of recurrent events on average, SparseGDN achieves 1.50-1.94x GDN-core prefill speedups over native FLA kernels, including online selection overhead.
[NLP-39] Comparative Evaluation of LLM -Based Review Paradigms for Academic Paper Quality Assessment EACL2027
【速读】： 该论文旨在解决生成式 AI 在学术同行评审中应用的关键问题，即如何有效设计基于大语言模型（Large Language Models, LLMs）的评审评估范式，明确不同输入表示与模型选择对评审结果的影响，并验证其预测研究影响力的可靠性。其解决方案的关键在于系统性地比较五种评估范式在三种输入形式（摘要、全文摘要、完整论文）和三种LLM下的表现，发现：直接评分（Direct Rating）虽在与人类评分的等级相关性上表现良好，但存在高分集中现象，导致大量应拒稿论文被误判为可接受；而通过基线匹配校准（base-rate-matched recalibration）后，对比评分（Comparative Ranking）仅提供相对位置而非更优排序，校准后的直接评分在一致性上优于Elo评分，且调用API次数减少76%；此外，仅使用摘要的输入方式优于完整论文摘要，模型选择的重要性不亚于范式选择；在大规模数据集（11,513篇投稿）上的复现进一步表明，绝对一致性依赖于评估集的分数分布，且无任何范式在准确率上超越“始终拒稿”基准；更重要的是，人类评分对引用量的预测能力最强，唯一例外是组评（Group Rating）在控制人类评分后仍保持引用相关性，并能准确识别接收池中的论文。因此，评审质量不能仅由排序质量衡量，模型选择、校准策略、阈值设定及目标指标均至关重要。
链接: https://arxiv.org/abs/2610.41391
作者: Fan Huang,Hongyu Zhou
机构: Indiana University Bloomington (印第安纳大学布卢明顿分校); Renmin University of China (中国人民大学); University of Antwerp (安特卫普大学)
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL)
备注: commit to EACL 2027
Abstract:Growing submission volumes have renewed interest in using large language models (LLMs) to support peer review. It remains unclear how such evaluation should be structured, how much the model matters, and whether LLM scores predict research impact. We evaluate five paradigms across three input representations and three LLMs on 232 stratified ICLR 2025 submissions. Direct absolute rating matches comparative ranking on rank correlation with human scores, yet compresses most predictions above the accept threshold, flagging almost none of the papers reviewers scored below it. A base-rate-matched recalibration control locates the cause: comparative ranking supplies placement, not better ordering, and after recalibration Direct Rating reaches higher agreement than Elo while making 24% as many API calls. Abstract-only input beats full-paper summarization, and model choice matters as much as paradigm choice. Replicating on all 11,513 scored submissions reproduces these patterns, shows absolute agreement depends on the evaluated set’s score spread, and finds none beating an always-reject baseline in accuracy. Human scores track citations better than any paradigm, with one exception: Group Rating retains its citation correlation after controlling for human scores, and alone ranks within the accepted pool. Ranking quality alone cannot assess review quality; calibration, thresholds, model, and target outcome all matter.
[NLP-40] What We Keep of What We Forget: Replacement Operators and Routing Semantics in Training-Free MoE Expert Pruning
【速读】： 该论文旨在解决训练自由的专家剪枝（training-free expert pruning）中的两个核心问题：一是如何决定移除哪些专家，二是当路由器仍选择被移除专家时，应如何确定该层的计算内容。现有方法通常同时调整这两个决策，缺乏系统性分析。本文的关键贡献在于提出一种分离式评估框架，固定剪枝掩码（mask），在OLMoE、DeepSeek-V2-Lite、Moonlight和Granite等模型上对比三类替换算子（replacement operators）的表现，并采用配对且多重性校正的统计检验方法。研究发现，一个常被隐含处理的重要因素——幸存专家门控值的缩放系数（survivors’ gate scale）——对性能影响显著：对于未使用top-k归一化的路由器，若对幸存门控进行归一化，性能损失达0.8–8.5点，甚至逆转“重路由”与MoNE均值替换策略的优劣关系；而在使用归一化的路由器上，经过训练的门控缩放在50%剪枝率下表现最优，胜出概率为50%。为此，作者提出“倾斜新手”（tilted novice），这是一种闭式低秩映射，可表示被剪枝专家输入的函数关系，其零秩情形即为MoNE的常数替换。在50%剪枝下，“倾斜新手”仅增加1.2–1.8%参数量，但在OLMoE和Moonlight上分别提升3.45和1.37点，且在不同校准样本和4/3比特量化下表现稳定。实验表明，不存在通用最优算子：在DeepSeek-V2-Lite上，训练后门控重路由最佳；而在两个模型上，部分复制被剪枝专家的方案与“倾斜新手”无统计差异。此外，评估指标会显著改变结论：在25%剪枝下，MoNE常数替换虽保持92–97%稠密模型多选题准确率，但仅保留5–65%的GSM8K得分和0–17%的代码能力，且校准域的影响是重采样的4–9倍。基于此，作者提出包含五项内容的报告清单，以提升剪枝研究的可复现性与透明度。
链接: https://arxiv.org/abs/2610.41366
作者: Ezza Ali,Sehrish Bibi,Momina Moetesum,Faisal Shafait
机构: National University of Sciences and Technology (NUST)(国家科技大学); National Center of Artificial Intelligence (NCAI)(国家人工智能中心)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 56 pages, 8 figures, 76 tables
Abstract:Training-free expert pruning makes two decisions: which experts to remove, and what the layer computes when its router still selects a removed expert. Published methods change both at once. Holding the mask fixed, we compare replacement operators from three families on OLMoE, DeepSeek-V2-Lite and Moonlight, with Granite as a fourth router, using paired, multiplicity-corrected tests. The largest effect is a choice usually left implicit: the survivors’ gate scale. On routers trained without top-k renormalization, renormalizing the survivors’ gates, a common implementation choice, costs 0.8-8.5 points and reverses whether rerouting beats MoNE’s mean-output replacement; on two routers trained with renormalization, the trained scale wins at 50% pruning. We introduce the tilted novice, a closed-form low-rank map of the pruned expert’s input whose rank-0 case is MoNE’s constant. At 50% pruning it adds 1.2-1.8% parameters and gains 3.45 and 1.37 points over the constant on OLMoE and Moonlight, stable across calibration draws and largely unchanged by 4- and 3-bit quantization. No operator dominates: trained-scale rerouting is best on DeepSeek-V2-Lite, and a partial copy of each pruned expert is statistically indistinguishable from the tilted novice on two models. Evaluation can change conclusions: at 25% pruning MoNE’s constant keeps 92-97% of dense multiple-choice accuracy but only 5-65% of GSM8K and 0-17% of code scores, and the calibration domain moves the mask 4-9 times more than resampling. We propose a five-item reporting checklist.
[NLP-41] SERIAL-7: Deriving Realizing and Preserving Machine-Verifiable Outputs Across Representations NEURIPS2026
【速读】： 该论文旨在解决大语言模型在生成结构化输出时，仅保证语义正确性不足以确保其在程序和下游系统中可靠执行的问题。当前方法普遍忽视了从形式、结构到任务特定语义的多层级验证需求，导致生成结果虽语义合理但无法被程序正确解析或执行。为此，论文提出SERIAL-7——一个韩英双语的基准测试框架，基于可执行验证机制，针对七种不同表示形式（representation）进行机器可验证的生成评估。其核心解决方案在于将结构化生成过程分解为三个关键阶段：语义推导（Derive）、受限的产物生成（Realize）与跨表示保真（Preserve），并通过43个原子级表示约束与可执行检查，实现从格式合规到完整产物有效性的渐进式验证。实验表明，尽管部分模型在最终答案和表示层面表现良好（如REALIZE-L3 HSR达42.0%–93.9%），但全合规性（Full Compliance）评估揭示出大量存在于产物层级的隐藏错误，说明可靠的结构化生成必须依赖显式的、可执行的跨表示实现与保真验证机制。
链接: https://arxiv.org/abs/2610.41308
作者: Youngkyung Seo,MiHyeon Kim,Jisoo Lee,Hwijung Ryu
机构: KT Corporation(KT公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: NeurIPS 2026 Workshop on AI for Verifiable Coding
Abstract:Large language models increasingly generate structured outputs consumed by programs and downstream systems, where semantic correctness alone does not guarantee reliable execution. We introduce SERIAL-7, a Korean-English benchmark built around an executable verification framework for machine-verifiable generation across seven representations. Using 177 validated canonical artifacts, SERIAL-7 separates semantic derivation (Derive), constrained artifact generation (Realize), and cross-representation preservation (Preserve). Its 43 atomic representation constraints are paired with executable checks over format, structure, and task-specific semantics, enabling progressively stricter verification from representation compliance to full artifact validity. Across ten models, REALIZE-L3 HSR ranges from 42.0% to 93.9%, while Full Compliance exposes additional artifact-level failures beyond correct final answers and valid representations. These results show that reliable structured generation requires explicit, executable verification of how artifacts are realized and preserved across representations.
[NLP-42] From Transcription to Reformulation: Defining and Benchmarking Grounded Generative Speech Recognition
【速读】： 该论文旨在解决传统自动语音识别（ASR）基准评估体系中“每段语音仅对应单一标准转录文本”的假设在面对生成式语音识别系统时的局限性，尤其当模型输出为可读性强、格式化或结构化的文本时，现有方法难以有效评估其质量。其核心解决方案是提出基于语义锚定的生成式语音识别（Grounded Generative Speech Recognition, GSR），将语音识别任务重新定义为生成与输入语音语义一致的文本，而非单一转录。关键在于构建了一个四层递进的重构能力层级：从字面转录到规范重构、语义重构，直至语音锚定的结构化生成，并据此设计了首个全面的评估基准GSR-Bench，涵盖会议转录优化、跨方言翻译、上下文感知理解及噪声/静音条件下的鲁棒性等多样场景。为克服传统词级评价指标的不足，研究进一步提出融合语音锚定评估与大语言模型（LLM）语义判断的多维评估框架，通过人工偏好实验验证其可靠性。实验揭示了转录保真度与生成重构能力之间的根本权衡，并识别出如声学幻觉、非支持生成和指令漂移等关键失败模式，为未来语义锚定生成式语音系统的架构设计与评估提供了重要洞见。
链接: https://arxiv.org/abs/2610.41292
作者: Qing Wang,Qihan Hu,Hang Lv,Yan Yang,Boqing Liu,Xuechao Zhu,Zehan Li,Hongjie Chen,Jian Kang,Jie Li,Yongxiang Li,Lei Xie
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Speech language models have expanded automatic speech recognition (ASR) from transcription to general speech-to-text generation. Yet existing benchmarks still assume a single ground-truth transcript per utterance, an assumption that struggles when models produce readable, formatted, or structured outputs. In this work, we introduce Grounded Generative Speech Recognition (GSR), reformulating speech recognition as generating semantically grounded text instead of a single transcript. We define a grounded reformulation space that characterizes all valid textual realizations supported by the input speech, and establish a four-level hierarchy of reformulation capabilities, including literal transcription, canonical reformulation, semantic reformulation and speech-grounded structuring. Based on this formulation, we present GSR-Bench, the first comprehensive benchmark designed to evaluate generative speech recognition systems beyond exact transcript matching. GSR-Bench covers diverse scenarios including refined meeting transcription, cross-dialect translation, context-aware speech understanding, and robustness under silent or noisy acoustic conditions. To address the limitations of word-level evaluation metrics, we develop a multi-dimensional evaluation framework combining speech grounding assessment and LLM-based semantic judgment, whose reliability is validated through human preference studies. Extensive experiments across representative architectures, ranging from conventional ASR systems to end-to-end Speech-LLMs, reveal fundamental trade-offs between transcription fidelity and generative reformulation ability. Our analysis further identifies critical failure modes, including acoustic hallucination, unsupported generation, and instruction drift, providing insights into the design and evaluation of future grounded generative speech systems.
[NLP-43] In-Model Context Compression with Agent ic Soft Tokens for Long-Horizon LLM Agents
【速读】： 该论文旨在解决长时序大语言模型（LLM）智能体在执行复杂持续任务时，因交互上下文不断累积而导致的计算开销剧增与关键决策信息被稀释的问题。现有方法通常依赖外部训练的压缩器或上下文管理智能体，将上下文重写为较短的硬标记序列，但此类外部选择机制难以与模型内部表征的动态演化对齐，且不可避免引入信息损失。本文提出无需训练的代理软标记压缩（Agentic Soft-Token Compression, ASTC），其核心在于利用模型自身的循环动态识别已吸收的信息，基于常用的混合门控增量网络（Gated Delta Network, GDN）-注意力架构，将这些信息以**代理软标记（agentic soft tokens）**的形式携带至后续阶段，同时保留原始精确值作为硬标记。该方法实现了上下文压缩与模型内在表示演化的自然协同，显著降低后续层的计算量，并生成可复用的键值（KV）缓存，从而在整个智能体轨迹中持续节省资源。大量实验表明，ASTC在长时序任务上达到或超越未压缩智能体的准确性，同时减少高达50.68%的端到端注意力对数，而训练压缩器则需多出15.4至103.7倍的注意力对数才能完成相同任务，验证了代理软标记作为模型原生上下文传递机制的有效性与高效性。
链接: https://arxiv.org/abs/2610.41291
作者: Xuning He,Kai Fan,Runyu Zhang,Binghuan Wang,Yuxiang Zhang,Xue Jiang,Yihong Dong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注: 26 pages, 9 figures, 11 tables. Code: this https URL
Abstract:Long-horizon LLM agents play an important role in solving complex and sustained tasks, with ever-growing interaction contexts. However, these accumulated contexts will raise substantial computational cost and obscure key decision-relevant evidence. Existing methods typically train external compressors or context-management agents to rewrite context as shorter hard-token sequences, but such external selection may not align with the LLM’s evolving representation and is necessarily lossy. In this paper, we introduce Agentic Soft-Token Compression (ASTC), a training-free compression method using soft tokens within LLM agent for long-horizon tasks. Specifically, ASTC uses the model’s native recurrent dynamics to determine the already absorbed information, leveraging the commonly-used hybrid Gated Delta Network (GDN)-attention. It then carries this information forward as agentic soft tokens while retaining exact values as hard tokens. The resulting compact sequence reduces later-layer computation and yields reusable KV caches, extending the savings across the agent’s trajectory. Extensive experiments on long-horizon tasks show that ASTC matches or exceeds the uncompressed agent’s accuracy with up to 50.68% fewer end-to-end Attention Pairs, while trained compressors require 15.4 to 103.7 times more Attention Pairs per successful task. These findings highlight agentic soft tokens as a model-native way to carry absorbed context forward as the task representation evolves.
[NLP-44] PsyInterviewee: Informational Invariance and Relational Adaptivity in Mental Health Interview Simulation
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）驱动的模拟面试对象在心理卫生访谈训练与评估系统中存在可靠性不足的问题。尽管现有研究侧重于通过更丰富的患者背景信息和行为建模来提升拟人化程度，但仅追求人类相似性无法确保模拟结果的临床可信度。其核心挑战在于如何在保持患者背景信息一致性（即信息不变性）的同时，实现对话过程中披露内容与互动行为的动态适应性（即关系可塑性）。为此，论文提出PsyInterviewee框架，通过分离“受保护的真实信息通道”与“动态交互通道”，将生成的临床陈述与患者档案进行验证以确保信息一致性；同时，采用非对称写权限机制，允许对话上下文更新披露状态与行为模式，但禁止修改基础临床事实。该设计分别实现了临床信息的精准锚定与响应表达的灵活调节。自动化评估结果显示，相较于四种代表性基线模型，PsyInterviewee在双维度（信息不变性与关系可塑性）上的多数指标均获得更高评分，且消融实验表明两个通道分别贡献于临床根基稳固性和响应调制能力。
链接: https://arxiv.org/abs/2610.41289
作者: Jiawei Zhang,Huachuan Qiu,Ruocheng Guo,Zijun Shi
机构: Harbin Institute of Technology (哈尔滨工业大学); Westlake University (西湖大学); Microsoft (微软); The Hong Kong University of Science and Technology (香港科技大学)
类目: Computation and Language (cs.CL)
备注: 38 pages, including appendices. Code: this https URL
Abstract:LLM-based simulated interviewees support interviewer training and the evaluation of mental health interviewing systems. Existing studies have primarily focused on improving human-likeness through richer patient profiles and behavioral modeling. However, human-likeness alone does not establish their reliability. Reliable simulation requires preserving profile-grounded clinical facts (informational invariance) while adapting disclosure and interaction behavior to the interview (relational adaptivity). To this end, we introduce PsyInterviewee, which separates a protected truth channel from a dynamic interaction channel and verifies generated clinical claims against the profile. Specifically, asymmetric write permissions allow dialogue context to update disclosure and behavioral states without authorizing new clinical facts; these states guide what profile-supported information is revealed and how it is expressed. In an automated evaluation of interactions between LLM-based interviewers and interviewees, PsyInterviewee achieves higher scores than four representative baselines on most metrics across both axes, as assessed by two LLM judges. Ablations suggest that the two channels contribute to clinical grounding and response modulation, respectively.
[NLP-45] SciGraphSelect: Auditable Scientific Data Selection through Knowledge–Reasoning Graphs NEURIPS2026
【速读】： 该论文旨在解决科学数据选择过程中缺乏可解释性的问题，即现有方法主要依赖相关性或效用评分，难以揭示文档被保留的内在原因。其解决方案的关键在于提出一种基于参考文献的可审计框架——SciGraphSelect，该框架将领域特定知识、可复用的推理操作以及经参考文献验证的关系组织为类型化的知识-推理图（knowledge–reasoning graph），并通过概念覆盖度、关系与路径共支持度以及方向一致性等多维度指标对候选文档进行评分。为避免对文档嵌入表示的过度解读，系统在参考端保留丰富的关系结构，而在候选端则采取保守策略，不假设关系蕴含。通过显式呈现每项选择背后的参考依据，SciGraphSelect实现了科学训练语料构建过程的可追溯性，并有效支持下游科学结论的验证。
链接: https://arxiv.org/abs/2610.41280
作者: MiHyeon Kim,Ji-Eun Han,Eunkyeong Lee,Jeehyun Lim,Hwijung Ryu
机构: KT Corporation
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: AI4Science
Abstract:Scientific data selection is typically driven by relevance or utility scores, providing limited insight into why a document is retained. We introduce SciGraphSelect, a reference-grounded framework for auditable scientific data selection. SciGraphSelect organizes domain-specific knowledge, reusable reasoning operations, and reference-attested relations into a typed knowledge–reasoning graph, then scores candidate documents using concept coverage, relation and path co-support, and directional alignment. To avoid over-interpreting document embeddings, rich relational structure is preserved on the reference side while candidate-side evidence is treated conservatively without assuming relation entailment. By exposing the reference-grounded evidence behind each selection, SciGraphSelect brings traceability to the upstream construction of scientific training corpora and complements downstream scientific verification.
[NLP-46] RIDENT and SHAKTI: A Framework and Benchmark for Hindi AI-Text Detection
【速读】： 该论文旨在解决多语言大语言模型（LLM）背景下，针对低资源语言如印地语（Hindi）的文本生成溯源难题，以维护信息真实性。当前研究仍以英语为主，缺乏针对低资源语言的高质量评估基准。为此，作者构建了SHAKTI——一个大规模印地语基准数据集，涵盖四种文体、八类主流大模型、多种提示策略及对抗性变换，通过人工标注验证其质量，并系统分析了人类撰写与AI生成文本在语言特征、语法结构和情感表达上的差异。基于上述发现，提出TRIDENT——一种基于往返翻译（Round-Trip Translation, RTT）的检测框架，包含三种变体：TRIDENT-S（静态阈值）、TRIDENT-D（动态阈值）和TRIDENT-C（利用多距离RTT特征的逻辑回归）。其核心机制在于利用原文与往返翻译后文本之间的语义一致性来识别AI生成内容。实验表明，TRIDENT显著优于传统及监督基线方法，其中TRIDENT-S表现最优；跨七种语言、对抗场景及未见大模型的评估进一步验证了其跨语言适用性与鲁棒性，但也揭示其对翻译模型选择具有敏感性。
链接: https://arxiv.org/abs/2610.41232
作者: Shifali Agrahari,Sujit Kumar,Sanasam Ranbir Singh
机构: Indian Institute of Technology Guwahati(印度理工学院古瓦哈蒂分校); A*STAR(新加坡科技研究局)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) become increasingly multilingual, distinguishing human-written from AI-generated text is essential for maintaining information integrity. However, research remains English-centric, with limited benchmarks for low-resource languages such as Hindi. To address this gap, we introduce SHAKTI, a large-scale Hindi benchmark covering four genres, eight LLM families, multiple prompting strategies, and adversarial transformations. We evaluate its quality through human annotation and analyze linguistic, grammatical, and sentiment-based differences between human- and AI-generated texts. Building on these findings, we propose TRIDENT, a round-trip translation-based detection framework with three variants: TRIDENT-S (static threshold), TRIDENT-D (dynamic threshold), and TRIDENT-C (logistic regression using multi-distance RTT features). TRIDENT leverages semantic consistency between original and round-trip translated texts to identify AI-generated content. Extensive experiments show that TRIDENT outperforms conventional and supervised baselines, with TRIDENT-S achieving the best overall performance. Further evaluations across seven languages, adversarial settings, and unseen LLMs demonstrate its multilingual applicability and robustness, while highlighting sensitivity to the translation model.
[NLP-47] Beyond Response Scoring: An AI-aware Cognitive Supervision Framework for Automated Mathematical Short Answer Grading
【速读】： 该论文旨在解决自动化短答案评分（ASAG）中现有方法在处理包含符号与公式的复杂数学答案时，难以实现语义理解、推理判断及准确评估的问题。现有仅依赖分数的评分方法存在泛化能力差、无法区分相同得分下不同认知水平学生表现的缺陷，且忽视了生成式人工智能（Gen-AI）使用带来的评分偏差。为此，本文提出AI-aware Cognitive-supervised Grading（AICG）框架，其核心创新在于构建双分支多任务架构：第一分支采用基于RoBERTa的回归、序数与分类联合头实现个体答案评分；第二分支通过专家设计的Q矩阵将回答与技能关联，联合预测基于G-DINA模型的技能掌握度与学习进步量，实现认知层面的监督。两分支共享编码器，使评分过程获得超越单一分数标签的认知指导。此外，AICG引入一个经监督对比预训练与线性分类微调的Gen-AI检测器，用于估计学生获取外部协助的概率，并将其转化为可靠性权重，在评估中抑制不可靠的认知证据。实验基于涵盖19个学术学期共49,402条教师评分数据，结果表明，相较于RoBERTa回归模型，AICG将平均绝对误差（MAE）降低50.3%，并使加权二次肯德尔系数（QWK）从0.798提升至0.927。消融实验证明，Q矩阵引导的认知监督是性能提升的主要来源，而AI感知的加权机制进一步使整体MAE降低4.2%，并在最近四个生成式AI广泛应用的学期中持续改善MAE与QWK，显著增强了模型在真实教育场景下的鲁棒性与公平性。
链接: https://arxiv.org/abs/2610.41215
作者: Shengran Guo,Min Chi,Feifei Qiao,Varad Patwardhan
机构: 未知
类目: Computation and Language (cs.CL)
备注: 12 pages, including references and appendix
Abstract:Transformer-based language models have achieved remarkable success in automated short answer grading (ASAG). However, current methods in ASAG struggle to comprehend, reason about and evaluate complex mathematical answers involving symbols and formulas. These score-only methods fail to generalize across diverse responses and skill states associated with identical scores. Moreover, they ignore the potential upward bias introduced by generative Artificial Intelligence (AI) usage in assessing students’ performance and knowledge mastery. To address these limitations, we propose AICG (AI-aware Cognitive-supervised Grading), a two-branch multi-task framework. The first branch combines RoBERTa-based regression, ordinal, and classification heads for individual-answer grading. The second branch uses an expert-designed Q-matrix to associate responses with assessed skills and jointly predicts G-DINA-derived skill mastery and learning gain. Through the shared encoder, these skill-level targets provide cognitive supervision for answer grading beyond score labels. AICG further uses a Gen-AI detector, trained through supervised contrastive pretraining followed by linear classification, to estimate assistance probabilities and convert them into reliability weights, attenuating unreliable cognitive evidence during learning assessment. We conduct experiments on 49,402 instructor-graded responses spanning 19 academic semesters. Relative to RoBERTa regression, AICG reduces mean absolute error (MAE) by 50.3% and improves quadratic weighted kappa (QWK) from 0.798 to 0.927. Ablations identify Q-matrix-guided cognitive supervision as the primary source of improvement, while AI-aware weighting further reduces overall MAE by 4.2% over the unweighted cognitive model and consistently improves MAE and QWK across the four most recent post-Gen-AI semesters.
[NLP-48] raceSpec: Understanding and Improving Speculative Decoding for LLM Agents
【速读】： 该论文旨在解决大语言模型（LLM）智能体在执行任务时，因反复调用模型并依赖动态演化的推理、动作与工具反馈历史所导致的生成效率瓶颈问题。现有方法中，推测解码（speculative decoding）的聚合度量无法有效定位需要干预的决策点，缺乏对生成轨迹中关键阶段的精准指导。其解决方案的关键在于提出TraceSpec框架，通过将轨迹评估与自适应草稿训练相结合，实现基于状态条件的动态监督分配。该框架融合动态执行与固定前缀重放机制，并构建了包含10,000个训练状态和2,500个测试状态的TraceSpec-Traj数据集，覆盖网页浏览与软件工程场景。研究发现，尽管整体接受率随上下文长度增加而上升，但在特定后期浏览行为中却呈现下降趋势，且工作负载与执行阶段进一步加剧这一差异，从而揭示了基于轨迹状态条件进行监督的必要性。TraceSpec通过在中间助手回合上训练，并利用状态感知的前缀感知接受差距估计，在固定预测预算下实现自适应分配，显著提升重放吞吐量（相比DSpark基线提升12.5%至22.6%），同时在Qwen3-4B模型上的消融实验表明，相较于固定全回合监督，自适应分配可使接受长度提升3.3%至10.0%，验证了状态条件与连续标记一致性的关键作用。该工作系统地连接了智能体执行分析与实际草稿优化策略，为高效生成提供了可复现的评估资源与训练范式。
链接: https://arxiv.org/abs/2610.41177
作者: Jihao Zhao,Shuaishuai Zu,Pengfei Li,Biao Qin,Guanghuan Fang,Ben Feng,Baochuan Yang
机构: Renmin University of China (中国人民大学); Huawei Technologies Co., Ltd. (华为技术有限公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:LLM agents execute tasks through repeated model calls over evolving histories of reasoning, actions, and tool feedback. Aggregate speculative-decoding metrics merge these distinct decisions, providing limited guidance on where draft supervision is needed. We present TraceSpec, a framework connecting trajectory evaluation with adaptive draft training. It combines dynamic execution with fixed-prefix replay and introduces TraceSpec-Traj, a corpus of 10,000 training states and 2,500 test states spanning browsing and software engineering. Analysis of five speculative baselines reveals that aggregate acceptance can rise with context length while declining within matched late browsing actions. Workload and phase further distinguish these trends, motivating supervision guided by trajectory conditions. TraceSpec trains on intermediate assistant turns and allocates a fixed prediction budget using state-conditioned estimates of prefix-aware acceptance gaps. Across three target sizes and two temperatures, it improves replay throughput by 12.5% to 22.6% over its DSpark base. Qwen3-4B ablations show that adaptive allocation increases accepted length by 3.3% to 10.0% over fixed full-turn supervision, with additional comparisons substantiating the roles of state conditioning and consecutive-token agreement. The resulting evaluation resources, trajectory findings, and training strategy connect the analysis of agent execution to practical draft improvement.
[NLP-49] S2TDR: Structured Multimodal Reconstruction for Sticker-Aware Dialogue Understanding
【速读】： 该论文旨在解决社交媒体对话中贴纸（Sticker）语义表达的非字面性与上下文依赖性问题，这类表达难以通过通用的视觉融合或独立的图像描述方法准确建模。其核心挑战在于如何将贴纸所承载的情感、立场与意图有效转化为自然语言文本，以实现与现有基于文本的对话理解模型的兼容。解决方案的关键在于提出一种结构化的多模态重构框架——贴纸到文本对话重构（Sticker-to-Text Dialogue Reconstruction, S2TDR），该框架将贴纸理解过程分解为三个阶段：视觉线索提取、情感-意图推断以及上下文感知的对话重构；仅在有光学字符识别（OCR）文本时将其作为辅助线索，避免对非语义性文字的过度依赖。实验表明，相较于直接整合贴纸的方法，S2TDR在情感分析、意图识别和对话摘要等下游任务上均显著提升性能，且通过组件消融、无OCR贴纸及重构质量分析验证了其可解释性与有效性。
链接: https://arxiv.org/abs/2610.41176
作者: Yuanchen Shi,Fang Kong
机构: Soochow University (苏州大学); Natural Language Processing Laboratory (自然语言处理实验室)
类目: Computation and Language (cs.CL)
备注: 12 pages, 4 figures, 5 tables
Abstract:Stickers are widely used in social media dialogues to express affect, stance, and intent beyond literal text. Their meanings are often non-literal and context-dependent, making them difficult to handle with generic visual fusion or standalone captioning. We propose Sticker-to-Text Dialogue Reconstruction (S2TDR), a structured multimodal reconstruction framework that converts sticker turns into contextually appropriate textual utterances. S2TDR decomposes sticker interpretation into visual cue extraction, affective-intent inference, and context-aware dialogue reconstruction, using OCR text only as an auxiliary cue when available. The reconstructed dialogue can then be consumed by existing text-based dialogue models. Experiments on sentiment analysis, intent recognition, and dialogue summarization show that S2TDR generally improves multiple downstream models over direct sticker integration methods. Further analyses on component ablations, OCR-empty stickers, and reconstruction quality indicate that structured reconstruction provides an interpretable and effective way to integrate sticker semantics into dialogue understanding.
[NLP-50] Lineage Is Memory: Parent Inheritance and Feedback Disclosure in Short-Budget LLM -Guided Optimization
【速读】： 该论文旨在解决生成式语言模型优化过程中迭代优化循环效率受限的问题，核心在于厘清“父代继承”（greedy parent inheritance）与“反馈广度”（breadth of per-round feedback）在优化进展中的相对贡献。现有方法普遍依赖于单轮反思（single-round reflection）的局限性，提出通过全集错误聚类、目标性小批量采样或更大规模提议模型以提升反馈多样性，但这些机制均在保持谱系追踪（lineage-preserving search）的框架下评估，导致父代继承效应与反馈信息量无法有效分离。本文通过在9个提示与程序合成基准上开展1,750次预注册实验，在控制计算预算的前提下，系统性地解耦了父代继承与反馈披露的影响。研究发现，贪婪式父代继承是推动优化进展的主要驱动力，其性能显著优于种子重置重采样（seed-reset resampling），即便后者在每轮中接收到所有失败案例的聚类摘要或使用更大型提议模型。尽管全集错误聚类和多样化失败选择有助于初始阶段的单步重写并加速搜索初期进展，但随着已解决错误模式被保留在候选方案中并从后续失败列表中过滤，父代继承迅速弥合这一差距。此外，对GEPA（Generative Evaluation and Propagation Architecture）的分项成本分析表明，提议模型反思仅占总计算开销的一小部分，而验证与小批量门控机制消耗了几乎全部预算，说明贪婪式继承原始失败样本即可以极低计算成本获得复杂反思循环的大部分收益。
链接: https://arxiv.org/abs/2610.41157
作者: Shanglin Yang,Zhan Shi
机构: Independent Researcher; Santa Clara University (圣克拉拉大学)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 17 pages, 2 figures, 10 tables. Code, pre-registrations, and 1,750 run logs at this https URL
Abstract:Iterative language-model optimizers refine prompts and programs by evaluating a candidate, showing a proposer model a sample of failing cases, and keeping revisions that improve performance. Recent work attributes the limits of this loop to narrow single-round reflection, introducing full-set error clustering, targeted minibatches, or larger proposer models so the optimizer sees more failure modes per step. Yet every existing evaluation tests these mechanisms exclusively inside lineage-preserving search, where each revision edits a surviving parent, leaving the contribution of parent inheritance confounded with the breadth of per-round feedback. Across 1,750 pre-registered runs spanning nine prompt and program synthesis benchmarks, we separate parent inheritance from feedback disclosure under matched evaluation budgets. We find that greedy parent inheritance drives the vast majority of optimization progress, outperforming seed-reset resampling even when seed-reset proposals receive a clustered summary of all failures in every round or use a larger proposer model. While full-set error clustering and diverse failure selection improve single-step rewrites from the seed and accelerate the opening rounds of search, sequential parent inheritance rapidly closes this gap as solved error modes are retained in the candidate and filtered from subsequent failure lists. Moreover, decomposing token expenditure in GEPA reveals that proposer reflection accounts for only a small fraction of total cost whereas validation and minibatch gating consume nearly the entire budget, showing that greedy parent inheritance over raw failures captures the gains of complex reflective loops at a fraction of their compute.
[NLP-51] HyperRepair: Recovering Forgotten General Capabilities of Fine-Tuned Language Models
【速读】： 该论文旨在解决语言模型在特定领域微调（fine-tuning）后出现的通用能力退化问题，即在提升领域专精性能的同时往往损害模型的通用泛化能力，且现有方法难以兼顾二者。其核心解决方案是提出HyperRepair框架，通过学习一个共享预训练主干（pretrained backbone）的超网络（hypernetwork），利用源模型上训练的修复数据生成可迁移的激活指纹（activation fingerprints），进而动态生成针对特定任务的修复侧网络（Repairing Side Net, RSN）。RSN采用提示条件门控机制（prompt-conditioned gate）实现选择性修正，仅在推理阶段引入额外计算开销，即可在不依赖目标领域样本或进一步优化的情况下，有效恢复模型的通用能力。该方法的关键在于：1）通过超网络实现跨模型、跨领域的通用修复能力；2）基于激活指纹捕捉微调带来的表征变化，从而精准生成适配的修正模块。实验表明，在两个模型家族、12个LoRA适配数据集及15个通用基准测试中，HyperRepair在保持99.5%微调后领域性能的同时，恢复了基线模型平均通用得分的93.4%。
链接: https://arxiv.org/abs/2610.41147
作者: Junrong Tang,Jianghui Wang,Hao Kong,Yang Lin,Yibo Yang
机构: Shanghai Jiao Tong University(上海交通大学); The University of Queensland(昆士兰大学); Wanwu-MindFlow.ai(万悟-心流科技)
类目: Computation and Language (cs.CL)
备注: 29 pages, 5 figures
Abstract:Fine-tuning improves a language model’s performance in a target domain but can weaken its general capabilities. Existing methods often face a trade-off between general capabilities and domain specialization, making it difficult to preserve both. We introduce HyperRepair, a framework for recovering general capabilities while retaining domain specialization at only inference cost. To make recovery generalizable across models sharing a pretrained backbone, HyperRepair learns a hypernetwork from repairs trained on source models. The hypernetwork uses activation fingerprints that summarize changes from the pretrained backbone to generate a tailored Repairing Side Net (RSN), whose prompt-conditioned gate applies corrections selectively. This enables recovery on models from unseen adaptation domains without target-domain examples or additional optimization. We evaluate HyperRpair across two model families, 12 LoRA adaptation datasets, and 15 general benchmarks. On the default split, HyperRepair achieves 93.4% of the base models’ average general score while retaining 99.5% of the fine-tuned models’ average domain score.
[NLP-52] Language Models Show a “Women-Are-Wonderful” Effect in Blame Attribution That is Not Just Leniency
【速读】： 该论文旨在解决语言模型在人际冲突责任归因中存在“女性皆美好”（Women-Are-Wonderful, WaW）效应的问题，即模型对女性施加了系统性偏袒，表现为对女性行为的评价更宽容，同时对伤害女性的行为则表现出更严厉的评判。其解决方案的关键在于构建配对反事实样本，通过系统性地改变被评判者（原始发帖人）与受影响者（对方当事人）的性别角色，控制其他变量以分离性别影响。研究发现，尽管性别变化仅影响少数判决结果，但这些变化始终有利于女性：当原始发帖人为女性时，被责难的概率降低9%–52%；而当对方当事人为女性时，被责难的概率则提升2.7–9.2倍。该效应具有显著的角色依赖性，且在沟通与关系类冲突中更为突出，在家庭冲突中则减弱。探索性分析进一步表明，女性身份会影响模型对同情、合理性、伤害程度及保护需求等判断维度的评估，说明WaW效应不仅是对女性自身的宽待，还体现为对针对女性行为的更高道德要求，揭示了生成式AI在社会性别认知上的深层偏见。
链接: https://arxiv.org/abs/2610.41116
作者: Anika Sharma,Snehalkumar ‘Neil’ S. Gaikwad,Snigdha Chaturvedi,Shashank Srivastava
机构: University of North Carolina at Chapel Hill(北卡罗来纳大学教堂山分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper investigates the Women-Are-Wonderful (WaW) effect in language-model blame attribution: a favorable bias in which women receive more positive evaluations than men. Using 11,270 interpersonal conflicts from r/AmItheAsshole, we construct matched counterfactuals that vary the gender of the person being judged (the original poster) and the person affected (the counter-party). Across four language models, gender changes a minority of verdicts, but those changes consistently favor women. The effect is strongly role-dependent. Making the original poster a woman reduces the odds of blame by 9–52%, while making the counter-party a woman multiplies them by 2.7–9.2X. Counter-party WaW also varies across conflict types, strengthening in communication and relationship conflicts and weakening in family conflicts. Exploratory probes suggest that counter-party gender can shift judgments of sympathy, reasonableness, harm and protection. WaW is therefore not just leniency toward women being judged: models also judge conduct more harshly when it affects a woman.
[NLP-53] MUON-RT Reusing the Teachers Trajectory for Momentum-Free Muon for LLM Self-Distillation
【速读】： 该论文旨在解决大语言模型自蒸馏训练中因引入指数移动平均（EMA）教师模型而带来的高持久内存开销问题，尤其是现有方法如Muon需额外维护动量缓冲区导致的存储负担。其核心解决方案是提出一种名为Muon-RT（Muon with Reused Teacher）的新算法，关键在于重用学生-教师之间的参数位移（teacher-student displacement），将其作为隐含的历史更新信息源，从而消除对独立动量缓冲区的需求。该位移向量在局部模型假设下可编码指数加权的历史更新轨迹，并有效抑制时间上不相关的噪声；通过去除其径向分量、进行谱归一化并经牛顿-舒尔茨（Newton-Schulz, NS）正交化后与当前梯度混合，实现更稳定的优化方向。理论分析表明，在显式的正则性与对齐假设下，目标函数差距的期望值呈线性收缩至一个残差误差水平。实验结果表明，结合自蒸馏微调（SDFT）在Qwen2.5-7B-Instruct上的测试显示，Muon-RT相较AdamW提升平均准确率2.6个百分点且持久内存减少42%，相较原版Muon提升5.4个百分点且内存降低27%；在Olmo-3-7B-Instruct上结合自蒸馏策略优化（SDPO）亦取得相比AdamW 2.0个百分点的准确率提升，验证了其高效性与优越性。
链接: https://arxiv.org/abs/2610.41104
作者: Aryan Das,Vinay Kumar Verma,Aakansha Mishra
机构: IIT Kanpur(印度理工学院坎普尔分校); Amazon(亚马逊)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注: Under Submission
Abstract:Self-distillation methods for large language models with an exponential moving average (EMA) teacher already retain a history of student updates. Muon nevertheless stores a separate momentum buffer, increasing persistent training memory. We propose Muon with Reused Teacher (Muon-RT), which reuses the teacher-student displacement to eliminate momentum buffers for hidden weight matrices. We show that this displacement encodes an exponentially weighted history of applied updates and smooths temporally uncorrelated noise under a local model. Muon-RT removes its radial component, normalizes it spectrally, and mixes it with the current gradient after Newton-Schulz (NS) orthogonalization. Under explicit regularity and alignment assumptions, we establish linear contraction of an expected objective-gap bound toward a residual error level. With Self-Distillation Fine-Tuning (SDFT) on Qwen2.5-7B-Instruct, Muon-RT improves average accuracy over AdamW by 2.6 points with 42% less persistent memory, and over momentum Muon by 5.4 points with 27% less persistent memory. With Self-Distillation Policy Optimization (SDPO) on Olmo-3-7B-Instruct, it improves average accuracy over AdamW by 2.0 points.
[NLP-54] EDU-Jev Training System-One Decision Models for Education
【速读】： 该论文旨在解决教育领域自动化决策中普遍存在的三大挑战：生成式语言模型（Generative Language Models）输出冗长、需额外解析、且其表达的置信度与实际正确概率不一致，导致推理成本高且难以可靠应用。针对这些问题，本文提出EDU-Jev系列单阶段（“System One”）决策模型，参数规模分别为0.8B、2B和4B，其核心创新在于采用无文本生成的直接概率输出机制——在给定评估上下文和预定义选项的问题下，模型通过指针头（pointer head）对每个选项进行打分，直接输出选项上的概率分布，确保预测严格限定于可选答案范围内。该方法避免了自然语言生成带来的开销，显著降低延迟并提升可解释性。训练采用单一监督阶段，以软交叉熵损失优化目标标签分布；模型校准通过实证评估完成。在十二个教育基准测试中，EDU-Jev 4B平均准确率达67.6%，较最强商业决策API高出9.4个百分点，较最佳开源4B决策模型高出18.7个百分点；在四个跨领域教育数据集上表现媲美最强商用API，但在通用决策与推理任务上仍略逊于后者。2B版本在单张H100 GPU上可实现约150次/秒的决策吞吐量，具备高效部署潜力。
链接: https://arxiv.org/abs/2610.41079
作者: Jiazheng Li,Yulan He
机构: 未知
类目: Computation and Language (cs.CL)
备注: 16 pages, 3 figures, 4 tables. Work in progress
Abstract:Many automated decisions in education involve choosing from a fixed set of options: assigning a rubric score, checking whether an answer meets a marking criterion, or identifying the first wrong step in a solution. Generative language models are increasingly used to make these decisions, but their responses require parsing, longer outputs increase inference costs, and confidence expressed in words does not necessarily reflect the probability of being correct. We present EDU-Jev, a family of single-pass (“System One”) decision models at 0.8B, 2B and 4B parameters. Given an assessment context and a question with predefined answer options, the model returns a probability distribution over those options without generating text. A pointer head scores a marker for each option, ensuring the predictions are restricted to the allowed choices. Training uses a single supervised stage with soft cross-entropy against the available target label distributions; calibration is assessed empirically. Across twelve education benchmarks, EDU-Jev 4B achieves an average accuracy of 67.6%, exceeding the strongest of five commercial decision APIs by 9.4 percentage points and the best open 4B decision model by 18.7 points. On four cross-domain education sets, it matches the strongest API, which remains ahead on general decision-making and reasoning tasks. The 2B model processes about 150 decisions per second on a single H100 GPU.
[NLP-55] Capability-Grounded Agent ic Planning
【速读】： 该论文旨在解决大语言模型（LLM）代理在处理复杂自然语言请求时，因依赖试错方式选择工具调用而导致计划不可行或执行失败的问题。其核心挑战在于：简单请求也可能涉及多个相互依赖的工具调用，而传统方法缺乏对任务需求与工具能力之间语义关系的系统性建模，导致生成的执行路径不可行或效率低下。解决方案的关键在于提出基于能力的代理规划（Capability-Grounded Agentic Planning, CGAP），其核心机制包括：将工具目录确定性地编译为领域描述语言（PDDL）模型，利用大语言模型（LLM）将自然语言请求转化为可形式化的目标，再通过经典规划器求解该抽象模型；由于工具输出值在执行前未知，模型仅追踪输入可用性而非具体值，从而保持规划的抽象性与鲁棒性；执行阶段由LLM补充实体特定的调用，并通过确定性检查确保不破坏原计划；所有操作参数均源自可信来源（如对话片段或先前工具响应），且每项结果必须经由独立验证器确认后才视为完成。该方法在ToolSandbox、ComplexFuncBench、BFCL多轮对话及\tau^2-bench零售基准测试中，相较标准工具调用代理最高提升28.9分，显著提升了规划的正确性与执行可靠性。
链接: https://arxiv.org/abs/2610.41040
作者: Shikha Shrestha,William Yeoh,Ning Zhang,Yevgeniy Vorobeychik
机构: Washington University in St. Louis (圣路易斯华盛顿大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 3 figures
Abstract:LLM agents serve natural-language requests through tools, data sources, and other agents, and even simple requests often need several interdependent calls. Typically, the LLM picks these calls by trial and error, with no guarantee of an executable plan. We present Capability-Grounded Agentic Planning (CGAP), which instead reasons about what a request requires and what tools provide. CGAP compiles the tool catalog deterministically into a PDDL model, uses an LLM to turn the request into goals, and solves the model with a classical planner. Since tool outputs are unknown in advance, the model is abstract, tracking which inputs are available, not their values. During execution, an LLM adds entity-specific calls that abstract plans omit, and deterministic checks reject any that break the plan. Every argument is drawn from a cited source, such as a conversation span or an earlier tool response, and each requested result counts as delivered only after an LLM verifier checks it against the request. With three LLMs on ToolSandbox, ComplexFuncBench, BFCL multi-turn, and \tau^2 -bench retail, CGAP outperforms a standard tool-calling agent by up to 28.9 points.
[NLP-56] Do Language Models Know What Compelling Scientific Evidence Looks Like?
【速读】： 该论文旨在解决当前语言模型在科学推理中难以有效进行证据权衡（evidence deliberation）的问题，即模型在面对来自不同研究来源、具有差异性统计可靠性与认识论相关性的实证发现时，缺乏对证据权重的准确评估能力。其解决方案的关键在于构建一个基于真实生物医学数据的科学推理基准——EDB-Bio，涵盖药物扰动转录组学、CRISPR筛选和人类全基因组关联研究（GWAS）三个领域，包含96,000个实验序列任务，要求模型预测目标假说的真实性概率。研究发现，未经微调的语言模型普遍表现出系统性低估置信度，且其贝叶斯误差（Brier score）接近仅依赖先验频率的基线模型。通过引入严格正确的评分规则（strictly proper scoring rules）进行后训练，显著提升了模型的校准性（calibration）与判别能力（discrimination）。进一步地，量化证据权重为贝叶斯因子（Bayes factor）揭示了模型信念更新过程，使其能够与专家生物学家在证据相关性判断上达成一致，并暴露其潜在不一致与偏差。最终，在序列实验选择任务中，所提出的Ibis0模型效率比最优前沿模型高出2.5倍。本工作将校准训练与不确定性分析拓展至真实的科学预测场景，对人类癌症生物学研究具有重要应用价值。
链接: https://arxiv.org/abs/2610.41015
作者: Conor Igoe,Gabriel Grand,Zane Koch,Albert Bou,Eli Moss,Michaela M. Hinks,Samuel G. Rodriques,Andrew White,Siddharth Narayanan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Expert scientists excel at weighing empirical findings from multiple sources that vary in both statistical reliability and epistemic relevance. To what extent are language models, which are increasingly driving autonomous discovery systems, capable of such evidence deliberation? We introduce EDB-Bio, a scientific reasoning benchmark curated from real-world data in three biological domains: drug-perturbation transcriptomics, CRISPR screens, and human genome-wide association studies (GWAS). Each of EDB-Bio’s 96k episodes presents a sequence of experimental results from published studies and asks the model to predict the probability that a target hypothesis is true. Out-of-the-box, open-weight and frontier models alike demonstrate systematic underconfidence and attain Brier scores comparable to a trivial base-rate forecaster. Post-training against strictly proper scoring rules significantly improves both calibration and discrimination. This calibration pays off downstream: in a sequential experiment selection task, our Ibis0 model is 2.5 times more efficient than the best frontier model. Throughout, quantifying evidence weights as Bayes factors reveals how models update their beliefs over time, whether they agree with expert biologists on relevance, and where they exhibit inconsistencies and biases. Our work extends calibration training and uncertainty analysis to a realistic scientific forecasting setting with applications to human cancer biology. We release all code, data, and trained models.
[NLP-57] Language Models Show Conformity Bias in RAG : When the Majority Endorses a Poisoned Claim
【速读】： 该论文旨在解决生成式 AI（Generative AI）在检索增强生成（Retrieval-Augmented Generation, RAG）框架下对误导性信息（poisoned claims）的检测能力受检索结果中虚假证据数量与一致性影响的问题。具体而言，研究关注当模型接收到的检索文档中存在多个相互一致的污染文档（poisoned documents）时，其识别虚假信息的能力是否会下降。解决方案的关键在于通过对比两种不同证据分布情境下的模型表现：一种是少量污染文档（1个）与多数健康文档（6个）共存，另一种是多数污染文档（6个）与少数健康文档（1个）共存。实验结果显示，对于Gemma-3-12b模型，其在“少数污染”条件下的中毒检测率为25%（50/200），而在“多数污染”条件下降至10%（20/200），且绝大多数（34/38）原本可被检测出的错误未被识别，仅4例相反。该现象表明，模型表现出类似“从众偏差”（conformity bias）的行为模式，即更倾向于接受占主导地位的、相互一致的错误信息，从而显著削弱了其对误导性内容的敏感性。这一发现为RAG系统在面对协同伪造证据时的脆弱性提供了初步实证支持。
链接: https://arxiv.org/abs/2610.41012
作者: Mehrdad Ghassabi,Audrina Ebrahimi,Zahra Zojaji,Amin Milani Fard
机构: University of Isfahan(伊斯法罕大学); University of Texas at Dallas(德克萨斯大学达拉斯分校); New York Institute of Technology, Vancouver Campus(纽约理工学院温哥华校区)
类目: Computation and Language (cs.CL)
备注:
Abstract:Retrieval-augmented generation (RAG) can expose language models to misleading evidence in retrieved documents. We investigate whether increasing the number of mutually agreeing poisoned documents affects a model’s ability to detect misinformation. We evaluate 200 randomly selected questions from SciFact (random seed 38), comparing contexts with one poisoned and six healthy documents against contexts with six mutually agreeing poisoned documents and one healthy document. For Gemma-3-12b, poison-detection rates decrease from 25% (50/200) in the minority condition to 10% (20/200) in the majority condition. Among the 38 questions with different outcomes across conditions, 34 change from detected to undetected, while only four change in the opposite direction (exact McNemar test, p = 6.04e-7). This pattern is consistent with a conformity-like response to a majority of mutually agreeing poisoned documents. Our results provide initial evidence of conformity bias in RAG-based misinformation detection: Gemma-3-12b is substantially less likely to detect poisoned claims when they dominate the retrieved evidence.
[NLP-58] MOSAIC: Addressing Disproportionate Task-Modality Dependencies via Modality-Partitioned Gradient Routing
【速读】： 该论文旨在解决多任务场景下跨模态融合架构中存在的模态依赖不均衡问题：传统融合方法强制所有任务共享统一的表示空间，导致各任务在优化过程中其梯度会无差别地影响所有模态的参数，从而产生在不同任务需求间妥协的次优特征。这一问题的根本原因在于缺乏对模态的显式分离机制，限制了模型根据每个任务对特定模态的实际依赖程度进行针对性优化的能力。为此，作者提出MOSAIC（MOdality-Separated Architecture with Inter-modal Cross-attention）框架，其核心创新在于保留各模态的独立表示，并通过跨模态交叉注意力实现可控交互，同时采用模态专用损失函数将梯度精确路由至对应模态分支。实验表明，在日常日语对话语料库（CEJC）上，MOSAIC在四项任务中平均宏F1提升7.4个百分点，且骨干参数量减少72%，验证了模态分离策略在提升性能与降低计算开销方面的有效性。
链接: https://arxiv.org/abs/2610.41004
作者: Aryan Chowdhury,Yizheng Sun,Thomas Carroll,Riza Batista-Navarro
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 9 Pages, 1 Figure, 3 Tables
Abstract:In multi-task settings, combining multiple modalities allows task representations to draw on signals that a single modality alone would not capture. However, individual tasks may depend disproportionately on particular modalities, a structure that conventional fusion-based architectures do not model explicitly, instead enforcing a unified representation space. Consequently, a task’s gradients can affect parameters tied to modalities it barely relies on, leaving features that compromise between competing demands. Although prior work has explored task-weighting and loss-balancing strategies, their effectiveness is limited by the absence of explicit modality separation, preventing models from optimising each modality according to its task-specific importance. To address these limitations, we propose MOSAIC (MOdality-Separated Architecture with Inter-modal Cross-attention), which preserves unimodal representations while enabling controlled interaction between them, and uses modality-specific losses to route gradients to their corresponding partitions. On the Corpus of Everyday Japanese Conversation (CEJC), MOSAIC improves average macro-F1 by 7.4 points across four tasks with a 72% reduction in backbone parameters relative to a fusion-based baseline. Our results demonstrate that modality partitioning yields consistent gains across tasks while requiring far fewer backbone parameters than a fusion-based design.
[NLP-59] Do Open Language Models Invent Scripture? BILAL-BENCH: Benchmarking Islamic-Source Fabrication in Bengali NEURIPS2026
【速读】： 该论文旨在解决生成式AI在处理伊斯兰教义相关文本时产生的“虚构”（Islamic-source fabrication）问题，即语言模型可能生成看似权威但实际上并不存在的《古兰经》经文或圣训，从而误导用户将其视为神圣文本。现有针对伊斯兰领域的幻觉评估基准多为阿拉伯语且以封闭式问答为主，而孟加拉语资源则集中于通用领域事实性错误，缺乏对开放性伊斯兰源文本生成的系统评估。为此，本文提出BILAL-BENCH，一个包含240道题目的专家验证基准，专门评估孟加拉语环境下生成式AI对伊斯兰文献的开放性虚构行为，涵盖《古兰经》归因、圣训、教法、当代议题及对抗性虚假前提提示等多个维度，并采用双轴评分标准，将“来源错误”与“断言强度”分离分析。实验评估了7个参数规模从10亿到1200亿不等的开源模型，在1,680条双标注回答上的表现显示，六款模型在79.3%至95.3%的源文本生成问题上表现出高度自信的虚构行为，且性能未随规模提升而单调改善；其中圣训出处识别能力最弱，315条响应中仅有2条正确且有据可查。尽管两个80亿参数的BanglaLLaMA变体在虚构率上相近，但错误模式各异；更重要的是，模型在无法支持其主张时选择回避（适当沉默或纠正虚假前提）的能力差异显著（6至46次），且该能力与参数量无明显关联。因此，解决方案的关键在于构建具有领域针对性、多维度评估能力且能区分“来源可靠性”与“陈述自信度”的开放性基准，揭示当前模型在宗教敏感场景下存在严重的虚构倾向及规避机制缺失的问题。
链接: https://arxiv.org/abs/2610.40996
作者: Sabah Nushra,Md Kishor Morol
机构: Islamic University of Technology(伊斯兰科技大学); Dhaka, Bangladesh(达卡, 孟加拉国); Facebook(脸书); Menlo Park, CA, USA(门洛帕克, 加利福尼亚州, 美国)
类目: Computation and Language (cs.CL)
备注: 15 pages, 6 tables. Accepted at the 7th Muslims in ML (MusIML) Workshop at NeurIPS 2026. Code and dataset: this https URL
Abstract:When a language model invents a Qur’anic verse or hadith, it gives false authority to text that readers may treat as sacred. Existing Islamic hallucination benchmarks are largely Arabic and often closed-ended, while Bengali resources mainly study general-domain hallucination or factual knowledge. We introduce BILAL-BENCH, a 240-question expert-verified benchmark for open-ended Islamic-source fabrication in Bengali. It covers Qur’anic attribution, hadith, fiqh, contemporary issues, and adversarial false-premise prompts, and uses a two-axis rubric that separates source errors from assertiveness. We evaluate seven open-weight models from 1B to 120B on 1,680 double-annotated responses. Six of the seven models confidently fabricate on 79.3-95.3% of source-production questions, with no monotonic improvement from scale. Hadith provenance is the weakest category across models: only two of 315 responses are correct and correctly sourced. The two 8B BanglaLLaMA variants have similar confident-fabrication rates but different error profiles. Models vary far more in whether they withhold a claim they cannot support than in source accuracy: appropriate abstention or false-premise correction ranges from 6 to 46 responses across the benchmark, and does not track parameter count.
[NLP-60] he psychophysics of affect in large language models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在决策过程中如何将结果映射为情感体验，以及这些情感映射是否被用于实际选择的问题。当前大语言模型（LLMs）正日益融入决策工作流，但其对结果情感的感知机制尚不明确。研究发现，当结果独立评估时，模型对收益和损失的情感反应表现出对幅度的递减敏感性，类似于人类的价值函数；然而，模型对收益的情感强度显著高于等量损失，形成一种与经典损失厌恶相反的“硅基收益放大”现象。当结果并列比较时，情感评价呈现线性化趋势。在实际选择任务中，收益-损失不对称性基本消失，且模型自身的情感评分无法比金钱效用更有效地解释其决策行为。该研究揭示了从结果到情感的映射具有高度可塑性，从而引发对代理型大语言模型是否及如何整合预期情感进行决策的深层质疑。
链接: https://arxiv.org/abs/2610.40985
作者: Joseph Marks(1),Marius C. Vollberg(2 and 3) ((1) NatWest Group, London, UK, (2) University of Zurich, Zurich, Switzerland, (3) Swiss Center for Affective Sciences, Geneva, Switzerland)
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 64 pages (26 main text, 25 Supplementary Information, 13 GUIDE-LLM reporting checklist), 5 figures. Code: this https URL ; data: this https URL
Abstract:Good decisions often require considering how outcomes will feel. Yet large language models (LLMs) increasingly enter decision workflows, despite little being known about how they map outcomes to feelings or use those mappings in choice. We tested how LLMs anticipated gambling or stock-trading outcomes would feel and whether they incorporated these ratings into choice. When outcomes were rated in isolation, ratings exhibited diminishing sensitivity to magnitude, resembling human value functions; but models rated gains more intensely than equivalent losses, an inversion of canonical loss aversion we call silicon gain amplification. When outcomes were rated together, enabling direct comparison, ratings linearised. In choice, the gain-loss asymmetry largely disappeared, and models’ own ratings did not reliably explain their choices better than monetary utility did. These findings reveal malleable mappings from outcomes to affect, raising questions about whether and how agentic LLMs should incorporate anticipated feelings into decisions.
[NLP-61] Fewer Heads Fewer Memories: Pruning Redundant Attention Heads Suppresses Verbatim Memorisation
【速读】： 该论文旨在解决大语言模型在推理过程中存在对训练数据中特定序列进行逐字复现（verbatim memorisation）的问题，这一现象引发了隐私泄露、知识产权暴露以及模型泛化能力受限的担忧。其解决方案的关键在于利用注意力头（attention head）的冗余性：通过在推理阶段剔除大量任务无关的冗余注意力头（task-redundant heads），可显著抑制模型对已记忆序列的复现行为，同时维持甚至提升语言建模性能。实验结果表明，在GPT-2-XL（1.5B）模型中移除729个冗余注意力头后，已知记忆序列的复现次数降低了91.5%，且在WikiText-103基准上的困惑度（perplexity）反而提升了0.92，证明了通过剪枝冗余注意力头即可在不重新训练或修改训练数据的前提下有效缓解记忆泄露问题。
链接: https://arxiv.org/abs/2610.40974
作者: Aryan Chowdhury,Riza Batista-Navarro
机构: University of Manchester(曼彻斯特大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 7 Pages, 5 Tables, 1 Figure
Abstract:Large language models have been shown to memorise and reproduce verbatim sequences from their training data, raising concerns about privacy leakage, intellectual property exposure, and the limits of generalisation. Recent theoretical work suggests that attention heads may contribute to this behaviour by functioning as associative memory mechanisms, with memorisation capacity scaling with the number of heads. At the same time, empirical work has shown that many attention heads are redundant and can be pruned with limited impact on downstream performance. Despite these parallel findings, the effect of pruning redundant heads on memorised-sequence reproduction remains unexplored. In this work, we demonstrate that zeroing out a substantial fraction of attention heads prior to inference can significantly suppress verbatim memorisation while preserving, or even improving, language modelling performance on the benchmark used to identify redundant heads. Evaluating GPT-2-XL(1.5B) on known memorised sequences, we find that pruning 729 task-redundant heads reduces the memorisation count by 91.5%, while improving WikiText-103 perplexity by 0.92. These findings suggest that attention-head redundancy can be exploited to mitigate memorisation at inference time, without retraining or modifying the training data.
[NLP-62] Better Retrievers Worse Labels: How Incomplete Relevance Judgments Distort Abstention Evaluation in Conversational RAG
【速读】： 该论文旨在解决当前对话式检索增强生成（Conversational RAG）评估中普遍采用的“是否包含黄金文档”作为答案可回答性判断标准所导致的偏差问题。现有基准如MTRAG和RETECO通过端到端方式评估系统在自身检索结果上的生成能力，其默认假设是：若检索结果中不包含黄金文档（gold documents），则该上下文为不可回答。然而，该研究发现这一简化策略存在显著缺陷——即使没有黄金文档，许多上下文仍可能包含完整答案，主要来源于难负样本（hard negatives）、其他对话轮次的黄金内容或黄金页面邻近段落。研究通过重新运行官方基线，在RECOR（RETECO第二赛道）上使用四种检索器（nDCG@10从0.18到0.59）与三种Qwen3阅读器进行实验，并由两组开源大模型与四名小型开放模型组成的评审团对上下文内容进行标注，另由另一家族的模型盲审250个样本以确保一致性。结果显示，随着检索器性能提升，无黄金文档但实际包含答案的上下文比例从28%升至72%，而基于“有无黄金文档”的快捷判断方法的精确率从0.68降至0.10（审计后为0.70降至0.21）。此外，Qwen3-4B在这些“假阴性”样本中仍能正确回答62%–67%，远高于对被标记为“不足”上下文的响应率（23%–30%）及完全无文档情况下的响应率（1%）。因此，该研究的核心解决方案在于：不应将“是否有黄金文档”作为判断答案可回答性的依据，而应基于对上下文内容的实际语义分析进行评分，并建议未来评估应以内容标签而非黄金文档的存在与否来衡量系统的拒绝行为。研究还发现，最优的充分性检测器竟是一种不读取文档内容、仅依赖检索得分的模型，暗示当前评估体系对文档内容的理解能力可能存在根本性局限。研究已公开代码、实验结果与标注数据，推动更准确的评估范式。
链接: https://arxiv.org/abs/2610.40966
作者: Ivan Kochergin,Vasily Konovalov
机构: Letovo School(列托沃学校); AXXX
类目: Computation and Language (cs.CL)
备注:
Abstract:Conversational RAG benchmarks such as MTRAG and RETECO evaluate retrieval and generation end to end: each system answers from its own retrieval, so abstention must be scored against whether each retrieved context is sufficient. A convenient shortcut reads sufficiency off the relevance judgments and treats a context without gold documents as unanswerable. We re-run the official baselines exactly and test this shortcut on RECOR (RETECO track~2) with four retrievers (nDCG@10 from .18 to .59) and three Qwen3 readers. Two open LLM judges and a panel of four small open models label each context by content, and a model of another family audits 250 contexts blind, with adjudication. The stronger the retriever, the larger the share of no-gold contexts that still contain the answer (28% to 72%) and the lower the shortcut’s precision (.68 to .10; .70 to .21 under the audit). A reader agrees: Qwen3-4B answers 62–67% of these false misses correctly, against 23–30% of no-gold contexts judged insufficient and 1% with no documents. In most false misses, the answer comes from a hard negative mined for the same question, another turn’s gold or a neighbouring chunk of the gold page. On MTRAG, whose judgments come from annotators, precision drops from .72 to .50. Under the shortcut, answerability AUROC is up to .19 too low, and the best sufficiency detector appears to be a retrieval score that never reads the documents. We release code, runs and labels, and recommend scoring refusals against content labels, not the presence of gold.
[NLP-63] Kernel-based Interpolatory Methods for Context Compression of Attention Mechanisms
【速读】： 该论文旨在解决现代语言模型中注意力机制（attention mechanisms）因上下文长度呈二次复杂度而带来的计算瓶颈问题，核心挑战在于如何高效压缩上下文信息，尤其是键值缓存（KV-cache）的存储与计算开销。其解决方案的关键在于将注意力机制在对应的核希尔伯特空间（Hilbert space associated with its kernel）中进行形式化建模，从而将注意力表示为两个线性映射之比，其插值可进一步分解为各自线性映射的插值。基于此理论框架，作者提出了两种新型的KV-cache压缩方法：基于广义经验插值法（GEIM）的GAIM和基于Q-离散经验插值法（Q-DEIM）的Q-DAIM。为降低计算复杂度，研究进一步引入基于泰勒展开、Performer及随机傅里叶特征（Random Fourier Features）的代理模型，使压缩过程可实现线性时间复杂度。此外，研究还扩展至流式注意力（streaming attention）场景，提出可依据历史插值信息动态调整插值节点选择并更新先前插值结果的新方法。数值实验表明，所提方法在残差收敛性与未来查询输出误差方面表现良好，并在LongBench-E基准上展现出与当前最优方法相当的性能，尤其验证了Q-DAIM方法能够有效揭示KV缓存中信息集中区域，从而支持更高效的压缩策略。
链接: https://arxiv.org/abs/2610.40943
作者: Michel Fabrice Serret,Laura Grigori
机构: Paul Scherrer Institute (保罗谢勒研究所); EPFL (瑞士联邦理工学院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Numerical Analysis (math.NA)
备注: 44 pages, 17 figures, 8 tables
Abstract:The bottleneck of attention mechanisms, which are the fundamental building block of modern language models, is their quadratic complexity in terms of context size. There has thus been an increasing interest in the compression of the context, also called KV-cache compression. In this article, we consider the problem of compressing the context of attention mechanisms by first formulating it in the Hilbert space associated with its kernel. In this formalism, attention can be represented as the ratio of two linear maps whose interpolant is also the ratio of interpolants, this allows us to consider the interpolation of the linear maps directly. We thus introduce two novel KV-cache compression methods which we call GAIM and Q-DAIM, based on the Generalized Empirical Interpolation Method (GEIM) and the Q-Discrete Empirical Interpolation Method (Q-DEIM), respectively. As the cost of these methods is quadratic in terms of context length, we propose surrogate models based on the Taylor series, the Performer and the Random Fourier Features method which allow the compression to be done in linear time. Finally, we extend this result to the streaming attention setting, providing new methods which allow one to penalize the selection of interpolation nodes based on the previously interpolated context and to update the previous interpolation based on new information. We then provide numerical results on the convergence of the residuals and the output error in future queries and preliminary results on the LongBench-E benchmark showing performance comparable to the state-of-the-art methods. These experiments suggest that the range revealed by our Q-DAIM method could help identify where information is concentrated in the KV cache, thus enabling an efficient compression.
[NLP-64] Adaptive Personalization Under Preference Epistemic Uncertainty
【速读】： 该论文旨在解决对话式人工智能在个性化响应中对动态变化的人类偏好适应能力不足的问题，尤其关注在认知不确定性（epistemic uncertainty）情境下如何实现自适应个性化。其核心挑战在于：现有语言模型虽能识别特定情境下的合适响应策略，但在开放生成过程中无法有效根据偏好不确定性调整策略，表现出“策略识别-生成”脱节现象，导致策略校准性能下降35%-46%。解决方案的关键在于通过监督模型内部策略表征，使其学习与当前状态条件相匹配的人类偏好模式，而非依赖静态偏好记忆。研究发现，仅靠提示工程无法缓解该问题，但经过针对性训练后，模型在跨分布人物角色和对话风格上的用户可接受性泛化能力显著提升，使通用性差距缩小约三分之一。
链接: https://arxiv.org/abs/2610.40930
作者: Priyanka Dey,Rajarshi Das,Vikas Bahirwani,Qi Zeng,Julia Kruk,Qi Qi
机构: Meta Superintelligence Labs (Meta); University of Southern California (南加州大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Human preferences change over time: an established preference may remain valid over time, become uncertain as context shifts, or be explicitly revised. Effective personalization requires conversational AI to adapt its response strategy to evolving preferences rather than relying on passive memory retrieval. We formalize this capability as adaptive personalization under epistemic uncertainty. A human preference study further confirms that people favor state-contingent response strategies as preference uncertainty changes. To evaluate whether language models mirror this adaptation, we create a multilingual benchmark spanning 250 personas, 5 languages, and 25K conversational turns that decouples strategy selection from response execution. Across eight closed- and open-weight models, we uncover a recognition-generation gap: models reliably identify appropriate strategies when selecting among candidate options, but collapse during open-ended generation, suffering a 35-46% drop in strategy calibration. Crucially, this failure is systematic: models default to exploit-first behavior, over-relying on existing preferences regardless of preference epistemic uncertainty. We show this failure is addressable: while prompting interventions fail, targeted training that supervises models’ internal strategy representations toward state-conditional human preferences narrows the user acceptability generalization gap by a third on out-of-distribution personas and conversational styles.
[NLP-65] CoFiBreak: A Coarse-to-Fine Diversity Reward for Effective Multi-Turn Jailbreak
【速读】： 该论文旨在解决自动化红队测试（automated red teaming）中攻击多样性不足的问题。当前基于强化学习的攻击方法在仅以攻击成功率作为奖励时，容易导致攻击策略收敛于少数重复性提示，从而引发安全评估偏差，使测试集中于近似重复的攻击模式而非深层有害意图。现有方法通常需在攻击成功率、推理成本与内容/策略多样性之间进行权衡。本文提出一种粗粒度到细粒度的多样性奖励机制（CoFiBreak），集成于组相对策略优化（Group Relative Policy Optimization, GRPO）框架中，通过双重奖励设计提升攻击多样性：粗粒度奖励鼓励各GRPO组内使用低频策略，增强策略层面的多样性；细粒度奖励则通过抑制去除有害意图信息后的提示嵌入中对同一角色设定、表述框架和输出格式的重复使用，扩展策略实现方式的多样性。该多样性奖励仅作用于已超过有效性阈值的攻击，确保多样性提升不依赖无效攻击，且所有奖励计算仅在训练阶段进行，保障推理效率。实验表明，在HarmBench上的前沿模型上，CoFiBreak发现的精细策略种类为最强基线的1.6–2.0倍；在AdvBench上达到98.8%的攻击成功率（ASR@10），且多样性得分高达最强攻击基线的2.1倍；所生成攻击可有效迁移至未见过的前沿大模型Claude-Opus-4.6，实现71.0%的ASR@10。核心解决方案在于通过分层多样性奖励机制，在不牺牲攻击效果和推理效率的前提下，显著提升攻击策略的广度与深度。
链接: https://arxiv.org/abs/2610.40926
作者: Jialin Song,Xiaodong Liu,Weiwei Yang,Mingqian Feng,Chengquan Guo,Qiyao Ma,Carolyn Buractaon,Anson Ho,Wuyang Chen,Christopher White
机构: Simon Fraser University (西蒙菲莎大学); Microsoft Research (微软研究院)
类目: Computation and Language (cs.CL)
备注: 10 pages main text, 24 pages total, 8 figures, 12 tables
Abstract:Automated red teaming trains an attacker large language model (LLM) to elicit harmful responses from a victim LLM, but reinforcement learning can collapse attackers onto a narrow set of prompts when attack success is the only reward. Such a collapse of attack diversity can bias safety evaluation toward repeatedly testing near-duplicate attack patterns rather than the underlying harmful intents. Existing methods often trade off attack success or inference cost for greater content or strategy diversity. We propose CoFiBreak, a coarse-to-fine diversity reward for Group Relative Policy Optimization (GRPO) that improves attack diversity while preserving strong attack effectiveness and low inference cost. The coarse reward broadens which strategies the attacker uses by encouraging strategies underrepresented within each GRPO group, while the fine reward broadens how those strategies are realized by discouraging reuse of the same personas, framings, and output formats after harmful-intent information is removed from prompt embeddings. Diversity rewards apply only to attacks that already exceed an effectiveness threshold, so diversity cannot be gained through ineffective attacks. Since both rewards are computed only during training, CoFiBreak also retains inference efficiency. Trained with Llama3.1-8B-Instruct as the victim, CoFiBreak discovers 1.6-2.0x as many distinct successful fine-grained strategies as the strongest baseline across frontier victims on HarmBench. It reaches 98.8% ASR@10 (attack success rate with 10 generations per intent) on AdvBench with the highest diversity score among all compared methods, which is 2.1x that of the strongest-attacking baseline. The resulting attacks transfer to unseen frontier LLMs, reaching 71.0% ASR@10 on AdvBench with the unseen victim Claude-Opus-4.6. The code will be released.
[NLP-66] Qistas: A Pre-Registered Portable Protocol for Fairness Evaluation of Large Language Models
【速读】： 该论文旨在解决当前大型语言模型（LLM）公平性评估基准过度依赖英语及美国社会范畴所带来的文化偏见问题，尤其指出这些基准在跨语言、跨文化迁移时未能适配目标社会的现实语境，且缺乏对假设前提的预先校验。其核心解决方案是提出Qistas（阿拉伯语“公正的天平”），一种可跨国家移植的评估协议，首次应用于伊拉克社会语境，用于检验模型对社会、教派与区域群体的处理方式。该协议的关键在于构建基于匹配反事实对的程序对称性测量体系，涵盖差分拒绝行为、模糊情境下的刻板印象依赖、以及事件表述的术语框架，并通过成对分析方法（如符号翻转置换检验、精确麦内玛尔检验、模板聚类自举区间、霍尔姆校正多重比较）实现严格统计控制。此外，该协议包含预注册机制，明确各项假设、指标、排除规则与分析流程，并通过功率分析确定各维度在模板聚类设计效应下可检测的最小效应量。研究释放了完整的可复现作者工具包（包括修剪后的3×5分类体系、类型化检查清单、累积裁决日志、结构审计与人工审阅笔记），并在首个实地测试中发现24/25模板组至少存在一项缺陷，揭示出反事实公平性评估中普遍存在的风险：评估标准泄露至提示、阿拉伯语词形一致性的破坏、认知失衡的反事实对、单一主题拒答探针以及未明确定义的方言孪生体误计为覆盖范围。因此，本论文定位为协议开发而非结果报告，尚未评估任何模型，强调其方法论的可迁移性与严谨性。
链接: https://arxiv.org/abs/2610.40893
作者: Nawar S. Alseelawi,Mustafa S. Aljumaily
机构: University of Misan (米桑大学); Missan Oil Company (米桑石油公司)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 19 pages, 1 figure, 5 tables. Protocol paper; no model results. Pre-registration: this https URL . Reference implementation: this https URL
Abstract:Fairness benchmarks for large language models (LLMs) are overwhelmingly English-born and American in their social categories; translated versions carry those categories into societies where they do not apply, and almost none fix their hypotheses before the models are run. We present Qistas (Arabic qistas, “the just balance”), a protocol for evaluating how LLMs treat the social, confessional, and regional constituencies of a society, portable across countries and instantiated first for Iraq. In its pilot form it measures only what needs no verdict on contested truth: procedural symmetry across matched counterfactual pairs, differential refusal, stereotype reliance under ambiguity, and terminological framing of documented events. Every measurement is paired by construction and analysed with paired machinery (sign-flip permutation tests, exact McNemar tests, template-clustered bootstrap intervals, Holm-corrected families), with a power analysis that states, before any data exist, the smallest effect each axis can detect after the design effect of template clustering. We release the authoring apparatus (a pruned 3x5 taxonomy with four cells closed by design, per-type checklists, a cumulative rulings log, a structural audit, and a human review notebook) and report its first field test: a semantic review of a 58-item seed bank in which 24 of 25 template groups failed at least one check. The defect patterns it exposed (evaluation criteria leaking into prompts, token-level swaps that break Arabic agreement, epistemically unbalanced pairs, single-theme refusal probes, unwritten dialect twins counted as coverage) are, we argue, general hazards of counterfactual fairness benchmarks. This is a protocol paper, not a results paper: no model has been evaluated, and the accompanying pre-registration commits the hypotheses, metrics, exclusion rules, and analysis plan that the pilot will follow.
[NLP-67] Incongruence-Aware Evaluation of Multimodal Speech Emotion Recognition in Audio Large Language Models
【速读】： 该论文旨在解决语音交互式大语言模型在处理言语内容与语调情感时，文本与音频模态间不一致（incongruence）对语音情感识别性能的影响问题。其核心挑战在于：当文本语义与语音情感表达存在冲突时，这种不一致性是否会导致模型判断错误。解决方案的关键在于提出一种基于双成分高斯混合模型的连续、无阈值的不一致评分机制，将文本与音频特征映射至共享的效价-唤醒度-主导性（valence-arousal-dominance, VAD）空间，并以二者之间的距离作为不一致程度的量化指标。通过与人工标注对比验证该评分的有效性，研究发现，在控制真实情感标签及特征空间距离后，不一致分数显著提升模型出错概率，且这一现象在多个数据集（eNTERFACE’05、MELD、CMU-MOSEI）中均成立。值得注意的是，尽管基于相同特征的监督分类器表现出相反趋势，表明问题根源并非信号本身，而在于模型融合原始证据的方式。此外，该不一致评分在去除情感词典依赖后仍能保持核心结论，且在六种简化替代方案中唯一能在所有三个数据集中稳定预测错误，凸显其鲁棒性与有效性。
链接: https://arxiv.org/abs/2610.40883
作者: Fengji Jing,Guy Laban
机构: The University of Hong Kong (香港大学); Ben-Gurion University of the Negev (本·古里安大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Voice-interactive large language models process both what a speaker says and how they say it, yet the two channels do not always agree. We ask whether that disagreement degrades speech emotion recognition. Mapping text and audio into a shared valence-arousal-dominance space, we convert the distance between them into a continuous, threshold-free incongruence score with a two-component Gaussian mixture, and validate the score against human judgement. We evaluate three audiocapable models on acted, conversational and spontaneous speech (eNTERFACE’05, MELD and CMU-MOSEI). With the model held constant across modalities, incongruence raises the odds of error whenever audio is present, in every corpus, after controlling for the ground-truth emotion and for distance in the feature space. Supervised classifiers trained on the same features behave in the opposite way, which suggests that the difficulty lies in how these models combine raw evidence rather than in the signal itself. Text-only effects are unreliable: swapping only the model that reads the transcript reverses their sign on one corpus. Rebuilding the score without the emotion lexicon reproduces the central result, and among six simpler alternatives it is the only label-free score that predicts error in all three corpora. Code, data tables and prompts are released.
[NLP-68] FARO: Function-Space Adaptive Return-to-Risk Optimization via Cross-Block Equal-Risk-Contribution Budget Allocation
【速读】： 该论文旨在解决大语言模型训练中优化器参数更新策略依赖人工先验与固定全局更新预算所带来的局限性问题。现有方法通过手动划分不同层类型（如注意力模块与前馈网络）采用不同的优化几何（如正交化动量与Adam风格更新），这种设计不仅受限于人类对模型结构的直觉判断，且无法动态适应各参数块的实际学习表现。其核心解决方案是提出FARO（Function-space Adaptive Optimizer），以参数块（parameter block）为基本决策单元，基于每个块在反事实（counterfactual）下的实际收益自适应地分配优化几何，并引入来自投资组合理论的风险平价原则（Equal Risk Contribution, ERC），实现跨块的动态预算再分配。该方法通过冻结各块的更新路径并按风险贡献均衡重分配资源，避免了对特定层类型的预设几何。实验表明，在FineWeb数据集上训练的GPT类模型中，FARO-Adaptive臂在完整训练周期内达到3.2780 nats的损失，与最优人工设计策略NorMuonAndAdam（3.2794 nats）相当，无需人工设定每层优化器配置；而进一步引入ERC机制的FARO-ERC臂则在6个随机种子下均显著降低干净数据损失（平均下降0.0136 nats），统计检验（符号检验、配对t检验、置信区间）一致支持该改进，且未增加计算或内存开销。其优势在于通过裁剪高风险块而非扩展低风险块来优化整体性能，并在数据流污染条件下仍保持更低的平均损失及多数种子胜出，展现出鲁棒性。
链接: https://arxiv.org/abs/2610.40858
作者: Xiaojun Wu,Cehao Yang,Xuhui Jiang,Chengjin Xu,Jia Li,Jian Guo
机构: The Hong Kong University of Science and Technology (Guangzhou); IDEA Research; DataArcTech Ltd.
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 20 pages, 5 figures
Abstract:Training a large language model is compute-bound, and current recipes divide the work between optimizers by hand: orthogonalized momentum for attention and MLP matrices, Adam-style updates for the rest. This division rests on human priors about which layer type deserves which geometry, and it fixes a single global update budget that every parameter block shares. We introduce FARO, a function-space adaptive optimizer that makes the partition of parameters into blocks the unit of decision. FARO gives each block a geometry by its realized counterfactual return, then freezes the routes and reallocates the frozen per-block budget by cross-block equal risk contribution (ERC), the risk-parity principle from portfolio theory. On a GPT-style model trained on FineWeb, the framework gives two results. First, its FARO-Adaptive arm reaches 3.2780 nats at the full schedule, matching NorMuonAndAdam (3.2794), the best hand-designed recipe: it recovers the human prior without hand-setting per-layer geometry. Second, FARO-ERC improves on that arm: it lowers the clean-data loss at every one of 6 seeds, by 0.0136 nats on average, a modest but robust gain. Its sign test, paired test, and confidence interval all agree, and it exceeds the measured run-to-run noise at no added time or memory. It works by trimming the riskiest blocks rather than expanding the safest, and it holds up under a corrupted stream, still lowering the mean loss and winning the seed majority. The source code is available at this https URL.
[NLP-69] he Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model
【速读】： 该论文旨在解决生成式语言模型在无验证机制下的高效独立生成问题，具体聚焦于连续扩散语言模型（Continuous Diffusion Language Models, CDLMs）与推测性草稿生成器（speculative drafters）在独立生成场景下的性能差异。其核心挑战在于：尽管两类模型均能实现高效生成，但传统评估方式分别依赖于迭代精炼或目标模型验证，难以公平比较其内在生成能力。为此，研究提出在无验证、无目标特征的条件下对两类模型进行对比，采用LangFlow作为CDLM基准，并将DSpark的马尔可夫头与递归头架构改造为独立生成模式。所有模型均从零训练，基于相同预处理、骨干维度及训练预算，在16、128和1024长度上生成无条件单块OpenWebText数据。通过外部语言模型计算生成困惑度（GenPPL）与使用MAUVE衡量分布相似性进行评估。结果表明，在128和1024长度下，LangFlow显著优于两种DSpark变体；而在16长度下，DSpark-RNN表现更佳。进一步的条件KL分析揭示了马尔可夫头仅依赖前一词元所导致的依赖关系缺失，而递归记忆虽缓解此问题并提升性能，但长序列差距依然存在。此外，教师概率匹配可降低马尔可夫结构的GenPPL但未带来MAUVE提升；增加LangFlow的去噪步数在固定权重下有效改善短序列（16和128）性能，并改变其与DSpark-RNN的相对排序。综上，该研究确立了LangFlow在长序列生成中的优势，强调了迭代全序列精炼机制对于提升独立生成质量的关键作用，超越了仅依赖递归头记忆带来的改进。
链接: https://arxiv.org/abs/2610.40847
作者: Yujian Chen,Hao Jiang,Guang Cheng,Weijian Luo,Tianyang Hu
机构: Polyadic Research Group; School of Computer Science, Peking University (北京大学计算机学院); SAGA Lab, The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）SAGA 实验室); The University of California, Los Angeles (加利福尼亚大学洛杉矶分校); Dots Studio, Xiaohongshu (小红书点滴工作室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Continuous diffusion language models (CDLMs) and speculative drafters offer efficient language generation but are typically evaluated differently: CDLMs generate independently through iterative refinement, whereas drafters propose text for target-model verification and correction. We compare their architectures without verification, using LangFlow for CDLMs and adapting DSpark’s Markov and recurrent-head architectures for standalone generation. All models are trained from scratch for unconditional, single-block OpenWebText generation at lengths 16, 128, and 1024, without target features or verification, using common preprocessing, backbone dimensions, and training-update budgets. We evaluate generation perplexity (GenPPL) with an external language model and distributional similarity to reference text with MAUVE. With default denoising steps equal to sequence length, LangFlow outperforms both DSpark variants on both metrics at lengths 128 and 1024, while DSpark-RNN leads at 16. A conditional KL analysis characterizes dependencies excluded by the Markov head’s previous-token conditioning. Recurrent memory removes this restriction and improves both metrics, but the longer-sequence gap persists. Teacher probability matching further lowers Markov GenPPL without observed MAUVE gains. Increasing LangFlow’s denoising steps at fixed weights improves both metrics at lengths 16 and 128, changing the length-128 ordering relative to DSpark-RNN. These results establish LangFlow’s longer-sequence advantage under the evaluated budgets and highlight iterative full-sequence refinement as a route to improving standalone generation beyond gains from recurrent head memory.
[NLP-70] Safin-1.5: Safety-Native Architecture and Verifiable Reasoning for Recursive Safety Improvement
【速读】： 该论文旨在解决大语言模型在递归自提升（Recursive Self-Improvement, RSI）过程中因持续迭代而引入新风险、削弱现有安全防护机制的问题。其核心挑战在于如何在不牺牲模型通用能力与实用性的情况下，实现安全性的持续增强。解决方案的关键在于提出“安全递归自提升”（Safety RSI）框架，通过三方面协同实现：首先，构建包含持久化安全状态与危险知识解耦的安全架构，使安全知识可被选择性调用，而高风险知识则被隔离至可移除的专家模块，从而实现安全强化的同时最小化对通用能力的影响；其次，建立发现-评估-修复的递归安全改进循环，基于实验反馈识别新漏洞并生成修复策略，仅采纳在提升安全性、维持通用能力及限制过度拒绝方面均表现优异的更新；最后，引入推理时形式化推理钩子（formal reasoning harness），结合语言理解与符号推理验证，通过证明义务引导的语义桥接恢复形式化表示中的缺失关系，使符号求解器能够在形式化前提和可接受的语义桥梁下构造可验证的证明。实验结果表明，Safin-1.5 在安全性和形式推理性能上均超越前沿模型，验证了该方法的有效性。
链接: https://arxiv.org/abs/2610.40751
作者: Jingnan Zheng,Cheng Jin,Ming Zhang,Huan Sun,Zhongtian Ma,Kaisen Yang,Ziyuan Zhou,Tong Wu,Dongcheng Zhang,Yi Zhang,Xiao Wang,Han Qi,Shu Yu,Ermo Hua,Zongji Yu,Chaozhuo Li,Xiaoyu Wen,Zhekai Chen,Jiawei Zhou,Sirui Chen,Ning Ding,An Zhang,Tat-Seng Chua,Xiangnan He,Xia Hu,Bowen Zhou,Qiaosheng Zhang,Youbang Sun,Xiang Wang,Zhongyu Huang,Chaochao Lu
机构: Shanghai AI Laboratory(上海人工智能实验室)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 29 pages, 5 figures
Abstract:Recursive self-improvement (RSI) enables large language models to advance their capabilities through successive updates, but these updates can also introduce new risks and weaken existing safeguards. In line with the AI-45° Law, we pursue Safety RSI, in which safety develops as an intrinsic capability alongside general model capabilities through continual risk discovery and repair, while preserving utility and limiting over-refusal. We introduce Safin-1.5, a model built on a safety architecture and trained through recursive safety improvement, with an inference-time harness that extends its capabilities to formal reasoning. Our approach comprises three components: i) The safety architecture combines persistent safety states with hazardous-knowledge disentanglement. The former retains and selectively invokes safety knowledge, while the latter relocates targeted hazardous knowledge into removable experts, enabling its removal with minimal impact on general capabilities. ii) Recursive safety improvement establishes a discovery-evaluation-repair loop. The loop identifies new vulnerabilities and uses experimental feedback to develop repair strategies. It accepts updates only when they improve safety while preserving general capabilities and limiting over-refusal. iii) The formal reasoning harness combines the model’s language understanding with symbolic inference and verification. Proof-obligation-guided semantic bridging recovers missing relations in formal representations, enabling symbolic solvers to construct checkable proofs under formalized premises and accepted semantic bridges. Experimental results demonstrate that Safin-1.5 achieves safety and reasoning performance, outperforming frontier models.
[NLP-71] Mage-VL-Cowork: A Full Training Recipe to Learn Hybrid Computer Use From Evolved Harnesses MICRO
【速读】： 该论文旨在解决现有计算机使用代理（Computer-Use Agents, CUAs）在整合视觉图形用户界面（GUI）交互、代码执行与终端操作时缺乏统一策略的问题，尤其关注如何在保持高效推理成本的前提下实现多模态能力的协同。其核心解决方案是提出一种40亿参数的混合型CUA——Mage-VL-Cowork，通过一个端到端、分五阶段渐进式的训练课程（涵盖中段训练、监督微调和强化学习），从通用多模态对齐的检查点出发进行训练。关键创新在于构建了可扩展的“Mage-CUA-Forge”框架，将传统的任务定制化代理构建转变为可复用的技能条件生成工作流与应用特定预处理器，从而在6,915种技能下合成293,806个桌面与网页任务，使每任务重复开销降低82.6%。此外，不采用直接蒸馏默认教师模型的方式，而是动态演化教师代理框架，并引入专用的CUA代理框架以生成高质量轨迹并提供阶段特异性监督信号，显著提升了训练效率与最终性能。最终，Mage-VL-Cowork-4B在OSWorld-Verified基准上达到66.0%的准确率，在同规模模型中处于领先水平，推动了本地化智能（on-device intelligence）的发展边界。
链接: https://arxiv.org/abs/2610.40747
作者: Xiaoyi Zhang,Haoqing Wang,Xinjie Zhang,Xiang Ming,Xing Wang,Senqiao Yang,Kaichen Zhang,Zhaoyang Jia,Andong Tan,Xinjiang Wang,Jinghao Guo,Yifei Shen,Xiao Li,Peng Zhang,Xun Guo,Jiahao Li,Shicheng Zheng,Jinglu Wang,Zongyu Guo,Wenxuan Xie,Bin Li,Yan Lu
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Project page: this https URL
Abstract:Effective computer-use agents (CUAs) should seamlessly combine visual GUI interaction with code and terminal execution within a single policy. We present Mage-VL-Cowork, a 4B hybrid CUA trained from a general multimodally aligned checkpoint through an end-to-end, progressive five-stage curriculum across midtraining, SFT, and RL. To power this curriculum at scale, Mage-CUA-Forge replaces per-task agent construction with reusable, skill-conditioned generation workflows and application-specific preparers, synthesizing 293,806 desktop and web tasks across 6,915 skills while reducing recurring per-task expenditure by 82.6%. Rather than directly distilling a default teacher, we evolve the teacher harness and instantiate specialized CUA harnesses to yield high-quality trajectories alongside stage-specific supervision. Mage-VL-Cowork-4B finally climbs to 66.0% on OSWorld-Verified through different training stages with more efficient reasoning cost, achieving state-of-the-art performance at its parameter size and pushing forward the frontier of on-device intelligence.
[NLP-72] Apriel Voice 27B Preview: An Open Model for Speech Reasoning and Tool Use
【速读】： 该论文旨在解决现有语音助手在理解用户请求、上下文推理、多轮对话维持以及通过工具执行任务等方面的局限性，尤其针对传统系统中语音处理与语言理解模块分离导致的延迟高、协同能力弱的问题。其核心解决方案是提出一个统一的开源语音-语言模型Apriel Voice 27B Preview，该模型基于预训练的Gemma 4 26B-A4B检查点构建，具备共享的推理主干（reasoning backbone），可直接接收语音输入并生成文本响应与工具调用，无需依赖独立的对话模型。关键创新在于采用两阶段训练机制：第一阶段建立语音接口，第二阶段通过监督微调、文本重播及源相关交叉熵与前向Kullback–Leibler散度联合监督，实现投影器与主干网络的协同优化；同时利用自动生成的语音条件对话数据进行对话训练，并结合本地教师服务、计算重叠与融合全词汇损失实现高效的在线知识蒸馏。实验表明，该模型在Big Bench Audio（98.90分）、Speech-BFCL（81.13分）和τ-Voice任务成功率（65.51%）等指标上表现优异，平均延迟仅1.91秒，且在英语EVA准确率（51.27%）和经验通过率pass@1（74.13%）方面亦有显著提升，最终部署于定制化服务平台以支持实时交互式语音交互。
链接: https://arxiv.org/abs/2610.40738
作者: Aman Tiwari,Akshay Kalkunte,Akintunde Oladipo,Jash Shah,Tara Bogavelli,Hoang Nguyen,Nishanth Madhusudhan,Vishal Juneja,Sridhar Nemala,Srinivas Sunkara,Sathwik Tejaswi Madhusudhan,Shruthan Radhakrishna,Masoud Hashemi,Kelechi Ogueji,Khalil Slimi,Saloni Mittal,Pulkit Pattnaik,Shashank Maiya,Dhruv Jhamb,Esakkivel Esakkiraja,Katrina Stankiewicz,Gabrielle Gauthier Melançon,Fanny Riols,Amit Saha,Lakshmi Sirisha Chodisetty,Nitin Aggarwal,Anil Madamala,Sagar Davasam,Segan Subramanian,Vikas Yadav,Sai Rajeswar Mudumba
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注:
Abstract:A capable voice assistant must do more than recognize words; it must understand requests, reason over context, sustain conversations, and act through tools. We present Apriel Voice 27B Preview, an open speech-language model that unifies these capabilities within a shared reasoning backbone while retaining text and image input modalities. Built on the post-trained Gemma 4 26B-A4B checkpoint, Apriel Voice directly processes speech and generates both responses and tool calls without requiring a separate dialogue model. We introduce a two-stage training procedure to adapt the model to speech. The first stage establishes the speech interface, while the second jointly adapts the projector and backbone through supervised fine-tuning, text replay, and source-dependent cross-entropy and forward Kullback–Leibler divergence supervision. Automatically constructed speech-conditioned dialogues provide conversational training data, while local teacher serving, overlapping teacher and student computation, and fused full-vocabulary losses enable efficient online distillation. In thinking mode, Apriel Voice achieves a score of 98.90 on Big Bench Audio, 81.13 on Speech-BFCL, and a task success rate of 65.51% on \tau -Voice, with a mean latency of 1.91 seconds. It also achieves 51.27% English EVA accuracy and 74.13% experience pass@1. Finally, we deploy Apriel Voice 27B Preview on a custom serving platform to support interactive voice interactions.
[NLP-73] Li2SAO: Lifting Light Singl-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping
【速读】： 该论文旨在解决异步强化学习（Asynchronous Reinforcement Learning, ARL）在大语言模型（Large Language Models, LLMs）后训练中因采样策略与训练动态不匹配而导致的稳定性与离策略行为不佳的问题，尤其针对代理型任务（agentic tasks）表现欠佳的瓶颈。现有方法如单轮次异步优化（Single-rollout Asynchronous Optimization, SAO）虽提升了性能，但依赖于闭源的预训练价值模型（value model），限制了其可复现性与泛化能力。为此，本文分析了组内采样策略（Group-wise Sampling, GRPO）与轻量级复现版本LiSAO在单轮推理与多轮代理任务上的表现差异，发现GRPO在推理任务上更优，而LiSAO在代理任务上更具潜力，但受限于未充分训练的价值函数导致早期性能滞后。为突破此局限，本文提出Li² SAO，其核心创新在于引入“组相对演员-评论家自举”（group-relative actor-critic bootstrapping）机制：在评论家尚处于冷启动阶段时，通过组相对优势（group-relative advantage）指导演员（actor）更新，并以组中心回报（group-centered return）引导评论家初始化，待模型收敛后再切换至单轮次广义优势估计（GAE）。实验表明，Li² SAO不仅在早期训练阶段快速追赶并超越GRPO，且显著提升后期性能上限，实现了高效、稳定且无需依赖外部预训练价值模型的异步训练方案。
链接: https://arxiv.org/abs/2610.40726
作者: Zichen Bai,Yanli Wang,Yang Liu,Junhao Shi,Xueqin Liu,Jinguo Sun,Muye Li,Shaoqiang Zheng,Xuefei Diao,Zhuwei Zeng,Nan Duan,Peng Cao
机构: JD.Com(京东)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Asynchronous reinforcement learning (RL) is becoming a mainstream paradigm for post-training large language models (LLMs), as it improves training efficiency by updating the model as rollouts arrive. Recent work argues that group-wise sampling in GRPO does not naturally fit asynchronous RL training because rollout lengths follow a long-tailed distribution, and proposes Single-rollout Asynchronous Optimization (SAO) to improve stability and off-policy behavior, especially on agentic tasks. However, SAO relies on a closed-source value model obtained from scaling value pretraining. In this paper, we analyze GRPO and LiSAO, our lightweight reproduction of SAO without the pretrained value model, on single-turn reasoning and multi-turn agentic tasks. We find that GRPO outperforms LiSAO on reasoning tasks, while the reverse holds on agentic tasks. Since the value model of LiSAO is not yet well trained , LiSAO lags behind GRPO substantially in early training steps. To address this issue, we propose Li ^2 SAO, which Lifts Light Single-rollout Asynchronous Optimization with group-relative actor-critic bootstrapping: while the critic is cold, a group-relative advantage trains the actor and a group-centered return trains the critic, after which training switches to single-rollout GAE. Experiments show that Li ^2 SAO catches up with or surpasses GRPO in early training and further raises the later performance ceiling. Code is available at this https URL.
[NLP-74] Leaner Training Lower Leakage: Revisiting Memorization in LLM Fine-Tuning with LoRA
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在微调阶段，尤其是针对参数高效微调方法LoRA（Low-Rank Adaptation）时的过拟合记忆（memorization）问题，这一现象可能导致数据提取攻击（data extraction attacks）。尽管预训练阶段的过拟合记忆已受到广泛研究，但其在微调阶段的表现，特别是LoRA微调中的影响仍缺乏系统探讨。论文的关键发现是：与传统全量微调或预训练阶段不同，模型规模和数据重复性等关键因素在LoRA微调中对记忆行为的影响呈现显著差异，表现出非预期的弱相关性。其解决方案的核心在于引入一种更宽松的基于相似性的记忆度量方式，证明了相较于全量微调，LoRA在显著降低记忆风险的同时，仍能保持优异的任务性能，从而为参数高效微调的安全性提供了新的理论支持与实践依据。
链接: https://arxiv.org/abs/2506.20856
作者: Fei Wang,Baochun Li
机构: University of Toronto(多伦多大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注:
Abstract:Memorization in large language models (LLMs) makes them vulnerable to data extraction attacks. While pre-training memorization has been extensively studied, fewer works have explored its impact in fine-tuning, particularly for LoRA fine-tuning, a widely adopted parameter-efficient method. In this work, we re-examine memorization in fine-tuning and uncover a surprising divergence from prior findings across different fine-tuning strategies. Factors such as model scale and data duplication, which strongly influence memorization in pre-training and full fine-tuning, do not follow the same trend in LoRA fine-tuning. Using a more relaxed similarity-based memorization metric, we demonstrate that LoRA significantly reduces memorization risks compared to full fine-tuning, while still maintaining strong task performance. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR) Cite as: arXiv:2506.20856 [cs.LG] (or arXiv:2506.20856v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.20856 Focus to learn more arXiv-issued DOI via DataCite
[NLP-75] Agent ic Systems for Calibrating Cosmological Simulations
【速读】： 该论文旨在解决宇宙学模拟校准这一高维、计算成本高昂的参数优化问题。传统方法如贝叶斯优化虽能实现自动化，但将模拟器视为黑箱，难以融入人类领域知识或科研工作流程。为此，本文提出AgenticTuner，一种基于当代大语言模型（Large Language Model, LLM）作为智能体（agent）的校准流水线，由该智能体主动提出下一组参数试验。关键创新在于：通过引入非正式的人类可读先验信息（如代码库、文档和经验性指导原则），大语言模型能够以极低的CPU开销实现与传统方法相当甚至更优的校准性能。该方案的核心在于将人类科学家的经验知识编码为自然语言形式的上下文输入，使生成式智能体在参数探索中具备更强的科学合理性与效率，从而推动迈向能够执行复杂、长周期科学任务的AI科学家。
链接: https://arxiv.org/abs/2610.40685
作者: Chaipat Tirapongprasert,Matthew Ho
机构: Columbia University (哥伦比亚大学); Institute for Advanced Study (高级研究院)
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computation and Language (cs.CL)
备注: Accepted to the 9th annual workshop for Machine Learning the Physical Sciences (ML4PS). A starter code is available on this https URL
Abstract:Calibrating a cosmological simulation is an expensive, high-dimensional parameter optimization task. Previous works employ Bayesian optimization for automation, which treats the simulator as a black box and precludes the incorporation of human domain knowledge or workflow into the selection policy. We propose AgenticTuner, an agentic calibration pipeline with a present-day large language model serving as the optimizer who proposes the next parameter trial. We show that these agents can achieve competitive calibration performance at a fraction of the CPU cost when they are given informal, human-readable priors (such as codebase, documentation, and heuristic guides that seasoned scientists may possess). We hope this work serves as an early step toward AI scientists that can carry out complex, long-horizon scientific tasks.
信息检索
[IR-0] Recommendation Feedback Loops Shape Urban Income Segregation and Venue Visitation RECSYS
链接: https://arxiv.org/abs/2610.41911
作者: Guangyuan Weng,Lin Chen,Ciro Beneduce,Giovanni Mauro,Massimiliano Luca,Takahiro Yabe,Bruno Lepri,Luca Pappalardo,Esteban Moro
类目: Information Retrieval (cs.IR); Computers and Society (cs.CY); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph)
备注: 13 pages, 5 figures, 7 tables. Code: this https URL
Abstract:Location-based recommender systems shape where people go in cities and learn from the visits they steer, forming a human-AI feedback loop. This feedback loop shapes whom people share places with across income groups and which venues keep attracting customers. Yet platforms judge these systems by their offline accuracy on past visits, a test in which the suggestions never change where people go and which therefore cannot capture long-term effects. Studying these effects in cities is challenging because, unlike online resources, travel constraints and limited venue capacity shape where people can go, while each recommended visit also changes whom other visitors encounter. To capture such features, we build a long-term simulation framework based on fine-grained human mobility data from four U.S. metropolitan areas, with distance and capacity constraints. Focusing on food venues, we study two urban consequences: how unevenly people’s exposure is distributed across income groups (experienced income segregation) and the share of venues receiving no visits from simulated residents (venue disappearance). We find that, under every algorithm and in every metropolitan area, at least a fifth of venues stop receiving visits, and that more than half of this loss arises from retraining. More importantly, more accurate algorithms leave people more segregated, and the most accurate algorithm leaves people 15-21% more segregated than they actually are. We then test a re-ranking intervention that favours less popular venues and runs on top of a platform’s existing algorithm. It lowers both venue disappearance and segregation across algorithms, largely without directing people to venues where they may feel less comfortable. Our results suggest that impact assessments of recommender systems should account for the feedback loop and report segregation and venue disappearance beyond accuracy.
[IR-1] Completeness and Cost of Retrieval Structures for Multi-Tool Requests in LLM Agents
链接: https://arxiv.org/abs/2610.41905
作者: Shuyang Zhang
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 17 pages, 3 figures, 9 tables
Abstract:An agent that must call several tools to fulfill a request fails when the tool retriever omits any one of them, yet tool retrievers are mostly built and evaluated on queries requiring a single tool. We compare representatives of six retrieval structures (flat lexical and dense retrieval, LLM query decomposition, LLM-navigated hierarchies, server-then-tool matching, dependency graphs, and trained set-level retrievers) across two multi-tool testbeds: 345 AppWorld queries with expert dependency labels and 103 MCP-Bench tasks over 257 MCP tools. We measure completeness@k, the share of queries whose entire required tool set appears in the top k, alongside the LLM calls, tokens, and latency that each structure incurs at retrieval time. A motivating analysis on all 7,961 ToolRet queries shows that aggregate completeness mixes two regimes: completeness at 10 is .58 to .67 on single-target queries and .08 to .17 on queries with four or more targets, and the ordering of retrievers on single-target queries reverses on multi-target ones. Across the two testbeds, none of the compared structures that is untrained on the testbed and makes at most one LLM call per query completes more than 10% of AppWorld queries at k = 16. The two structures that do better incur different costs: a set-level retriever trained on 75 to 90 in-domain queries reaches .33 on AppWorld and .58 on MCP-Bench with no LLM calls, while an LLM-navigated hierarchy reaches .30 and .40 with a median of 31 to 38 LLM calls and about five minutes per query. Their failure modes also differ: the trained retriever mostly ranks the missing tools just below the cutoff, whereas the hierarchy finds the target API and stops before collecting its prerequisites. In a downstream check with the official AppWorld agent, tasks with a complete retrieved set succeeded 21 of 21 times, whereas incomplete ones succeeded 2 of 39 times.
[IR-2] Peeling the Matryoshka: Extracting Ontological Structure from Retrieval Embeddings EMNLP2026
链接: https://arxiv.org/abs/2610.41785
作者: Eunchan Kim,Inwhee Joe
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2026 (Main Conference). 13 pages, 6 figures, 7 tables. Code: this https URL
Abstract:Taxonomy construction is essential for knowledge-intensive NLP yet remains costly: clustering-based methods (i) need careful per-corpus tuning and yield unstable hierarchies, while LLM-based pipelines (ii) depend on expensive task-specific training that does not transfer across domains. We report an empirical finding that bears on both: in Matryoshka Representation Learning (MRL) embeddings, where each leading prefix is itself a usable, coarser vector, prefix length aligns with the coarse-to-fine granularity of gold taxonomies; shuffled- and reverse-dimension controls isolate the alignment to the learned early prefix dimensions. The pattern holds across three architecturally distinct MRL models. To test whether this regularity is usable, we introduce PEEL (Peeling Embeddings to Extract Latent ontologies): a minimal probe over these frozen embeddings, trained once with an order-embedding objective that represents hierarchy as vector containment; a minimum spanning arborescence then decodes taxonomic DAGs. Using only a concept vocabulary (no documents), PEEL recovers taxonomic DAGs with high structural fidelity on two OLLM benchmarks, transferring to a new domain with no target-edge supervision. The end-to-end OLLM pipeline, which jointly discovers concepts and induces structure from document corpora, is reported as a cross-task reference. Pre-trained retrieval embeddings encode a decodable granularity gradient; a minimal probe renders it as explicit parent-child (is-a) structure.
[IR-3] Search When Needed Proceed When Reliable: Metacognitive Reliability Supervision for Efficient Agent ic Search
链接: https://arxiv.org/abs/2610.41738
作者: Minhao Sun,Zhijing Wu,Xian-Ling Mao
类目: Information Retrieval (cs.IR)
备注:
Abstract:Efficient agentic search requires large language models (LLMs) to determine whether they can reliably proceed with their current knowledge or need additional external evidence. Supervision based on final-answer correctness or trajectory-level feedback evaluates realized trajectory performance. However, observed trajectory performance is jointly shaped by stochastic generation, retrieval quality, and downstream reasoning. Such signals therefore provide only indirect evidence of how reliable the current decision state was and how retrieved evidence changed its reliability. An effective reward should encourage search when external information is needed and favor searches that contribute to the answer. To this end, we propose a metacognitive framework for training LLMs to make search decisions based on the reliability of their intermediate reasoning states. Our framework estimates the reliability of local conclusions to distinguish whether the current information supports proceeding without search from how much retrieved evidence improves that reliability. The current-state reliability score assesses whether the model can proceed without search. The search-action score measures how retrieved evidence changes estimated reliability. Together, these scores define a reliability-aware process reward for Group Relative Policy Optimization (GRPO), providing finer-grained supervision for search and no-search behavior. Experiments across seven question-answering benchmarks demonstrate that our method achieves a better accuracy-efficiency trade-off than strong agentic-search baselines. Our code and model checkpoints will be open-sourced.
[IR-4] owards Trustworthy Multi-stakeholder Recommender Systems
链接: https://arxiv.org/abs/2610.41674
作者: Katarina Marcincinova,Adrian Gavornik,Matus Mesarcik,Michal Kompan
类目: Information Retrieval (cs.IR)
备注: this https URL
Abstract:Recommender system (RS) nowadays has to reflect, combine, and solve often contrasting requirements and expectations from dozens of stakeholders. In fact, the problem is even more complicated as each of the stakeholders can simultaneously have various objectives creating a diverse and complicated environment with several stakeholders and their objectives in which a RS operates. To address this problems the Multi-objective and Multi-stakeholder RS have been studied in the literature. However, a limited attention has been paid to trustworthiness aspects of such RS, which slowly becomes a new standard. In this paper, we highlight open challenges and important questions which need to be addressed on the way to trustworthy Multi-stakeholder RS.
[IR-5] Enhancing Knowledge Entity Relation Extraction in Abstracts and Full-text Content of Academic Articles Using Large Language Models
链接: https://arxiv.org/abs/2610.41552
作者: Yingyi Zhang,Chengzhi Zhang
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
备注:
Abstract:This study investigates the effectiveness of large pre-trained language models (LLMs) for knowledge entity relation extraction (KERE) from academic papers. Existing studies have examined individual types of LLM-based strategies and evaluated model performance using metrics such as precision and recall. To provide a broader evaluation, this study investigates multiple strategies, including closed-source LLM-based CoT prompting, LoRA-adapted open-source LLMs, LLM-enhanced small pre-trained language models (SLMs), and SLM-enhanced closed-source LLMs. In addition, this study introduces two strategies, namely SLM-enhanced open-source LLMs and closed-source LLM-enhanced open-source LLMs. These strategies are evaluated from multiple perspectives, including metrics, their ability to mitigate errors observed in SLMs, and potential directions for model optimization. This study manually constructs two annotated datasets in the NLP domain, covering Abstract and Full-Text datasets. Experimental results show that the LoRA-adapted T5 model achieves the highest macro F1 scores of 0.7408 and 0.7013 on the Abstract and Full-Text datasets, respectively. SLMs also benefit from LLM-generated entity descriptions under the LLM-enhanced SLM strategy. Error analysis further reveals that the effectiveness of LLM-based strategies in mitigating SLM errors varies across error types. Based on the identified limitations and error patterns of these strategies, several optimization directions are proposed. In addition, combining strategies with different error patterns further improves KERE performance.
[IR-6] KITE: Retrieval as a Decision Procedure for Long-Term Conversational Memory
链接: https://arxiv.org/abs/2610.41521
作者: Feng Li,Kailun Tan,Zhibo Zhou,Youxiang Wang,Lejun Liu,Huaigu Li,Fei Du,Huilong An
类目: Information Retrieval (cs.IR)
备注: 15 pages, conference
Abstract:Many long-term memory systems for LLM agents are built around embedding-based retrieval. Al- though effective for semantic matching, similarity ranking alone does not establish whether re- trieved memories satisfy the conditions implied by a question. In this paper, we introduce KITE (Knowledge-Indexed Temporal Evidence), a vector-free framework that formulates memory re- trieval as an explicit decision procedure. KITE organizes multi-session conversations as structured knowledge indexed by a governed topic vocabulary, which evolves through controlled expansion and consolidation. At query time, questions are compiled into inspectable retrieval plans over the same knowledge index that guides memory construction. The plans are then executed through fixed lookup and ranking operations, while sparse lexical retrieval recovers surface details omitted from the structured representation. Experiments on LoCoMo and LongMemEval-S show that KITE achieves 93.5% and 86.0% accuracy while using only about 1.6k reader-context tokens per ques- tion. These results demonstrate that conversational memory can move beyond similarity ranking by unifying memory construction and query compilation around a governed index, turning retrieval into an inspectable decision procedure for determining which evidence satisfies the question.
[IR-7] How BERT-based Cross-Encoders Model Relevance in Information Retrieval?
链接: https://arxiv.org/abs/2610.41425
作者: Mathias Vast,Basile Van Cooten,Laure Soulier,Benjamin Piwowarski
类目: Information Retrieval (cs.IR)
备注: Under review
Abstract:Cross-encoders are the most effective but also the least efficient models in Information Retrieval. Despite their success, the mechanisms underlying their relevance predictions remain poorly understood, particularly how they process to match queries and documents. This lack of interpretability limits their optimization, can lead to suboptimal retrieval, especially out-of-domain, and raises efficiency concerns for over-parameterized models. To address these challenges, this paper investigates two under-explored aspects of cross-encoder behavior. First, to understand the relevance prediction process, we trace the flow of information at the different stages of the process and between different input segments. We study the nature of the signals that compose this flow and that allow cross-encoders to predict the relevance of a passage to a query. Our work particularly focuses on the matching performed by the cross-encoders between queries and documents. Second, we explore how cross-encoders behave with out-of-domain documents and/or queries. Through a series of interpretability experiments, we evaluate three BERT-based cross-encoder models, varying in size and pre-training strategies, across both in-domain and out-of-domain datasets. Overall, this study both consolidates what is known about how cross-encoders model relevance and extends it by exploring the role of matching signals.
[IR-8] Fidelity Synopses: Pre-Execution Plan Selection for Approximate Vector Search
链接: https://arxiv.org/abs/2610.41407
作者: Adithya Ramanatha,Sanskar S. Singh,Maya Ramanath
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Vector search has become a database operator with many physical implementations: different index families, build-time and search-time parameters, and compressed or disk-resident variants. Each trades cost against fidelity, the fraction of a query’s exact top-k nearest neighbors that a configuration returns. Deployments nevertheless choose one configuration offline and use it for every query, so they are sized for their hardest queries and overpay for the rest. On a 5.8M-item LAION benchmark with four embedding models, running each query on the cheapest configuration that suffices for it would roughly halve the cost in a typical setting. Exploiting this requires predicting each configuration’s fidelity before running it, which is hard because fidelity is defined by the very exact search that approximate indexes avoid. Relational optimizers solve the analogous problem for cardinality with offline synopses; we propose one for vector search. A fidelity synopsis divides the embedding space into cells around a set of landmarks and records, for every cell and configuration, how often that configuration met a fidelity target on a sample of queries. At query time, one nearest-landmark lookup yields a calibrated estimate for every configuration, and an optimizer chooses configurations so that the workload misses the target no more often than its requirement allows. We test the two premises the synopsis relies on, that fidelity varies smoothly across the embedding space and that its loss is specific to each configuration. Held to the same requirement a static deployment is built to meet, synopsis-guided selection lowers cost in all 16 combinations of model and query direction, to a median of 0.72x the static deployment, with a batched lookup of 21-32 microseconds per query. The saving persists against the best static choice among the same indexes, and within a single index at no extra memory.
[IR-9] ProSearch: Benchmarking Multi-Constraint Protocol Retrieval in Experimental Science NEURIPS2026
链接: https://arxiv.org/abs/2610.41396
作者: Wenliang Liang,Zhiyuan Ning,Haolin Chen,Yuanchun Zhou,Wenjuan Cui,Yi Du
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to NeurIPS 2026 (Evaluations and Datasets Track). 48 pages, 4 figures. Wenliang Liang and Zhiyuan Ning contributed equally. Code: this https URL . Data: this https URL
Abstract:In AI for Science scenarios, experimental design and method reproduction require retrieving protocols that satisfy multiple interdependent constraints, yet existing benchmarks lack systematic evaluation of this capability. To address this gap, we introduce ProSearch, a benchmark for multi-constraint protocol retrieval in experimental science. ProSearch has three key features: (1) realism, with queries grounded in real experimental records and expert-defined templates; (2) complexity, with 60 experimental fields and their dependencies; and (3) diagnosticity, with 3-5 explicit constraints per query, including numerical and negation constraints. We construct ProSearch through LLM-based extraction, data-driven constraint selection, controlled query generation, and evidence-based relevance validation. The resulting benchmark contains 1,515 queries across six experimental science domains. We conduct extensive experiments on ProSearch with 19 representative retrievers and complementary analyses. The best-performing retriever achieves an average nDCG@10 of only 0.290, which improves to 0.474 with reranking. Structured diagnostic analysis shows that explicit constraint matching provides a strong ranking signal, while manual error analysis identifies joint constraint binding as a key bottleneck. ProSearch provides a testbed for systematically evaluating scientific retrieval systems.
[IR-10] Hierarchical Dual-Graph Retrieval for Long-form Audio Reasoning
链接: https://arxiv.org/abs/2610.41390
作者: Eunsang Lee,Taein Ju,Jiyoung Lee
类目: ound (cs.SD); Information Retrieval (cs.IR); Audio and Speech Processing (eess.AS)
备注:
Abstract:Large audio language models (LALMs) struggle with long-form audio reasoning, as processing entire recordings often limits to capture query-relevant content distributed across distant segments while incurring substantial computational cost. We introduce AudioHelix, a hierarchical DualGraph framework tailored for LALMs’ retrieval-augmented generation (RAG) systems. Unlike existing text-only or non-graph RAG approaches, AudioHelix connects related textual and acoustic evidence across temporally distant segments for multi-hop reasoning. Extensive experiments on TraceAV-Bench, MMAU-Pro, and AudioMarathon demonstrate that AudioHelix consistently outperforms full-audio processing and existing RAG methods. The project site is this https URL.
[IR-11] BrowseFlow: Sparse Event Flow Matching for Generative User Simulation
链接: https://arxiv.org/abs/2610.41374
作者: Yitian Pang,Jiayi Zhang,Zhenjia Ma,Xiaowei Yue,Dagui Chen,Jian Xu
类目: Information Retrieval (cs.IR)
备注:
Abstract:User simulation aims to reproduce how users interact with content and products across online platforms. Recent generative approaches have enriched user simulation with behavioral history and contextual information. However, realistic browsing simulation still faces two challenges. (i) Stochastic Responses: Even with the same recommendation feed, the same user may produce different browsing responses, yielding distinct behavioral trajectories. A logged trajectory records only one such realization. (ii) Sparse Key Events: Clicks and likes provide important feedback but occur sparsely within browsing trajectories dominated by progression actions such as scrolling. Many stepwise simulators predict these actions in a dense sequence, where key events contribute relatively few training targets. To address these challenges, we propose BrowseFlow, a conditional discrete flow matching method for generative user simulation. For Sparse Key Events, BrowseFlow represents each response by its browsing extent and an ordered sequence of key events, directly modeling their counts and locations under exposure constraints. BrowseFlow learns the conditional distribution of complete browsing responses through probabilistic extent prediction and discrete flow matching over key events. Random event retention defines probability paths over partial event sequences, while transition rate matching learns stochastic insertion dynamics. For Stochastic Responses, we use the energy score, a proper scoring rule, to evaluate response fidelity and generated variability, complemented by the Wasserstein distance between key event count distributions. We further introduce TaobaoBrowse, a benchmark pairing real recommendation contexts with fine-grained browsing trajectories. Experiments across three datasets demonstrate that BrowseFlow consistently achieves the best distributional performance.
[IR-12] Defining Page-Centric Visual Document Retrieval and Evaluating Existing Benchmarks
链接: https://arxiv.org/abs/2610.41360
作者: Chongzhi Zhang,Yuqi Zuo,Aixin Sun
类目: Information Retrieval (cs.IR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In modern information retrieval (IR), documents are increasingly encountered as structured sequences of visually rich, paginated units, rather than as flat text. Example documents encountered in our daily life include Word files, PDFs, and slide decks. Free text-centric retrieval systems either ignore this structure entirely or destroy it through arbitrary passage chunking. In this paper, we formulate page-centric Visual Document Retrieval (VDR) with pages as the fundamental indexing units and document context available for assessing page relevance. The formulation distinguishes two retrieval granularities, page and document, and three retrieval paradigms: independent-item, evidence-set, and iterative retrieval. We then assess 42 benchmarks using eight criteria covering corpus characteristics, query construction, relevance annotation, and ranking support. Our analysis shows two main findings on current VDR research. First, evaluation remains concentrated on page-level independent-item retrieval, with fewer resources supporting document-level, evidence-set, and iterative settings. Second, queries originating from real user information needs independently of target corpus content and graded relevance judgments are uncommon, while the coverage and verification of relevance judgments vary substantially. Both findings reveal gaps between the capabilities applications require and those existing evaluation protocols can establish. We provide benchmark selection guidance and identify priorities for future benchmark design, emphasizing alignment between task requirements, relevance semantics, and evaluation protocols.
[IR-13] Pix2Evidence: A Multimodal RAG System for Text and Visual Evidence
链接: https://arxiv.org/abs/2610.41335
作者: Ziniu Liu,Xiaoyang Chen,Dezhi Ye,Jie Liu,Jin Ma,Hao Deng
类目: Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-augmented generation (RAG) over visually rich documents must find both relevant content and the evidence that supports an answer. Screenshot retrieval preserves images and page structure, but written facts within screenshots also need to be searchable. Moreover, topical relevance alone does not identify which retrieved tile contains evidence for the answer. We introduce Pix2Evidence, a multimodal RAG system that encodes each screenshot tile together with its available transcription into a single fused image–text representation, so that layout and readable text are considered jointly during retrieval. To guide evidence selection, a vision-language teacher describes the visible content that supports each training question. We use these descriptions to adapt the retriever, then rerank the retrieved candidates before answer generation. Extensive experiments demonstrate that Pix2Evidence outperforms state-of-the-art methods. Across five widely-used Wikipedia QA benchmarks, the non-reranked Pix2Evidence leads the main comparison in answer accuracy on four. On MMSearch, it reaches 26.0%, 4.4 percentage points above PixelRAG. We will release the code and share derived data where licensing and privacy reviews permit.
[IR-14] MILES-Rec: Uncovering Implicit Intents from Mobility Behaviors for Cross-Domain POI Recommendation
链接: https://arxiv.org/abs/2610.41319
作者: Yi Zou,Wei Luo,Xuewen Zhang,Zhuang Yan,Pengjie Wang
类目: Information Retrieval (cs.IR)
备注: 12 pages, 7 figures, 10 tables
Abstract:Map applications bridge everyday mobility with local services, creating opportunities to recommend places by uncovering users’ implicit intents. However, utility-driven map interactions provide only indirect and incomplete evidence of consumption demands, making it challenging to infer personalized service needs from observed behaviors. We propose MILES-Rec, a cross-domain POI recommendation framework that infers latent service intents from heterogeneous map behaviors and expresses them as prioritized natural language queries. It first learns from progressive reasoning demonstrations that synthesize user profiles and map histories for situation understanding, intent expansion, and query prioritization. We then introduce intent-aligned group relative policy optimization, using subsequent user interactions to reward query relevance and ranked service coverage while penalizing redundancy and excessive response length. A two-level credit assignment mechanism integrates shared list-level advantages with query-specific advantages, coordinating individual query quality with the complementary coverage of the complete query list. We further construct MILES-Bench from real user interaction logs on a large-scale mapping platform, supporting training and evaluation across diverse services under rich and sparse behavioral histories. Extensive experiments against general-purpose LLMs and specialized recommendation baselines demonstrate superior POI retrieval utility and strong service and semantic alignment, with consistent gains from behavioral policy optimization across four backbone scales.
[IR-15] Context as Infrastructure: Knowledge Compilation for Retrieval in Agent ic Systems with Harnesses
链接: https://arxiv.org/abs/2610.41172
作者: Zhichang Yu,Baining Cao,Sbaaoui Idriss,Qi Wang,Gangqiang Xu,Ling Qin,Xiaoling Jin,An Liu,Shaohao Cai,Xiaoyang Bao,Shengle Chen,Changchang Ding,Pengfei Zhu,Jingxiang Xu,Yifeng Mao,Lu Liu,Hai Jin,Yingfeng Zhang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 13 pages, 2 figures
Abstract:Retrieval-augmented generation is becoming agentic. A search harness writes queries, reads results, reformulates, and decides when to stop. This largely solves accuracy: a harness with only BM25/grep and MiniMax M3 answers 85% of BrowseComp-Plus and 91% on multi-hop FRAMES in our runs, without compiled or vector indexes. It exposes a context problem: an undisciplined loop repeats queries, rereads documents, and fills the window with unused passages. The missing layer is knowledge compilation: a configurable ingestion pipeline where LLMs enrich documents with context no chunk contains alone–navigation hierarchies with claim-evidence leaves, entity graphs, timelines, mind maps, ontologies, wikis, and memory. Compilation is not a better retriever: under a competent harness, structural artifacts gave no detectable accuracy gain on FRAMES, and a PageIndex-style outline degraded at scale. It is a supply layer. A navigation tree whose leaves are claim-evidence pairs–the mechanism we measure–cuts query tokens by 91% when evidence suffices and adds 1.9% when it does not, a 38% average reduction; this implies a 2% break-even sufficiency rate and a measured rate near 43%. A decision gate cuts latency from 140 s to 30 s, though substitution’s token saving is bought with retrieval rounds and carries its own latency cost. Other artifacts–ontology, graph, wiki, timeline, mind map, memory–pay off in deployment (governance, integration, onboarding, temporal orientation, continuity) where accuracy benchmarks cannot see them. We describe RAGFlow’s pipeline and harness (open source at this https URL), which turns the loop into five disciplined stations and keeps the context entering the model the smallest sufficient set; together they form an agent-native database: a store that hands the agent compiled, governed context instead of a ranked list of passages.
[IR-16] WebOverlay: High-Throughput Vision-Only Distillation for Extreme Aspect-Ratio Web Overlay Detection
链接: https://arxiv.org/abs/2610.41037
作者: Jiannan Huang,Jerry Ding,Li Yu,Zhenting Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 9 pages, 2 figures, 2 tables
Abstract:Web overlays, such as cookie consent banners, newsletter prompts, and age gates, obstruct automated web crawlers and degrade downstream document representations. While proprietary multimodal Large Language Models (MLLMs) can accurately identify overlays, their latency and inference cost preclude web-scale ingestion. Conversely, lightweight vision models struggle with full-page renderings characterized by extreme vertical aspect ratios, where standard isotropic resizing collapses critical text features and causes false positives. In this work, we present WebOverlay, a high-throughput vision-only detector trained via distillation from a frontier vision-language teacher. We establish a dynamic tiling strategy that partitions extreme aspect-ratio web renders into aspect-preserving patches with spatial overlap, coupled with an aspect-ratio-aware boundary adjudication mechanism to eliminate boundary artifacts. To eliminate cross-modal latency overheads, our student model relies purely on visual cues, bypassing DOM tree parsing. WebOverlay significantly outperforms DOM-based heuristics and achieves competitive precision and recall against heavy proprietary MLLM baselines while delivering multiple orders of magnitude higher throughput and substantially reduced deployment costs.
[IR-17] FieldGuideRAG : Image-Grounded GraphRAG for Fine-Grained Recognition with Vision-Language Models
链接: https://arxiv.org/abs/2610.40971
作者: Jiacheng Ding,Yanliang Qi,Cong Guo,Peihua Mai
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注:
Abstract:We present FieldGuideRAG, a training-free GraphRAG method in which a vision-language model, or VLM, recognizes fine-grained species by comparing the query with real photographs stored on the nodes of a knowledge graph. Multimodal GraphRAG systems describe each entity in text, and text conveys little of the visual detail that separates look-alike species. Our graph stores one photograph on every species node and links each species to its relatives, its geographic range and its ecological facts. A frozen VLM verifies the query against the photograph of each candidate node. FieldGuideRAG fuses the verification scores with the range edge and answers questions about the organism from the fact edges of the verified species. On CUB-200, a Wikidata graph and four iNaturalist domains, the node photograph improves verification over the species name on every graph, while text descriptions add little. Verification of the candidate pool exceeds a SigLIP retriever by up to 16 points, the range edge adds 7 to 19 points on iNaturalist, and answering from fact edges is correct on up to 89.5% of 28,820 ecological questions. The verification scores also detect queries whose species is missing from the graph. Graphs, prompts and code will be released.
[IR-18] ProdCrawl: A Replayable Test Collection for Focused Crawling
链接: https://arxiv.org/abs/2610.40855
作者: Manuel Rey-Escobar,David Otero,Javier Parapar
类目: Information Retrieval (cs.IR)
备注:
Abstract:A focused crawler searches for web pages on a specific topic under a constrained download budget. Since it can only fetch a limited number of pages, its success depends on choosing which links to follow. Focused crawlers are usually evaluated on live websites by harvest rate, the fraction of downloaded pages that are relevant. This has two problems: on the one hand, websites change over time, so a crawl cannot be repeated under the same conditions; on the other hand, there is no ground truth for which pages are relevant. As a result, comparisons between crawlers are unreliable. We present ProdCrawl, a replayable test collection for focused crawling. It comprises static mirrors of 621 company and organization websites, about 1.1 million pages, with each page labeled as a product page or non-product page based on a majority vote among three large language models. Any crawler can be run on exactly the same sites and scored against the same labels. We use ProdCrawl to compare simple baselines (breadth-first, depth-first and text classifiers) with an approach that scores each link by searching for similar link texts whose target pages have already been labeled. Our results show that text classifiers and keyword matching do not significantly outperform breadth-first crawling, whereas neural embeddings, which compare link texts by semantic meaning, outperform all simple strategies. Because every run evaluates over identical pages and labels, these results are exactly reproducible. The collection, codebase, and models are publicly released to support experimental replication and fair crawler comparison.
[IR-19] Retrieve-then-Disambiguate: Scalable Reviewer Discovery
链接: https://arxiv.org/abs/2610.40768
作者: Vicente Amado Olivo,Joe Petrecca,Wolfgang E. Kerzendorf
类目: Information Retrieval (cs.IR); Digital Libraries (cs.DL)
备注:
Abstract:The growing number of scientific submissions is straining the peer review process, while a large pool of relevant researchers remains unidentified and is therefore not considered as potential reviewers. We formulate the discovery of these researchers as a retrieval problem: given a submission, we retrieve and rank researchers based on domain similarity. We present retrieve-then-disambiguate, an open-source algorithm that identifies domain relevant researchers for a query submission by retrieving similar publications from the scientific literature and resolving ambiguous author names into unique researcher identities. We evaluate the algorithm on 500 astronomy and physics publications. We use ORCID-linked first authors, coauthors, and cited researchers as proxy labels to evaluate the recovery of researchers with known connections to each query publication. When retrieving the top 500 most similar papers, the algorithm recovers roughly 68% of first authors, 60% of coauthors, and 45% of cited researchers. Our code is available at this https URL.
[IR-20] Prevented is Not Protected: What Attack Success Rate Hides about RAG
链接: https://arxiv.org/abs/2610.40742
作者: Chahana Dahal,Zuobin Xiong
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-Augmented Generation (RAG) is vulnerable to knowledge poisoning, where adversaries manipulate retrieved evidence to influence model outputs. Existing defenses are primarily evaluated using attack success rate (ASR), which obscures defense-induced failures and the mechanisms behind apparent attack prevention. To address this issue, this work introduces \textbfAligned RAG Evaluation, a matched-query protocol that jointly examines clean and poisoned responses, with and without defense. By combining matched conditions analysis, LLM-judge endorsement, and retrieval dependence controls, the proposed framework distinguishes prevented from introduced failures, target mentions from LLM endorsement, and single-passage influence from evidence composition. Experiments on HotpotQA and Natural Questions reveal that existing defenses can introduce unexpected target-positive answers, although they can prevent some attacks, while evaluation criteria can also change defense rankings. Our findings expose critical blind spots in ASR-based evaluation and motivate a more comprehensive assessment of RAG poisoning defenses. The code and the evaluation artifact are available at this https URL.
[IR-21] Explain Then Forget: Recommendation Unlearning under Asymmetric Access Constraint ICDM2025
链接: https://arxiv.org/abs/2610.40658
作者: Haichao Zhang,Chong Zhang,Wen Wang,Shi Qiu,Peiyu Hu,Jia Wang
类目: Information Retrieval (cs.IR)
备注: 10 pages, 6 figures. Accepted ICDM 2025 (IEEE International Conference on Data Mining)
Abstract:Recommendation unlearning aims to remove the influence of specific historical interactions that should no longer affect future recommendations, such as noisy clicks or outdated user preferences. However, performing recommendation unlearning becomes much harder in modern Machine-Learning-as-a-Service (MLaaS) architectures, where the recommendation model is remotely hosted by a model provider (MP) and accessed by service operators (SO) only through APIs. In this setting, a service operator serves end users but cannot access or modify internal model parameters. As a result, existing unlearning methods are difficult to apply because they require access to model internals for retraining, fine-tuning, or other parameter updates. To address these challenges, we propose Causal Explanation-based Unlearning (CEU), a novel unlearning framework tailored for MLaaS environments. Instead of modifying internal model weights, CEU operates through a two-step output-level score intervention. First, it estimates how the forgotten interaction causally contributes to the current recommendation. Second, guided by this causal signal, the system removes the influence of the forgotten interaction by adjusting the final ranking scores of affected items. Because CEU leaves model parameters untouched, it naturally supports continuous unlearning requests while avoiding the error accumulation that can result from repeated parameter updates. Extensive experiments on three public datasets show that CEU achieves unlearning effectiveness comparable to full retraining while preserving recommendation utility and significantly reducing computational cost. The source code is available at: this https URL.
人机交互
[HC-0] GazeRIB: Systematic Evaluation of Multi-Reader Gaze-Guided Chest X-ray Learning
链接: https://arxiv.org/abs/2610.42152
作者: Nattakorn Kittisut,Roy M. Gabriel,Marly van Assen,Mohammadreza Zandehshahvar,Carlo N. De Cecco,Elizabeth A. Krupinski,Ali Adibi
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
备注: 20 pages on main manuscripts (3 tables + 4 figures), 14 pages supplementary (10 tables + 8 figures)
Abstract:Radiologists’ visual attention can supervise medical image learning, but how reader panel composition affects its value remains untested. We evaluated GazeRIB (Gaze-guided Radiologist-Informed Blackout), which masks unviewed regions during training and uses unmasked images at inference. Eight experts repeatedly read the same 80 chest X-rays (CXRs). We compared individual readers with three-, five- and eight-reader panels across seven architectures and 10 seeds, evaluating 910 models on a separate 2,897-CXR cohort for four-class severity prediction. Every panel improved macro F1 over the no-gaze baseline by 5.33-7.47 percentage points (pp; Bonferroni-adjusted p 0.007); no individual reader reached significance on this endpoint. Three readers captured 73-80% of the eight-reader gain in macro F1, the area under the receiver-operating-characteristic curve (ROC-AUC) and the area under the precision-recall curve (AU-PRC). On external validation without retraining, all four panels improved ROC-AUC on SIIM-FISABIO-RSNA by +1.93 to +4.24 pp (p = 0.004); on MIMIC-Eye no panel differed significantly from baseline, whereas the single-reader average declined (-1.40 pp, p = 0.008). Reader composition therefore influenced performance, although transfer depended on the cohort.
[HC-1] AutoAnalysis: An automated self-updating meta-analysis of human-AI collaboration
链接: https://arxiv.org/abs/2610.42001
作者: Michelle Vaccaro,Abdullah Almaatouq,Thomas W. Malone
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Digital Libraries (cs.DL)
备注: Please visit this http URL to explore the data and view the most recent results
Abstract:As generative artificial intelligence (AI) becomes more capable and widely used, understanding when and how humans should work with these systems is becoming increasingly important. However, the empirical evidence in this space offers conflicting recommendations, which leaves individuals and organizations without clear guidance on when to combine human and AI capabilities. Existing meta-analyses can help reconcile these findings, but they require substantial manual effort and cannot readily incorporate new evidence as it emerges. To address this challenge, we introduce AutoAnalysis: an automated, self-updating meta-analysis of when humans and AI work better together, and when they work better alone. AutoAnalysis’ most recent run synthesizes 1,955 effect sizes from 420 papers. Across this body of work, we found that, on average, human-AI combinations performed significantly better than humans alone, but they performed significantly worse than the better of humans or AI alone. These findings highlight the important distinction between improving human performance and achieving human-AI synergy and underscore the need to evaluate collaboration against both human and AI performance alone. As new studies emerge, AutoAnalysis will integrate their findings into the meta-analysis, providing cumulative evidence about when humans and AI perform better together and how human-AI performance varies across AI agents, tasks, and processes.
[HC-2] SimXAI: A Simple Drop-in Layer Increases Interaction Quality and Understanding in Conversational XAI EMNLP2026
链接: https://arxiv.org/abs/2610.41856
作者: Van Bach Nguyen,Qianli Wang,Nils Feldhus,Jörg Schlötterer,Christin Seifert
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted at EMNLP 2026 Findings
Abstract:Conversational explainable AI (ConvXAI) agents often use GUS-style architectures with separate modules for intent recognition, dialogue management, and template-based response generation. While faithful to the underlying XAI methods, these pipelines are hard to adapt and often struggle with context-dependent follow-up questions. We present SimXAI, a lightweight LLM-centric conversational layer that replaces these modules with a single tool-calling LLM that bridges access to existing XAI resources. Rather than a standalone agent, SimXAI serves as a modular interface that enables conversational access to external XAI resources. We further introduce an evaluation suite for single- and multi-turn parsing, faithfulness, and contextualization. Across existing and new datasets, SimXAI achieves strong function-selection accuracy and high faithfulness while better handling context-dependent requests. In a user study replacing only the conversational layer of TalkToModel, a representative GUS-based agent, users completed tasks more accurately and in fewer turn. Overall, SimXAI improves GUS-based design while preserving faithfulness and improving conversational quality.
[HC-3] MERIDIAN: Memory-Encoded Rituals for Intimacy across Distance in an Interactive Asynchronous Neighbourhood
链接: https://arxiv.org/abs/2610.41776
作者: Jingyi Chen,Yifan Yang
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 13 pages, 14 figures, 5 tables. Playable game: this https URL ; source code: this https URL
Abstract:How can shared memories become playable structures for partners who live in different time zones? We present MERIDIAN, a pre-deployment design case of Yi Town, a bilingual browser game developed by one member of a long-distance couple as a gift for the other. Nine levels translate personally meaningful events, places and cultural references into tasks within a navigable pixel town. A birthday prologue and a fictional wedding frame the intended experience. Drawing on research through design, we reconstruct four design changes from 109 development commits, 28 changelog versions and three design specifications. These records support six situated design commitments, including memory-derived mechanics, staged access, local progress storage and bilingual coverage. The implementation combines a union-based progress model with manually exchanged, checksummed codes and time-zone-dependent scenery. An implementation audit reproduced 153 passing unit tests and examined all 1,024 canonical progress codes, while identifying limits involving resets, unversioned level registries and residual transcription errors. The application has no author-operated backend for progress, but external media services and browser speech recognition limit its privacy and offline guarantees. No recipient evaluation or relationship outcomes are reported. The contribution is an inspectable account of design decisions and their trade-offs, including the tension between authoring a personal gift and preserving the recipient’s freedom to interpret, decline or reshape it. The game is playable at this https URL and its source is public at this https URL.
[HC-4] When Architectural Spaces Start to Act
链接: https://arxiv.org/abs/2610.41724
作者: Alex Binh Vinh Duc Nguyen
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:Architectural spaces are increasingly gaining robotic capabilities. Architectural robots such as robotic partitions, ceilings, furniture, facades, and other spatial components can autonomously move and reconfigure in response to occupants’ needs and activities. Yet, when architectural robots start shaping the spatial conditions in which people continuously live, work, and interact, we may begin to experience the architectural space itself as robotic. What does this shift mean for how we inhabit such spaces? Drawing on our previous studies, I reflect on how architectural robots interact with people not only through their motions, but also through the spatial configurations that persist after those motions stop; how their actions may benefit some occupants while disadvantaging others; and how multiple robots may together make a whole architectural space appear to act. I argue that as computation gains the capacity to physically reconfigure our environments, interaction extends beyond the moment of computational action into the spatial conditions that people continue to inhabit. Understanding robotic architecture therefore requires us to look beyond short-term human-robot interaction and toward how people inhabit, negotiate, and maintain control over spaces that can autonomously reconfigure.
[HC-5] Designing wearable haptic cueing for neurological gait rehabilitation: user requirements across residential care and community settings
链接: https://arxiv.org/abs/2610.41680
作者: Riasat Islam,Annemarie Zijlema,Theodoros Georgiou,Simon Holland,Blaine Price,Paul Mulholland,Oliver Pearce,Allan Perry
类目: Human-Computer Interaction (cs.HC)
备注: 38 pages, 2 figures, 4 tables. Preprint; manuscript currently under review in following journal - Disability and Rehabilitation: Assistive Technology
Abstract:Wearable haptic cueing offers rhythmic prompts for neurological gait rehabilitation while leaving hearing available for communication and environmental awareness. We investigated the user, design, and implementation requirements needed to make these systems usable in practice. We analysed six linked studies involving 17 people with neurological conditions affecting gait and three physiotherapists across formative sessions, residential care, and community self-management. Wearable devices served as technology probes. Observations, interviews, diaries, physiotherapist records, gait analysis, and activity logs informed thematic analysis and cross-study comparison. Seven recurring requirement areas concerned haptic perceptibility and placement, wearability and fastening, interaction burden, rhythm personalisation, real-world walking contexts, supported use, and perceived value beyond gait outcomes. Requirements interacted: perceptible cue locations could be difficult to access for fastening or control, while fixed rhythms did not always accommodate terrain, fatigue, interruptions, or changing pace. Carers and clinicians supported configuration, operation, safety, and interpretation. Participants described confidence, routine formation, and willingness to walk; these accounts were interpreted as perceived value rather than evidence of therapeutic efficacy. Low sensation, cognitive burden, and mobility limitations could make wearable cueing impractical. Future systems should offer adjustable haptic output, flexible placement, accessible fastening, simple controls, personalised rhythms, and robustness during everyday walking. Design should accommodate support from carers and clinicians and consider trade-offs between requirements. These findings inform wearable rehabilitation design rather than establish clinical efficacy.
[HC-6] Look Over There! Dynamic Visual Guidance for AI-Narrated Slides
链接: https://arxiv.org/abs/2610.41645
作者: Jiongning Allen Lian,Yutong Alison Mei,Ho Ting Cheung,MK Michael Cheung,Xiangzhi Eric Wang,George Baciu,Henry Been-Lirn Duh,Qing Li,Ou Yang,Zackary P. T. Sin
类目: Human-Computer Interaction (cs.HC)
备注: 21 pages, 7 figures
Abstract:Slides present information spatially, but narration unfolds linearly, leaving the viewer struggling to locate what the narration refers to. This split attention between audio and visual channels forces viewers to search the slide manually, imposing extraneous cognitive load and disrupting comprehension. The problem is especially acute on mobile devices, where dense layouts compressed onto small screens make visual search particularly effortful. In teacher-led settings, however, instructors naturally direct attention through pointing, pacing, and emphasis. Inspired by this, we present GuideSlide, a system that automatically generates Dynamic Visual Guidance for slide presentations, providing viewers with narration-synchronized visual cues that direct attention in real time. Given a PDF slide deck, GuideSlide automatically produces region-level zoom and time-aligned highlights without manual annotation. In a within-subjects study with 24 participants, GuideSlide substantially reduced perceived cognitive load and yielded higher immediate comprehension scores compared to static slides with identical narration. Participants strongly preferred the Dynamic Visual Guidance condition, reporting that it made narration easier to follow and reduced uncertainty about where to look. These findings suggest that narration-synchronized visual guidance is a valuable design strategy for slide-based presentations, with potential to extend to other instructional and presentation scenarios.
[HC-7] Altered Visual Hand Feedback in Virtual Reality: An Exploratory Study in Essential Tremor
链接: https://arxiv.org/abs/2610.41496
作者: Alejandro Pascual-Valdunciel,Marcos Sacristán-Moraleda,Verónica Giménez de Béjar,Julián Benito-León,Juan Marín-Lahoz,Jaime Ibañez,Daniel Martin
类目: Human-Computer Interaction (cs.HC); Graphics (cs.GR)
备注: 12 pages, 5 figures, 3 tables. Under revision
Abstract:Pathological tremor is the most prevalent movement disorder worldwide, characterized by involuntary muscular oscillations within a frequency of 3 to 12 Hz. Despite extensive research, the underlying mechanisms that generate and modulate tremor are not yet understood, and treatments are usually insufficient. While it is known that visual feedback is an important afferent pathway, little is known about the impact of altering the hand’s visual representation on tremor expression. With the development of immersive technologies, VR poses an excellent tool to study how alterations to the representation of the hands affect tremor. In this work, we study this through two complementary experiments. In the first one, we show that a consumer-level hand-tracking system substantially attenuates tremor-band movements without preventing successful task completion. In the second one, with eight patients with essential tremor, we observe task-dependent differences in physical tremor between VR and the real world, with several patients exhibiting lower tremor-band activity in both hand kinematics and muscle oscillations, particularly during reaching tasks. Our findings represent an initial step toward validating the use of VR as an experimental tool for the research and treatment of pathological tremor, while highlighting strong user- and task-dependent effects.
[HC-8] A prospective randomized study of patient-centered AI for clinical visit preparation
链接: https://arxiv.org/abs/2610.41437
作者: Xincheng Tan,Matthew Thompson,Xiang Ji,Angelica Willis,Will Vaughan,Roma Ruparel,Michael A. Sanchez,Sean Chang,Joseph Xu,Bhavna Daryani,Preeti Singh,Pinal Bavishi,Kejia Chen,Fan Zhang,Priya Gupta,Ernest Lim,Christina Chen,Bob Lou,Dimitrios Antos,Zach Wasson,Ray Luo,Fadi Yousif,Florence Thng,Mark Malhotra,Joseph Breda,Daniel McDuff,Kavi Goel,Katherine Chou,Dale R. Webster,Avinatan Hassidim,Yossi Matias,Po-Hsuan Cameron Chen,Yun Liu,Mike Schaekermann,Quang Duong,Jackie M. Barr
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 50 pages (16 main text, 34 appendix), 6 figures
Abstract:Patients often arrive at brief, time-constrained clinical visits unsure of what to expect or ask, and millions of adults now turn to general-purpose AI chatbots to prepare beforehand. Yet whether conversational AI improves visit readiness remains unclear. We designed a large language model (LLM)-based visit preparation agent to guide patients before an appointment by gathering clinical context, setting visit expectations, providing relevant health information, building a prioritized list of questions for their clinician, and generating a summary. In a prospective consented randomized study in the Google Health app, 802 U.S. adults held 866 health-related conversations with the visit preparation agent (standard or concise variant) or a minimally prompted control chatbot built on the same LLM. The visit preparation agent significantly outperformed the control across blinded clinician evaluations and patient self-reports. In clinician reviews of completed sessions (n = 272), the agent achieved a 16.6% higher rate of comprehensive preparation (67.2% vs. 50.6%; P 0.05) and an 8.5% higher rate of alignment with patients’ health context (98.9% vs. 90.4%; P 0.01), while maintaining high medical safety and appropriateness across all study arms. Patient post-surveys (n = 460) showed 16.8% higher visit preparedness (73.8% vs. 57.0%; P 0.01), 14.3% higher confidence in knowing what to ask (81.4% vs. 67.1%; P 0.01), and 12.0% higher overall satisfaction (74.0% vs. 62.0%; P 0.05). Clinician and participant metrics were not significantly correlated, indicating that expert review and patient self-assessment capture complementary dimensions of patient-facing AI. These findings suggest that LLM chatbots can safely help patients prepare for clinical visits, with greater benefit when following a structured workflow, and motivate future studies on in-visit communication and clinical outcomes.
[HC-9] Hybrid Computer Use: A Unified Programmatic Semantic and Visual Interface for General Agents
链接: https://arxiv.org/abs/2610.41432
作者: Haoqing Wang,Xiaoyi Zhang,Sicheng Fan,Zhexin Zhang,Xinjie Zhang,Jiahao Li,Bin Li,Yan Lu
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注:
Abstract:Computer-use agents increasingly tackle tasks ranging from everyday desktop activities to long-horizon professional workflows. Yet interfaces centered on screenshots and mouse-and-keyboard actions can impose a human-interaction prior, requiring repeated screen interpretation while leaving the computer’s semantic and programmatic capabilities underexposed. We argue for exposing these capabilities and letting models choose and combine them. We introduce Hybrid Computer Use (HCU), a unified interface for general agents that combines three complementary channels: programmatic interaction through code and command-line tools, semantic interaction through accessibility (AX), and visual interaction through graphical user interfaces (GUI). Our HCU supports efficient AX interaction and guards against stale-state actions during channel switching. A supplementary HCU skill guides agents in selecting and coordinating channels to use the unified interface effectively. Our designed AX tools return compact observations, execute actions in batches, and report their effects as AX diffs, so the model needs fewer round trips. On OSWorld 2.0, HCU achieves the highest Partial and Binary scores among the compared systems. Analyzing agent trajectories, we find that all evaluated models improve with HCU yet allocate work across the channels differently and consistently outperform compared baselines in terms of both efficiency and task success, highlighting the value of unified computer-use interfaces that allow flexible channel selection and combination.
[HC-10] When History Overrides Intent: Relational Schema Lock-In in Human-LLM Interaction
链接: https://arxiv.org/abs/2610.41367
作者: Wei Yang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Long-context interaction and persistent memory allow LLM assistants to develop history-grounded understandings of users. When users change direction, however, an understanding built through earlier exchanges may conflict with their current instruction. We introduce relational schema lock-in to describe cases in which an assistant grants that accumulated understanding authority to override the current instruction without user authorization for the intervention. Study 1 documents reports of AI assistants using prior interactions to question or alter requested actions. Study 2 reproduces the pattern across controlled histories and six models: models retained history-grounded positions but differed in whether and how those positions shaped task execution. In Study 3 with 360 participants, artifact substitution preserved task completion while reducing faithful enactment: 75.0% sent the AI’s suspension letter, including 11 participants who reported cancellation as their final stated intent. Overt resistance reduced task completion while leaving sent artifacts faithful.
[HC-11] oward Low-Burden Image-Based Dietary Assessment Through Human-AI Collaborative Correction
链接: https://arxiv.org/abs/2610.41300
作者: Darren Liu,Andrew Lu,Zhiyuan “Ryan” Wang,Hanqi Luo,Xiao Hu,Runze Yan
类目: Human-Computer Interaction (cs.HC)
备注: 28 pages, 5 figures, 5 tables. Includes appendices
Abstract:Image-based dietary assessment can automate initial food logging, but meal photographs often leave food identity, hidden ingredients, preparation, and portion uncertain, making correction a recurring part of use. We introduce Human-AI Collaborative Correction, which reframes correction as communicate intent \rightarrow regenerate \rightarrow verify: users express what should change through text, voice, or guided clarification, the AI regenerates the linked dietary record, and users verify the result. In a within-participant study with 30 participants, compared with conventional Manual Search-and-Edit Correction, collaborative correction was associated with lower workload (Raw TLX: 35.83 vs. 55.00; p .001), 21.0% lower recorded usage time, 51.1% fewer recorded clicks, and more favorable user-experience ratings; 90.0% preferred it overall. An exploratory nutrient analysis also found lower energy MAE. These findings support intent-driven regeneration as a promising interaction pattern for reducing correction burden while preserving user verification and making image-based dietary tracking easier to sustain.
[HC-12] Imputing Missing Data in Longitudinal Life-Logging Athlete Data
链接: https://arxiv.org/abs/2610.41226
作者: Saaz Kaur Sahdra,Kalina Yacef,HaeJin Lee,Judy Kay
类目: Human-Computer Interaction (cs.HC)
备注: Submitted to IMWUT
Abstract:Long-term, diverse personal informatics data enables athletes to understand and act on their health and performance goals, whether assessing readiness right now, tracking how soreness relates to training load over time, or predicting how sleep affects next-day performance. However, the inherent missingness in longitudinal data, if handled inappropriately, can bias these analyses. To our knowledge, no prior work has systematically benchmarked classical and deep learning imputation methods on long-term self-tracking data from athlete populations. We evaluate six imputation methods across four missingness mechanisms at missingness rates spanning 10-80%. Using seven diverse long-term self-tracking datasets from 231 individuals, we assess both point accuracy and the preservation of temporal and multivariate structure. Our results show that no single method dominates across all conditions and reveal a critical trade-off: while PAIN is superior on point-wise accuracy, deep learning methods (BRITS, GRU-D, and GRIN) are better at preserving multivariate and temporal structure, particularly under higher missingness. Our findings offer a practical, goal-conditioned framework that guides researchers in selecting imputation strategies aligned with both the underlying missingness mechanism and the specific analytical goal of long-term self-tracking studies.
[HC-13] When Should AI Agents Ask? Clarification Requests Under Limited Human Attention
链接: https://arxiv.org/abs/2610.41129
作者: Hanzhao Wang,Shengjie Liu,Zhengchao Wang
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:AI agents increasingly carry out delegated tasks in parallel, with one person overseeing several of them at once. In practice, an agent often lacks information its task requires, such as an unstated preference, constraint, or specification. It can ask the human to clarify, or it can search for the information itself, for example in past records, which takes time and effort and may fail. In this paper, we study how to coordinate these decisions across several agents that share one human with limited attention. We build a stylized model in which each agent can search, ask, or continue the parts of its task that do not depend on the missing information, and we formulate the problem as a dynamic program. We first show that whether an agent should ask depends on more than its own uncertainty: an agent with other work to do can be better off asking earlier, and which question the human should answer first depends on all waiting questions, so no ranking computed agent by agent is optimal. We then propose the Pairwise Coordination Policy (PCP), which uses exact one- and two-task problems to estimate how pairs of tasks delay each other when they share the human. Theoretically, PCP is exactly optimal when at most two requirements remain unresolved and costs at most twice the optimum when every unresolved requirement must be resolved by the human. We evaluate PCP on 864 synthetic cases and in a case study with LLM agents. On the synthetic benchmark, PCP’s mean gap to optimal cost is 0.07%, and it reduces cost by an average of 18-31% relative to three standard rules for when to ask. In the LLM case study, agents completed every task correctly, and PCP achieved the lowest mean cost in all tested settings.
[HC-14] Flash Word : ESL Listening Support with Subtitle Skimming
链接: https://arxiv.org/abs/2610.41098
作者: Yuzu Saijo,Jun Rekimoto
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Listening to English audio with English subtitles is thought to be effective for ESL students to improve listening comprehension skills. However, for those who learn English as a second language it can be hard to finish English subtitles. Also they are unlikely to focus on listening while being busy reading subtitles. We propose Flash Word, a subtitling system which generates and presents shortened intralingual subtitles for English audio by detecting keywords from the script. Flash Word extracts keywords from scripts using an existing algorithm, and shows them one at a time like a flashcard. The system calculates when the keywords are being pronounced and aligns them correctly. Flash Word aims to enable ESL students to focus better on listening by minimising the cost of reading subtitles.
[HC-15] Out of sync: Accommodation evaluation and emergence in five weeks with an agent ic AI teammate
链接: https://arxiv.org/abs/2610.41057
作者: Nia Nixon,Jaeyoon Choi,Pedro Martins de Bastos,Mohammad Amin Samadi
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 21 pages, 5 figures. Submitted to British Journal of Educational Technology
Abstract:As agentic AI in education shifts from reactive tools toward partners that take initiative and persist over time, a central question is how learners relate to, evaluate, and benefit from artificial agency. We conducted a longitudinal experiment in an undergraduate learning analytics course, in which 17 student teams (N = 48) collaborated with a persistent LLM-based teammate across weekly sessions. The agent decided when to intervene through a trigger-based architecture, and its social role was manipulated between teams: a Cognitive Agent that challenged quick consensus or a Supportive Agent that invited quieter members to contribute. Combining chat discourse, post-session surveys, and spoken reflections, we identified distinct patterns unfolding across three timescales. Students accommodated their language to the agent’s role immediately, using more social language toward the Supportive Agent from the first session. Their helpfulness evaluations were recalibrated from session to session in relation to the agent’s observed behavior, rather than remaining anchored to students’ pre-existing attitudes toward AI. A slower crossover emerged in perceived collaboration process and positive-affect word use in reflections: Supportive-Agent teams began below Cognitive-Agent teams and ended above them, although students rated the Supportive Agent less helpful throughout. Because these responses follow different trajectories, single-session evaluations of agentic AI may miss changes that emerge only through repeated interaction.
[HC-16] Measuring Personal Information Exposure in Mental Health-Related Conversations with LLM s
链接: https://arxiv.org/abs/2610.41045
作者: Synthia Qia Wang,Ranya Sharma,Christine Lee,Sarah Keedy,Marshini Chetty,Nick Feamster
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language models (LLMs) are increasingly used for informal mental health support, yet the privacy implications of conversational interaction remain poorly understood. Prior LLM privacy research has focused largely on model-side risks such as memorization and adversarial extraction, leaving personal information inferred from ordinary conversation understudied. We introduce a profile-recovery framework that measures personal information exposure by comparing user profiles with attributes inferred from conversation alone. We generate 240 synthetic multi-turn mental health conversations from profiles sampled from a nationally representative health survey and validate them against real counseling transcripts, finding comparable overall disclosure volume. Across 21 attributes, 33-41% of each profile is recoverable, with 76.5% first-inference and 94.3% any-of-3 accuracy. 239 of 240 conversations expose at least one sensitive mental health attribute, and 45.0% expose all six. Our findings reveal a gap between disclosure and inferability, motivating HCI approaches to make inference risks legible and support privacy-aware interaction design.
[HC-17] Nedimo: An Evidence-Grounded Multilingual Pipeline for AI-Generated Sleep Stories
链接: https://arxiv.org/abs/2610.40970
作者: Georgios Makridis,Stefanos Kampouris
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注: 7 pages, 3 figures, 3 tables
Abstract:Generative audio systems can produce large libraries of personalized bedtime content, but ordinary media objectives (retention, completion, and novelty) are poorly aligned with the goal of supporting a transition toward sleep. Direct evidence for narrated bedtime audio in adults is mixed, and a recent active-controlled trial did not support such audio as a standalone treatment for sleep disturbance. We translate this uncertain evidence into conservative engineering requirements and present a multilingual pipeline for generating and narrating low-arousal sleep stories. The implemented prototype separates deterministic planning from large language model generation, represents content and delivery parameters explicitly, shapes each script through a four-phase attention taper, validates structural and lexical constraints, performs a bounded error-aware retry, and quarantines unresolved outputs. Provider-agnostic text-to-speech synthesis is followed by paragraph-aware joining, two-pass loudness normalization, and a publication gate that re-measures the saved, encoded file against fixed loudness, loudness-range, and true-peak limits. The product data model records stop position, replay, early abandonment, and completion while treating these events as ambiguous behavioral proxies rather than clinical outcomes. We contribute an evidence-to-requirement mapping, a traceable guardrail architecture with explicit acceptance thresholds, technical evaluation criteria, and a prospective protocol for testing sleep-onset effects. No clinical efficacy is claimed; the system is positioned as a relaxation and attentional-support tool whose benefit and possible reliance must be evaluated empirically.
[HC-18] Robot Navigation and Canine Welfare: A Pilot Study Surfacing Challenges for Cross-Species Legibility and Proxy Validity in Animal-Machine Interaction
链接: https://arxiv.org/abs/2610.40921
作者: Haoxiang Zhang,Nancy Smith
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 8 pages, 2 figures, Accepted at ACI2026
Abstract:Autonomous delivery robots increasingly share residential spaces with companion animals, yet navigation behavior is rarely designed with animal welfare in mind. This paper presents a pilot study evaluating a curve-out maneuver, a laterally arcing pass-by trajectory triggered at the dog’s alert distance, proposed as a candidate welfare-informed navigation intervention grounded in canine social communication research and legible motion theory. Following a research through design approach, we developed and evaluated a first design iteration of the artifact in a naturalistic home environment with a companion dog. Contrary to our hypothesis, the curve-out produced a more acute stress response than the straight-line baseline. We argue that discontinuous execution limitation, the robot stopping and reorienting multiple times to approximate the arc, undermined legibility, which in itself became a stressor. The pilot surfaces the legibility of robot behavior to non-humans’ eyes and proxy stimulus validity as structural challenges for AMI and repositions the design problem for future work.
[HC-19] Personalised 3D Mnemonics for Vocabulary Learning in Context
链接: https://arxiv.org/abs/2610.40760
作者: Karolina Trajkovska,Matjaž Kljun,Maheshya Weerasinghe,Klen Čopič Pucihar
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Augmented reality (AR) can support vocabulary learning by placing mnemonic cues in the physical contexts where new words are encountered. Prior work has augmented real objects with bilingual labels, audio, keywords, and 3D visualisations of those keywords interacting with the objects, but these cues are typically predefined by system designers. This limits scalability and reduces learners’ ability to construct personal mnemonic links. By grounding mnemonics in objects that learners already recognise, use, and associate with prior experiences, a system could connect new vocabulary to existing mental models rather than requiring learners to adopt designer-defined associations. We outline a proof of concept and a storyboard of a generative AR approach in which learners create personalised 3D mnemonics by transforming familiar objects in their surroundings into situated mnemonic anchors. We position this concept within a timeline of related work and argue that recent advances in generative 3D asset creation enable a new design point: personalised 3D mnemonics that do not merely place content in the physical learning environment, but modify familiar real-world objects so that they become part of the mnemonic itself.
计算机视觉
[CV-0] SemEllipsoid: Semantic-aware Shape Abstraction with Ellipsoid
链接: https://arxiv.org/abs/2610.42145
作者: Kuankuan Cheng,Haocheng Yuan,Changjian Li
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Representing 3D shapes using a set of primitives - known as shape abstraction - is an actively studied problem in computer graphics with wide applications in shape understanding, analysis, and simulation due to the compactness and computational efficiency of the primitive-based representation. Prior work has primarily focused on geometry-aware shape abstraction, where the objective is to approximate the input shape with a minimal set of primitives while preserving geometric fidelity. However, these methods often overlook semantic consistency, inevitably leading to primitives that span across multiple semantic parts. Such inconsistency can cause undesirable artifacts in downstream tasks such as part-level shape editing or manipulation. In this paper, we introduce a new task - semantic-aware shape abstraction, where the goal is to produce abstractions that are both geometrically faithful and semantically consistent, ensuring that each primitive adheres strictly to a single semantic part without crossing semantic boundaries. We propose SemEllipsoid, a novel method to address this problem using ellipsoidal primitives. The core idea is to inject semantic constraints into the primitive optimization process leveraging the availability of semantic cues from large-scale foundation models. We design a fully differentiable projection-blending-evaluation scheme to measure semantic divergence against input semantic masks, and formulate an alternating optimization strategy to iteratively solve for optimal semantic assignment and primitive configurations. Our method is non-learning-based, requiring no data collection or network training, and generalizes well to shapes from arbitrary categories. It also supports different levels of abstraction according to user demand. Extensive experiments and evaluations validate the effectiveness of our approach across several 3D shape collections.
[CV-1] RoboArchitect: Automating the Design of Robotic Agents with Persistent Memory
链接: https://arxiv.org/abs/2610.42141
作者: Shengguang Wu,Suning Huang,Cherie Ho,Ria Doshi,Hao Zhu,Serena Yeung-Levy
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Website: this https URL
Abstract:Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand and redesigned for each new robot or benchmark. We show that harness design can be automated, offering a strong alternative to finetuning on a novel setup. In RoboArchitect, a coding model observes a robot’s rollouts, diagnoses failures, and iteratively revises the harness around the frozen policy. The harness evolves on a set of episodes for its optimization and is evaluated on held-out scenes and tasks. Without updating any model weights, RoboArchitect raises average success from 9.7% to 43.3% on unseen scenes (4.5x) and from 12.7% to 40.2% on unseen tasks (3.2x), across multiple benchmarks and policies. The gains are largest where the benchmark is far from the policy’s training data: wrapping a general-purpose policy, the evolved harness yields a specialist that often outperforms even the finetuned version. Intuitive control strategies are automatically designed, such as lifting a jammed arm clear or reaching in horizontally when an obstacle blocks the way from above. The harness also evolves its own persistent memory, carrying learned experience, such as effective grasp depths, into later decisions. These results suggest that substantial headroom in robotic manipulation lies in the agent harness, and its design can be effectively automated and optimized without weight updates.
[CV-2] ReLift: Dense 3D Tracking as Lifted Trajectory Repair
链接: https://arxiv.org/abs/2610.42136
作者: Assaf Singer,Maroun Khriesh,Noam Rotstein,Yoni Kasten,Or Litany
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dense 3D tracking from monocular video requires establishing point correspondences over time, estimating their depth, and inferring their trajectories during occlusion. Existing methods typically learn these tasks jointly, a demanding objective given the scarcity of 4D supervision. We show that strong pretrained 2D trackers and 3D geometry models provide an effective foundation: lifted trajectories, with occluded segments simply interpolated, already rival dedicated 3D trackers using the same geometry. However, lifted trajectories inherit correspondence and depth errors, and under occlusion, their depth comes from the occluder rather than the tracked point. We introduce ReLift, an efficient feed-forward framework that recasts dense 3D tracking as geometric trajectory repair and completion. At its core, a trajectory corrector alternates attention along and across trajectories to preserve reliable observations, repair corrupted positions, and complete missing motion. Using only geometric inputs allows us to train directly on 3D trajectories with simulated lifting errors, decoupling training from images and from any specific tracker or geometry model. Concentrating 4D supervision on trajectory refinement yields a reusable model that consistently improves lifted trajectories across trackers and geometry providers without retraining, so it benefits directly from future advances in either component. On WorldTrack, ReLift achieves state-of-the-art mean accuracy, improving on the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points, with faster configurations remaining competitive with leading 3D trackers while running faster than 4RC. Visit our supplementry page for more examples and code.
[CV-3] A Better Critic Matters for Distribution Matching Distillation
链接: https://arxiv.org/abs/2610.42135
作者: Zongze Wu(1),Xianbang Wang(2),Zhengyang Geng(1),Mingdeng Cao(1),Zhoutong Zhang(1),Richard Zhang(1),Eli Shechtman(1),Ruiming Cao(1) ((1) Adobe, (2) MIT)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 35 pages, 16 figures. Project page and code: this https URL
Abstract:Distribution Matching Distillation (DMD) trains a few-step student using the difference between a teacher’s score and an online critic’s estimate of the student distribution’s score. We investigate what the critic needs, in capacity and in information, to provide a reliable training signal. On the capacity side, controlled experiments across seven SiT model sizes on ImageNet show that increasing teacher size while keeping a small critic fixed can degrade student quality and cause training collapse. Scaling the critic alongside the teacher, a strategy we call joint scaling, prevents collapse in our experiments and allows the student to benefit from larger teachers. On the information side, rather than forcing the critic to model a step-agnostic mixture of student outputs, we introduce a step-aware critic (STAC) that conditions on the student’s generation step to estimate step-specific scores. Together, joint scaling and STAC consistently improve few-step distillation across class-conditional ImageNet generation, text-to-image generation with SDXL and FLUX.2, and text-to-video generation with Wan2.1.
[CV-4] Rethinking Asymmetry in Self-Supervised 3D Geometry Learning
链接: https://arxiv.org/abs/2610.42092
作者: Gyeongjin Kang,Freeman Cheng,Botao Ye,Weijie Lyu,Ming-Hsuan Yang,Eunbyung Park
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Website: this https URL
Abstract:Self-supervised learning relies on asymmetry between what a model observes and what it must explain. Recent methods learn 3D geometry from unposed images by predicting cameras and scene structure, then rendering held-out views. To avoid trivial solutions, they impose this asymmetry in the input space, restricting how views interact through separate branches or masked attention. We revisit this design with geometric accuracy, rather than rendering quality, as the main objective. When a held-out view lies between context views, the context already constrains the geometry it observes. We therefore impose the asymmetry in the 3D representation space, excluding held-out views only from the reconstructed scene while a shared backbone uses all views for correspondence. This improves depth and pose estimation over input-space designs. We further exploit view density: sparse and dense observations depict the same scene but provide different amounts of correspondence. Aligning sparse-context features with dense-context features supplies a geometric training signal alongside rendering, without a pretrained geometric teacher. We additionally align features with a frozen vision foundation model, which brings further gains. None of these signals requires geometric annotation, and the resulting model improves geometry estimation over prior self-supervised methods. Controlled experiments isolate the contribution of each component, and self-supervised pretraining also improves subsequent supervised training.
[CV-5] hink in 360°: Benchmarking and Bootstrapping Panoramic Video Reasoning
链接: https://arxiv.org/abs/2610.42082
作者: Lidong Lu,Lidong Zou,Xinyi Mao,Jiaxin Lv,Zhenquan Li,Liangrui Dong,Tong Lu,Guo Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A panoramic video captures all directions around the camera, but relevant evidence can appear in different directions and at different times. Understanding it requires models to connect observations across the viewing sphere and time. We introduce PanoVideoBench, the first manually annotated benchmark for comprehensive panoramic video understanding. Its 1,000 questions span five categories and 15 question types, with diagnostic annotations for target search and panoramic capabilities. Evaluation of 11 proprietary, open-source, and panoramic-specialized models identifies cross-view tracking and sphere-time evidence integration as their weakest capabilities. To strengthen these capabilities, we construct PanoVideoCoT, the first reasoning dataset grounded in real panoramic video, through sphere-time-grounded synthesis that converts verified panoramic observations and successful agentic explorations into 13,465 QA-CoT pairs. Starting from a general video MLLM, PanoVideoReasoner learns from this corpus through Panoramic CoT SFT and Evidence-Privileged On-Policy Self-Distillation, a panoramic instantiation of Vision-OPD’s OPSD in which the teacher uses verified perspective views to guide student-generated trajectories. It reaches 55.7% on PanoVideoBench, improving the backbone by 12.5 points and the previous SOTA by 2.6. The gains concentrate on the same spatial, cross-view, and temporal capabilities exposed by the benchmark, showing that grounded sphere-time supervision can equip a general video MLLM with panoramic reasoning.
[CV-6] REA: A Routed Embodied Agent Runtime for Asynchronous Brain-Cerebellum Control
链接: https://arxiv.org/abs/2610.42053
作者: Pengfei Zhou,Yixing Ma,Zhengfeiyang Zhang,Haoyuan Chen,Zhenglin Wan,Hexin Wang,Wangbo Zhao,Yang You
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 32 pages, 14 figures
Abstract:Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy’s measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded recovery. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA reaches 95.9% success and beats the best single-agent baseline in its own policy pool (94.7%) while reducing control steps by 25% and control time by 17%. In a separate 100-episode RoboTwin 2.0 study, REA achieves 84% success with VLA policies and 86% with VLA+code, compared with 80% for the strongest single VLA baseline MEDex. These results demonstrate learned and programmatic policies operating within the same runtime. REA also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid, achieving the highest observed success among the compared methods.
[CV-7] Learning Clean Generative Processes from Noisy Measurements
链接: https://arxiv.org/abs/2610.42036
作者: Fabian Kruse,Lorenzo Baldassari,Ivan Dokmanić
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require posterior predictions at every noise level. We first show that it is possible to learn these predictions from independent noisy views using Noise2Noise regression, but only at or above the noise level of the training data. To learn the predictions below that level, needed to generate clean images, we impose consistency: a prediction should equal its expectation after continuing the sampling process. We derive conditions under which consistency uniquely determines the continuation to the clean image distribution and verify them for Poisson, binomial, Gamma, and negative-binomial measurements. Experiments on CIFAR-10, photon-counting microscopy, and synthetic aperture radar show that the required posterior predictions can be learned from noisy views alone and that the consistency relation repairs the continuation beyond the noise level of the data, which severely deteriorates without consistency.
[CV-8] Any Forcing: Any-Step Generality and One-Step Stability for Autoregressive Video Generation
链接: https://arxiv.org/abs/2610.42031
作者: Jiahua Xu,Yijie Fang,Nannan Wang,Xunhao Lin,Li Cao,Lei Hao,Mingrui Zhu,Fei Gao,Xinbo Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Autoregressive video generation
Abstract:Autoregressive video generation enables efficient inference through streaming prediction. However, existing methods are typically restricted to fixed sampling steps, limiting the flexibility to balance generation quality and efficiency, while one-step generation suffers from degraded visual quality and error accumulation over long sequences, leading to content drift and generation collapse. To address these issues, we propose Any Forcing, a unified framework for achieving Any-Step Generality and One-Step Stability. Specifically, we introduce a unified adversarial discriminator that jointly performs real-distribution alignment, inter-step consistency alignment, and first-frame quality enhancement, enabling flexible sampling while preserving one-step generation quality. Furthermore, we propose a training-free adaptive long-term memory selection mechanism that selects historical states based on global semantic relevance and local value contribution, while remapping their temporal positions to preserve temporal relationships within the memory cache, thereby enabling stable one-step long-video generation. Experiments demonstrate that Any Forcing achieves high-quality and stable one-step autoregressive video generation, achieving 20.9 FPS and 24.7 FPS for short and long video generation on a single H100 GPU, respectively.
[CV-9] GeoFlip: Benchmarking Geometry-Sensitive Physics in Video Generation
链接: https://arxiv.org/abs/2610.42002
作者: Yuqi Li,Junming Liu,Bowen Hu,Ruize Niu,Huiran Duan,Yingli Tian,Hao Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures
Abstract:Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video generation benchmark centered on physical outcome flips induced by geometry. With shared physical settings and textual prompts held fixed, GeoFlip applies controlled interventions to support extent, object placement, and mechanism configuration, constructing paired scenes with contrasting reference events, such as remaining stable versus falling or tipping. These pairs test whether models translate geometric differences in the first frame into correct physical evolution. The benchmark comprises 482 scene pairs and 964 reference videos across six physical categories, with reference outcomes established through procedural scene construction, physics simulation, and numerical validation and verification of physical events. We evaluate 16 models for image to video generation along three dimensions: event correctness, structural integrity, and similarity to reference videos. We examine evaluation consistency with independent vision language judges. On the 442 conditions shared by all models, the highest event correctness averaged across tasks is 27.98%, revealing substantial limitations in physical generation conditioned on geometry. Model strengths vary across physical mechanisms, and the three evaluation dimensions capture distinct capabilities. Typical failures include ignoring geometric changes, generating incorrect object motion, bending rigid objects, and introducing additional components. Rank correlations between judges reach 0.906 for event correctness and 0.929 for structural integrity. By combining controlled geometric interventions, verifiable physical references, and multidimensional evaluation, GeoFlip assesses progress from visual realism toward physical consistency conditioned on geometry.
[CV-10] MoETok: Sparse Expert Routing for 1D Visual Tokenizers
链接: https://arxiv.org/abs/2610.41992
作者: Jiahao Guo,Bao Tang,Haoxiang Cao,Jingfeng Yao,Bo Li,Wenyu Liu,Kun Gai,Changqian Yu,Xinggang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Modern visual tokenizers aim to preserve fine-grained visual details while learning high-level semantic representations for downstream generation and understanding. Query-based 1D visual tokenizers encode images into flexible latent sequences, yet how individual learnable queries gather and encode image information remains poorly understood. We reveal that these queries spontaneously develop complementary roles under semantic supervision: a small set of Content queries captures high-level semantic information, while a larger set of Detail queries preserves fine-grained visual details for reconstruction. To adapt token processing to these heterogeneous roles, we introduce MoETok, a 1D visual tokenizer that routes tokens through sparsely activated experts. Jointly optimized with reconstruction and semantic objectives, the router learns role-specialized processing paths adapted to each token’s representation. Across model scales and token budgets, MoETok consistently improves reconstruction, semantic representation, and autoregressive generation over dense tokenizers under matched supervision and active computation. On ImageNet, it combines strong reconstruction and semantic representation with state-of-the-art class-conditional generation within the 1D tokenizer family. We further validate MoETok at scale in a unified autoregressive model jointly trained for understanding and generation with a next-token cross-entropy objective. The resulting model combines competitive visual understanding with state-of-the-art generation among unified discrete models, matching or surpassing diffusion-based models such as BAGEL and Qwen-Image on benchmarks including DPG-Bench (87.61), GenEval2 (40.04 GM), and WISE (0.49).
[CV-11] FoAViS: Analyzing Learned Fixation Strategies in a Foveated Active Vision System
链接: https://arxiv.org/abs/2610.41987
作者: Valentin Hassler,Jonathan Henrich,Yohaï-Eliel Berreby,Haoze Zhu,Alexander Ecker
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 79 pages, 6 figures. Code: this https URL
Abstract:Human vision is foveated and active: resolution is highest at the fovea and falls off towards the periphery, and fixations are actively directed to locations of interest. Building a machine system that processes information in the same way lets us study gaze behavior in a controlled setting where the system’s components and objectives are known. The emergence of gaze characteristics can therefore be examined systematically, which may in turn inform our understanding of biological vision. This requires a model that can sample like the fovea, handle various tasks from classification to dense prediction, and be trained concurrently with a fixation policy. In our study, we present such a system and study its capabilities and the fixation policies that emerge from it. Our system, FoAViS, samples visual input by adopting the established log-polar foveation and accumulates information in a 2D spatial memory. Due to its 2D structure, the same memory can be read out globally for classification or densely for semantic segmentation, without any architectural change. Fixations are drawn from a 2D map by a policy trained concurrently with the task. We find that learned fixation policies select more informative, task-specific locations than non-learned heuristics. These policies also shape the task representations in a beneficial way: for both classification and segmentation, models trained concurrently with a policy outperform models trained with random locations, even when the latter are provided with learned scanpaths. Moreover, these scanpaths exhibit similarities to human gaze behavior by capturing aspects of empirical scanpath characteristics. Our work marks an important step away from standard passive, single-pass architectures, towards biologically grounded models of vision. It opens a path for studying goal-directed gaze behavior and information representation in the visual system.
[CV-12] winPast: Benchmarking Historical Evidence Use in Streaming Video Understanding with Evidence Contrastive Video Pairs
链接: https://arxiv.org/abs/2610.41983
作者: Haotian Yang,Yong Chen,Cheng Peng,Haobo Wang,Zhongle Xie,Xuanru Li,Gang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High accuracy on streaming video benchmarks does not establish whether a model uses visual history. Our review of OVO-Bench and StreamingBench finds that recent frames suffice for 32% of sampled historical questions, while models also answer many questions correctly from the question and options alone. We introduce TwinPast to test whether answers correctly follow changes in history, using 350 human-reviewed video pairs spanning five historical tasks. Each pair holds the recent video context, question, and ordered options fixed while changing critical historical evidence and the correct answer. Each video is evaluated independently, and PairAcc requires correct answers for both histories. We evaluate 34 model and input configurations. For Qwen3-VL-8B and DeepSeek-Flash, historical inputs improve performance on TwinPast, reversing the recent-frame advantage on existing benchmarks. The strongest evaluated streaming system reaches 49.14% PairAcc, compared with 66.57-91.43% for the evaluated general-purpose models. This gap persists across all five tasks. Case studies further show that models select states from the wrong time or contradict recorded event order despite retaining the relevant evidence. These findings highlight the need to connect historical memory and retrieval with reasoning that uses evidence according to the question’s temporal references and event relations.
[CV-13] Movens-Zero: Scaling Humanoid Motion Foundation Models with Large-Scale Human Videos
链接: https://arxiv.org/abs/2610.41877
作者: Wendong Bu,Tinghong Ye,Qizhou Wang,Jiacheng Li,Zhiqi Ge,Yuze Lin,Zhongqi Yue,Yi Su,Jingyu Wang,Kai Shen,Jingdong Liu,Chengyuan Deng,Xiaoxuan Chen,Xin Tan,Jiaming Ji,Wenqiao Zhang,Chenghong Jiang,Tuoyu Li,Siliang Tang,Jun Xiao,Tat-Seng Chua,Yueting Zhuang,Xingxing Wang,Juncheng Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report. 86 pages, 31 figures, 15 tables. Project page: this https URL
Abstract:Scaling humanoid motion foundation models is constrained by the scarcity of diverse, robot-executable data and by the difficulty of jointly learning from heterogeneous multimodal inputs while supporting real-time closed-loop control. We present Movens-5M, constructed from raw Internet videos through clip generation, quality filtering, semantic annotation, motion recovery, and physical refinement with humanoid retargeting and execution verification. It contains 15 million human-motion sequences and 5 million physically executable humanoid-motion sequences spanning over 10,000 hours, aligned with text, RGB video, speech, and music. Building on this data, we introduce Movens-Zero, a scalable understanding-generation-execution architecture that supports joint learning across modalities and real-time closed-loop execution. Task-mixed flow-matching training jointly optimizes a single action expert across seven generation tasks, enabling direct motion generation from text, speech, music, and video. Multimodal Real-Time Chunking connects overlapping chunks under continuously updated instructions and robot states for continuous closed-loop execution. We further introduce Movens-Intent, a four-track benchmark with 8,192 expert-verified condition-motion pairs across text, speech, music, and video. Experiments show consistent gains across all four modalities when scaling execution-verified data from 1% to 100% and action-expert capacity from 64M to 911M parameters, while demonstrating that data quality and cross-modal supervision improve generation and execution.
[CV-14] Open-Weight MLLM s Cannot Compare What They Cannot Name
链接: https://arxiv.org/abs/2610.41841
作者: Wenxin Su,Anna Kreshuk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) are increasingly capable of understanding images and performing complex visual reasoning, suggesting the possibility of using them to automate biological image analysis. This need is becoming increasingly urgent as advances in technology continue to generate ever larger and more diverse microscopy datasets, already exceeding the scale at which manual visual analysis is feasible. With the final aim to use MLLMs for visual comparison of complex phenotypes, we investigate a very simple baseline: comparison of the size of body parts in synthetic images of ants. We find that state-of-the-art open-weight MLLMs fail for some body parts, but not for others, even though the comparison itself is visually trivial for all of them. We evaluate different in-context learning strategies and analyze performance on the related object recognition task. Our results suggest a recognition-driven grounding failure: structures that the models cannot recognize are instead associated with a recognizable semantic concept, impairing the grounding and comparison of both. Further analysis of the vision encoder is consistent with the hypothesis that fine-grained visual structure may be less well preserved without semantic support. Together, our results reveal a surprising limitation of current MLLMs: semantic recognition can gate simple visual comparisons that do not intrinsically require semantic understanding. Our code and data are available at this https URL
[CV-15] ReuScope: Auditing Data Reuse and Evaluation Sensitivity in Capsule Vision 2024 NEURIPS2026
链接: https://arxiv.org/abs/2610.41835
作者: Eunseob Choi,Kyeonghun Kim,Hyuk-Jae Lee,Nam-Joon Kim
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026, Track on Evaluations and Datasets. 39 pages, 3 figures. Code and data: this https URL
Abstract:We develop ReuScope, a protocol for auditing data reuse and evaluation sensitivity, using Capsule Vision 2024 as a case study. It combines measurements of data relationships with two comparisons: changing the evaluation population while holding predictions fixed, and changing training data while holding evaluation fixed. Byte-identical files cross the public training-validation boundary, and all 41 Kvasir-Capsule recordings that contribute frames occur in both partitions. Shared source recordings establish cross-split overlap; missing cross-recording patient identifiers prevent counting unique shared patients. Across ten paired seeds, removing all training rows attributed to Kvasir-Capsule, plus five rows from other sources flagged as near-matches to it, lowers public-validation balanced accuracy from 82.5% to 61.2%. Evaluating the same fitted models on the validation rows of the other sources reverses the comparison: removal improves balanced accuracy by 1.38 percentage points. The public-validation drop measures sensitivity to source removal, not an amount of leakage-induced inflation. Matched controls show that sample size and class counts alone do not explain the observed score differences. An exploratory experiment removes the training neighbors of validation frames that have close training matches, or equally many distant training rows, and yields a difference-in-differences estimate, relative to matched unflagged frames, of -0.355 percentage points in true-class probability; the paired-t interval for accuracy spans zero. The resulting procedure distinguishes observed reuse from conditional performance sensitivity and specifies the population, class support and uncertainty unit for each conclusion.
[CV-16] ObjectGuidedMap: An Object-Aware Model for Robust Birds Eye View Map Prediction in High-Occlusion Scenarios
链接: https://arxiv.org/abs/2610.41834
作者: Christian Bohn,Harsh Yadav,Klaus Friedrichs,Hasan Tercan,Tobias Meisen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate BEV map prediction is a critical component of autonomous driving systems, enabling downstream planning and control. Recent approaches such as Mask2Map achieve strong performance by predicting vectorized, instance-level map elements directly from multi-view images. However, existing BEV map prediction models operate largely independently of object-level scene understanding, despite the fact that traffic participants frequently occlude or implicitly encode information about road geometry. In this work, we propose a multitask architecture that jointly predicts BEV maps and 3D objects, explicitly enabling information exchange between the two tasks. Building upon Mask2Map, we introduce an object detection branch and incorporate cross-task feature fusion at both the BEV feature level and the instance-query level via deformable attention and cross-attention mechanisms. This design allows map instances to leverage object context while remaining robust to occlusions caused by traffic participants. Experimental results on the nuScenes and Argoverse 2 datasets demonstrate that our approach consistently improves BEV map prediction accuracy, with particularly significant gains of 21% mAP in scenes containing heavy object occlusion.
[CV-17] PGGT-Loc: Primitive-Grounded Geometry Transformer for Feed-Forward Camera Localization NEURIPS2026
链接: https://arxiv.org/abs/2610.41833
作者: Hanqiao Ye,Yuzhou Liu,Mengqi Rong,Jian Liu,Yangdong Liu,Shuhan Shen
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted at NeurIPS 2026. 24 pages, 14 figures, including appendices. Code and models: this https URL
Abstract:Camera localization methods are mostly multi-stage pipelines relying on dedicated and complex maps that are costly to construct, store, and maintain over time. Alternatively, well-structured maps that organize scenes with compact yet expressive primitives are often readily available, providing rich structural and semantic cues that humans can interpret. However, their potential for direct camera localization remains largely unexplored due to the significant modality gap between map primitives and visual observations. This motivates a new concept for localization, which estimates camera poses by grounding query observations to map primitives. In this paper, we instantiate the concept in the context of planar primitives with PGGT, the Primitive-Grounded Geometry Transformer network. Given a single query image and a set of planar map primitives, the network is trained to predict multiple localization targets for feed-forward 6-DoF camera localization that generalizes to unseen environments. With this straightforward and scalable architecture, our method pushes the state-of-the-art localization performance on both dense surface maps and minimal layout models. The code and models are available at this https URL
[CV-18] A Patch is More Than a Token
链接: https://arxiv.org/abs/2610.41814
作者: Zixiong Wang,Haiping Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 54 pages, 16 figures, 12 tables. Code and experimental assets: this https URL
Abstract:Vision Transformers represent each image patch through a canonical token produced under one observation condition. That token is a powerful interface, but it is still one realized state of the patch. We introduce PMTT, which establishes two foundations: a patch representation object can extend from one canonical state to a canonical-centered Representation State Family, and identity-preserving variation opens Patch-Level Local Feature Space. We develop four parts of this representation program. Patch Information Acquisition and Dense Orbital Views expose lawful same-patch states. Visible-to-Hidden prediction tests whether observed states reveal structure beyond themselves. Intrinsic analysis identifies low-dimensional, energy-concentrated, patch-specific, predictive geometry. Representation Organization separates acquiring more information from organizing that information, with an analytic Manifold as the central structured representation. We realize PMTT on frozen DINOv3 S/B/L/H+ backbones without neural post-training of the representation. Across two independent 4,096-anchor cohorts, Manifold explains 70.8-79.6% of Hidden variation, compared with 55.0-72.3% for directly Observed states, and wins on more than 95% of anchors at every scale. Effective rank falls from about 2.5 to 2.0 while dominant energy concentration rises with backbone scale. Across six visual capabilities and four scales, Observed exceeds the canonical Center in 21 of 24 conditions, Manifold exceeds Center in 23, and Manifold exceeds Observed in 16. The strongest current consequence appears in physical correspondence, where MegaDepth satisfies Center Observed Manifold at every scale. These results show that pretrained visual backbones contain richer patch information than the standard single-token interface directly exposes. Code and experimental assets are available at this https URL.
[CV-19] Different Models Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation NEURIPS2026
链接: https://arxiv.org/abs/2610.41790
作者: Eunseob Choi,Kyeonghun Kim,Hyuk-Jae Lee,Nam-Joon Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026, Track on Evaluations and Datasets. 47 pages, 8 figures. Code and data: this https URL
Abstract:Average segmentation accuracy and model count do not reveal whether models fail on the same cases. We present CoFailMed, an audit of correlated failures in pretrained-encoder pools for medical segmentation. Across five 2D tasks, we evaluate 11 public pretrained encoders (a controlled sample, not a census) using frozen features and separately trained 1\times 1 readouts with four seeds per encoder. Shared-checkpoint pairs have the highest mean per-case Dice correlation. An expanded 17-encoder analysis shows that distinct checkpoints within the same encoder family remain more correlated than cross-family pairs. At the pooled 33rd-percentile Dice threshold, four-member pools drawn from distinct encoders have mean effective sizes of 1.56-2.92, estimated from failure correlations in pools with enough failures and passes per member. In the primary pool, richer readouts and fine-tuning narrow the Dice-correlation gap between pairs sharing a checkpoint (or the DINOv2-B/S release) and all other pairs. Yet dependence between different encoders can increase, and fine-tuning does not consistently increase effective pool size at 33rd-percentile thresholds recomputed for each training recipe. A smaller between-group gap can coexist with greater dependence. Pool evaluation should therefore report absolute dependence and threshold-specific effective size alongside accuracy and nominal pool size. The CoFailMed release provides aligned per-case traces, analysis scripts and study code to reproduce the central audit results.
[CV-20] Hard Sample-Conditioned Decorrelated Attention for Robust Face Recognition WACV2027
链接: https://arxiv.org/abs/2610.41789
作者: Tahar Chettaoui,Guray Ozgur,Eduarda Caldeira,Naser Damer,Fadi Boutros
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2027)
Abstract:Multi-head attention in Vision Transformers (ViTs) allows independent attention patterns across heads, but without explicit regularization, heads may learn redundant patterns rather than complementary roles. Correlation-based penalties encourage head disentanglement, yet apply uniform regularization across all training samples. In face recognition (FR), hard samples whose embeddings lie close to competing identities are critical for optimization and generalization. Motivated by this, we propose ViT-HarDis, which conditions attention head disentanglement on sample difficulty: samples with high cosine similarity to negative class centers receive stronger head decorrelation, enforcing specialization where identity ambiguity is highest. Experiments show improvements over both the baseline and uniform disentanglement on most benchmarks. Our analysis shows that ViT-HarDis reduces the similarity of hard samples to competing class centers while decreasing inter-head attention similarity, and that applying disentanglement only to the final transformer layers outperforms regularizing the entire network. Trained on WebFace4M, ViT-HarDis achieves SOTA results among ViT-based methods on IJB-B, IJB-C, and TinyFace Rank-1, while achieving the best IJB-B and TinyFace results when trained on MS1MV3, improving FR without architectural modifications or additional inference cost. Code: this https URL.
[CV-21] WorkIng: A Real-World and Synthetic Highway Work-Zone Benchmark Dataset for Autonomous Navigation WACV2027
链接: https://arxiv.org/abs/2610.41772
作者: Xujun Xie,Dominik Rößle,Adithya Mohan,Daniel Cremers,Torsten Schön
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2027. 19 pages including supplementary material. © 2027 IEEE. Personal use of this material is permitted
Abstract:Highway work zones are critical edge cases for autonomous vehicles (AVs), combining atypical geometries and jurisdiction-specific regulations that existing benchmarks fail to capture: real-world datasets lack 360° multi-modal, full-scene 3D work zone annotation, and no single recorded site can span the layouts and regulatory variants needed for generalizable models. We present WorkIng, a dual-domain benchmark and training resource. WorkIng-Highway provides a 360° multi-modal work zone benchmark with synchronized 128-channel LiDAR, six RGB cameras, and RTK-GPS across 32 km, yielding 12,000+ annotated frames and 227,399 tracked 3D bounding boxes. WorkIng-CARLA complements this with procedural simulation spanning 24 regulation-aware scenarios across six canonical topologies and six international traffic standards, populated by 100+ custom 3D assets. Benchmarking representative 3D detection and end-to-end planning architectures shows that real-world data remains the primary performance driver, and that scenario-stratified synthetic augmentation improves architectures across 2D recognition, 3D detection, and trajectory planning. Resources: this https URL
[CV-22] Measuring What Matters: A Causal Reliance Score for Histopathology Image Segmentation
链接: https://arxiv.org/abs/2610.41763
作者: Samuel Thomas,Daniel Tweneboah Anyimadu,Ahmed Karam Eldaly,Somaia Elsheikh,Mohammed M. Abdelsamea
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 8 figures, accepted into Medical Image Understanding and Analysis (MIUA) 2026 conference
Abstract:Reliable histopathology segmentation requires models that rely on pathology-relevant structures rather than spurious background correlations. In this work, we argue that improving architectures alone is insufficient if models continue to rely on spurious correlations rather than pathology-relevant features. We introduce a causality-inspired evaluation framework based on controlled interventions that explicitly probes model sensitivity to foreground tissue structure and background/domain variation. Building on this, we propose the Causal Reliance Score (CRS), a metric that quantifies the relative sensitivity of model predictions to foreground perturbation versus background/domain variation under controlled interventions. To support this analysis, we design augmentation strategies that act as implicit counterfactual interventions during training, encouraging invariance to nuisance factors while preserving morphology. We demonstrate how these interventions reveal behavioural differences between models that are not captured by standard metrics such as Dice or IoU. While we instantiate our framework using a self-supervised transformer (CAUSE-ViT), the primary contribution of this work is not a new architecture, but a principled intervention-based methodology and evaluation metric for auditing model behaviour in medical image segmentation. Our results suggest that evaluating model behaviour through counterfactual interventions is essential for developing reliable and clinically robust medical image segmentation systems.
[CV-23] SAR-TS-BERT: Physics-Guided Self-Supervised Pre-Training With Polarimetric Features for SAR Time-Series Crop Classification
链接: https://arxiv.org/abs/2610.41728
作者: Jieqiu Wang,Yuhan Zhang,Bo Zheng,Shaoming Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 13 figures, 18 tables. Jieqiu Wang and Yuhan Zhang contributed equally to this work
Abstract:Multitemporal synthetic aperture radar (SAR) imagery provides continuous, cloud-free observations of crop phenology for agricultural monitoring in cloudy and rainy regions, yet high-accuracy classification typically requires extensive labeled samples. Although self-supervised pre-training mitigates label dependency in remote sensing, existing frameworks primarily target optical imagery or generic spatial SAR scenes, underutilizing temporal backscatter dynamics and polarimetric scattering mechanisms. We propose SAR-TS-BERT, a physics-guided BERT-style self-supervised pre-training framework for SAR time-series crop classification. SAR-TS-BERT integrates backscatter intensities and polarimetric ratio features (VV, VH, and VV/VH) with dual-polarimetric Cloude-Pottier decomposition parameters ( H/\alpha/A ) into a six-band input representation through two core designs: 1) a physics-guided span masking strategy (SpanPhysics) that adaptively selects contiguous masked spans based on temporal backscatter change rates to prioritize key phenological transitions; and 2) a dual-branch cross-modal fusion encoder that models backscatter and polarimetric decomposition features through separate Transformer branches followed by bidirectional cross-attention fusion. On independent test sets in Estonia (EuroCropsML) and the USA (California CDL), SAR-TS-BERT achieves overall accuracies (OAs) of 96.78% and 87.29%, respectively, and attains a mean monthly OA of 99.37% on Chongming Island, Shanghai. On the Estonia benchmark, pre-training improves OA and Macro F1 by 1.17 and 1.59 percentage points over training from scratch under full supervision, and by 2.58 and 5.19 percentage points using only 1% labeled data. Incorporating SAR scattering physics into temporal self-supervised pre-training effectively enhances crop classification accuracy, parcel-level mapping consistency, and few-shot transferability.
[CV-24] Nereus: A Large-Scale Underwater Dataset for Fine-Grained Attribute Understanding and Grounded Counting Perception NEURIPS2026
链接: https://arxiv.org/abs/2610.41705
作者: Jingtao Deng,Zihao Jin,Yuehe Chen,Feixiang He,Shuiwang Li,Dan Zeng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS 2026 Evaluations and Datasets Track
Abstract:Underwater imagery records rich ecological information and supports applications such as science communication, fishery monitoring, ecological conservation, and oceanographic research. Beyond general scene understanding, these applications require models to recognize fine-grained biological traits and reason over spatially grounded evidence. However, existing underwater datasets often provide coarse category labels, generic captions, or total-count supervision, which limits their use for organism-level analysis and ecological reasoning. We introduce Nereus, a large-scale dataset for Underwater Fine-grained Perception that jointly supports Fine-Grained Object Attribute Understanding and Grounded Counting Perception. Nereus contains 88K images and 2.68M question-answer pairs, including annotations for 136K marine organism objects across 2,690 species and 1,515 genera, as well as 65K point annotations and 67K grounded counting QA pairs. Experiments with representative multimodal models show that Nereus improves downstream adaptation over existing underwater-domain data and provides a foundation for future research on fine-grained marine perception.
[CV-25] Retrieval-Augmented Initialization with Dual-Guided Inversion for Flow-Based Inverse Problem Solvers WACV2027
链接: https://arxiv.org/abs/2610.41695
作者: Hansam Cho,Seoung Bum Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV 2027
Abstract:Flow models have recently emerged as powerful generative priors for solving inverse problems. Despite their success, most existing flow-based inverse problem solvers initialize the reconstruction process with random Gaussian noise. However, recent studies in image generation have shown that the choice of initial noise can significantly influence generation outcomes. The role of initialization as a distinct design choice in flow-based inverse problem solving remains comparatively underexplored. In this work, we systematically evaluate noise initialization strategies while holding the downstream flow-based inverse solver fixed. We propose Retrieval-Augmented Initialization with Dual-Guided Inversion (RA-DGI), a method that constructs improved initial noise by leveraging retrieved proxy clean images and dual-guided inversion. Extensive experiments across multiple inverse problems demonstrate that the quality of the initial noise substantially affects reconstruction fidelity and perceptual performance. RA-DGI outperforms standard random initialization and achieves superior results compared to inference-time scaling strategies, while requiring less computational overhead. These findings highlight initialization as a critical yet previously overlooked factor in flow-based inverse problem solvers.
[CV-26] OT-CAL: Optimal-Transport-Guided Adaptive Sample Selection for Efficient Visual Learning
链接: https://arxiv.org/abs/2610.41677
作者: Suryansh Talukdar,Jin Soung Yoo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures. Submitted to the IEEE BigData 2026 Workshop on Efficient AI
Abstract:Deep neural networks for image recognition are typically trained using mini-batches, with a forward pass to compute predictions and losses followed by a more computationally expensive backward pass to update model parameters. However, not all training samples contribute equally to learning, suggesting that computation can be reduced by performing backward passes only on informative samples. The challenge is that sample informativeness changes throughout training: difficult samples may be valuable early on, improving samples become important later, and class diversity becomes increasingly relevant toward the end. Existing methods often rely on fixed selection rules or computationally expensive per-sample gradient calculations. We propose OT-CAL, an adaptive data selection method that automatically adjusts its sampling preferences during training. OT-CAL represents each sample using five informativeness features, including difficulty, learnability, and uncertainty. A curriculum vector describes the currently preferred sample characteristics, and optimal-transport-based matching assigns selection weights according to how closely samples match this target. The curriculum vector is then updated using the selected samples, forming a feedback loop that adapts without a manually defined schedule. Experiments on three standard visual learning benchmarks show that, when selecting 50% of each mini-batch, OT-CAL improves performance by 1.3–2.1 points over AdaSelection while achieving 1.18–1.24 times faster training than full-data training.
[CV-27] A Self-Supervised Pose Estimation Approach for Construction Machines
链接: https://arxiv.org/abs/2610.41673
作者: Alaa Alshubbak,Daniel Görges
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 Pages, 12 figures, submitted for the International Symposium on Visual Computing conference
Abstract:Pose estimation is a computer vision task used to estimate a skeleton of dynamic systems to predict future movements. Most of the research in this direction is based on a supervised learning approach which requires a massive amount of labeled datasets. In this paper, a self-supervised three-stage model based on a contrastive learning approach is introduced for estimating a skeleton of dynamic construction machines; such as excavators without using any labeled images for the first stage. The whole model structure is divided into three stages: the pre-train stage using the SimCLR contrastive approach, and two fine-tuning stages for the transfer learning and the downstream task. The model can leverage the features and learn from a huge unlabeled dataset called ACID to two small datasets generated from NVIDIA Isaac and MATLAB Simscape simulators as well as transfer the knowledge to a smaller dataset with a ratio of 3.5% from the original ACID dataset. The results show that the proposed approach can improve the accuracy of pose estimation for heavy construction machines in real images by 11% and 13% in comparison to the normal self-supervised approach with two backbones ResNet-50 and HRNet-W32, respectively.
[CV-28] OpenVIO: A Versatile Open-Source Visual-Inertial Odometry System
链接: https://arxiv.org/abs/2610.41671
作者: Seungwon Choi
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 6 figures
Abstract:Visual-inertial odometry (VIO) is a fundamental topic in robotics research. Beyond research, VIO has been adopted in a wide range of products. In this manuscript, we present OpenVIO, an open-source VIO system for research and practical applications. It is released under the MIT license. OpenVIO supports pinhole and fisheye cameras and extends this support to stitched 360° images. We describe each part of the system together with its design considerations. We also report benchmark results with other open-source VIO systems on public datasets. OpenVIO is available as open source at this https URL.
[CV-29] When Depth Lies: Benchmarking Vision-Language Models on Mirror-Induced RGB-D Ambiguity NEURIPS2026
链接: https://arxiv.org/abs/2610.41651
作者: Hao Yin,Tianchen Guo,Heming Du,Yan Ke,Yanbin Liu,Xin Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: NeurIPS 2026 ED Track accepted
Abstract:Vision-language models (VLMs) have made significant progress in spatial reasoning, particularly by integrating RGB images with depth data, which is often assumed to faithfully reflect physical distances. However, mirrors violate this assumption: they distort depth readings while reproducing object appearances, making reflections difficult to distinguish from real objects. As existing benchmarks rarely consider such reflective scenarios, VLM performance in mirror scenes remains largely unexplored. In this work, we introduce a mirror-centric RGB-D dataset from reflective scenes to evaluate the spatial reasoning capabilities of state-of-the-art VLMs in these scenarios. Specifically, we collect 1,011 RGB-D images from 525 real-world scenes, spanning diverse indoor and outdoor environments, six reflective surface types, and both daytime and nighttime conditions. All images are annotated with reflective surface region masks and mask annotations for both directly observed and mirror-reflected objects. Then, we curate a benchmark named MIRA-Bench (Mirror-Induced Reasoning Ambiguity Benchmark), comprising 14,140 QA pairs across eight sub-tasks organized into three cognitive levels: Reflection-aware Perception, Spatial Reasoning, and Scene-level Decision-making. We evaluate state-of-the-art VLMs on MIRA-Bench and find consistent failures across tasks: models misclassify whether objects are directly observed or reflected, fail to match reflected instances to their real-world counterparts, and produce erroneous spatial inferences. Remarkably, providing depth data actually degrades reasoning performance, implying that VLMs cannot identify incorrect depth in mirror scenes. The code and data are available at this https URL
[CV-30] ADP: Dual-Branch Temporally Adaptive Differential Privacy for Expression-Aware Video Anonymization WACV2027
链接: https://arxiv.org/abs/2610.41642
作者: Feng Xu,Xun Li,Lars Petersson,Yulei Sui,David Ahmedt-Aristizabal,Dadong Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV2027 accepted
Abstract:We present TADP, a temporally adaptive differential privacy (DP) framework for inference-time privacy-preserving video generation under fixed-length clip-replacement adjacency. TADP uses a dual-branch design to extract identity and expression representations, clips and privatizes both branches with temporally adaptive Gaussian mechanisms whose noise scales depend only on preceding privatized releases within the same clip and are bounded below by history-uniform calibrated minima, and accounts for their joint release via adaptive Rényi Differential Privacy (RDP) composition. The clip-level RDP guarantee is converted to (\varepsilon,\delta) -differential privacy, while soft quantization and subsequent temporal processing are applied only after privatization as DP post-processing. Experiments on facial expression datasets show reduced identity leakage under evaluated attacks, usable perceptual quality and temporal stability, and high facial expression recognition utility relative to the evaluated privacy-preserving baselines. TADP formally satisfies clip-level DP and empirically provides a favorable privacy–utility trade-off under the evaluated threat models and datasets.
[CV-31] Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation WACV2027
链接: https://arxiv.org/abs/2610.41618
作者: Chanseul Cho,Jaesung Jun,Dongheon Lee,Youngmin Ro
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to WACV 2027
Abstract:Chain-of-Thought (CoT) prompting has become a standard reasoning strategy in Multi-modal Large Language Models (MLLMs), showing strong performance in complex vision-language tasks such as reasoning segmentation. However, our analysis reveals that indiscriminate CoT usage can lead to degraded performance by inducing unnecessary reasoning, a phenomenon we term overthinking. In reasoning segmentation, overthinking often leads to misalignment between the query’s intent and visual grounding. To address this, we propose Difficulty-Aware Chain-of-Thought (DA-CoT), a training-free adaptive framework that selectively triggers CoT based on the input’s intrinsic difficulty. We find that the entropy of the model’s attention maps reliably reflects task difficulty: low entropy indicates confident, localized grounding, while high entropy suggests uncertainty and potential benefit from CoT. DA-CoT uses this entropy signal to determine whether to apply CoT prompting. Additionally, we introduce a Bidirectional Rethinking mechanism that enables the model to automatically flip its reasoning decision when the initial output fails, enhancing robustness by recovering from failures induced by overthinking and underthinking. DA-CoT demonstrates its effectiveness by achieving state-of-the-art performance on the Reasoning Segmentation benchmarks while significantly improving inference efficiency.
[CV-32] DrawSplat: Atlanta World-Guided Implicit Gaussian SLAM for Indoor Reconstruction
链接: https://arxiv.org/abs/2610.41602
作者: Fan Zhu,Zhisong Xu,Ziyu Chen,Zhenjun Zhao,Takayuki Okatani
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:RGB-D SLAM requires accurate online tracking, high-quality rendering, and geometrically consistent reconstruction. Existing Gaussian SLAM methods mainly optimize Gaussian primitives with photometric, depth, or local frame-to-model constraints, which are often insufficient in indoor scenes with weak textures, large planar structures, noisy depth, and long trajectories. The resulting maps may suffer from pose drift, surface thickness artifacts, noisy normals, and blurred structural boundaries. We propose DrawSplat, an Atlanta World-guided implicit Gaussian SLAM framework for indoor reconstruction. DrawSplat follows a drawing-like process: explicit point-line geometry first sketches the structural layout, and implicit neural Gaussians then fill in high-fidelity appearance and continuous geometric details. It estimates camera poses, sparse geometry, and gravity direction through point-line joint tracking, infers structural planes from tracked lines using the shared-gravity component of the Atlanta World prior, and converts them into differentiable regularization terms on Gaussian centers and normals. To improve compactness and efficiency, DrawSplat further combines an anchor-based implicit neural Gaussian representation with adaptive structural sampling, which allocates Gaussian capacity to structurally informative regions. Experiments on multiple datasets show that DrawSplat achieves robust tracking, high-fidelity rendering, and strong geometric reconstruction among Gaussian-based SLAM systems, while maintaining a practical balance among quality, efficiency, and model size.
[CV-33] Scout-Refine-Correct: Teaching Flow Models to Refine Their Own Samples
链接: https://arxiv.org/abs/2610.41598
作者: Sergey Karpukhin,Aibek Alanov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: preprint
Abstract:The quality of image generation models trained with diffusion or flow matching is largely fixed by the training budget: at inference, the only available lever is the number of sampling steps, and additional steps improve quality only up to a point. We present a training recipe, Scout-Refine-Correct (SRC), that instead gives a flow-matching generator a genuine inference-time refinement capability. At each sampling step the model takes a scout rollout – K steps ahead along its own trajectory – extrapolates a full-sample estimate, and feeds that estimate back to itself as a self-conditioning signal before taking the real step. This can be repeated for R refinement rounds. Two correction losses, applied during training at states the model’s own rollouts actually reach rather than only on the ground-truth path, teach the model to use this signal to reach new samples instead of collapsing to a suboptimal one. On ImageNet-256, SRC improves LightningDiT-B from FID~14.5 to FID~7.26 at NFE~550, matching a privileged best-of- N baseline that uses an oracle FID verifier to choose among samples. SRC uses no external verifier, no reward signal, and, after ablation, almost no extra parameters over the base architecture. Combining SRC with best-of- N improves the result further, to FID~3.81 at NFE~4400, showing that the two axes are complementary. We ablate the main design choices – the architectural pathway for the scout signal, the scout horizon, each correction loss, and the training schedule to demonstrate key working components of training.
[CV-34] ACO: Trajectory-Aware Curvature Observation for Memorization Localization and Mitigation in Diffusion Models
链接: https://arxiv.org/abs/2610.41520
作者: Ruxi Gu,Zilei Wang,Wei Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages, 10 figures
Abstract:Text-to-image diffusion models can reproduce memorized training content within localized image regions, making precise localization essential for targeted mitigation. However, curvature observations at a single denoising state can contain transient responses, while spatially uniform mitigation may disrupt prompt-relevant content. We present Trajectory-Aware Curvature Observation (TACO), a unified framework for local memorization detection and mitigation in diffusion models. Our empirical analysis reveals that memorization-related curvature becomes pronounced during late-stage denoising and persists at consistent spatial locations across neighboring states. Motivated by these observations, TACO aggregates prompt-dependent curvature responses along the late denoising trajectory and applies latent-guided spatial refinement to improve spatial coherence. The resulting memorization map serves both as a localization signal and as a spatial control signal for mitigation. Specifically, TACO uses this map to steer existing inference-time interventions toward memorization-related regions through spatial reweighting and localized guidance correction. Extensive experiments demonstrate that TACO consistently outperforms competing localization methods, and improves the Pareto trade-off between memorization suppression, semantic alignment, and perceptual quality. These results establish trajectory-aware spatial analysis as an effective link between memorization localization and targeted mitigation.
[CV-35] Parameter-Efficient Fine-Tuning of Transformer Encoders for Audio-Image-Symbolic Music Retrieval
链接: https://arxiv.org/abs/2610.41512
作者: Eliseo Fuentes-Martinez,Antonio Hidalgo-Centeno,Jorge Calvo-Zaragoza,Antonio Javier Gallego
类目: Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Score-based multimodal music retrieval requires representations that are both discriminative within each modality and compatible across audio recordings, sheet music images, and symbolic scores. This paper investigates the effectiveness of parameter-efficient fine-tuning (PEFT) for adapting pretrained music encoders and examines whether modality-specific improvements translate into better multimodal representations. We compare full fine-tuning, LoRA, and QLoRA across nine pretrained audio, image, and symbolic score encoders on TriScore and the Musical Theme Dataset. Encoders are first evaluated and selected through supervised unimodal classification, using frozen-encoder linear probing as a reference. We then introduce a controlled 2x2 design crossing encoder adaptation with task-specific shared projection, comparing frozen and adapted representations before and after projection. LoRA or QLoRA is selected in 14 of 18 modality-target combinations, frequently matching or outperforming full fine-tuning while optimizing substantially fewer parameters. However, these unimodal gains do not transfer uniformly to multimodal classification. Adaptation and projection are complementary for composer classification on TriScore, largely redundant for TriScore instrument and the Musical Theme Dataset instrument and ensemble tasks, and partially interfering for composer classification on the Musical Theme Dataset. The final adapted-and-projected title representation on TriScore supports piece-level retrieval, reaching average Top-5 and Top-10 accuracies of 55.32 and 64.68, respectively. Cross-modal exclusion and neighborhood analyses further reveal strong modality-specific local structure in the projected spaces. Overall, PEFT provides an effective and computationally efficient alternative to full fine-tuning, but improved unimodal discrimination alone does not guarantee better multimodal organization.
[CV-36] VoxLution: Evolving Voxels for High-Fidelity 3D Scene Generation
链接: https://arxiv.org/abs/2610.41501
作者: Daoyi Gao,Xinyi Zhang,Alexey Bokhovkin,Lei Li,Angela Dai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL Video: this https URL
Abstract:High-resolution 3D indoor scene generation is crucial for immersive applications such as virtual reality, spatial computing, and robotics simulation. We introduce VoxLution, a coarse-to-fine transformer-based approach that generates high-fidelity indoor scenes with rich details, overcoming the overly smooth geometry typical of existing generative models. First, we generate a low-resolution spatial layout; then, based on this structural information, we progressively refine the scene geometry locally in chunks. Key to our approach is formulating 3D super-resolution as a sequence modeling problem over multi-resolution sparse voxel grids. To achieve this, we introduce a training-free, resolution-invariant tokenization scheme that compactly encodes the geometric information of eight octants into a single decimal number. We formulate the transformer sequence to include multiple resolutions, allowing the coarse layout to guide the generation of fine local details. To ensure coherence across local chunks, we incorporate adjacent chunk boundary information directly into the transformer sequencing. VoxLution achieves scene generation at a finer voxel resolution than existing baselines, demonstrating state-of-the-art performance on standard benchmarks with 59.6% lower FPD and 51.1% lower FID on 3D-FRONT, and 70.5% lower FPD and 73.8% lower FID on Scannet++.
[CV-37] VariaFlow: Uncertainty-Aware Flow Matching for Unsupervised Anomaly Detection WACV2027
链接: https://arxiv.org/abs/2610.41494
作者: Arnela Hadzic,Martin Urschler,Cosmin I. Bercea
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for WACV 2027
Abstract:Reconstruction-based anomaly detection no longer fails at reconstruction. Generative models produce high-quality pseudo-healthy reconstructions, and the remaining difficulty is the step of scoring residuals. The residual between a scan and its reconstruction has two indistinguishable sources, genuine pathology and the anatomical variation that separates one healthy brain from another. Scoring against a single reconstruction treats both as anomalous, inflating false positives where healthy anatomy varies most while small or subtle lesions are lost in the surrounding reconstruction error. We identify the missing quantity as the predictive variance, how much a healthy reconstruction can vary at each location. We introduce VariaFlow, which estimates this variance from an ensemble of mask-guided restorations under an unconditional flow matching prior, each started from a different noise initialization, and which normalizes the residual by the resulting standard deviation. Few-step flow matching keeps the ensemble computationally affordable, and restoring at two spatial scales captures lesions of different size. Across three brain MRI datasets spanning stroke and tumor, VariaFlow is trained once on healthy anatomy and generalizes across all three, attaining the highest AUROC (0.94, 0.80, 0.78) on each and the highest AUPRC on both cross-domain datasets, exceeding the strongest baseline by 37% and 50%. Under domain shift, the same baseline drops by up to 52% in AUPRC, whereas VariaFlow stays more stable and achieves the best small-lesion Dice on all three datasets.
[CV-38] BDNet: Fast-slow thinking is all you need
链接: https://arxiv.org/abs/2610.41485
作者: Chenzhi Zheng,Zhuoran Zheng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 2 figures, this manuscript is an early draft
Abstract:We present BDNet, a hierarchical image-classification backbone built around fast image features and slow statistical references. Its baseline deformation operator (BDO) transforms the deviation of a feature from a stage-specific reference through a low-rank projection and a learned channel scale. Rather than computing pairwise token similarity, the branch operates directly on baseline-relative residuals with complexity linear in the token count. Each of four reference buffers accumulates detached stage statistics during training and remains frozen at inference. A four-stage pyramid with depths (2,2,6,2) and widths (96,192,384,768) yields a 20.70M-parameter classifier at 224\times224 resolution. We organize an ImageNet-1K study around clean classification, eight controlled ablations, and Gaussian-noise, contrast, and brightness perturbations. The central hypothesis is that a slow reference can improve the robustness of a compact representation while retaining a simple tensor implementation. We distinguish this training-time statistical mechanism from input-dependent attention and make its linear inference behavior explicit.
[CV-39] Beyond Trial-and-Error: A Geospatial-Guided Active Reasoning Framework for Robust Aerial Vision-and-Language Navigation
链接: https://arxiv.org/abs/2610.41446
作者: Jiawei Ge,Wenkang Gong,Jiuxin Cao,Bo Liu,Lei Cui,Haoran Wang,Xuelin Zhu,Biwei Cao,Chang Liu,Chen Feng,Ioannis Patras
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Aerial Vision-and-Language Navigation (VLN) requires UAVs to locate language-specified goals in large-scale urban environments, where objects often appear small and visually ambiguous from high-altitude viewpoints. Existing methods largely follow a reactive paradigm common in indoor VLN, repeatedly updating navigation states from local observations while underutilizing the geospatial structure that remains stable during navigation. As a result, visually similar distractors can cause a reactive goal search to repeatedly reinforce locally plausible goal hypotheses based on passively accumulated evidence, leading to unreliable localization. To address this, we propose GeoReasoner, a robust framework that shifts the paradigm of Aerial VLN from reactive trial-and-error to geospatial-guided active reasoning. Specifically, GeoReasoner first performs pre-search geospatial reasoning to derive region-level priors and constrain the goal hypothesis space. Building on these, it further conducts hypothesis-driven exploration to actively acquire discriminative evidence and refine competing goal hypotheses. Finally, consistency-aware goal verification adversarially assesses complementary evidence sources before stopping, hence reducing premature or erroneous goal confirmations. Experiments on the real-world CityNav benchmark demonstrate that GeoReasoner sets a new SOTA among zero-shot Aerial VLN methods while competing favorably with supervised approaches.
[CV-40] FlexReader: Towards Adaptive Decoding Paths for Efficient Arbitrary-Length Scene Text Recognition
链接: https://arxiv.org/abs/2610.41426
作者: Zhibin Ma,Pengwen Dai,Zhigang Yao,Xugong Qin,Wenqi Ren,Xiaochun Cao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figures
Abstract:Existing Scene Text Recognition (STR) models are inherently coupled with a specific decoding strategy by their training paradigms, limiting their flexibility in deployment across diverse recognition scenarios. In this paper, we propose FlexReader, an STR model that supports multiple decoding strides, enabling a single model to achieve an optimal accuracy-efficiency trade-off across different datasets. Specifically, we first introduce a Sub-string Perception module to perceive the target sub-string from visual features, followed by multiple parallel decoding blocks that independently decode each character in the sub-string. During inference, FlexReader supports decoding with arbitrary strides within the perceived sub-string range. Furthermore, we propose Adaptive Prefix Decoding, which adaptively selects the decoding stride at each step based on the model’s confidence in the decoded characters, thereby identifying the optimal decoding path for each input. Experimental results demonstrate that FlexReader exhibits robust recognition capability across a diverse range of benchmarks, including those featuring long text, curved text, and occlusions. It consistently outperforms existing decoding strategies in recognition accuracy while achieving up to 6-flod decoding speedup over autoregressive decoding.
[CV-41] SACQ: Structure-Aware Codebook Quantization for Ultra-Low-Bit Vision-Language-Action Models
链接: https://arxiv.org/abs/2610.41408
作者: Haesung Lee,Insu Han
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Vision-Language-Action (VLA) models enable capable robot behavior, but their large memory footprint limits deployment on resource-constrained robotic platforms. Weight quantization is a natural remedy, yet post-training quantization (PTQ) recipes inherited from large language models degrade when applied to VLAs at ultra-low precision. Existing low-bit PTQ methods often fail to explicitly account for the structure of downstream operations and allocate precision without considering its effect on the policy actions. We introduce SACQ, an ultra- low-bit weight-only vector-codebook quantization framework for VLAs. SACQ combines (1) structure-aware codebook fitting with role-specific quadratic metrics that reflect input statistics and downstream sensitivity; (2) action-aware mixed-bit allocation based on the increase in final-action error as individual layers are compressed; and (3) two-stage post-quantization refinement that reassigns codeword indices with second-order error compensation and calibrates codebooks and row-group scales against the decoded action. Using GR00T N1.7, SACQ achieves 93.85% average success across four LIBERO suites at 1.5 bits per language/action weight while reducing model memory by 4.83x. With INT8 activation quantization and low-precision attention, it retains 93.55% success while running 1.51x faster than BF16. On SimplER, SACQ further achieves 63.76% and 52.27% success under Visual Matching and Variant Aggregation at 2 bits per language/action weight, compared to 58.90% and 53.45% for BF16, respectively.
[CV-42] DUGO: Dual-Scale Gaussian Memory for Online Temporal Action Localization in Egocentric Procedural Videos
链接: https://arxiv.org/abs/2610.41402
作者: Mohammed Aman Bhuiyan,Md. Sazzad Hossain Adib,Samiul Basir Bhuiyan,Mir Faiyaz Hossain,Syed Mohammed Shamsul Islam,Md Zakir Hossain,Shafin Rahman
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: This paper has been accepted at the 2026 IEEE International Conference on Big Data (IEEE BigData 2026)
Abstract:Online Temporal Action Localization (OnTAL) aims to detect and localize action instances from untrimmed streaming videos in real time. Unlike offline approaches, OnTAL models have no access to future frames and cannot go back and refine earlier predictions, which makes the task considerably harder given the strict real-time constraints and narrow temporal window available. Most existing OnTAL methods lean on either brief anchor-based context or a single-scale memory of past frames, which limits how well they can capture actions unfolding across multiple time scales or requiring long-range reasoning in Procedural Videos. To address these gaps, we present DUGO (Dual-Scale Encoding and Gaussian Memory Online), a new OnTAL framework that jointly models local motion dynamics and long-range temporal dependencies. At its core, DUGO uses a Dual-Scale Temporal Encoder, pairing depth-wise local representations with global self-attention so it can pick up both fine-grained temporal patterns and broader contextual cues. We also introduce a Gaussian Memory Bank, which represents action memory as stochastic, input-conditioned Gaussian prototypes, allowing for richer temporal memory through gated cross-attention applied at both the encoder and anchor-query stages. Extensive experiments on procedural egocentric datasets (EGTEA, EPIC-Kitchens-100) demonstrate that DUGO achieves state-of-the-art performance while also providing substantial improvements on non-egocentric benchmarks such as Muses. The project page is available at: this https URL
[CV-43] Parse Before You Execute: Efficient Spatial Reasoning with Executable Primitives
链接: https://arxiv.org/abs/2610.41389
作者: Xiwei Liu,Yulong Li,Xinling Zhuang,Xuhui Li,Yichen Li,Haolin Yang,Zhixiang Lu,Yutong Xie
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Spatial reasoning in Vision-Language Models (VLMs) has increasingly benefited from tool-integrated pipelines that ground computation in explicit geometry. However, existing systems often compile queries into relatively open-ended artifacts, such as code, task constraints, tool plans, or gathered evidence, leaving what must be resolved before execution under-specified. This can lead to unnecessary tool acquisition and iterative refinement, while still producing plausible reasoning traces from incomplete spatial commitments. We identify this failure mode as under-committed query compilation. To address it, we propose PEPS, a training-free agentic paradigm Parsing Executable Primitives for Spatial reasoning under a typed FESM schema over Frame, Entity, State, and Metric. PEPS follows a parse-before-execute principle: the Parser commits each query to a compact set of executable requirements; the Executor selectively grounds the required primitives through fixed geometric APIs and deterministic computation; and the Verifier checks execution sufficiency, enabling early termination or targeted repair. Extensive experiments across challenging spatial reasoning benchmarks show that PEPS consistently improves reasoning accuracy while substantially reducing refinement rounds, API cost, and end-to-end latency over strong tool-integrated baselines. These results demonstrate that explicit requirement commitment can make tool-integrated spatial reasoning not only more reliable, but also more execution-efficient.
[CV-44] Queryable Gaussian View Index: Text Retrieval and Selective Camera Localization
链接: https://arxiv.org/abs/2610.41373
作者: Tianyu Xiong,Rui Li,Jiaqi Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Includes supplementary material; 3 figures, 47 references
Abstract:We present Queryable Gaussian View Index (QGVI) for querying existing Gaussian scene assets and their associated calibrated reference data. Text-view retrieval and selective RGB camera localization return poses in the asset’s coordinate frame. QGVI-T turns Gaussian-rendered views into a language-searchable index of scene locations, binding structured captions to known camera poses and place identifiers. Semantic and lexical retrieval select views from distinct places without online rendering, VLM calls, or a scene-specific pose network. The image route, QGVI-I, filters LoGS-based geometric estimates using distinct inlier query keypoints. It omits photometric refinement and preserves accepted pose coordinates. On nine development scenes, QGVI-T achieves 58.3% Top-1 and 78.3% Top-5 joint relevance-and-validity success, compared with 26.1% and 34.6% for our CityLoC reimplementation with refinement. QGVI-I achieves 97.3% scene-mean pose success at 1 m/2 degrees, with pooled false-acceptance and false-rejection rates of 0.707% and 1.142%. A paired cached evaluation retains all 1,775 initializer poses correct at this threshold. Recorded median text query latency is 0.020 s. Matched controls show competitive BM25 retrieval and slightly lower false acceptance for thresholded full LoGS; grid augmentation suggests modest gains on novel-view-derived descriptions. We report model-judged semantic success separately from source-camera recovery, with source-verified or estimated scales for geometric evaluation.
[CV-45] ask-Performance Routing for Multi-Teacher Distillation in CT Medical Image Segmentation MICCAI2026 CEC
链接: https://arxiv.org/abs/2610.41362
作者: Jiaye Yang,Yao Liu,Die Dai,Hanwen Zhang,Peiyuan Jiang,Qiao Liu,Yutong Xie,Peng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2026. 11 pages, 3 figures, 4 tables. Code: this https URL
Abstract:Pre-trained models offer complementary strengths in CT organ segmentation, but their relative accuracy varies across organs and spatial regions. Multi-teacher knowledge distillation can combine these strengths in a single student, yet global or instance-level teacher weights overlook variation within a 3D volume. We propose Task-Performance Routing (TPR), which uses region-wise segmentation errors to determine teacher weights during training without learned routing parameters. In hard regions, TPR favors teachers with lower prediction errors; in easy regions, it uses uniform weights to combine their knowledge. A hierarchical spatial partition provides fine-grained routing in shallow encoder stages and coarse-grained routing in deep stages. Experiments on BTCV, MSD-Liver, and MSD-Pancreas with two teacher pairs and two backbone architectures show higher Dice scores than the individual teachers, adaptive ensembles, and evaluated multi-teacher distillation baselines. On MSD-Pancreas, TPR achieves Dice scores of 65.40% with SwinUNETR and 68.25% with U-Net, compared with 64.38% and 67.45% for the corresponding adaptive ensembles. Inference uses the student alone, with no routing or teacher overhead. Code is available at this https URL.
[CV-46] CalibSwitch: Observation-Driven Camera-LiDAR Extrinsic Recovery and Selective Maintenance for Multimodal Perception
链接: https://arxiv.org/abs/2610.41352
作者: Haifa Zhang,Yijing Wang,Zhiqiang Zuo
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera–LiDAR perception on mobile robots and intelligent vehicles depends on reliable sensor geometry throughout deployment. Installation variations create uncertainty in the initial extrinsics, while changing observation quality makes frame-wise estimates fluctuate during operation. This paper presents CalibSwitch, an observation-driven framework that separates absolute extrinsic recovery from selective state maintenance. A cross-modal absolute extrinsic generator (CAEG) combines projection-free bidirectional feature interaction with geometry-regularized learning to recover complete SE(3) candidates from image–point observations and coarse view context, without a supplied numerical initial transform. A selective recurrent calibration policy (SRCP) combines observation quality and temporal history to determine when a candidate should replace the working geometry consumed by a frozen multimodal detector. On nuScenes, CAEG achieves mean translation and rotation errors of 0.248 m and 5.884 ^\circ . Under sustained geometric transitions, SRCP reaches a mean composite error of 1.46 with 9.70% candidate commitments, outperforming quality-based and temporal-consistency gating at comparable commitment rates. Relative to unconditional per-frame updating, it improves frozen-detector mAP and NDS by 1.43 and 0.92 points. Paired geometry–observation uncertainty tests, two real-vehicle platforms, and 13.5-FPS single-camera Jetson execution demonstrate the system value of selective geometric-state maintenance for continuous perception.
[CV-47] Joint-Health-Aware Reinforcement Learning for Robust Robot Control Under Actuator Degradation
链接: https://arxiv.org/abs/2610.41348
作者: Haifa Zhang,Yijing Wang,Zhiqiang Zuo
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robots deployed in unstructured environments are susceptible to actuator-authority degradation, which reduces the effective control authority of individual actuators and can cause reinforcement learning policies trained under nominal dynamics to fail. Existing robustness approaches rely on discrete fault labels, broad domain randomization, or global latent variables, often without explicitly modeling continuous per-joint residual actuation capability. We propose a joint-health-aware reinforcement learning framework for robust robot control under simulated actuator degradation. Our framework encodes actuator condition in a continuous joint-health vector and models degradation as multiplicative attenuation of executed actions. A dedicated HealthNet estimator infers per-joint health from recent observations and actions, allowing the policy to redistribute control effort across healthy and degraded joints. We evaluate the method on Walker, Go2, Cassie, and Panda-based ManiSkill manipulation tasks under diverse degradation masks and severity levels. The method outperforms nominal and degradation-trained no-health baselines on the main evaluated metrics. Relative to the nominal baseline, task-level locomotion returns improve by up to 250.1% and manipulation success rates by up to 15.2 percentage points; in the high-degradation Cassie group, return increases from 3.94 to 40.50 (+928.0%). Ablations support the contribution of per-joint health estimation, action-degradation modeling, and health-conditioned control.
[CV-48] GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
链接: https://arxiv.org/abs/2610.41314
作者: Ke Yang,Zhenyu Zhang,Jun Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must preserve scene state while tracking how spatial relations evolve over time. Meanwhile, video generation models (VGMs), trained to synthesize temporally coherent world dynamics, offer a rich source of spatiotemporal representations. We present GenDSR, a framework that transfers intermediate representations from VGM into VLM for dynamic spatial reasoning. Rather than treating a VGM feature as a monolithic prior, GenDSR constructs two complementary branches: a raw branch preserving holistic generative context and a delta branch emphasizing temporal variation. Task-conditioned tokenwise gates then fuse both signals and inject the result into the native visual representation. On DSR-Bench, GenDSR achieves 67.2%, surpassing the previous best result and establishing a new state of the art.
[CV-49] STAR-FER: Adaptive Selective Restoration for Edge-Based Low-Resolution Facial Expression Recognition
链接: https://arxiv.org/abs/2610.41310
作者: ZhongYang Wu,ZhenFeng Gan,Di Zhao,XiaoJun Zhu,HouDe Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Variable interaction distances continuously degrade facial resolution in edge-based affective interaction, making low-resolution facial expression recognition (FER) difficult under limited computation. Full-face image or feature restoration incurs substantial overhead, whereas lightweight recognition pipelines may miss local cues that remain discriminative after degradation. We propose STAR-FER, a scale-and-task-adaptive selective restoration framework that balances recognition performance and resource constraints. A lightweight image-scale conditioned Gate jointly predicts a pixel-wise spatial ranking and a continuous restoration budget, then applies exact-support projection to obtain an input-dependent free-form mask. The mask-conditioned restorer retains complete facial context while concentrating sparse restoration and residual insertion on the selected support. Across seven degradation scales from x2 to x8 on the complete RAF-DB test set, STAR-FER achieves 80.92% mean accuracy and 71.64% Macro-F1, improving the low-resolution ResNet-18 baseline by 2.25 and 2.71 percentage points, respectively. It surpasses SR-FER by 1.04 accuracy points while requiring 8.84 GFLOPs, a 54.29% reduction, with 28.01% mean mask coverage, and maintains effective recognition at unseen non-integer scales. These results show that spatial-ranking and continuous-budget driven selective restoration reduces spatial modification and computation while maintaining low-resolution FER performance, offering an efficient path to resource-constrained affective interaction.
[CV-50] SketchDirector: Complex Scene Sketching via Agent ic Layout Reasoning and Detail Refinement
链接: https://arxiv.org/abs/2610.41260
作者: Yiqi Liang,Xiaodong Cun
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 13 figures
Abstract:Existing sketch generation methods often rely on image guidance and are largely limited to individual objects or simplified scenes, leaving text-guided complex scene sketching underexplored. We present SketchDirector, an agentic framework that mimics the human sketching workflow for automatic multi-concept scene sketching. Given a scene-level text prompt, SketchDirector first employs a large language model~(LLM) to decompose the scene into multiple draft assets, where each is represented by a set of Bézier curves. Then, an agentic reasoning loop iteratively plans and executes their spatial arrangement, guided by self-evolving memory. Finally, a detail refinement module leverages diffusion priors to enhance sketch quality while preserving geometric structure. To support future research and applications in multi-concept scene sketch generation, we further introduce the \textitMCSketch8K dataset, comprising editable vector assets, compositional layouts, and object-level manipulation annotations. Extensive experiments show that SketchDirector outperforms state-of-the-art baselines in semantic and compositional quality while effectively preserving overall contour organization during refinement. The project page is: \hrefthis https URLthis https URL.
[CV-51] ReVEAL: Eliciting Role-Aware Latent Visual Evidence from MLLM s for Reasoning Video Object Segmentation
链接: https://arxiv.org/abs/2610.41257
作者: Zhongyuan Bao,Cunjie He,Lejun Zhang,Zenglin Xu,Yanwei Fu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reasoning video object segmentation (ReVOS) requires segmenting the target specified by an implicit query throughout a video. Existing MLLM-based methods typically require costly cross-model alignment, tying MLLM training to a downstream segmentation model. We present ReVEAL, a decoupled thinking-with-video framework for ReVOS that elicits role-aware latent visual evidence from MLLM reasoning to directly guide segmentation. ReVEAL introduces Visual Evidence Tokens (VETs) into video reasoning to associate necessary visual regions or entities with target, background, or distractor roles, while a Dynamic Attention Layer Router adaptively aggregates VET-to-video attention across layers into role-specific grounding evidence. We train the MLLM to perform VET-augmented reasoning on our constructed ReVEAL-47K dataset, with a Contrastive Attention Grounding Loss as auxiliary supervision to apply role-specific constraints to VET attention and optimize layer routing for reliable latent grounding. At inference, ReVEAL identifies the target through this thinking-with-video process and fuses the resulting role-specific evidence to guide a frozen SAM2 in predicting target masks. Experiments on five benchmarks show that ReVEAL outperforms alignment-free baselines and remains competitive with alignment-based methods.
[CV-52] Hawkeye: Reading Illegible Text with Vision Language Models
链接: https://arxiv.org/abs/2610.41254
作者: Brian Tang,Kang G. Shin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: For datasets, code, models: this https URL
Abstract:While modern Vision-Language Models (VLMs) have been remarkably successful in general scene text recognition, they consistently struggle with the long-tail distribution of text that is degraded by motion blur or low resolution. These limitations are particularly apparent in egocentric applications like smart glasses, where text is often captured in noisy environments. In this paper, we introduce the \textbfBSTRB (Blurry Scene Text Recognition Benchmark), a novel benchmark of 8,398 noisy image samples with high-fidelity annotations derived from 1,699 real-world video sequences. To support this, we present \textbfHawkeye, a VLM fine-tuned on BSTRB to read and reason about illegible text by leveraging contextual priors and pixel-level alignment. Our results show that \name matches much larger VLMs on BSTRB overall and exceeds them on text at or below 20 pixels, in a single pass suited to real-time deployment. We characterize the resolution at which current VLMs begin to fail.
[CV-53] Coarse3D: Controllable and Faithful Sparse Structure Generation for Image-to-3D
链接: https://arxiv.org/abs/2610.41231
作者: Gaige Wang,Bowen Pan,Hao Ren,Jiangjing Lyu,Chengfei Lyu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Coarse-to-fine 3D-native generation relies heavily on the first-stage sparse structure, which determines the global orientation and overall geometry for subsequent refinement. However, existing coarse generators suffer from two key limitations: their orientation is difficult to control due to ambiguous semantic canonicalization, and their structures are often inconsistent with input images due to weak 2D-3D grounding. We propose Coarse3D, a sparse-structure generation model that produces orientation-controllable and structurally faithful voxel geometry from arbitrary unposed images. Coarse3D introduces three designs: a first-frame anchored canonical space that aligns generation with the first input view while preserving object-centric normalization; a dual-tower DiT that couples generation with auxiliary feed-forward reconstruction to learn spatially grounded features; and spatial-prior guided attention that regularizes cross-attention with pixel-to-voxel correspondence. Coarse3D can directly replace the coarse stage of existing pipelines. Experiments show that it substantially improves sparse structure quality and consistently enhances final 3D generation, achieving state-of-the-art performance.
[CV-54] nyAstral: Transferring Self-Evolving Harnesses to Tiny Generalist Manipulation Agents
链接: https://arxiv.org/abs/2610.41216
作者: Kehong Gong,Xiaoxu Zheng,Guanli Hou,Seunghun Lee,Mengqi Guo,Runjia Tan,Zhengyu Wen,Qi Wang,Xiaoyu He,Mingyuan Zhang,Michael Bi Mi
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 5 figures. Project page: this https URL
Abstract:Robot agents built on vision-language models depend on both the planner and its harness: the tools, feedback, memory, and recovery logic around it. Can improvements to a large teacher’s harness be carried into a compact agent with lower inference compute cost? We present TinyAstral, a two-stage approach. We first freeze an open-weight teacher and improve its harness through recursive self-improvement (RSI): frontier LLM agents read the teacher’s trajectories and propose harness edits, and each edit is accepted only if it passes a paired statistical test. We then freeze the final harness, collect teacher trajectories with reasoning and failure recovery, and distill a small student that replaces the teacher in the same harness. On RoboTwin, harness evolution raises the teacher’s success from 33.4% to 63.6%; the final 4B student reaches 62.8%. Across four harness versions, distilled 4B students score close to their corresponding teachers, and later versions yield stronger students, showing that harness evolution gains carry into compact models. The harness gains also transfer to other frozen planners without retraining. On identical episodes, the final 4B student outperforms open 4B agents, the open 27B harness-RSI model SenseNova-RoboRSI, and our own teacher model placed in three competitors’ harnesses. It stays close to its teacher on all three training benchmarks, and on three benchmarks never used for RSI or training it scores close to a 552B planner running the same harness. On the measured deployment episodes, it uses about one fifth of its teacher’s accelerator time.
[CV-55] Beyond Scarcity: When Encoder Adaptation Is Worthwhile
链接: https://arxiv.org/abs/2610.41213
作者: Zexing Zhang,Jichao Li,Tianyang Lei,Yiyin Zhang,Kewei Yang
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-tailed recognition often learns a shared representation before correcting the classifier for class imbalance. Once the classifier has been rebalanced, however, final accuracy does not reveal whether further encoder adaptation remains worthwhile. Standard imbalance benchmarks obscure this question because they jointly vary the number of distinct examples available to rare classes and the frequency with which those examples shape the representation. We introduce a controlled evaluation framework that separates unique support from representation allocation and estimates the incremental value of encoder updates through matched classifier-only and full-network continuations from a common corrected model. The framework combines stage-specific controls, multiple representation and adaptation objectives, whole-distribution metrics, and hierarchical inference over class assignments, support reconstructions, and optimization runs. Across the controlled study, the same low-support regime moves from one in which classifier-only training is adequate to one in which encoder adaptation yields a practically meaningful benefit solely as representation allocation becomes more imbalanced. The transition persists across classifier corrections, representation objectives, normalization and augmentation controls, and ImageNet-scale evaluation, while preserving frequent-class accuracy. Under the prespecified one-percentage-point operating margin, the two regimes are separated by simultaneous confidence bounds. These results show that benchmark construction can alter a post-correction training decision and that support size alone does not characterize the remaining value of representation adaptation.
[CV-56] From Evaluation to Evolution: Benchmarking Image Editing and Evolving Image Models
链接: https://arxiv.org/abs/2610.41210
作者: Jun He,Canmiao Fu,Ying Zhang,Zhipeng Huang,Chenyu Mu,Yazhen Xie,Chen Li,Jing Lyu,Weijia Li,Xiang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 58 pages, 42 figures
Abstract:Real-world image editing requires coordinating multiple visual transformations with content preservation and reference constraints, yet aggregate scores can readily obscure unmet local requirements. Effective evaluation must therefore identify local failures and translate them into priorities for subsequent model improvement. We introduce WeGenEditBench, comprising 2,740 evaluation instances and 148 task types across single-image, text-intensive, and multi-reference editing. Pre-generated checklists shared across models decompose each request into atomic requirements; item-level deductions and visual evidence are established first and then summarized into scores for edit execution, preservation, visual quality, and task-specific correctness. To support the experience-acquisition component of recursive self-improvement (RSI), we develop WeGenEvoStudio, a unified multi-agent harness for RSI-oriented learning that integrates model-conditioned failure diagnosis, retrieval-grounded data construction, supervised adaptation, and re-evaluation. Among 15 evaluated configurations, GPT-Image-2 leads single-image editing with an Overall score of 4.076, while Seedream-5.0-Pro leads text-intensive editing at 4.291. Evolution experiments across four generation and editing benchmarks demonstrate substantial targeted gains: on their report-selected Top-5 single-image targets, FLUX.2 [dev] improves Overall from 1.083 to 3.517, while Qwen-Image-Edit-2511 gains 13.2%. Together, our benchmark and harness provide a foundation for developing more reliable image models through interpretable evaluation and feedback-driven learning.
[CV-57] Shifting Focus by Reliability: Geometric Fusion Modulation for Robust RGB-T Detection WACV2027
链接: https://arxiv.org/abs/2610.41204
作者: Liuyin Shi,Chengqi Li,Wenbo He
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to WACV 2027
Abstract:RGB-thermal (RGB-T) object detectors combine visible and thermal imagery for robust detection, but signal corruption in either stream can substantially degrade accuracy. Restoration-based methods incur significant latency and do not consistently improve downstream accuracy, raising a more direct question: rather than restoring corrupted inputs, can we efficiently adapt the detector to their varying signal quality? Conventional PEFT offers an efficient route, but its input-independent corrections cannot track diverse test-time corruptions. Our controlled corruption sweeps show that corruption types have similar effects near clean conditions but diverge increasingly with severity, motivating corruption-aware, per-input adaptation. We therefore propose PG-FM (Prompt-Guided Feature Modulation), which adapts RGB-T object detectors to the estimated corruption type and severity of each modality. Under controlled RGB-T corruption simulations, PG-FM preserves clean-set performance, improves mAP50 by up to 20.8 points on FLIR and 19.9 points on KAIST, and transfers without retraining to unmodified, naturally acquired external RGB-T datasets.
[CV-58] Beyond CLIP: Calibrated (Few-Shot) Defect Detection With System One Decision Models
链接: https://arxiv.org/abs/2610.41199
作者: Fadel M. Megahed,Ying-Ju Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures
Abstract:Cameras and vision sensors are widely used for product and process inspection in manufacturing environments. Traditionally, this process required thousands of images of both nominal and defective products to train such models. Recent research shows that vision transformer (ViT) models, such as CLIP, can reduce this requirement to 50-100 images per nominal and defective class. However, collecting even 50 defective images can be difficult in high-yield or low-volume manufacturing processes. In this paper, we examine the utility of two recently proposed structured decision models (Jev and Clef-Flash) in applications using as few as k=2 reference images per class at inference. In the process, we provide three main contributions: (a) a framework for k-shot learning of these models for image classification/inspection applications; (b) an agentic approach for automatically refining the text descriptions that guide image classification in these decision models; and © an approach to calibrate the resulting defect probabilities to improve their reliability for inspection decisions. We test these contributions in five manufacturing case studies. Using two reference images per class, both Jev and Clef-Flash outperform CLIP’s test accuracies with the same references (0.75-1.00 [Jev] and 0.71-0.99 [Clef-Flash] vs 0.67-0.84 [CLIP]). Furthermore, we show that our agentic refinement of text descriptions significantly improves accuracy across 6 of 8 model-application combinations, and our calibration approach reduces the Brier score in 13 of the 15 model-application configurations. To support the reproducibility of our work, we provide our replication code and have created the R “decisions” package for evaluating additional models. We also provide a publicly available web app that allows practitioners to inspect their own images with no code.
[CV-59] FlowMoDE: Coarse-to-fine Flow Matching for Detail-aware Sim-to-Real Monocular Depth Estimation
链接: https://arxiv.org/abs/2610.41190
作者: Liangjing Shao,Wanhao Liu,Jinsong Lin,Zhiwei Fang,Hongliang Ren
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 Pages, 7 Figures, Under Review
Abstract:Monocular depth estimation is a fundamental capability for 3D spatial perception in robotic and intelligent systems. While data-driven discriminative methods have achieved compelling performance for monocular depth estimation, generative approaches can reconstruct fine structural details with stronger generalization. However, most existing generative diffusion models suffer from low inference efficiency caused by multi-step sampling, whereas single-step diffusion models struggle to recover high-fidelity depth maps. To mitigate these drawbacks, this paper presents FlowMoDE, a coarse-to-fine flow-matching-based framework for efficient, structure-aware monocular depth estimation supporting sim-to-real and cross-scene generalization. The core architecture first conducts coarse-level decoding conditioned on diffused features extracted from a pre-trained semantic encoder, and subsequently restores fine-grained structural patterns via expanded decoding layers. Trained exclusively on a single synthetic indoor dataset, our proposed method outperforms prior methods across five real-world indoor and outdoor datasets, yielding an average 14.1% reduction in relative error against state-of-the-art works as well as 9.9% reduction in chamfer distance for point cloud reconstruction. We further validate the generalization capability of FlowMoDE on realistic robotic scenes, with qualitative comparisons against state-of-the-art methods. The project page is this https URL
[CV-60] Mechanistic Interpretability for End-to-End Self-Driving
链接: https://arxiv.org/abs/2610.41185
作者: Manav Gagvani,Benjamin Namikas,Sivamurugan Velmurugan,Nysa Kumar,Shrey Sharma,Thomas Peterson,Pratyush Mathur,Hyunseo Chang,Rohan Paleja
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to the Conference on Robot Learning (CoRL) 2026. Project page: this https URL
Abstract:End-to-end (E2E) autonomous driving models achieve strong planning performance but remain largely opaque, limiting observability and trust. We apply mechanistic interpretability to E2E driving by training Sparse Autoencoders (SAEs) on the internal activations of a simple, single-camera end-to-end path planning model. Unsupervised clustering of SAE latents reveals interpretable structure aligned with route intent, vehicle speed, and braking behavior, recovering route intent with 98.3% accuracy without supervision. Through paired counterfactual image edits, we further show that specific SAE features are visually grounded and systematically respond to scene changes such as traffic light state and stop sign presence. Steering individual SAE latents produces predictable, monotonic changes in planner outputs including lateral displacement, curvature, and braking. Finally, we demonstrate a practical application of these representations: we build a lightweight adapter trained on SAE latents that reduces trajectory selection error by 3.44%, outperforming an equivalent adapter that omits SAE latents, confirming that the sparse representation captures useful information for interpretability. Code and visualizations are available at this https URL
[CV-61] MATS: A Multi-Agent Framework for Controllable and Reflective Tumor Synthesis
链接: https://arxiv.org/abs/2610.41159
作者: Zhongnan Shi,Keyang Zhou,Siyi Li,Qichao Ying,Xinchen Ma,Shuyi Gong,Wei Tang,Sheng Li,Zhenxing Qian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: IEEE BIBM 2026
Abstract:Synthesizing realistic tumors on medical images helps address data scarcity for clinical AI. Existing diffusion-based methods, however, lack fine-grained attribute-level controllability, closed-loop quality verification, and awareness of which morphological patterns are underrepresented in the target distribution. We present MATS, a multi-agent framework for text-controllable, cross-organ tumor synthesis. Given a target disease, MATS first profiles its morphological distribution over a fine-grained attribute space covering tumor geometry and appearance, and constructs a generation plan that amplifies underrepresented subtypes while keeping common ones dominant. It then localizes the target organ, proposes anatomically valid placement, and executes two-stage diffusion to produce the tumor mask and render texture, each conditioned on the corresponding attributes. A closed-loop reflective mechanism verifies attribute alignment at three checkpoints throughout synthesis and provides targeted feedback to guide re-generation when misalignment is detected. Trained exclusively on a single-source pancreatic neuroendocrine tumor dataset, MATS consistently improves downstream segmentation both in-domain and across LiTS, KiTS, and MSD-Pancreas benchmarks, whereas most prior generative augmentation methods fall below real-only training.
[CV-62] One Verdict Is Not Enough: Regression Testing Learned World Models and Their Test Harnesses
链接: https://arxiv.org/abs/2610.41130
作者: Junyu Huo,Zihao Wan,Ziqi Mao,Gouri Ginde
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases. Work on model updates already counts per-sample regressions, or negative flips, that an accuracy check hides. Two problems remain: a test can pass under one configuration and fail under another, and a harness fault can produce erroneous scores and wrong verdicts without a crash. We present TetraState, a regression-testing protocol for paired possible/impossible video tests. Each test runs under four fixed model-native configurations; its number of correct verdicts assigns one of four outcome classes. Inconclusive tests and failed executions are reported separately alongside the verdict rate. Revisions are compared by per-test outcome transitions, and the harness is versioned as part of the system under test. On all 506 IntPhys 2 Main tests and two heterogeneous systems, Cosmos 3 Nano and V-JEPA 2, two V-JEPA 2 harness revisions move modal accuracy by only 0.2 and 2.6 points yet change 11 and 214 test labels; in the second, accuracy rises although fewer tests are judged correctly, and 43 regressions keep the modal verdict, so a modal-verdict flip check misses them. These results outline a research agenda on configuration-level regression oracles for learned components.
[CV-63] MotorSense: A Video-EMG Dataset of Motoric Representations for Action Understanding WWW NEURIPS2026
链接: https://arxiv.org/abs/2610.41127
作者: Eadom Dessalene,Michael Maynord,Amir-Hossein Shahidzadeh,Botao He,Yasamin Tabatabaee,Ruoshi Liu,Ruohan Gao,Paul Pu Liang,Yiannis Aloimonos
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: NeurIPS 2026. 27 pages, 9 figures, 5 tables. Project page: this https URL
Abstract:Computer vision models learn hand action only from its visual consequences, without access to the underlying embodied motor signals. We address this absence with MotorSense, the first large-scale paired egocentric video and surface electromyography (EMG) dataset. MotorSense pairs bimanual EMG bands with egocentric video and yields synchronized video, bimanual EMG and IMU, and audio across 100 hours of unscripted manipulation. EMG offers a powerful scalable alternative to manual annotation: it captures finger movement, hand configuration, and the forces exerted between hand and environment as a dense signal free of occlusion and labeler bias, whereas manual annotation is costly, subjective, and unreliable under occlusion. We use MotorSense to learn sensorimotor alignment, producing an organization of action that is shared across the visual and motoric spaces, through cross-modal pre-training between video and EMG. The resultant vision models are then transferred to downstream tasks that benefit from sensorimotor alignment: in this case, action recognition, 3D hand reconstruction, and 3D contact reconstruction. We further demonstrate a generative application in which EMG motor signals are used to condition a video diffusion model, yielding videos of fine-grained hand manipulation. Across four action-centric computer vision tasks and eight popular video datasets, we demonstrate that sensorimotor alignment provides a stronger foundation for visual representation learning than conventional self-supervised learning methods over video, outperforming every baseline across all tasks and datasets evaluated.
[CV-64] CompoSET: A Controlled Single-Edit Benchmark for Vision-Language Compositionality
链接: https://arxiv.org/abs/2610.41118
作者: Laure Delisle,Pietro Perona
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Dataset: this https URL | Code: this https URL | 9 pages main text, 38 pages total
Abstract:Vision-language compositionality is the ability to identify which properties, relations, quantities, and states bind to which entities in both an image and a caption, and to match those bindings across modalities. Existing benchmarks evaluate this ability through image-text matching: a model ranks candidate image-caption match similarities and succeeds when the correct match comes on top. In such datasets, paired images differ by more than a single binding: scene, layout, and content all differ, with 97% of pixels changing on average. Such pairs do not isolate compositionality: results are confounded by global image differences. We introduce Compositional Single-EdiT (CompoSET), a benchmark dataset in which each base image-caption pair has an edited counterpart, or foil, that preserves the scene but differs by a single controlled edit, such as color, count, spatial relation, or object state. Base and foil images preserve naturalistic scene complexity yet are visually near-identical: only 8% of pixels change on average. Our dataset has many foils per base scene (~22) in order to separate scene difficulty from edit difficulty, a distinction one-foil-per-base benchmarks cannot make. CompoSET contains 1,776 instances across 80 naturalistic scenes, spanning 16 edit types in 4 semantic categories, with captions at 3 verbosity levels. We test 22 dual-encoder VLMs and find that all models perform at or below chance level on CompoSET, despite scoring well on prior benchmarks. This suggests that apparent progress under prior image-text matching protocols may be an artifact arising from confounds in benchmark design, and that compositional understanding in dual-encoder VLMs is far from solved. CompoSET addresses the confounds and offers a better controlled benchmark to measure future progress.
[CV-65] An Empirical Study on Learning Rooftop Solar Potential from Satellite Imagery Directly Without Explicit Geometry
链接: https://arxiv.org/abs/2610.41111
作者: Joshua Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Rooftop solar potential estimation often relies on LiDAR, digital surface models (DSMs), or pipelines that explicitly reconstruct roof geometry before estimating solar potential. We propose Direct Rooftop Solar Prediction (DRSP), an end-to-end formulation that maps satellite imagery and coarse regional sunlight information directly to application-level solar outputs. DRSP neither requires explicit geometric representations as input nor predicts roof orientation, slope, height, DSMs, or other geometric quantities as intermediate outputs, and therefore requires no geometric intermediate supervision. To enable large-scale study of this formulation, we develop a programmatic data-curation pipeline that converts per-panel information from the Google Solar API into usable-rooftop masks and spatial solar-energy targets, yielding two geographically distinct datasets containing approximately 60K rooftops from the U.S. Midwest and 87K from Texas. We demonstrate the feasibility of DRSP and systematically characterize the effects of geographic training distribution, model depth, image resolution, regional sunlight conditioning, and heatmap regression objectives. Overall, our results suggest that useful rooftop-scale solar information can be learned directly from 2D imagery without explicit geometric intermediate representations, offering a simpler path toward large-scale solar assessment. Our code and dataset are available at this https URL.
[CV-66] Grounding Image Composition with Label-Conditioned Visual Evidence WACV2027
链接: https://arxiv.org/abs/2610.41080
作者: Armin Dadras,Tingyu Lin,Robert Sablatnig,Franziska Proksa,Markus Seidl
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:Image composition labels such as center, diagonal, or rule of thirds describe visual structure rather than object categories. KU-PCP provides image-level labels and class-specific geometric elements, but does not show which visible regions people associate with each of its nine classes. We introduce KU-PCP Elements (KU-PCPE), a label-conditioned region-evidence benchmark for photographic composition. Given an image and a label already judged applicable, the task is to segment the visible regions that explain why the label applies. KU-PCPE contains 4,093 single-annotator training masks and a 1,327-task expert test split with three masks per image-label task. The annotations also include exploratory segment-versus-line ratings that describe whether region or semantic-guideline evidence better explains the label. A held-out expert reaches .867 Dice against the other experts, while the strongest deployable model reaches .664, a proposal oracle .672, and the best vision-language model (VLM) proposal selector .395. KU-PCPE exposes the gap between recognizing a composition label and grounding the human-aligned visual evidence that makes it plausible in a particular image.
[CV-67] Single-Annotation Multi-Hypothesis Segmentation with Semantic-Spatial Residual Banks WACV2027
链接: https://arxiv.org/abs/2610.41069
作者: Armin Dadras,Robert Sablatnig,Franziska Proksa,Markus Seidl
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at WACV 2027
Abstract:Multi-rater segmentation datasets show that a single image can admit several valid masks, yet most training sets expose only one annotation per image. We study single-annotation multi-hypothesis segmentation: each training image exposes one fixed mask, while the model must return a finite bank that covers held-out human annotations at test time. Our experiments reveal a representation tradeoff. Banks learned directly from images recover variations in local boundaries and extent but lose structural hypotheses, whereas frozen semantic features preserve structural grouping but underrepresent spatial variation. We introduce the Semantic-Spatial Residual Bank (SSRB), which freezes a semantic bank trained with balanced assignments and learns a residual from the image for each hypothesis. Across five medical endpoints, SSRB improves coverage of held-out masks at K=8 over a Probabilistic U-Net under the same one-mask constraint. On LIDC and LIDC-hard, it also produces substantially more distinct useful masks. On BSDS, it preserves the semantic bank’s performance in grouping regions. Its advantage grows as more masks are returned, showing that residuals learned for corresponding heads improve coverage of local annotation variation while preserving how regions are grouped.
[CV-68] Segment Anything Pancreas: A Unified MRI Segmentation Model for Pancreatic Cancer Radiotherapy
链接: https://arxiv.org/abs/2610.41063
作者: Zach Eidex,Mojtaba Safari,Kundan Thind,Parag J. Parikh,Zhen Tian,Xiaofeng Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Background: Limited tumor annotations and incomplete labels across MRI datasets hinder unified segmentation of pancreatic tumors and organs at risk (OARs) for MRI-guided radiotherapy. Purpose: We propose Pancreas-MedNeXt, a unified model that segments the whole pancreas, its subregions, tumors, and surrounding OARs from one MRI volume. Methods: The model uses a shared MedNeXt encoder-decoder with supervised whole-pancreas and subregion pretraining, followed by multitask fine-tuning. Three output heads predict the pancreatic subregions, tumor, and OARs (stomach, duodenum, kidneys). An annotation-aware loss allows training on partially labeled data without treating unannotated structures as background. Pancreas and tumor were evaluated by three-fold cross-validation on 142 PANTHER patients (92 diagnostic, 50 MR-Linac), subregions and OARs on Cyst-X and AMOS test sets, and pancreas and OARs on 33 external T1-weighted TotalSegmentator scans. Comparisons with nnU-Net, MedNeXt, VISTA3D, and Swin UNETR used the Dice similarity coefficient (DSC) with paired bootstrap 95% CIs. Results: Pancreas-MedNeXt achieved the highest mean pancreas and tumor DSC on diagnostic and MR-Linac MRI. Tumor DSC was 58.41% and 42.55%, exceeding task-specific MedNeXt by 4.94 (95% CI: 0.28-9.45) and 5.89 (95% CI: 2.37-9.72) percentage points, respectively. Whole-pancreas DSC was 85.73% and 70.66%. Pretraining improved head, body, and tail DSC by 2.01, 3.00, and 3.78 percentage points over training from scratch. It also achieved the highest DSC for the pancreas, duodenum, and right kidney on AMOS, and for all five organs on external MRI, exceeding the next-best method by 4.92-8.85 percentage this http URL: A single Pancreas-MedNeXt model segments tumors, pancreatic subregions, and OARs on diagnostic and MR-Linac MRI, which could streamline contouring for pancreatic cancer radiotherapy.
[CV-69] Dreams Are Cheaper Than Flights: Curated World-Model Data Improves Aerial Detection
链接: https://arxiv.org/abs/2610.41030
作者: Rugved Katole,Vedant Patil,Jenna Kline,Christopher Stewart
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 6 pages, Corl 2026
Abstract:Robot perception is increasingly data driven, yet field data is slow, costly, and sometimes disruptive to collect. Video world models offer a cheaper source: seeded with real frames, they extend a recorded scene into new footage with the deployment’s sensor, altitude, and environment. We ask whether such synthetic data matches the utility of real field data and whether detectors trained with it perform in the field. Seeding a 7B-parameter world model with wildlife drone video, we more than double the training set with curated frames. On held-out videos from the same drones, mAP@50–95 rises 4.3% when these frames are added to real data and 10.3% when they replace it, and up to 33.5% on an urban benchmark. Although an expert rejects an estimated 87.5% of generated clips, the data costs about \ 1,100 of GPU time, against more than \ 3,200 of field labor alone for a less useful real training set. Synthetic data thus shifts data costs from fieldwork to compute.
[CV-70] Prior-Guided Single-Exposure HDR Novel View Synthesis via Base-Gain Representation
链接: https://arxiv.org/abs/2610.41011
作者: Armin Shafiee Sarvestani,Sheyang Tang,Jialu Xu,Zhou Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Single-exposure high dynamic range (HDR) novel view synthesis (NVS) recovers HDR scenes from low dynamic range (LDR) views captured at a single exposure and estimates camera response function (CRF) for LDR re-exposure. Without HDR ground-truth, using single-exposure LDR views as the sole source of direct supervision underconstrains HDR reconstruction and cannot ensure its faithful recovery, motivating the need for learned LDR-to-HDR reconstruction priors. Moreover, single-exposure LDR views supervise CRF only over a limited range, necessitating a learnable prior for reliable re-exposure beyond it. We propose PriorGS, a 3D Gaussian Splatting framework that addresses both challenges by (1) guiding HDR recovery with pseudo-HDR supervision from 2D single-exposure HDR reconstruction methods while anchoring it to input LDR views through a closed reconstruction loop; and (2) regularizing CRF modeling with a learnable prior that guarantees monotonicity and promotes plausible responses. Yet cross-view inconsistencies in independently generated pseudo-HDR targets impair scene reconstruction when HDR is encoded directly as Gaussian’s color attribute. We therefore introduce a 3D base-gain HDR representation that factors HDR radiance into an LDR base and an overlaid 3D gain field, promoting robustness to such inconsistencies and enabling 3D HDR appearance editing. Extensive experiments demonstrate PriorGS’s substantial superiority over baselines.
[CV-71] RADI3N: GeometRic ImAge EDiting via ParametrIc 3D CoNtrol
链接: https://arxiv.org/abs/2610.40973
作者: Abdul Wasi,Akhil Gorugantu,Mahesh Bhosale,Anjan Dutta,Junsong Yuan,Sauradip Nag,David Doermann
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent diffusion-based methods have substantially advanced image editing, yet parametric 3D control at the part level remains challenging. Existing approaches either operate on whole objects with manual rigging, or revert to 2D handles at the part level. They also struggle to model appearance and texture under large transformations, leading to unrealistic outputs in geometry-intensive edits. To address these limitations, we introduce RADI3N, a framework for semantically-grounded part-aware object manipulation with parametric 3D control. Our CS-ARAP deformation module suppresses rotation-field discontinuities at part boundaries via neighborhood covariance smoothing, producing structurally consistent deformations under complex part-level edits. We further introduce an inversion-free selective texture transfer mechanism that suppresses high-frequency positional biases and scales up low-frequency semantic cues within cross-image attention. This eliminates strict spatial bias and supports semantic matching, allowing source appearance to follow the edited part onto its new geometry. Experiments demonstrate that RADI3N achieves state-of-the-art performance in geometric fidelity, viewpoint consistency, and controllability.
[CV-72] alkFlow: Structured Latent Flow Matching for Speech Driven Holistic Motion Generation
链接: https://arxiv.org/abs/2610.40972
作者: Xinmu Wang,Fumi Honda,Xiang Gao,Xianfeng Gu,Xiyun Song,Heather Yu
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Holistic co-speech motion generation aims to synthesize coordinated facial expressions, hand gestures, and body movements from speech, which is challenging due to heterogeneous part-specific dynamics, long-range temporal coherence, and cross-part coordination. Directly modeling high-dimensional motion can be unstable, while overly compact or unstructured latent spaces may lose fine-grained motion details. We present \textbfTalkFlow, a structured latent flow framework for speech-driven holistic motion generation. TalkFlow learns a continuous three-stream latent representation for upper-body motion, lower-body motion, and facial expressions, preserving part-specific dynamics without forcing heterogeneous motion components into a single latent space. To maintain holistic coordination, lightweight cross-stream interactions are incorporated in both latent representation learning and flow-based generation. Conditioned on multi-level speech features, TalkFlow performs speech-conditioned latent flow matching, learning a continuous transport process from source latents to realistic motion latents. Experiments on the BEAT2 dataset demonstrate strong performance across facial expression accuracy, body gesture quality, and speech-motion synchronization. Qualitative comparisons and user studies further show that TalkFlow generates more natural, expressive, and speech-aligned holistic motion than state-of-the-art methods. The project page is available at this https URL.
[CV-73] CAMEL: Cross-Architecture Masked Embedding Learning for Ultra-Lightweight Retinal Foundation Models
链接: https://arxiv.org/abs/2610.40954
作者: Andre Guarnier De Mitri,Giovani Decico Lucafo,Miller Matheus Lima Anacleto Rocha,Diego Furtado Silva
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 6 figures, pre-print
Abstract:Recent progress in deep learning has substantially advanced automated grading of ophthalmologic pathologies, supported by large annotated datasets and improved representation learning. However, a significant gap remains between state-of-the-art performance and clinical scalability. In particular, foundation models have reshaped image-based medical screening and diagnosis. These large-scale architectures trained on extensive datasets learn rich, generalizable feature representations that can be adapted to a wide range of downstream tasks. In ophthalmology, RETFound, one of the most relevant foundation models for retinal images, performs strongly across classification, segmentation, and risk prediction tasks. Despite their effectiveness, foundation models are often computationally prohibitive, hindering their deployment in real-world clinical environments where hardware constraints and latency are critical. To bridge this gap, we introduce CAMEL (Cross-Architecture Masked Embedding Learning), a novel cross-architecture distillation framework that uses an asymmetric masking strategy during embedding learning to effectively transfer knowledge from massive teacher foundation models to compact, lightweight student models. Our experimental evaluation across four datasets shows that CAMEL preserves accuracy while drastically reducing computational complexity, enabling lightweight yet highly accurate models suitable for resource-limited clinical settings.
[CV-74] AVID: A Weakly Aligned RGB-Infrared Dataset and Perturbation Benchmark for Tiny Aerial Targets
链接: https://arxiv.org/abs/2610.40949
作者: Dharini Raghavan,Vishnu Rao,Sethu Selvi S
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 8 figures
Abstract:Long-range detection of small drones is complicated by their visual similarity to birds and by the limited detail available from ground-based cameras. Combining visible and infrared observations offers complementary information, but their usefulness depends on target size, cross-modal alignment, and image quality. We present AVID, a benchmark for studying these factors using real RGB-infrared bird-drone surveillance footage originating from the Drone Detection Dataset and curated for the IEEE ICIP 2025 Video and Image Processing Cup. AVID comprises 32,040 corresponding frame pairs from 115 videos, with 35,952 existing bird and drone annotations at 320\times256 resolution. Targets occupy an unusually narrow size range: 68.1% have a geometric-mean bounding-box side of 8–16 pixels, and 99.0% meet the COCO small-object criterion. To characterize the geometric difficulty of combining the modalities, we introduce an object-relative alignment ratio and an alignment burden curve. In a sample of 300 frame pairs, 32.3% have estimated global translations greater than 5 pixels; among the 264 pairs with matching annotations, 16.3% exceed one median target side. We further introduce AVID-C, which simulates seven families of image degradation at five severity levels independently in RGB, infrared, or both. A fixed-feature analysis examines how scale, degradation, and added cross-modal shifts affect fitted bird-drone class overlap. At 12–16 pixels, the Bhattacharyya error bounds are 0.148 for RGB, 0.198 for infrared, and 0.046 for their combined features. These values describe separation in the specified feature and density models, not detector accuracy. Together, AVID and AVID-C support scale-aware, alignment-aware, and sensor-specific studies of tiny aerial targets.
[CV-75] UFMF: Radar-Guided Kinematic Priors for Real-Time 4D Occupancy Forecasting
链接: https://arxiv.org/abs/2610.40947
作者: Zeping Zhang,Zhuoya Zhao,Robert Laganière
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-time 4D occupancy forecasting requires reliably extrapolating both ego motion and surrounding actor motion under strict latency budgets. Recent occupancy world models improve long-horizon fidelity through generative decoders and 3D volumetric modeling, yet their computational cost makes real-time deployment challenging. Light-weight BEV forecasters meet latency requirements but often blur or suppress fast-moving actors at longer horizons. We present UFMF (Ultra-Fast Multi-modal Forecaster), a real-time forecaster that keeps an attention-free diagonal state-space temporal core while injecting targeted kinematic and geometric structure where lightweight models typically fail. A radar-guided velocity-aware dynamic prior (VADP) decomposes future displacement into a linear extrapolation plus a learned horizon-dependent residual. The resulting per-cell flow fields transport a compact dynamic latent subspace during rollout. Two auxiliary training constraints complement this branch: world-frame dynamic supervision enforces inter-horizon trajectory coherence, and class-conditioned vertical-profile supervision regularizes BEV-to-3D lifting. UFMF operates at 122 FPS on a single RTX 4090 and reaches 0.45 fast-displacement mIoU on nuScenes, outperforming recent occupancy world models at a fraction of their computational cost.
[CV-76] Let LiDAR Decide Where to Look: Geometry-Measured Token Selection for Efficient Sparse Latent World Models
链接: https://arxiv.org/abs/2610.40935
作者: Zeping Zhang,Zhuoya Zhao,Robert Laganière
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-centric autonomous driving models encode thousands of image tokens per frame, yet most depict sky, buildings, or empty road—content irrelevant to planning. Worse, these tokens dilute the planner’s attention: we show that randomly discarding 75% of tokens already outperforms the dense model, because the planner can concentrate on the remaining driving-critical content. Building on this finding, we present Geometry-Aware Token Selection (GATS), which uses LiDAR and radar point projections to select a small fraction of image tokens before the vision backbone runs. Because the selector operates on measured 3D geometry rather than visual features, the backbone encodes only the selected patches, cutting end-to-end FLOPs by up to 18\times . Each kept token receives a 3D anchor from its LiDAR point and is fed to a self-supervised latent world model that predicts future features and 3D displacements without box annotations. At 25% budget, GATS achieves a 3-sec L2 displacement error of 0.520 m on nuScenes and an EPDMS of 88.9 on NAVSIM—surpassing the dense baseline (0.573 m / 85.9) at 6.6\times fewer total FLOPs. At an extreme 10% budget (24 GFLOPs, 18\times reduction), it still beats the dense baseline on both nuScenes (0.537 m vs. 0.573 m L2) and NAVSIM (87.9 vs. 85.9 EPDMS). On 739 intent-critical scenarios, GATS covers 95.8% of hazardous vehicles and achieves a 0.09% collision rate—73.5% lower than the dense model (0.34%).
[CV-77] No Channels Left Behind: Understanding Diffusion Acceleration through Residual Gates
链接: https://arxiv.org/abs/2610.40914
作者: Junha Park,Jaemyung Yu,Jaehui Hwang,Jong-Seok Lee,Dongyoon Han
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:Training acceleration methods for diffusion transformers, such as REPA and dispersive regularization, are commonly explained by additional supervision, spatial-structure priors, or improved feature geometry. We revisit this view and uncover a striking internal change: the AdaLN-Zero residual gate \alpha , which directly controls how conditioned updates enter the residual stream, is substantially reorganized by these methods. This observation motivates our hypothesis that acceleration arises, at least in part, from reshaping the residual gating pathway rather than solely from the feature structures explicitly imposed by auxiliary objectives. Supporting this view, accelerated models are markedly more sensitive to perturbations of \alpha , and their gate geometry reflects semantic relationships among classes. We further introduce the normalized participation ratio (nPR) to characterize the channel-wise organization of the gates and find that, across REPA teachers, projector architectures, and radial-objective targets, broader gate participation accompanies better generation quality. Guided by this insight, we propose a simple teacher-free radial objective that promotes broader gate participation through feature-norm regularization, without pairwise feature comparisons. On ImageNet 256\times256 with SiT-B/2, our method improves FID over dispersive loss while increasing mean gate participation over timesteps and classes, further supporting the link between gate organization and training acceleration. Our results identify residual-gate reorganization as a measurable axis of diffusion training acceleration and suggest a simple direction for further acceleration.
[CV-78] Coupled or Independent? Fault Coupling in Agent ic Medical Vision Systems
链接: https://arxiv.org/abs/2610.40913
作者: Nishi Kanta Paul,Md Shihabul Islam Shovo,Md Mostafijur Rahman,Jamil Fayyad
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: 9 pages, 3 figures, 5 tables
Abstract:Vision-language agents equipped with tools expose intermediate outputs for inspection and comparison with the model’s reasoning, which may offer safety benefits over single-shot models. This paper tests that potential benefit when the sensor reading itself is untrusted, so that the tool and reasoning channels share a common visual fault. We consider a dermatology agent based on segmentation, contrastive retrieval, and an iterative vision-language reasoner and define a coupling taxonomy that distinguishes single-channel errors from joint errors that agree or disagree. On PAD-UFES-20, we show that typographic injection produces detectable directional retrieval capture, whereas physical corruption does not. Crop-first retrieval preserves the payload at a readable scale and produces 55% joint wrong agreement across all cases (71% of faulty cases); full-image retrieval produces 35% (43%). Under a binary decision, if both channels are wrong, their predictions must agree, so hard-label channel comparison cannot detect joint failures, limiting disagreement recall to 0.29 (crop-first) versus 0.57 (full-image). The direction of the order effect is reproduced on the independent HIBA test set.
[CV-79] Sparse Autoencoders for Expert-Assisted Interpretation of Histopathology Foundation Model Embeddings
链接: https://arxiv.org/abs/2610.40905
作者: Šimon Šmída,Wanda Benesova
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Histopathology foundation models learn rich embedding representations, but their internal structure remains difficult to inspect. Concept-level interpretability is particularly relevant in computational pathology because pathologists reason through recurring morphological and tissue-level patterns, including tissue architecture, cellular morphology, staining variation, and artifacts. In this work, we investigate whether sparse autoencoders (SAEs) can decompose embeddings from histopathology foundation models into sparse latent features suitable for expert-assisted interpretation. We train vanilla and Batch-TopK SAE variants on NCT-CRC embeddings, using CONCH as the primary foundation model and UNI2-h as an additional robustness evaluation, and characterize the resulting latent features through reconstruction fidelity, sparsity, coherence, label enrichment, and downstream class-signal preservation. The learned sparse representations preserve most downstream tissue-class information, and their latent features retrieve more coherent and label-concentrated patch groups than raw embedding dimensions or PCA components. Discussions with pathology experts further motivated a WSI-level latent visualization workflow that places feature activations into larger tissue context. Overall, the additional UNI2-h evaluation suggests that the observed behavior is not specific to CONCH, supporting the use of sparse autoencoders for expert-assisted analysis of histopathology foundation-model embeddings, while emphasizing that latent features should be treated as candidate concepts requiring pathological assessment rather than automatically validated histopathological concepts.
[CV-80] AquaProbe: Efficient Adaptation of Frozen Vision Models for Underwater Images
链接: https://arxiv.org/abs/2610.40900
作者: Md Shihabul Islam Shovo,Nishi Kanta Paul,Trung Dong,Xiaomin Lin
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 10 pages, 4 figures, 3 tables
Abstract:Reliable visual perception is essential for safe autonomous underwater exploration. Yet vision models pretrained on terrestrial images become less reliable underwater, where turbidity, backscatter, and wavelength-dependent attenuation alter scene appearance. Existing adaptation methods either spread adapters equally across the encoder depth or select layers using the divergence between two fixed domains. In this paper, we introduce AquaProbe for measuring how the location of the domain gap changes with water type and imaging range. AquaProbe separates newly introduced divergence from that accumulated through a frozen vision encoder using paired clean and physically rendered images and allocates a fixed LoRA budget according to the measured profile. The segmentation mean Intersection-over-Union (mIoU) for the proportional LoRA rank allocation, based on the measured profile, is 70.1; this is a better result than uniform LoRA rank allocation by 1.9 mIoU points while having the same parameter budget of 0.59 million adapter parameters. The three-way router, conditioned on estimated condition clusters, achieves 70.6 mIoU on the standard SUIM split and 63.4 AUDRC under held-out degradation.
[CV-81] Spatial Atlas-Informed Loss Ensembles for Whole-Body PSMA PET/CT Metastasis Segmentation
链接: https://arxiv.org/abs/2610.40834
作者: Grant N. Chau,Bashirul A. Biswas,Biratal Wagle,Matthew E. Maeder,Michael S. Leapman,James B. Yu,Indrani Bhattacharya
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 6 figures
Abstract:Existing whole-body PSMA PET/CT prostate metastasis segmentation methods generally rely on images alone, without explicitly incorporating where metastases tend to occur. We investigate whether a population metastasis atlas can improve lesion detection when integrated into model training. We constructed voxel-wise mean and standard deviation maps from 375 expert-annotated training scans and used them to weight the cross-entropy loss of an nnU-Net. The additional penalties for missed lesions and false positives depend on spatial atlas statistics and diminish as predictions become confidently correct. Seven configurations were compared on validation data; three were combined by majority voting into SAILS (Spatial Atlas-Informed Loss enSemble). We compared SAILS with a baseline nnU-Net and inference-time atlas fusion on AutoPET~III (110 disease-positive cases) and Deep-PSMA (100 external cases). No atlas or registration is needed at inference. Results: On AutoPET~III, SAILS improved whole-body Dice (0.531 vs. 0.520; p=0.006), reduced missed-lesion volume (9.5 vs. 12.1 cc/case; p0.01), and increased pooled lesion sensitivity (0.857 vs. 0.843; p=0.004). On Deep-PSMA, SAILS detected 126 additional reference lesions (sensitivity 0.769 vs. 0.751; p0.001), without a significant whole-body Dice change (-0.009; p=0.36). High-risk bone Dice improved in both cohorts (0.576 vs. 0.552 and 0.667 vs. 0.653; both p0.01). False-positive volume increased by 2.3 and 18.2 cc/case, respectively (p0.001). Atlas-weighted training improves lesion detection, especially in high-risk bone, without test-time atlas registration, but increases false positives.
[CV-82] EgoHSI3R: Online 4D Hand-Scene Interaction Reconstruction from Egocentric Video
链接: https://arxiv.org/abs/2610.40832
作者: Mingzhi Pei,Zhangchen Ye,Ruoqu Chen,Liu Cao,Mengdi Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Egocentric videos offer an abundant source of hand-scene interactions for embodied learning, which requires recovering hands, camera, and scene consistently in a shared 3D world. Existing methods focus on either camera-frame hands or world-space hand trajectories, without jointly recovering the scene pointmaps or interaction information. Building a complete reconstruction from such specialized predictions therefore typically requires per-sequence optimization. We propose EgoHSI3R, a unified feed-forward framework for online 4D hand-scene interaction reconstruction from egocentric monocular video. To jointly recover these components, it maintains a recurrent scene representation that accumulates geometric evidence over time and provides a shared world space and scale. We introduce hand-centric prompts that fuse pretrained hand priors and local scene context, while MANO vertex queries attend to scene features to predict dense per-vertex contact. These designs enable a single network to jointly estimate camera motion, dense scene geometry, world-space bilateral MANO hands, and per-vertex hand contact. Despite operating causally, EgoHSI3R achieves the lowest world-space hand joint error on ARCTIC among the evaluated methods, while offering faster inference than existing world-space hand reconstruction pipelines and competitive camera and scene reconstruction accuracy.
[CV-83] Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes
链接: https://arxiv.org/abs/2610.40801
作者: Jiahe Wu,Bing Cao,Xingyue Guo,Ruipu Zhao,Beiming Yu,Jiekang Feng,Qinghua Hu,Pengfei Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 13 figures
Abstract:The reasoning capabilities of vision-language models (VLMs) can be limited by incomplete or inaccurate visual observations in the general domain. In unmanned aerial vehicle (UAV) scenes, small targets, subtle attributes, and changing viewpoints further complicate perception and understanding. Therefore, we introduce Drone-PUR, a dataset and benchmark organized around visual clue discovery, interpretation, and integration, corresponding to the three capability dimensions of perception, understanding, and reasoning. Reasoning samples are designed to require models to combine multiple visual clues to derive visually grounded conclusions. Its 11 tasks provide 175k training and 20k test samples spanning single images, image sequences, cross-modal images, and multiple viewpoints. All test samples undergo human review. Evaluating 22 general-purpose VLMs reveals persistent limitations in spatial judgment and cross-modal and cross-view reasoning. Across models, stronger perception is associated with better reasoning, while single-task fine-tuning further reveals selective bidirectional transfer between these capabilities. To examine domain adaptation, we develop UAV-PUR from Qwen3-VL through staged fine-tuning with auxiliary supervision. Its 4B and 8B variants improve overall multiple-choice accuracy by 18.33 and 17.52 percentage points over their pretrained counterparts, respectively. The dataset and project resources are available at this https URL.
[CV-84] DyCraft: An Agent ic System for Long-Horizon 4D Scene Generation
链接: https://arxiv.org/abs/2610.40754
作者: Jeff Tan,Hang Dai,Zeqian Long,Yanzhe Lyu,R. Kenny Jones,Shangzhe Wu,Chen Geng,Jiajun Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:While frontier coding agents can generate high-quality 3D scenes and articulated assets, they struggle to create coherent long-horizon animations. When equipped with general-purpose harnesses, they frequently produce physically implausible outputs, generating objects that interpenetrate, lack physical support, or move unnaturally. Such bare agents struggle to repair these errors because they default to scripting the full animation in one pass, making it difficult to verify their work and disentangle complex spatiotemporal constraints. Drawing on the practice of human animators, we introduce DyCraft, a system for animated 3D scene generation that breaks animation into steps that coding agents can more reliably solve. DyCraft first expands the user’s prompt into a detailed plan and builds a static scene. It then creates a 3D storyboard of key animation poses, where each frame is a static 3D scene that the agent can build and debug in isolation. Storyboarding decomposes the problem into pairs of verified start and end states, allowing the agent to author local motion between them with a collection of motion authoring tools. We evaluate DyCraft on 24 long-horizon, multi-shot animation tasks covering humanoid robots, human and animal characters, and physical mechanisms. Compared with a native GPT-6 agent in Codex and prior agentic baselines, DyCraft substantially reduces physical violations and generates higher-quality animated 3D scenes that are more often preferred by human raters.
[CV-85] PathoJev: Efficient Pathology Decisions from Shared Visual Context
链接: https://arxiv.org/abs/2610.40752
作者: Chengyang Zhang,Wenchuan Zhang,Bo Li,Xinyu Liu,Jiaming Yang,Mengran Li,Jie Chen,Wei Ju,Yuhao Yi,Hong Bu,Jiancheng Lv
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Pathology workflows require multiple visual judgments about the same slide, from identifying tumor type to assessing grade and invasion. Generating and parsing a separate textual response for each structured question adds latency between visual evidence and usable decisions. Building on the System One paradigm, we introduce PathoJev, a pathology model that directly predicts answer probabilities for whole-slide and region-level assessment. PathoJev encodes each slide once and evaluates independent question suffixes in batches over the shared visual context. Reusing the hybrid backbone’s recurrent states further limits the computation that grows with slide-context length for each additional question. Across three whole-slide benchmarks, the 9B and 27B models achieve mean accuracies of 77.2% and 78.1%, exceeding SlideChat by 5.4 and 6.3 percentage points. Both scales also maintain competitive accuracy on five ROI benchmarks. On WSI-Bench, the 9B model runs 9.1x faster than SlideChat. Shared context accelerates eight-question workloads by 6.9x and 5.9x over separate prefix encoding, rising to 11.2x and 8.9x for 64 questions. These results provide a reusable probabilistic interface for combining multiple visual judgments in whole-slide and local tissue assessment. Code: this https URL.
[CV-86] IGDA-ICMH: an Importance Guided Feature and Network Dual Adaptation Framework towards Image Compression for Machine and Human Vision
链接: https://arxiv.org/abs/2610.41924
作者: Yanbo Gao,Xinyu Zhao,Shuai Li,Xun Cai,Mao Ye,Li Li
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Published in IEEE TCSVT
Abstract:With the surge of machine vision applications, image compression for both machine and human vision (ICMH) has become an important research area. Existing studies mainly focus on task-specific adaptation or multi-task joint optimization, yet they often overlook the unequal contributions of different features and network layers to downstream machine vision tasks. To address this issue, we first quantitatively investigate the impact of different feature and network layers on machine vision performance, providing a principled understanding of their importance. Based on this analysis, an Importance Guided feature and network Dual Adaptation (IGDA) framework is developed for ICMH. It adopts implicit neural representation as the basic architecture and designs an efficient adaptation structure with both feature and network weight adaptation. An Importance-Guided Network Adaptation (IGNA) module is developed with a low-rank adaptation strategy to selectively adjust key network layers according to their importance in different tasks. An Importance-Guided Feature Adaptation (IGFA) module is developed with a low-rank and Kronecker product-based transform to explore the non-local property of the features in order to reduce the number of adaptation parameters. Extensive experiments on representative machine vision tasks, including image classification and object detection, demonstrate that our IGDA-ICMH framework achieves significantly better performance than the existing implicit neural representation-based methods for ICMH.
[CV-87] Enhancing Medical Lesion Segmentation via Several Simple Modules
链接: https://arxiv.org/abs/2610.41652
作者: Ruichen Li,Zhao Yao
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 16 pages, 6 figures, 5 tables
Abstract:This work introduces three lightweight enhancement modules for medical lesion segmentation to improve clinical applicability, particularly under data-scarce and computationally constrained environments. Current mainstream methods struggle with the lack of high-quality annotations for rare pathologies, which often leads to severe morphological distortions during inference, despite achieving seemingly ideal DSC scores. Furthermore, the isolated development of individual segmentation tasks restricts cross-domain methodological innovation, underscoring the need for leveraging shared knowledge across different lesions within the same anatomical region. However, constrained access to historical data and annotations hinders efficient knowledge extraction and triggers catastrophic forgetting when adapting to new classes. To address these challenges, we first propose DT-Morph Combined Loss with continuous spatial pooling to optimize differentiable topological structures and boundary continuity. Second, we introduce 3D Group Prompt, which extracts domain-universal knowledge via curated volumetric image-mask alignment prompts together with text prompts built on SAT baseline. Finally, we replace conventional output layers with Many-to-One Conditional Segmentation Head equipped with synonym-resolved routing, which brings negligible computational cost and enables fast incremental learning for new categories without sacrificing performance on old tasks. Ablation studies on abdominal and extra-abdominal datasets covering both organ and lesion segmentation verify the efficacy of our modules.
[CV-88] opoHyperClinicalGraph: A Geometry and Topology-Aware Multimodal Framework for Cancer Survival Prediction
链接: https://arxiv.org/abs/2610.41556
作者: Noaman Khan,Zubair Shah
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We developed TopoHyperClinicalGraph, a multimodal framework combining hyperbolic tissue geometry, persistent topology and clinical graph learning for cancer survival prediction. Pre-extracted UNI2-h features are reduced from 1,536 to 1,024 dimensions using training-fold PCA. A pathology backbone combines hyperbolic neighborhoods with Lorentz-centroid attention pooling. Once frozen, its survival logits are adjusted using persistence descriptors from up to 64 landmarks per slide and sampling controls. A five-node clinical graph then learns a further correction from age, sex, stage, T and N. Five-fold patient-level evaluation covered eight TCGA and CPTAC cohorts with 2,364 patients. Mean validation-selected C-indices for TopoHyperClinicalGraph versus pathology-only spatial PatchGCN were 0.7563 versus 0.7386 in COADREAD, 0.6220 versus 0.5998 in HNSC, 0.6755 versus 0.6535 in BRCA and 0.6409 versus 0.6176 in STAD. CPTAC scores were 0.5984 in HNSC, 0.6060 in PDA, 0.8055 in CCRCC and 0.7192 in LUAD. CPTAC-HNSC used TCGA-initialized adaptation; other CPTAC backbones were trained within their cohorts. Geometry accounted for most pathology-only gains, with smaller persistence effects. Clinical graph fusion exceeded its matched node-wise control in five cohorts, tied in one and scored lower in two; clinical-only prediction exceeded fusion in three CPTAC cohorts. The framework supports separate evaluation of geometric, topological and clinical contributions. All scores were validation-selected; independent testing is needed to establish generalizability. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2610.41556 [eess.IV] (or arXiv:2610.41556v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2610.41556 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-89] PRIN: A Perceptual-Structural Retinex-Inspired Network for Low-Light Image Enhancement
链接: https://arxiv.org/abs/2610.41338
作者: Qiyi Xie,Wei Chen Ng,Ziyao Li,Yuning Wu,Huasen Wu,Tianshi Miao,Jian Ding Tan
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Low-light image enhancement (LLIE) focuses not only on visibility recovery but also on preserving structurally meaningful content and perceptual naturalness. Existing learning-based methods may achieve favorable pixel-wise fidelity while still producing over-smoothed textures, color deviations, or spatially incoherent illumination. Retinex-inspired methods provide interpretable decomposition into reflectance and illumination components, however, the asymmetric restoration roles of these components are rarely encoded through branch-specific frequency-domain refinement. To address this issue, we propose a Perceptual-Structural Retinex-Inspired Network (PRIN), which integrates Retinex priors with restoration-role-aware enhancement. PRIN employs a shared encoder and asymmetric decoders to estimate reflectance and illumination components. The reflectance branch performs attention-guided and edge-aware wavelet refinement to preserve textures and boundaries, whereas the illumination branch promotes spatially coherent brightness adjustment. After the recomposition of reflectance and illumination branches, a curve-based post-composition enhancement head calibrates residual tone and exposure deviations. Experiments on LOL-v1, LOL-v2-Syn, and LOL-v2-Real show that the proposed network ties for the best structural similarity index (SSIM) and obtains the second-lowest learned perceptual image patch similarity (LPIPS) on LOL-v1, achieves the best SSIM and LPIPS on LOL-v2-Syn, and obtains the highest peak signal-to-noise ratio (PSNR) and the second-lowest LPIPS on LOL-v2-Real.
[CV-90] Bayesian PET Reconstruction with Learned Flow Matching Priors via Langevin Sampling
链接: https://arxiv.org/abs/2610.41337
作者: Hengjia Ran,Huafeng Liu,Bo Zhao
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Selected as the Best Student Paper Award for 2026 IVMSP
Abstract:Image reconstruction for positron emission tomography (PET) is a challenging inverse problem due to its inherent ill-posedness and Poisson counting noise, particularly in low-dose acquisitions. In this work, we introduce a novel Bayesian PET reconstruction framework that leverages a flow-matching generative prior learned from high-quality PET images. By incorporating this prior, we formulate PET reconstruction as a Bayesian inference problem and solve it via latent-space posterior sampling using the unadjusted Langevin algorithm. We evaluate the proposed method through numerical experiments and benchmark it against classical reconstruction methods and state-of-the-art deep learning-based baselines. The proposed method achieves improved reconstruction performance while enabling posterior uncertainty quantification.
[CV-91] With foundation-model features aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
链接: https://arxiv.org/abs/2610.41328
作者: Nheng Vanchhay,Ali Hussain,Khan Ziaullah,Moon-Il Joo,Md Ariful Islam Mozumder,Rashedul Islam Sumon,Won-Ae Lee,Min-Sun Cho,Hyeong-Chan Shin,Yujun Park,Baek Hwan Cho,Nam-Hoon Cho,Heejung An,Heung-Kook Choi,Haeyoun Kang,Hyunki Kim,Hee Cheol Kim
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Whole-slide image classifiers are usually developed within one hospital and often go untested at unseen hospitals. We assembled 5,036 haematoxylin-and-eosin-stained gastric slides from five Korean hospitals, scanned at 20x and 40x, and compared multiple-instance learning aggregators under leave-one-hospital-out validation on frozen pathology foundation-model features. A hierarchical patch-region-slide graph aggregator (CHIME-MIL) with site alignment and two-scale fusion reached a mean area under the receiver operating characteristic curve (AUC) of 0.837, above six conventional attention and transformer aggregators trained without these steps. Under a matched recipe it did not exceed attention-based pooling, and global max pooling given the same alignment and fusion was better (AUC 0.855, P = 0.0002). The alignment, which subtracts each hospital’s mean feature vector without labels, onboarded a new hospital from ten unlabelled slides and cost 0.002 AUC without held-out-hospital data, but its accuracy gains were small and inconsistent. The model had low top-label calibration error (0.053) but under-confident malignancy probabilities, and removing its most-attended patches lowered the malignancy probability far more than removing random patches. With these features, more complex aggregators did not improve discrimination; what a deployable system adds is label-free onboarding, calibration checks and auditable evidence.
[CV-92] ART of PIV: Agent ic Real-Time Particle Image Velocimetry for Active Flow Control
链接: https://arxiv.org/abs/2610.41304
作者: Antonio Terpin,Francesco Banelli,Alan Bonomi,Raffaello D’Andrea
类目: Fluid Dynamics (physics.flu-dyn); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Particle image velocimetry (PIV) measures fluid motion from images of tracer particles, but its accuracy depends on choices throughout the estimation pipeline, including image preprocessing, flow estimation, and postprocessing. These choices are typically left to expert tuning, and a fixed configuration can become less accurate as conditions change. This matters especially when PIV supplies feedback for real-time control, where measurements must be both accurate and fast, and flow conditions vary. We introduce ART of PIV – short for Agentic Real-Time and also a nod to the art of tuning a PIV pipeline – which automates pipeline choice for each image pair. A learned agent selects a configuration according to a chosen measurement objective from a library assembled for a specified speed budget. Across cross-correlation, classical optical-flow, and learning-based estimators, ART of PIV reduces average end-point error (AEPE) by 12.2-22.4 % relative to the best single estimator at matched speed, with gains extending to 1000-4000 image pair/s. In a simulated local velocity regulation task, it reduces mean normalized closed-loop cost by 13.24-13.59 percentage points relative to a tuned fixed estimator, with the same classical controller in both cases. In a tabletop water channel, we illustrate two ways to tune ART of PIV for a fluidic pinball of three rotating cylinders: selecting between complementary optical-flow tunings for accuracy, and selecting regions of interest with coherent rotation or shear using a common flow estimator. For the first variant, we train a DreamerV3 reinforcement learning controller directly in the physical world and demonstrate tracking of a prescribed flow field in the wake of a fluidic pinball.
[CV-93] PBSR: Pseudo-Burst Super-Resolution via Zero-Shot Novel View Synthesis
链接: https://arxiv.org/abs/2610.41258
作者: Yuki Kondo,Norimichi Ukita
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in New Generation Computing. 17 pages, 10 figures, 3 tables
Abstract:Burst super-resolution (BSR) achieves higher reconstruction fidelity than single-image super-resolution (SISR) by aggregating sub-pixel information from multiple frames. However, it requires a burst of frames captured under near-static conditions, and such frames are unavailable for single photographs or archival images. We propose Pseudo-Burst Super-Resolution (PBSR), which brings BSR-style reconstruction to the single-image input setting by synthesizing pseudo-burst frames via zero-shot novel view synthesis (NVS). PBSR first generates pseudo-burst frames with realistic parallax using a viewpoint sampling strategy calibrated to the pixel-shift distribution of real burst datasets. The frames are then processed by a BSR backbone equipped with a geometry-aware alignment module that reuses the NVS camera parameters and estimated depth. While the NVS stage is zero-shot, the backbone is fine-tuned with an artifact-aware adaptation so that it can exploit the synthesized frames despite their generative artifacts. Experiments on the BurstSR dataset show that PBSR consistently improves in PSNR, SSIM, and LPIPS over the single-frame baseline with the same reconstruction backbone.
[CV-94] Inter-Frame Predictive Contour Coding for Semantic Map Compression
链接: https://arxiv.org/abs/2610.40887
作者: Runyu Yang,Hyomin Choi,Fabien Racapé,Ivan V. Bajić
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 4 figures, to be presented at the 2026 IEEE International Conference on Visual Communications and Image Processing, Singapore, December 2026
Abstract:Dynamic semantic maps have become an important intermediate representation for many computer vision applications. Efficient lossless compression of such maps is essential for reducing storage and transmission costs. Existing contour-based semantic map compression methods mainly exploit spatial redundancy within individual frames while largely ignoring the strong temporal correlation between adjacent semantic maps. In this paper, we propose an inter-frame predictive contour coding framework for lossless semantic map compression. The proposed framework first predicts blob positions and label indexes from the reference frame and then performs contour prediction using corresponding chain-code units in the reference contours. A mode selection mechanism is further employed to choose between intra and inter coding according to the estimated coding cost. By exploiting temporal contour redundancy while preserving the advantages of contour-based representation, the proposed framework significantly improves coding efficiency. Experimental results on the VSPW dynamic semantic segmentation dataset demonstrate that the proposed method consistently outperforms state-of-the-art lossless semantic map compression methods. Compared with the recent context-adaptive extended chain coding framework, the proposed inter-frame coding achieves an average bitrate reduction of 3.0% without significant increase in the average encoding time, demonstrating an effective trade-off between compression performance and computational complexity. The source code is made publicly available at this https URL.
[CV-95] A small HER2 reader for routine HE slides: real-world deployment
链接: https://arxiv.org/abs/2610.40662
作者: Thang Tran(CloudKites AI Lab, New South Wales, Australia),Lan Dang(Monash Business School, Monash University, Victoria, Australia)
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: 32 pages, 12 figures, 17 tables (including supplementary material). Research use only. Weights and data: this https URL
Abstract:HER2 status decides who receives HER2-targeted therapy. Pathologists score HER2 protein by immunohistochemistry (IHC); equivocal IHC 2+ tumours, their hardest calls, wait for in situ hybridisation (ISH). We built a HER2 reader for routine HE slides, owned end to end, as a baseline for pathologists. A 22M-parameter ViT-S/16 tile encoder was trained on public TCGA slides by self-supervision, distillation from H-optimus-0 and two rounds of JEPA refinement; five cross-validated attention-based slide classifiers average their probabilities; and an operating point is fixed on training data. Compared under one paired ten-seed protocol, readers on our encoder rank first of 7 on external validation from TCGA-BRCA to Yale-HER2 (AUC 0.812), level with readers on encoders 4 and 50 times larger. We also rebuilt PathBench’s encoder benchmark on public cohorts; our replica reproduces its published order of reference encoders, and under it our encoder ranks first of 7 on TCGA-BRCA (0.689) and sits level with H-optimus-0 and Phikon on Yale-HER2 (0.897). On three external hospitals our deployed reader reaches AUC 0.82 on Yale-HER2 (about 0.81 with training slides balanced for tissue size), 0.75 on HEROHE and 0.63 on BCNB biopsies, and among HEROHE’s 85 IHC 2+ cases it ranks ISH-amplified above non-amplified tumours at 0.76, best among our TCGA-trained readers. Per-laboratory score normalisation and a laboratory’s own slides improve it further on a new site. Without a GPU our reader needs 5-6 s per slide. It serves as a research-use pre-read in a pathology platform, where it proposes a probability and a call beside IHC and helps prioritise ISH for equivocal cases. Weights, per-slide predictions and the data behind every figure are public at this https URL.
人工智能
[AI-0] Latent Sparse Attention: Towards Subquadratic Ultra-Long Context Attention
链接: https://arxiv.org/abs/2610.42147
作者: Ales Kubicek,Andrea Caciolai,Liangzu Peng,Sukrit Sumant,Benjamin Hoover,Fraser Greenlee,Ruairidh M. Battleday,Radim Urban,Torsten Hoefler,James C.R. Whittington
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic workflows are increasingly shifting towards long-horizon tasks, such as open-ended scientific work, where models benefit from long context windows. However, at ultra-long context (above 1M tokens), full attention is prohibitively expensive. A promising approach is sparse attention, in which a dedicated indexer selects a subset of keys for the attention. Yet current methods of indexing either still have quadratic cost in context size or lose accuracy at ultra-long context scale. We address this with Latent Sparse Attention (LSA), an efficient attention method that maintains accuracy at ultra-long context. In LSA, keys are hierarchically clustered in a learned latent space, and queries score the hierarchy of clusters instead of scoring every key, making prefill, decode and training faster at long context. We propose two variants, Distilled-LSA (D-LSA), which can be applied to pre-trained models via distillation, and Native-LSA (N-LSA), which is trained natively on the next-token loss alone. On GLM-5.3-FLASH (QWEN-3.8-27B) at 10M tokens, D-LSA speeds up one attention layer by 22.7x (72.4x) in prefill and 4.6x (42.5x) in decode over its original sparse attention (full attention), while keeping similar accuracy across a variety of tasks. N-LSA matches the accuracy of full attention with a training speedup of 1.6x per layer at 128k tokens. LSA offers practical and efficient attention for models to benefit from ultra-long context.
[AI-1] Hierarchical World Models with Implicit Dynamics
链接: https://arxiv.org/abs/2610.42146
作者: Gaoyue Zhou,Yvonne Wu,Zichen Jeff Cui,Nicolas Ballas,Mido Assran,Lerrel Pinto,Yann LeCun
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning. However, long-horizon tasks remain hindered by cumulative errors and search complexity. Existing hierarchical methods often use rigid macro-actions or latent skills, leading to brittle representations and limited fine-grained control. We introduce ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control. Our framework utilizes a high-level model to sample feasible transitions across the state manifold and a low-level world model as an inverse dynamics solver to ground these transitions into precise actions. This allows the planner to identify reachable subgoals without being constrained by low-level execution details. By prioritizing local physical feasibility over global behavioral patterns, ID-WM synthesizes novel long-horizon plans from short segments, transcending the specific behavioral modes seen during training. We demonstrate its effectiveness across three navigation and manipulation suites, achieving a 38.5% improvement over state-of-the-art policy and diffusion-based planners. Crucially, by leveraging this hierarchical structure to mitigate compounding errors, our approach yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks. Videos can be viewed at this https URL
[AI-2] ACCT: Asymmetric Cross-LLM Context Transfer
链接: https://arxiv.org/abs/2610.42116
作者: Yuzhe Liu,Haolin Tian,Tonghan Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In real-world deployments, large language model (LLM) systems are increasingly evolving from single-model invocation toward heterogeneous deployments in which different models are dynamically invoked for inference. When the same context is reused across models, however, each newly selected target model typically has to process it again, repeatedly incurring substantial computational costs. Therefore, we propose ACCT (Asymmetric Cross-LLM Context Transfer), which targets KV cache transfer between models of different scales within the same model family. ACCT transforms the KV cache produced by a source model into sparse KV states that can be directly consumed by a target model, thereby enabling cross-model reuse of context computation and decoupling context computation from downstream inference. ACCT consists of three lightweight components: the Cartographer identifies query-relevant context tokens for each target layer and KV head; the Allocator determines how many context tokens each target layer and KV head retains under a shared total KV budget; and the Compiler transforms selected source KV into sparse target KV. The target model can therefore perform inference using the transferred KV without reprocessing the original context. We evaluate ACCT in two complementary settings comprising five general-capability benchmarks and six highly context-dependent tasks. Experiments show that ACCT matches or exceeds the target model’s original inference performance even when reducing the KV cache size by 75%. At a context length of approximately 19K tokens, ACCT’s KV conversion achieves a 6.5-21.2 times speedup over target context prefill. These results demonstrate the feasibility of transferring context computation across models under a limited KV budget.
[AI-3] CausalWAM: Learning the Causes of Actions in World Action Models
链接: https://arxiv.org/abs/2610.42096
作者: Juntao Cheng,Jinzhou Tang,Fan Feng,Henrik I. Christensen,Yujia Zheng
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO); Machine Learning (stat.ML)
备注:
Abstract:World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions. The prediction has to be of the whole scene, whereas only a portion of the scene causes actions. WAMs fitted to demonstrations do not, in general, guarantee that they will generate actions solely from the latent causes of the demonstrated actions (the action-relevant latent variables), as the model could just as well learn to imitate in this way whilst entangling the action-relevant latent variables with the nuisance variables. We therefore separate out what causes action from what prediction depends on. We prove that under a sparsity constraint the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone and the true structure is identified up to a permutation of the latent variables. This requires neither sufficient temporal dependence nor any assumptions on how the unobserved regimes evolve and holds even when the action-relevant latent variables are statistically dependent on the rest. We therefore propose CWAM (Causal WAM), which incorporates the constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged. Experiments on both synthetic data and real-world robots validate the theory as well as showcase the improved efficiency in closed-loop success.
[AI-4] Same Question Different Answers: Auditing Persona-Dependent Variation in Six LLM s on Smoking-Cessation Advice
链接: https://arxiv.org/abs/2610.42039
作者: Artin Yousefi,Aizierjiang Aiersilan,Lorien C Abroms,David A Broniatowski
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Hundreds of millions of people now ask large language model (LLM) chatbots for health advice, which raises a question that is simple to pose and hard to answer: does everyone get the same advice? We audit six widely deployed LLMs from four providers on smoking cessation, a domain with codified clini- cal guidance and substantial public-health stakes, by putting the same four cessation questions to twelve user personas that differ in life stage, self-described expertise, and smoking history, for a total of 2,880 responses. What the models say depends on who appears to be asking. Every model named FDA-approved prescription cessation medications less often to teen personas than to adult personas, by 32 to 67 percentage points, and in five of six models a persona presenting health- care expertise received fewer facts than a persona presenting none. We then evaluate one deployable intervention: a short system prompt specifying a factual floor, the set of core med- ical facts every user should receive when they are relevant, while leaving tone, vocabulary, and framing free to adapt. The prompt narrows persona differences in every model, but its effect on overall fact coverage varies in both magnitude and direction across models, so a prompt validated on one system cannot be assumed to transfer to another. Two cautions frame these results. Equal mention rates are not always the correct clinical target, because prescription cessation pharmacother- apy is not recommended for minors, so a defensible factual floor must be age-aware rather than uniform. And because our personas vary on several attributes at once, we report scenario- level differences rather than isolated effects of age or expertise. We release all 2,880 responses and the analysis code.
[AI-5] here Is No Free Loop: When Does Looping a Trained Model Pay for Its Inference Compute?
链接: https://arxiv.org/abs/2610.42030
作者: Rhea Sanjay Sukthanker,Pashmina Cameron
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Looping re-applies a block of layers so that each token passes through the block more than once. Adding a loop to a trained model is attractive because it leaves the parameter count, and hence the disk size, the memory footprint and the training cost unchanged. Unfortunately, every generated token pays for the loop. Re-applying m of N layers makes each token about (N+m)/N times as expensive. Loops that a model learns during large-scale pretraining justify this price, but whether loops added after pretraining justify this cost remains unclear. We compare a looped model with two simple baselines. We first compare it with the same model trained identically without the loop (equal parameters). Secondly, we compare it with the best unlooped use of the same inference floating-point operations (equal FLOPs). Across Qwen3 and Qwen3.5 models from 0.8B to 9B parameters, only the looped 0.8B model beats both baselines. At 4B and 9B, the looped model does not beat the first baseline, i.e. the same model trained without the loop. This is not because the benchmarks are too easy, since on the same benchmarks the unlooped 9B model still scores up to 13 points higher than the 4B model, showing that there is ample room for improvement. Introducing loops in an already pretrained model therefore only pays for small models with a fixed parameter budget.
[AI-6] Know When the Equation Is Wrong: Structural Uncertainty for LLM -Guided Symbolic Regression under Extrapolation
链接: https://arxiv.org/abs/2610.42024
作者: Mostafa Cham,Bayu Adhi Tama,Jianwu Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can help discover mathematical equations from data, but most LLM-guided symbolic regression methods focus on a single best equation without quantifying uncertainty over alternative functional forms. This is challenging because distinct equations can fit the observed data similarly while producing substantially different extrapolations. Our experiments on the LLM-SRBench dataset show that standard conformal intervals achieve nominal in-distribution coverage but frequently fail under extrapolation. To tackle this challenge, we introduce LLM-SR-UQ, a framework that retains competing equation structures and uses their disagreement to characterize uncertainty about the underlying functional form. We extend the standard fixed-width conformal approach to quantify structural intervals, which measure disagreement among candidate equations and identify where prediction intervals should widen. Extensive experiments show our framework substantially improves coverage attainment by incorporating structural disagreement while preserving in-distribution coverage. Our approach also shows good generalizability when it is applied to other symbolic-regression methods.
[AI-7] radeWar: A Benchmark for Execution-Grounded Trade Negotiation with Adaptive LLM Agents
链接: https://arxiv.org/abs/2610.42004
作者: Xiangyu Kong,Ruirui Yang,Xia Hou
类目: Artificial Intelligence (cs.AI)
备注: Accepted to DAI’26
Abstract:Multi-turn negotiation remains a challenging problem for large language model (LLM) agents. While LLMs can conduct natural-language dialogue, existing benchmarks predominantly assess only agreement rates, overlooking whether negotiated commitments actually create value once executed. To address this gap, we introduce TradeWar, a multi-country simulation benchmark that translates negotiation dialogues into concrete tariff and trade-share policies, enforces capacity and network constraints during settlement, and evaluates outcomes via an Economic Health Index (EHI). To solve this execution-grounded task, we propose TACTIC (Trade-aware Adaptive Consolidation and Theory-of-mind for International Commerce), an agent that iteratively refines its negotiation strategy across episodes-without updating base LLM weights-by leveraging prior experience, opponent behavior, and past settlement outcomes. Despite using zero-shot chain-of-thought (CoT) prompting, PromptLLM attains a 75% agreement rate but only 57.1 final EHI (*100), whereas TACTIC reaches 68.8. Across 20 sequential episodes and three seeds, TACTIC’s final EHI improves from 58.2 to 73.2, compared with 65.2 for an ablation without cross-episode updates. Under technology-controls and energy-crisis shocks, TACTIC also exhibits smaller performance degradation than PromptLLM and MemoryLLM. These results indicate that CoT deliberation and interaction memory alone do not fully address delayed execution feedback, whereas settlement-grounded adaptation improves long-horizon negotiation in TradeWar.
[AI-8] SecPO: Principled Adversarial Training for Prompt Injection Security
链接: https://arxiv.org/abs/2610.41998
作者: Sizhe Chen,Yibo Peng,Jaewon Chang,Chawin Sitawarin,David Wagner
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Recognized as a top threat to AI agents, prompt injection allows instructions from untrusted data (email, documents, websites) to manipulate the agent’s operation. The existing best-defended LLM, Meta-SecAlign, is trained with the DPO loss but still suffers from attack success rates (ASRs) approaching 100% under adaptive prompt injections. One reason is that DPO anchors the model around the undefended reference model, which is insecure. We show an idealized reference model—an injection-invariant, utility-preserving oracle—can nevertheless be approximated, as its output distribution in training is interestingly accessible. This new observation motivates a theoretically principled SecPO loss. Another Meta-SecAlign shortcoming is that it trains on weak simulated attacks. We implement an efficient adversarial training (AT) pipeline that synthesizes strong injected inputs (best@240), and train the model on them. Our AT reduces ASRs by 1.5-14.5 \times across three SoTA adaptive attacks, compared with Meta-SecAlign; the SecPO loss reduces them by a further 2.4-4.6 \times compared with using DPO loss. In agentic workflows, our SecPO + AT Qwen3.6-27B is much more secure than Meta-SecAlign: 8.8% vs. 78.8% AgentDojo ASR@800 under the AttackerMovesSecond attack. Moreover, our defended model shows no noticeable reduction in benign utility in nine benchmarks covering agentic tool calling, instruction following, science, and math. We release our \href//github.com/Sizhe-Chen/SecPO\textcode , \href//huggingface.co/datasets/Sizhe-Chen/Qwen3.6-27B-Instruct-SecPO-trainset\texttraining data (with optimized injections from AT), and \href//huggingface.co/Sizhe-Chen/Qwen3.6-27B-Instruct-SecPO\textmodel .
[AI-9] ID2Pattern: Pretraining Generalizable Identity Behavior Patterns for Transaction Risk Foundation Models
链接: https://arxiv.org/abs/2610.41988
作者: Ze Zhao,Haoyu Jiang,Weiyu Han,Ruobing Zuo,Xiao Tan,Shuai Chen,Tianyi Zhang
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 6 figures
Abstract:Large-scale behavioral pretraining offers a promising route to generalizable representations in online payment systems. However, high-cardinality identity fields such as card, device, and account IDs, though rich in behavioral signals, remain underexploited. Their continually growing vocabularies and limited semantic meaning make existing vocabulary-based and semantics-based encodings ill-suited. Their generalizable signal lies not in specific values, but in the identity behavior patterns they exhibit across a transaction sequence. Building on this insight, we propose ID2Pattern, a pretraining framework that brings these patterns into transaction foundation models. ID2Pattern first encodes identity fields with a Local Anonymous ID (LAID) tokenizer, which captures how each identity appears and recurs within the sequence, independent of its specific value. It then pretrains a multi-scale pattern codebook on unlabeled sequences, which abstracts LAID sequences into discrete behavior patterns for downstream prediction. Experiments on an industrial dataset with over 100M sequences and six public datasets show that ID2Pattern consistently outperforms strong baselines, improving industrial by 4.13% and public datasets by 8.83% on average. Notably, ID2Pattern remains equally effective even when all identity values are unseen during training, highlighting its potential to generalize to new users and markets.
[AI-10] When Does Test-Time Search Help World Action Models? An Empirical Audit of Candidate Selection and Simulator Integrity
链接: https://arxiv.org/abs/2610.41970
作者: Jun Liu,Dajie Sun,Zhenglun Kong,Zhenjiang Fan,Peiyan Dong,Fan Yang,Wenbin Zhangg,Geng Yuan,Xue Lin,Yanzhi Wang,Dong Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:World-action models increasingly apply test-time scaling, spending extra inference computation on candidate selection and adaptive allocation to improve control. Such gains are credible only when candidates carry exploitable signal, nominal compute savings become real speedup, and evaluation stays reliable. We audit all three on Cosmos Policy and RoboCasa with a premise-first, pre-registered methodology. On clean data, a learned value selector is 1.66 pp worse than random; a hindsight oracle gains only 1.40 pp, with an episode-level interval spanning zero; and 82% of states show identical candidate outcomes, leaving little selection headroom. Token pruning cuts per-forward FLOPs by up to 47% but yields at most a measured 1.15 \times end-to-end speedup on the deployed path (1.25 \times on a decode-excluded path), short of a 1.5 \times target, and our adaptive-allocation experiment is inconclusive under a power gate. We further identify and fix a silent rendering corruption whose rate rises sharply along compute-heavy planning paths while leaving the official direct-policy result unaffected, and release a detector, fix, and scripts. These findings show why test-time scaling should be audited as a chain of premises (decision signal, realized efficiency, evaluationintegrity) rather than by success rate alone. Conclusions are scoped to the tested configuration.
[AI-11] Prune Gradually Adapt Sparsely: Efficient Pruning of Large Language Models at Extreme Sparsity
链接: https://arxiv.org/abs/2610.41968
作者: Jean Lucien Randrianantenaina,Hung Nguyen,Nasib Ullah,Jinbin Zhang,Nilesh Gupta,Inderjit S. Dhillon,Rohit Babbar
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Weight pruning can reduce the storage and inference cost of large language models (LLMs), but preserving quality at high sparsity levels remains challenging. Existing methods face a quality-cost trade-off in this regime. One-shot pruning is cheap but degrades sharply as sparsity increases, even when followed by post-pruning recovery. A growing line of work instead optimizes the mask directly from the dense model under the end-to-end objective; among these methods, [Lee et al.,2026], the recent state of the art at extreme sparsity, reaches high quality but at the cost of model-sized optimizer state and auxiliary variables. To address this gap, we propose ETAPS, which prunes gradually and adapts sparsely: it tightens a global sparsity constraint in progressive stages while training a sparse delta with a small, fixed budget. Our key observation is that gradual weight removal keeps each pruning step small enough for sparse adaptation to compensate, allowing the adapted model to provide end-to-end gradients that guide the next pruning decision without model-sized optimizer state. Across six models from 125M to 13B parameters, ETAPS achieves lower perplexity than ELSA at every evaluated sparsity from 50% to 90%, with perplexity reductions of up to 42%. ETAPS is also far cheaper: at 90% sparsity, it prunes LLaMA2-7B on a single H200, where ELSA uses four, with 5.0\times smaller total persistent state and 49% fewer GPU-hours. Together, our findings point to a promising direction for achieving high quality in demanding regimes such as extreme sparsity at substantially lower cost.
[AI-12] Chronos-2: A foundation model for multivariate time series forecasting
链接: https://arxiv.org/abs/2610.41956
作者: Abdul Fatir Ansari,Oleksandr Shchur,Jaris Küken,Andreas Auer,Boran Han,Pedro Mercado,Syama Sundar Rangapuram,Huibin Shen,Lorenzo Stella,Xiyuan Zhang,Mononito Goswami,Shubham Kapoor,Danielle C. Maddix,Pablo Guerron,Florian Saupe,Markus Reichstein,Tony Hu,Junming Yin,Nick Erickson,Prateek Mutalik Desai,Hao Wang,Huzefa Rangwala,George Karypis,Yuyang Wang,Michael Bohlke-Schneider
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 41 pages, 9 figures. Extended version of the Chronos-2 technical report ( arXiv:2510.15821 ), adding scientific case studies (epidemiology, macroeconomics, carbon flux, hydrology, hierarchical forecasting), additional baselines, ablation studies, and calibration analysis. Code: this https URL
Abstract:Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences. Yet most forecasting models still need to be developed and trained separately for each domain-specific dataset. Time series foundation models (TSFMs) aim to remove this limitation and enable the application of the same model across domains through large-scale pretraining. However, most existing TSFMs treat each time series independently and therefore cannot model interactions among time series and external drivers. Here, we introduce Chronos-2, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications. The model infers relationships among time series directly from historical context and generates probabilistic forecasts without task-specific treatment. Across various benchmarks, Chronos-2 achieves state-of-the-art accuracy on average by improving skill scores by 5.2 percentage points relative to the strongest baselines and increasing win rates by 10 percentage points. Chronos-2 is also computationally efficient, generating forecasts for 100 time series in under one second on a single mid-range GPU, 50x and 1000x faster end-to-end than the best task-specific point and probabilistic forecasters, respectively. In addition, we demonstrate its effectiveness in diverse scientific settings – including infectious-disease forecasting, macroeconomic modeling, carbon-flux prediction, and runoff simulation – where the model matches or surpasses specialized methods developed for each domain. These results show that a single pretrained model can capture structure shared across temporal systems.
[AI-13] Optimizing Stochastic Policy Geometry for Few-Step Flow Reinforcement Learning
链接: https://arxiv.org/abs/2610.41949
作者: Zhuguanyu Wu,Congjia Chen,Ruihao Gong,Yang Yong,Shiqiao Gu,Yi Xu,Xianglong Liu
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Few-step flow models generate samples through a handful of large denoising transitions. Applying GRPO to such models requires introducing stochasticity into the denoising transitions, and when a single transition spans a wide range of noise levels, the choice of the resulting stochastic policy becomes a major factor in optimization. Recent constructions constrain the policy to preserve the target flow coefficients, but this constraint admits a family of policies and offers no criterion for choosing among them. We introduce Geo-FSRL (Geometry-Optimized Few-Step Reinforcement Learning), built on a selection principle: at a fixed conditioning state and timestep pair, choose the admissible policy that minimizes the conditional KL divergence induced by any given nonzero velocity-output update. Within a coefficient-preserving family of isotropic Gaussian transitions, this criterion reduces to minimizing a single scalar geometry factor that determines the conditional KL for every velocity-output update. The same factor governs velocity-space Fisher information and conditional transition log-likelihood-ratio variance. We derive the unique closed-form optimizer within this family, shared across all nonzero update magnitudes and directions, with transition coefficients determined by both the source and target noise levels. Because the minimized geometry factor still varies across timesteps, we further adopt gradient-scale matching from prior work and derive closed-form policy-gradient and KL weights that normalize explicit score scales and reference-KL curvature. Geo-FSRL trains on the original few-step schedule and retains the deterministic solver at inference. Our method achieves higher final reward scores than Flow-GRPO and Flow-CPS on both image generation and video generation models under the 4-step setting. Code is available at this https URL
[AI-14] Sensorium: An Open-Source Multimodal Data Collection and Curation Library for Embodied Surgical AI ALT NEURIPS2026
链接: https://arxiv.org/abs/2610.41946
作者: Lorenzo Mazza,Ariel Rodriguez,Stefanie Speidel
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: GenAI for Health Workshop @ NeurIPS 2026, Sydney
Abstract:Embodied surgical AI requires reliable multimodal data, yet surgical data collection remains costly, difficult to repeat, and strongly dependent on the hardware setup. We present Sensorium, an open-source Python library for recording, processing, annotating, and curating multimodal surgical data from configurable robot and sensor streams using the Robot Operating System (ROS) and ROS2 middleware. The library combines operator-facing data acquisition, configurable processing, semantic annotation, and episodic dataset generation. We evaluate Sensorium across 1516 recordings of phantom, ex vivo, and in vivo porcine experiments involving multiple surgical tasks and sensing configurations, and report preliminary usability feedback.
[AI-15] Observable-Specific Validity Tests for Scientific AI: Controlled Approximations and Intervention Response
链接: https://arxiv.org/abs/2610.41939
作者: Xiaoshn Nee,Haobo Zhong,Xiaomin Ni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL)
备注:
Abstract:An approximation can reproduce an energy while missing its response, or satisfy an error tolerance without being certified by a given bound. We formulate observable-specific validity records for scientific artificial intelligence (AI) that link a reduction to its reference model, error measure, tolerance, parameter domain, and transformations or interventions. Explicit displacement and velocity certificates for a singularly perturbed oscillator recover distinct convergence orders and provide sufficient validity guarantees. Exact Hubbard dimer relations resolve the different coupling domains accepted by energy and response criteria. Across 16 repeated neural fits, imposing dimensional homogeneity reduces energy scale transfer error from 52.3% to 0.0768% and raises the median fraction jointly satisfying gap and response tolerances from zero to 100%. Paired transformations verify the protected scaling relation, while a coupling crossover identifies the additional information needed for response transfer. A causal model recovers a positive intervention response beneath a strong negative observational association. For two mechanisms observed with Gaussian noise whose responses first differ at order x^k , we derive an exact fixed control query requirement scaling as x^-2k and verify its full success curve numerically. Together with a 256-task evaluation interface, these results provide independently checkable tests of controlled approximation, physical transfer, and mechanism discrimination in scientific AI.
[AI-16] Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling
链接: https://arxiv.org/abs/2610.41909
作者: Anran Liu,Benzhao Hou,Zongsheng Cao,Daichao Wang,Lang Chen,Shitong Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) can turn natural-language optimization tasks into solver-checked answers, but current agents remain brittle under narrative rewrites and often store reusable experience at the instance level. We introduce \textscArchOR, an archetype-centric skill agent that builds on \textscOptSkills by adding two retrieval-focused modules. \emphCentroid-Aware Representation Learning (CARL) refines the ordering of cached skill representations through a low-rank prototype residual. \emphSignature-Guided Retrieval (S\textsuperscript3R) attaches open-dimensional structural signatures to skills, prioritizes candidates by weighted Jaccard overlap, and bounds semantic re-ranking to a top- K set. A baseline-preserving arbitration rule determines when the structural route should replace the original selector. With the same DeepSeek-V4.1-Flash backbone and released skill library, \textscArchOR reaches 65.00% Micro-Avg.\ Pass@1 across five benchmarks, improving over \textscOptSkills by 1.64~pp Micro-Avg.\ and 2.68~pp Macro-Avg.
[AI-17] ReX-GG: a LLM Ensemble Pipeline for Relation-extraction and Graph Generation LREC2026
链接: https://arxiv.org/abs/2610.41901
作者: Giacomo Magnifico,Eduard Barbu
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 4 figures, accepted at KGLLM @ LREC 2026
Abstract:Current LLM ensemble frameworks focus on multi-step setups with additional modules for answer ranking, often opting for token and span analysis rather than structured outputs, leading to heavyweight architectures with potential fail states along the pipeline. Faster, lighter solutions are more vulnerable to hallucination propagation and can lack output control in more complex pipelines. This paper proposes a customisable, lightweight ensemble workflow of coordinated Large Language Models that leverages JSON-structured outputs and anonymous peer-review ranking to mitigate hallucinatory outputs and single-model failure points. The pipeline is demonstrated on a relation extraction task applied to popular science articles in English, targeting four ontologically-grounded relation types (strong causation, weak causation, contrastive, and compositional), with semantic node canonicalisation and interactive, colour-coded HTML causal graphs as the final output. Performance is evaluated through an anonymous user study, achieving an average perceived accuracy of 0.778 against a human-annotated gold standard. The modular architecture supports flexible deployment across both API-based and in-house LLM setups, and the full framework is released under an open license to foster reproducibility and collaborative research.
[AI-18] Using Machine Learning to Measure Self-Efficacy Belief Through Learners Interaction Traces in Technology-Enhanced Learning
链接: https://arxiv.org/abs/2610.41900
作者: Maeva Somny(LIRIS),Linda Chouati,Marie Lefevre
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Self-efficacy beliefs play a critical role in learners’ performance, academic choices, and, more broadly, in their future career trajectories. As Technology-Enhanced Learning (TEL) environments have demonstrated strong potential for supporting both performance and motivation, investigating self-efficacy within such environments is of particular interest, especially for implementing remedial practices as early as possible to support learners’ self-efficacy beliefs. Our research aims to develop a system capable of detecting low self-efficacy based on learners’ interaction traces while they solve exercises in a TEL system. In this paper, we present initial results and future perspectives on a detection approach based on data mining and machine learning techniques to identify features indicative of low self-efficacy beliefs. Because the detection of learners’ self-efficacy beliefs through their use of TEL systems remains largely unexplored, this work is exploratory in nature. To this end, we conducted an experiment across two universities to collect interaction traces and self-reported measures of self-efficacy. We then developed a machine learning pipeline comprising multiple classification models, enabling us to highlight the importance of closely monitoring of learners’ self-efficacy beliefs for improved prediction performance, given their highly dynamic nature during learning.
[AI-19] Automated Keyword Generation Using LLM s in a High-Volume Data Virtualization Service
链接: https://arxiv.org/abs/2610.41893
作者: Saiful Khan,Yiwen Xing,Joyraj Chakraborty,Afrah Farea,Philip Beaucamp,Samuel Jackson,Min Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In modern data infrastructure, a data virtualization service (DVS) allows users to create many versions of source datasets for different processing tasks. For example, a multivariate or multi-modality dataset can be used for different machine learning tasks (e.g., classification, detection, segmentation, forecasting, and so on). Data selection, participation, integration, and preprocessing in different tasks result in many virtual datasets [Khan et al., SSC 2026]. Meanwhile, different users need to discover existing virtual datasets frequently and easily to avoid duplicate efforts. To enable an effective search engine in a DVS, it is desirable to automatically generate keywords for each virtual dataset. In this work, we present a solution for generating keywords for virtual datasets using large language models (LLMs). We propose five strategies for selecting different collections of text data for processing by the LLMs. We applied the five strategies to virtual datasets created in five application case studies with different source datasets. We conducted experiments with three LLM generators and two embedding techniques. The experiments show that the performances of the five strategies depend on the quality of the text descriptions. The results help us adopt a multi-strategy implementation of a new keyword generation service (KGS) as part of an extended DVS. The work demonstrates that the capability of automated keyword generation is indispensable for a high-volume DVS that is dataset-rich and frequently accessed.
[AI-20] Fuzzy selective state model for forecasting the dynamics of consumer preferences
链接: https://arxiv.org/abs/2610.41879
作者: P.A. Golovinski,A.A. Drobyshev,A.O. Shatalova
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We consider forecasting the dynamics of consumer preferences of retail chain customers. A customer’s preference is described by a triangular fuzzy number. The evolution of the preference over time is governed by a Mamba-type selective state space model whose hidden state is a triangular number, with computations carried out in M.G.~Matveev’s two-component algebra of fuzzy numbers. The recurrence parameters are computed from crisp features, while the fuzzy part of the model remains linear with non-negative coefficients. We show that the state then always remains a proper triangular number and is a convex combination of past observations, so that the fuzziness of the forecast does not accumulate over time and the algebraic computations coincide with Zadeh’s extension principle. On a synthetic example with competitor promotions and customer switching, a lightweight 107-parameter model reaches a mean absolute error of the forecast centre of 0.154 versus 0.163 for the constant-inertia evolution operator and 0.179 for the naive forecast, matches a crisp model of the same architecture and additionally provides a linguistically interpretable estimate of forecast vagueness. We also show how the linguistic readout of the forecast can be used to detect a permanent switch of a customer to a competitor.
[AI-21] Learning to Interact and Direct: Co-Evolving Video Director Agents
链接: https://arxiv.org/abs/2610.41865
作者: Yingzhao Jian,Zhongan Wang,Yuxuan Hou,Hehe Fan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Video director agents orchestrate multistage production to realize users’ creative visions. Emerging research on their self-evolution advances screenplay planning effectively. However, the mutual reinforcement between directing and user interaction remains largely overlooked. Improved interaction clarifies user intent and informs workflow refinement, while more reliable execution shifts collaboration from troubleshooting to creative decisions. To this end, we introduce LIT (Learning to Interact and DirecT), a framework comprises a director agent, a simulated user, and a meta engineer. Simulated user assigns creative tasks to probe the director’s weaknesses, while the meta engineer uses the task traces to revise the director’s interaction and production strategy. Although this approach can address discovered interaction and production issues, it still relies on passive attribution by the meta agent. This limits its ability to adapt to broader user interaction patterns and production techniques. To solve this problem, we equip LIT with a co-evolution paradigm. The meta agent proactively learns external knowledge, thereby improving its ability to discover problems and propose solutions. The meta agent, simulated user, and director together form an evolutionary closed loop, collaboratively enhances the system’s directing and interaction capabilities. Across the Test and OOD Test splits, LIT improves production and Interaction scores by 19.1% and 32.0%, respectively, relative to the initial pipeline. Controlled ablations reveal the correlation between improvements in production and interaction, where the co-evolution mechanism further encourage directing skill and interaction mode exploration.
[AI-22] Dynamical Memory Transformer: Selective Recurrent Memories with Constant Cost Attention
链接: https://arxiv.org/abs/2610.41850
作者: Yannis Bendi-Ouis(Mnemosyne),Xavier Hinaut(Mnemosyne)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Transformers retain an explicit history of the sequence, allowing attention to directly retrieve past representations. However, the size of the KV-Cache and the cost of a new token increase with the context length. State Space Models compress the past into a fixed-size recurrent state, at the cost of reduced addressability. We present the Dynamical Memory Transformer (DMT), a hybrid architecture that separates sequence encoding from information reading. The encoder uses several memory units, each implemented by a dynamical system with a complex diagonal recurrent matrix. An input-dependent leak rate controls their updates. The decoder then uses attention to read and combine their outputs, without going through the token history. The formulation of the recurrence allows an associative scan with O(T ) total work and O(log T ) parallel depth, with fixed dimensions. During autoregressive inference, the recurrent states and the convolution cache remain fixed in size. We compare DMT with Mamba and Transformer on cognitive and language modeling tasks, and show its strengths and limitations: DMT achieves the best results on several cognitive tasks and language modeling performance close to that of Transformer, but remains behind Mamba in language modeling. On selective copy, DMT also shows stronger length extrapolation than Mamba and Transformer. These results demonstrate that parallel dynamical memory units are a promising mechanism for retaining and using information over sequences.
[AI-23] RECAST: REconstruction of Chain-of-Thought via Auditable Structured Traces
链接: https://arxiv.org/abs/2610.41813
作者: Qi Wang,Kang Xu,Chengcheng Wan,Jiangtao Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large closed-source reasoning models expose only their final answers. The intermediate reasoning that produces those answers is hidden behind API surfaces, tool-call policies, and safety filters. Existing chain-of-thought (CoT) extraction methods assume a side channel: a visible reasoning field, a scratchpad carried through tool calls, or a signed/encrypted reasoning block. We reproduce three representative state-of-the-art extraction methods and find that, on Claude-Sonnet-5.5 accessed through the standard chat-completions interface, all three return zero CoT tokens. The failure is structural rather than a bug: the reasoning field is absent, named tool selection is rejected, free-text scratchpad arguments are filtered, and no replayable reasoning block is exposed. We therefore take a different route. We present RECAST, a reconstruction-based framework that conditions a decoder on the question and the target model’s final answer, reconstructs a first-person structured rationale, cleans it, and builds SFT-ready data. To the best of our knowledge, RECAST is the first system to reconstruct CoT from frontier closed models such as Claude-Sonnet-5.5, GPT-6.1, and Gemini 3 without using hidden reasoning channels. We instantiate RECAST on Claude-Sonnet-5.5 and additionally obtain successful qualitative reconstructions on GPT-6.1 and Gemini 3 with the same answer-only protocol. The system is open-source and packaged as an agent skill: one prompt deploys it and runs the complete pipeline to produce SFT-ready CoT data. This preprint describes the system, its threat model, and a controlled artifact validation; it does not claim large-scale benchmark improvements.
[AI-24] Backpropagation-free Deep Learning for Time-Series Classification
链接: https://arxiv.org/abs/2610.41805
作者: Miha Kapš,Blaž Bertalanič,Carolina Fortuna
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 16 pages, 12 figures
Abstract:Deep neural networks for time series classification (TSC) are trained almost exclusively by error backpropagation, which imposes update locking and the weight-transport problem. Backpropagation-free learning rules such as Direct Feedback Alignment (DFA), Direct Random Target Projection (DRTP), and the diffusion-inspired NoProp relax these constraints but have been evaluated almost exclusively on 2D image benchmarks. We adapt these three learners to univariate TSC with 1D convolutional architectures and present the first systematic study of them across 112 datasets from the UCR time series archive, with one hyperparameter-optimized configuration per method. The optimized learners reach mean F1 scores of 0.76 to 0.79, below ROCKET (0.83) and InceptionTime (0.84). We propose an analysis framework that relates the per-dataset gap to InceptionTime to dataset properties and tests convolutional feature learning by replacing the flatten readout with global average pooling, measured as a difference-in-differences (DD) against a backpropagation control on the identical architecture. Intra-class temporal variability is the dataset property most consistently associated with the gap for all three learners (partial Spearman \rho from 0.28 to 0.40 ). Pooling harms DFA and DRTP more than backpropagation (DD -0.025 and -0.055 ), consistent with near-random convolutional features, whereas NoProp tolerates pooling as backpropagation does (DD +0.013 ). Relative to backpropagation on the same architectures, DRTP reduces training floating-point operations (FLOPs) by 33% and trains faster only on series longer than about 700 time steps, DFA yields no computational reduction, and NoProp reduces peak memory by 38% at the cost of 48% longer training.
[AI-25] Accurate and Auditable ECG Classification via Visibility Graph Neural Networks
链接: https://arxiv.org/abs/2610.41795
作者: Artur Xavier,Guilherme Henrique Gomes Evangelista,Aitazaz Khan,Antonio Luiz Pinho Ribeiro,Mohammed J. Zaki,Srinivasan Parthasarathy,Wagner Meira Jr
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 43 pages, 10 figures, 21 tables, including Supplementary Information
Abstract:Physicians read electrocardiograms (ECGs) from waveform shape and timing, yet accurate deep-learning classifiers rarely show which waveform regions drive their predictions. We introduce LYNX-ECG, a graph neural network that represents each heartbeat as a sparse visibility graph of timestamped turning points. On CODE-Test, LYNX-ECG reaches a macro F1 score of 0.889, outperforming five baselines including the ECGFounder foundation model, while its graphs need about 14% of the storage of graphs built from every sample. Because each node maps to a specific ECG time, we can delete clinically defined waveform regions and alter beat timing to audit predictions. For conduction disorders, the clinically expected region was the strongest evidence in more than 90% of correct predictions. Bradycardia and tachycardia calls follow heart rate with conduction intervals held fixed, and atrial fibrillation calls vanish when beat-to-beat variation is removed. Sparse visibility graphs thus pair accurate ECG classification with clinically grounded, testable explanations.
[AI-26] Whose Values Which Contexts and What Signals? Grounding Cultural Value Alignment with Re3Culture
链接: https://arxiv.org/abs/2610.41793
作者: Jing Yao,Hyunjin Hwang,Weihua Zheng,Bryan Chen Zhengyu Tan,Jia Wang Peh,Dylan Raharja,JinYeong Bak,Roy Ka-Wei Lee,Rui Cao,Yuki Arase,Jacki O’Neill,Zhicheng Dou,Xing Xie,Xiaoyuan Yi,Furu Wei
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Cultural value alignment requires not only model optimization but also a well-specified target grounded in real-human evidence. Yet how such targets should be grounded in human data remains largely unexplored, raising three fundamental questions: (1) whose values should be represented? (2) where should cultural values be observed? and (3) what signals reveal underlying values? We investigate these questions through Re3Culture, a large-scale dataset linking (i) Representative communities: 700+ human participants across four cultures recruited through cultural-affiliation screening and demographic-aware sampling; (ii) Realistic scenario: culturally sensitive scenarios selected by community members, e.g., family and kinship; and (iii) Revealed value signals: 1,400+ value surveys, 20,000+ topic-grounded behavioral principles and explicit value priorities with responses for 6,000+ situated questions. Our analysis shows why these choices matter for defining the target, with three implications. First, estimating cultural values requires both community affiliation and broad internal coverage. Substituting other cultures or restricting subgroup coverage shifts value estimates. LLM simulations also disproportionately reflect particular subgroups. Second, values must be observed in concrete, community-guided situations. Abstract questionnaire profiles weakly recover situated priorities, and communities differ in which scenarios they identify as culturally value-revealing. Third, value priorities should be elicited explicitly, not learned from response preferences or bottom-line answers alone. Preferences conflate value agreement with other response-level qualities, and similar answers can be derived from different value priorities. By addressing these requirements, Re3Culture provides the first human-grounded resource that explicitly informs target definition for cultural value alignment.
[AI-27] Implicit Assumptions About Tasks in Continual Learning NEURIPS2026
链接: https://arxiv.org/abs/2610.41792
作者: Kathrin Korte,Christian Metzner,Akshay Bedhotiya,Viet Anh Khoa Tran,Eleni Nisioti,Sebastian Risi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to the Workshop on Continual Learning in the Era of Foundation Models and Embodied Agents at NeurIPS 2026
Abstract:Continual learning is the study of how a model accumulates knowledge from data received over time, rather than all at once. In practice, this is typically achieved by dividing a dataset into tasks and presenting them sequentially. Explanations of the resulting measurements take into account factors such as the similarity between segments, the rate at which the data changes and the order of presentation. Each of these factors can be defined in terms of the underlying data, the partition imposed on it, or the learner that encounters it. These levels do not necessarily coincide, and it is usually left implicit which level a reported quantity refers to. We separate these and identify which properties acquire a value at each level. In controlled teacher–student experiments, we demonstrate that partitioning the same underlying trajectory at different levels of granularity, changing the order of presentation, and varying the similarity structure of the underlying data all result in different measured outcomes. We review representative benchmarks to determine which of these properties are stated. Where the underlying data cannot be inspected, as in foundation models built on undisclosed pre-training corpora, its properties are inherited by the model’s initial state, and the training applied on top of it can be described.
[AI-28] Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions
链接: https://arxiv.org/abs/2610.41784
作者: Pranav Patel,Siva Kannathasan,Prerak Patel,Jigar Patel,Vansh Patel,Sheel Patel
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY)
备注: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
Abstract:Healthcare increasingly relies on connected medical devices, clinical information systems, cloud services, and distributed-care technologies, creating cybersecurity risks across clinical workflows. AI-enabled capabilities can accelerate vulnerability discovery and attack-path analysis, increasing the need to limit compromise propagation. This paper proposes a clinical-protocol-aware cyber-resilience framework based on bounded clinical micro-networks and controlled interconnections. The framework integrates clinical factors into cybersecurity design, management, and evaluation, covering prevention, containment, and incident response. A parameterized Monte Carlo simulation distinguishes technical cyber exposure from modeled clinical consequence. Experiments investigate architectural connectivity, adversarial capability, security controls, protocol dependency, risk alignment, communication directionality, lifecycle integration, and response timing. Under the simulation assumptions, connected architecture increases mean cyber exposure by 56.3% relative to segmented architecture, while combined prevention and containment reduce exposure by 60.1% relative to no controls. Increasing protocol dependency raises the modeled clinical-impact index from 1.021 to 7.567 without changing mean cyber exposure, demonstrating that technical exposure and clinical consequence can diverge. Risk-alignment experiments further indicate higher modeled clinical impact when greater technical risk corresponds to greater clinical significance. The findings support comparative architectural analysis, not calibrated estimates of real-world cyber risk, control effectiveness, or patient harm. The framework provides a clinical-context-aware approach to security-boundary design, control prioritization, and coordinated containment in connected healthcare environments.
[AI-29] Multimodal In-Context Learning with Explicit Rule Induction
链接: https://arxiv.org/abs/2610.41754
作者: Yuquan Li,Xinyang Jiang,Yuhao Xue,Cairong Zhao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A central challenge in multimodal in-context learning (MICL) arises from tasks that are ill-defined without context and require uncovering the underlying rules from demonstrations. Existing methods predominantly treat MICL as direct input-output mapping, overlooking the importance of explicitly inducing and reasoning over such rules, limiting both performance and interpretability. We propose ERIM, Explicit Rule-Induction for Multimodal In-Context Learning, a unified framework that enhances MICL by explicitly inducing the latent rules governing demonstration samples. Leveraging the reasoning ability of multi-modal large language model (MLLM), ERIM constructs rule-centric MICL datasets, introduces explicit supervision for rule induction, and fine-tunes MLLM to perform explicit rule-induction via a two-stage training strategy that combines mixed cold-start supervised fine-tuning with reinforcement learning. Extensive experiments show consistent performance improvements across diverse MICL settings and demonstrate that correctly inducing underlying rules directly leads to stronger and more stable performance.
[AI-30] Latent-Lens: Visual Perception in Small Language Models Through Latent Communication NEURIPS2026
链接: https://arxiv.org/abs/2610.41751
作者: Mo Shahdloo,Faegheh Sardari,Mathew Salvaris
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Published at NeurIPS 2026
Abstract:Bridging independent vision-language models (VLMs) and small language models (SLMs) in a modular multi-agent system typically forces them to exchange information as discrete text, which creates a lossy bottleneck for spatial and fine-grained details. This constraint is especially acute for SLMs, whose limited capacity makes them more reliant on the quality of the input signal. Recent latent-communication methods exchange states between language models. How a VLM can pass visual information to a separately pretrained text-only SLM though remains unexplored. We propose \textbfLatent-Lens, a lightweight codec that bridges the representation gap between a frozen VLM and a frozen SLM directly in latent space, enabling them to communicate purely through hidden states. The codec consists of a domain encoder that projects hidden states from the VLM into the SLM’s embedding space, and an attention decoder formalised as a LoRA that teaches the SLM to attend to these visual prefixes, while training only \sim1.6 M parameters (0.2% of the combined model size). On visual question answering (CLEVR, GQA), Latent-Lens achieves 81.0\pm7.9 % and 65.4\pm5.4 %, compared with 58.3\pm5.7 % and 61.8\pm1.0 % for text communication with the same LoRA budget (+30.2–69.8pp relative to frozen text communication), while on image captioning (Flickr8k) it improves over the same baseline by +16.5 (+18.8 frozen). We also evaluate Latent-Lens with TinyLlama-1.1B and InternVL2-1B, which use different architectures, vocabularies, and hidden dimensions.
[AI-31] Simple Features Beat Most AI Control Monitors NEURIPS2026
链接: https://arxiv.org/abs/2610.41713
作者: Shubhrangshu Debsarkar,Valmik Nahata
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 19 pages (5-page main body plus appendix), 7 figures, 10 tables. Accepted at the EvoRobust workshop at NeurIPS 2026. Code and data: this https URL
Abstract:AI control relies on monitors to catch a capable model that sabotages its work. These monitors are evaluated on their ability to differentiate honest from backdoored code. We show that on a standard evaluation dataset, simple features beat most monitors without ever running the code, outranking 20 of 24 open-weight models prompted as monitors. The dataset gives its labels away in two ways. People wrote the honest solutions and older language models the backdoors, so the style of the code reveals the class. Additionally, problems that LLMs can backdoor have different characteristics, so basic facts about the problem reveal it too. Swapping one class at a time for code by Claude Sonnet 5 shows that these features reward a contrast between authors, since human code against Sonnet 5’s backdoors is easier to separate than the original pairs, 0.95 against 0.79. One in seven backdoors in the standard test set fails our validity checks, which distorts comparisons between attackers. A Sonnet 5 monitor appears to catch 77% of the older models’ backdoors but 37% of Sonnet 5’s, and 78% against 61% once both are verified. We do not show that published monitors rely on these shortcuts, but our findings suggest that a high score on such a dataset is not by itself evidence of sabotage detection. We release the rebuilt dataset and checks.
[AI-32] Advancing Clinical Trial Outcome Prediction with Domain-Specific Large Language Models
链接: https://arxiv.org/abs/2610.41710
作者: Burak Bakkar,Hana Moalla,Mohamed Salem Jedidi,Haifa Ben Messaoud,Nicolas Lopez Carranza
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical trial failures are a major barrier to efficient drug development, incurring substantial costs and delaying patient access to effective therapies. Structured approaches such as the Hierarchical Interaction Network (HINT) predict trial outcomes by explicitly modelling interactions between drugs, diseases and protocols, but make limited use of the free text in trial registrations. We evaluate two domain-specific language models, BioBERT (108M parameters) and BioMistral (7B parameters), for phase-level outcome prediction on the TOP benchmark (12,477 trials), representing each trial as text built from ten this http URL fields. We compare fine-tuning (full fine-tuning for BioBERT, low-rank adaptation for BioMistral) with probing, in which the pretrained model is frozen and only a lightweight classifier is trained on its embeddings. On the held-out test set of each phase, all four language-model variants achieved higher ROC-AUC (0.613–0.772) and PR-AUC (0.649–0.901) than the published HINT results, corresponding to relative PR-AUC gains of 10–20%. F1 differences were small and mixed, and no model, including HINT, clearly exceeded the F1 of a trivial classifier that predicts success for every trial. Under the configurations tested, probing performed comparably to fine-tuning: fine-tuned models scored highest on ROC-AUC and PR-AUC in Phases I and II, and frozen BioMistral embeddings in Phase III (ROC-AUC 0.772; PR-AUC 0.901), although most differences between variants were below 0.03 and were not tested for significance. These results suggest that pretrained biomedical representations already encode signal relevant to trial outcome, and that probing is a computationally cheaper alternative to fine-tuning for this task.
[AI-33] When the Past Shapes Action: Understanding Embodied Memory
链接: https://arxiv.org/abs/2610.41688
作者: Siqi Li,Ziyi Bai,Xinrun Xu,Tinglei Huang,Börje F. Karlsson
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in generative foundation models have expanded the capabilities of embodied AI, enabling increasingly general navigation, manipulation, and long-horizon task execution. However, effective interaction with dynamic environments requires more than only strong perception and reasoning: agents must act with partial knowledge and retain, retrieve, and update information from past observations, actions, and experiences. This survey examines embodied memory as a mechanism for preserving decision-relevant information beyond the current observation. We organize existing approaches along two complementary dimensions: six fundamental memory requirements, spanning world-state persistence, task progress, belief maintenance, and experiential learning; and eight architectural categories, including recurrent states, fast weights, visual and symbolic representations, spatial memory, and experience-based systems. We analyze their capabilities and limitations, and examine how existing benchmarks evaluate memory-dependent behavior, distinguishing genuine use of historical information from general task performance. Finally, we identify open challenges in selecting relevant experiences, maintaining consistent beliefs in changing environments, and consolidating knowledge across episodes. Our analysis provides a framework for understanding memory as an essential component of reliable, adaptive, and lifelong-horizon embodied intelligence.
[AI-34] Radial Kernel Attention for Long-Range Modeling in Polymer Machine Learning Force Fields
链接: https://arxiv.org/abs/2610.41675
作者: Wenbo Fu,Chenghui Mao,Chengwen Hu,Haifeng Lv,Zhou Lv
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Radial Kernel Attention (RKA) for long-range modeling in polymer machine learning force fields. Local neighbor graphs constrain communication across polymer chains and their surrounding environments, while explicit all-pair attention incurs quadratic cost. RKA maps atomic distances from the cluster centroid to learned radial features and combines their overlap with content-dependent queries and keys. Factorized aggregation and readout enable global feature exchange at linear cost in atom count for fixed feature dimensions. We insert the invariant scalar update between EquiformerV3 local blocks and obtain conservative forces by differentiating the total energy, including its radial coordinate dependence. We evaluate energy and force prediction for finite polymer-derived clusters in OPoly26. Our models are trained on the official training split for 12 epochs and evaluated directly after training on the complete official validation split. A matched 5M comparison shows that RKA reduces per-atom energy and force MAEs by 67.4% and 36.3%, respectively. The 20M RKA model is compared with UMA-S-1.2 and UMA-S-1.2.1 on the same validation structures. The 20M model attains a total-energy MAE of 15.81 meV and a force MAE of 2.619 meV/angstrom; its per-atom energy MAE is 0.1182 meV/atom. RKA provides a compact global communication pathway that improves energy and force prediction on the evaluated polymer-derived clusters.
[AI-35] Breaking Information Islands in Sparse Tuning via Small-World Connectivity NEURIPS2026
链接: https://arxiv.org/abs/2610.41665
作者: Xianchao Guan,Zijun Xiong,Yifeng Wang,Yubo Cui,Yaowei Wang,Guo-Sen Xie,Xin Li,Zheng Zhang
类目: Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026. 23 pages, 6 figures
Abstract:Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but also on the feature-interaction topology of the sparse update’s structural support. Limited or uneven connectivity in this support may isolate subsets of feature dimensions, forming information islands that restrict direct task-specific interactions through the trainable update. To address this issue, we propose Small-World Induced Fine-Tuning (SWIFT), a sparse tuning framework inspired by small-world connectivity. SWIFT combines block-diagonal sparse updates for local feature mixing with a fixed deterministic scattering permutation that routes the mixed outputs across feature groups without additional trainable parameters. We further show theoretically that certain random sparse-update models and block-diagonal updates can exhibit information islands, whereas a sufficiently scattering permutation guarantees positive block-level expansion when the trainable block-diagonal factor consists of equal-sized, structurally dense square blocks. Extensive experiments on commonsense reasoning, natural language generation, and image classification demonstrate that SWIFT outperforms the evaluated parameter-efficient fine-tuning (PEFT) baselines on average and can match or surpass full fine-tuning. Implementation is available at this https URL
[AI-36] Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning
链接: https://arxiv.org/abs/2610.41647
作者: Adam Abdin(LGI)
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome variability inherent to safety-critical spaceflight. This paper presents an adaptive planning approach for multi-target servicing campaigns in low Earth orbit and applies it to inspection missions flown by a low-thrust servicer. The approach is based on Distributional Reinforcement Learning, which learns the full distribution of mission returns and thereby supports explicit risk-sensitive decision-making at each planning step. Specifically, a Quantile-Regression Deep Q-Network (QR-DQN) agent selects which target to inspect next by jointly considering the expectation and the variance of the predicted return through a mean-variance criterion. The mission environment models circular orbits with a spread in inclination and uses Edelbaum’s combined-manoeuvre low-thrust transfer estimates. The observation comprises the orbital elements, the visit history, the observed risk state of each target and the remaining mission-time budget. The risk state of each target changes stochastically during the campaign, making the outcome of an inspection uncertain and creating operationally meaningful distinctions between risk-neutral and risk-averse planning strategies. Because the mission-time budget generally admits only a subset of the candidate targets, the planner must also select which targets to inspect. Numerical experiments on scenarios with five target satellites show that one risk-aversion weight moves the policy across a range of mean return and return variance, over an unchanged environment and reward. Risk-averse agents reduce the variance of the episodic return by approximately one half relative to risk-neutral agents and lower the probability of a mission loss, at a cost in mean return. To validate the learned policies, the efficient frontier, which gives the highest mean return attainable at each level of return variance, is computed exactly by enumerating the admissible visit orders. At equal variance, the learned policies lie close to this frontier in mean return, and the risk-averse policies have a lower probability of a mission loss than the frontier policies.
[AI-37] Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
链接: https://arxiv.org/abs/2610.41639
作者: Chloé Carer(LGI),Adam Abdin(LGI)
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints involves a complex combinatorial problem constrained by coverage, fuel, and safety considerations. This paper models the viewpoint selection as a Constrained Markov Decision Process (CMDP) and applies Proximal Policy Optimization with Lagrangian relaxation to solve it. The system combines a high-level planner, which sequences camera viewpoints on an observation sphere around the target, with a low-level model that assesses each transfer using the Clohessy–Wiltshire (CW) equations for relative motion. The deterministic constraints on single-impulse reachability and \Delta v cap are enforced directly on the action set. Safety margins are affected by navigation and manoeuvre errors, making the actual clearance stochastic. To address this, a chance constraint on the realised trajectory is implemented, with a Lagrangian multiplier governed by PID control to adapt the penalty during training. The approach is tested on a Galileo-class navigation satellite in medium Earth orbit, modelled as a point cloud where visibility from each viewpoint considers field-of-view and line-of-sight occlusion. Across various risk budgets, the trained policy maintains the keep-out violation rate within the specified limit, achieving coverage close to its maximum and staying within the mission \Delta v budget. Compared to the usual practice of applying a fixed margin to the keep-out radius and planning deterministically, the chance-constrained policy attains less than half the violation rate at comparable coverage. This makes the collision risk an explicit design parameter, eliminating the need for re-tuning per case. The learned policy selects a viewpoint without the repeated Monte Carlo risk estimates required by a greedy, chance-constrained planner at every decision point.
[AI-38] What Should LLM s Forget: Reinforcement Unlearning of Knowledge Associations
链接: https://arxiv.org/abs/2610.41616
作者: Wutong Xu,Yun Qu,Haiyan Feng,Qi Wang,Xiangyang Ji
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) acquire extensive factual knowledge during pre-training, which may include sensitive, private, or copyrighted information, motivating the development of unlearning methods that selectively remove specific knowledge while preserving general capabilities. However, existing approaches typically optimize entire responses without explicitly identifying the knowledge to be forgotten, resulting in coarse and uncontrolled forgetting. To address this, we revisit the forgetting target of LLM unlearning and introduce Knowledge Association (KA), which characterizes the association between a query context and its target knowledge and is instantiated by the corresponding conditional predictive distribution, as the fundamental target of forgetting. Based on KA, we propose Knowledge Association Reinforcement Unlearning (KARU), a unified reinforcement learning framework that suppresses forgotten knowledge associations while preserving retained ones. Specifically, KARU derives complementary mechanisms from the KA-based formulation: token-level reward construction for finer-grained forgetting supervision, distribution-level regularization for precise knowledge suppression, and proximity-aware retain sampling to mitigate collateral degradation. Experiments across diverse LLMs on representative benchmarks demonstrate that KARU consistently improves forgetting effectiveness over existing baselines while maintaining comparable retention performance and general utility.
[AI-39] VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression
链接: https://arxiv.org/abs/2610.41609
作者: Minsu Jang,Jae-Woo Choi,Youngwoo Yoon,Dohyung Kim
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 17 pages, 3 figures, 13 tables. Code: this https URL
Abstract:A robot should act only on an instruction from a person or on an intent that the robot explicitly represents and generates. Unintended actions taken without such an instruction can cause risk or inconvenience to people and the environment. We examine how recent vision-language-action (VLA) policies respond when the instruction is missing or meaningless. We apply a paired protocol with empty, neutral, nonsense, infeasible, and wrong-task instructions to six model checkpoints in LIBERO-Goal simulation. Holding the initial state fixed and varying only the instruction, we ask three questions: whether the robot starts to move, which task it completes, and what the instruction determines. Even when the instruction was empty, the robot started moving in every episode with all six checkpoints, and with five of them it often completed a task that the instruction had not specified. On two GR00T checkpoints, the instruction determined which task was executed but not whether the robot started moving; the robot moved even on infeasible instructions. We then tested three methods for suppressing the start: prepending “if no task is given do not move” to the instruction; a gate that holds the robot when the first action chunk for the given instruction nearly equals that for an empty instruction; and fine-tuning the policy to hold still on non-task inputs. The prefix stopped the robot in none of the six checkpoints, and the gate held it on non-task inputs in five of the six checkpoints. The fine-tuned policy never completed a task on non-task inputs, but it did not reliably keep the robot still, and it failed to suppress the start on infeasible instructions.
[AI-40] Real Computation or Merely Naming? A Pre-Registered Mechanism Audit of 25 Cognitive-Architecture Modules in an LLM -Based Agent System
链接: https://arxiv.org/abs/2610.41604
作者: Peter Chen,Ni Zhang,Ying Xu,Siyuan Zhang
类目: Artificial Intelligence (cs.AI)
备注: 40 pages. Static audit of 25 cognitive-architecture modules (18 carry no computed value to any production output), pre-registered interventions on 11 modules, 61 frozen criteria (53 confirmed / 5 falsified), two repair controls (0.028 - 1.000 task success; -52% error). Artifact: this https URL
Abstract:Agent systems built on local large language models are increasingly marketed through the vocabulary of cognitive architecture: modules named global workspace, theory of mind, causal reasoning, or active inference are listed as features, and the presence of the name is taken as the presence of the mechanism. We ask the prior question – does the named computation actually happen – and contribute a transferable verification method for the claim-implementation gap: a four-question static audit protocol with line-number evidence and call-chain tracing, demonstrated on two systems. We audit a system claiming 25 cognitive mechanisms, pre-register falsifiable predictions for 11 modules and execute the interventions, replicate the protocol on the IIT-phi core of a second, independent system with its own pre-committed falsification criteria, and run two repair experiments as positive controls. In 18 of 25 modules the computed value never reaches any production output; three idle-wiring shapes new to the dead-code and code-smell literatures emerge (connected-but-never-called, called-but-inert, called-but-content-blind). Of 61 frozen criteria across 12 interventions, 53 were confirmed, two partially adjudicated, five falsified, one adjudication-ambiguous. Repairs raised planner task success from 0.028 to 1.000 and cut state-controller error by 52%. Module names and module computations must be audited as independent claims; the protocol and the five-class failure taxonomy are reusable for the claim-implementation gap at two-system, case-level depth.
[AI-41] Patch-Based Deep Kernel Learning for Data Assimilation and Bias Correction with Extremely Sparse Observations
链接: https://arxiv.org/abs/2610.41596
作者: Emanuele Mele,Angelo Coluccia,Massimo Cafaro,Italo Epicoco,Paola Nassisi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Data Assimilation (DA) reconstructs the state of a spatio-temporal system by combining sensor observations with the output of a numerical model. Since model states are high-dimensional while observations cover only a small fraction of the domain, DA is a challenging large-scale estimation problem, central to weather forecasting and oceanography. Classical variational methods such as 3D-Var rely on a hand-crafted background error covariance that assumes unbiased errors, and therefore cannot capture state-dependent correlations or systematic model biases. Moreover, their cost grows with the size of the domain. Deep learning (DL) methods can perform DA and bias correction (BC) non-linearly, but typically offer limited interpretability and no calibrated uncertainty estimates. We propose a hybrid framework that couples a convolutional neural network (CNN) with a learnable-kernel Gaussian Process (GP) to jointly perform DA and BC from extremely sparse observations. The model is trained end-to-end by maximizing the marginal log-likelihood, learns a data-driven covariance over the whole grid, and provides both the Maximum A Posteriori analysis and its uncertainty by design. A patch-based strategy reduces the O(N^3) training cost of exact GPs over N grid points to O(N) for a fixed patch size. Inference remains exact and only requires solving a linear system whose O(m^3) cost depends on the number of observations m \ll N , not on the domain. The learned covariance can also replace the hand-crafted one in 3D-Var, improving its accuracy without changing the variational formulation. Experiments on sea surface temperature and salinity, with both synthetic and real, extremely sparse in-situ observations, show that the framework outperforms 3D-Var, a classical GP and a competing DL model, while reducing inference time by more than 98% with respect to 3D-Var.
[AI-42] Connectivity-Aware Client Selection in Decentralized Federated Learning
链接: https://arxiv.org/abs/2610.41570
作者: Usevalad Milasheuski,Monica Nicoli,Stefano Savazzi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Decentralized federated learning (DFL) has emerged as a scalable alternative to centralized federated learning, removing the single point of failure and communication bottleneck at the server. However, most existing decentralized approaches rely on full-neighborhood broadcasting, where each client transmits its model update to all of its neighbors in each communication round. While effective, this results in significant communication overhead and high energy consumption. This paper applies spectral graph tools as lightweight client selection methods that leverage the graph topology by analyzing the spectral properties of the graph Laplacian over a fixed communication graph. Specifically, we exploit eigenvector embeddings and heat/diffusion kernel similarities to guide each client in selecting only a subset of structurally relevant neighbors, i.e., those that are most beneficial for the convergence of the federation process. This topology aware randomization matches or outperforms uniform neighborhood subsampling and approaches the accuracy of full broadcasting. The largest gains, about 3 percentage points, emerge under strong data heterogeneity on larger, structurally rich graphs, while the total number of transmitted messages is reduced by 50% compared to naive broadcasting.
[AI-43] Verify–Backtrack Loop: Structured Precondition Recovery for Long-Horizon Language Agents
链接: https://arxiv.org/abs/2610.41564
作者: Mingyang Liu,Qingcan Kang,Shixiong Kai,Tao Zhong,Mingxuan Yuan,Xin Li,Linqi Song
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Language agents increasingly tackle long-horizon tasks in interactive environments, where actions often depend on latent preconditions. The core difficulty is not trajectory length but recovery: when a prerequisite is missing or an earlier decomposition fails, trying another plausible action rarely helps. ReAct and later reflection or adaptive-planning methods support local correction, but express recovery as free-form text, with no explicit control state linking the terminal goal, the active goal, and the failure source. We introduce the Verify–Backtrack Loop (VBL), a training-free extension of ReAct that turns precondition discovery and failure diagnosis into structured planning-state updates. VBL maintains the task decomposition as a bounded goal stack and, on goal activation, execution failure, or material state change, jointly verifies two horizons—whether the active goal contributes to the terminal goal and whether it is feasible in the current state. These dual-horizon judgments, combined with typed failure attribution, trigger explicit operations—Push, Pop, retry, or BacktrackTo the parent. We evaluate VBL with Qwen3.5-9B, GPT-4o, and DeepSeek-V3.2 on AppWorld, ALFWorld, and ScienceWorld. VBL improves over ReAct and reflection-based control on every backbone–benchmark pair. On the challenging AppWorld setting, it improves task success by up to 22.07 percentage points over ReAct. Ablations show that removing either verification or diagnosis degrades performance, in several settings below ReAct, confirming that the two mechanisms are complementary. These results show that reliable long-horizon interaction benefits from revising the planning state, not merely the next action.
[AI-44] Specification-Guided Generation for Large Language Models
链接: https://arxiv.org/abs/2610.41511
作者: Alessandro Previti,Konstantinos Vandikas,Mario Alviano,Mats Carlsson,Albin Larsson Forsberg,Alexandros Nikou,Athanasios Karapantelakis,Aneta Vulgarakis
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can generate structured outputs, but existing constrained-decoding methods mainly enforce syntax and cannot directly enforce satisfaction of a formal specification. We introduce a neuro-symbolic, specification-guided decoding framework that combines LLM generation with symbolic reasoning. At each step, the framework refines the decoding grammar by filtering out values that cannot be extended to an output satisfying the specification. We present a forward-only variant that guarantees specification satisfaction by construction, and a backtracking variant that revises earlier feasible choices when their downstream consequences leave only values that the LLM itself judges unsuitable. The framework is model-agnostic, requires no retraining, and builds on existing grammar-constrained decoding mechanisms. Experiments on configuration and constraint-solving benchmarks show that the forward-only variant reliably produces specification-compliant outputs while reducing repeated LLM inference compared with rejection sampling and incurring modest symbolic-reasoning overhead. The backtracking variant further reduces cases in which the model is forced to select values that it has itself judged unsuitable, thereby substantially improving adherence to the LLM’s stated interpretation of the prompt.
[AI-45] Multi-Test Nation Bias Benchmarking of LLM s with Explicit Unbiased Baselines ACL
链接: https://arxiv.org/abs/2610.41486
作者: Jonghyeon Choi,Yeonjun Choi,Beakcheol Jang
类目: Artificial Intelligence (cs.AI)
备注: Published in Transactions of the Association for Computational Linguistics (TACL), 2026. 17 pages. Equal contribution: Jonghyeon Choi and Yeonjun Choi
Abstract:While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relations (IR) applications, many existing evaluations still lack a clearly specified unbiased reference (ground truth), limiting fully quantitative and cross-setting measurement. Using an expanded corpus of 722 official UN Security Council (UNSC) records, we measure nation-level bias with five complementary tests, thereby extending prior approaches to cover a broader set of bias expression channels: opinion ranking (WOW), association sorting (AK±), vote simulation (PVS), opposition attribution (F-Oppose), and framing (Headliner). To enable grounded bias evaluation, we explicitly define an unbiased status for each test; in particular, for WOW and AK± where ground truth is difficult to specify, we propose an action-based pseudo unbiased status derived from observable voting behavior to construct a quantifiable baseline. Benchmarking 4 LLMs (GPT-4o-mini, Llama 3.3-70B, Mistral-22B-Small, and Qwen3-30B) across the five permanent members of the UNSC, we find a test-agnostic macro-pattern: relative to the reference baselines defined for each test, model outputs lean toward the opposition (red) side for China and Russia and toward the supportive (blue) side for the U.K., the U.S., and France. These results underscore the need to audit IR-facing LLM deployments with grounded, multi-test benchmarks tied to explicit reference points.
[AI-46] EvoMCP: Co-evolving MCP agents and training data
链接: https://arxiv.org/abs/2610.41479
作者: Xuanang Gao,Kepeng Lin,Xinyan Shi,Yong Deng,Shuheng Shen,Changhua Meng,Xiaofeng Wu,Jie Yang,Wei Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Supervised fine-tuning of Model Context Protocol (MCP) agents is typically conducted on a static data distribution, which fails to adapt to the student’s evolving capabilities and leaves newly exposed weaknesses insufficiently addressed. To address these challenges, we propose \method, an agent–data mutual evolution framework that enables MCP agents to improve through a closed loop of task evolution, trajectory evolution, and supervised fine-tuning. Specifically, \method\ assesses the student’s established capabilities and capability gaps from rollout feedback to identify learning targets. Task evolution then translates these targets into executable tasks by adapting the instructions and observable environment states of compatible verified seed tasks while keeping their verifiers aligned. To provide reliable and efficient demonstrations for these tasks, trajectory evolution uses reusable skills to recover failed teacher rollouts and refine successful ones. The resulting task–trajectory pairs are used to fine-tune the student, while accepted tasks seed subsequent evolution. To close the loop, we re-evaluate the updated student and use its feedback to guide the next round of data evolution. Experiments on Toolathlon, MCPMark, and MCP-Atlas demonstrate consistent improvements over the initial student, yielding absolute gains of 16.5%, 10.2%, and 12.6%, respectively. Project page: this https URL.
[AI-47] Evidence-Guided Recovery for AI Agents with Ambiguous Tool Outcomes and Partial Execution
链接: https://arxiv.org/abs/2610.41453
作者: Genliang Zhu,Xiaobo Chen,Ziyuan Wang,Chu Wang
类目: Artificial Intelligence (cs.AI)
备注: 24 pages, 1 figure, and 10 tables. Includes a finite recovery model, model-facing confirmation study with two local-model bundles, independent replay, and an explicit provider-capability scope
Abstract:Tool timeouts can leave AI agents facing ambiguous tool outcomes and partial execution: an action may have failed, committed or remained in flight while other workflow steps have already completed. We formulate evidence-guided task recovery over approved obligations, provider capabilities and states compatible with public evidence. A shared admissibility check handles key expiry, authorization timing, dependencies and residual effects; a terminal certificate separates actual goal satisfaction from publicly supported safe completion. The known-model implementation combines standard observation-contingent planning with a separately implemented effect-history adjudicator. In a finite study of 37 assignments, planning and three active controls each certify 28 completions; four saved synthetic fee units incur additional computation. A preregistered assignment-held-out comparison then evaluates planning advice against evidence prompting with two local model bundles. Across 58 paired units in 19 previously developed task families, advice certifies 30 completions versus 9, a 36.2-percentage-point difference (exact family sign-flip p=0.0078), using 272 proposals versus 399. All 116 trajectories pass independent replay. Both models exhibit a positive completion difference. The comparison measures the evaluated advice interface under a common shield; search-versus-rule advice requires a separate matched comparison. Unknown and handoff outcomes remain explicit, with guarantees tied to the declared provider capabilities rather than arbitrary third-party exactly-once execution.
[AI-48] From Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models
链接: https://arxiv.org/abs/2610.41445
作者: Wenxi Dai,Jingyu Hu,Yanting Yang,Mengnan Du
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Membership inference aims to determine whether a particular example was included in a model’s training data. Such membership signals are often broadly described as memorization, while the underlying mechanisms can involve both how information is represented internally and how it is accessed by the model to produce behaviors. We therefore present a mechanistic study of membership signals in large language models (LLMs). We introduce Decodable Information Score, Used Information Score, and Readout Accessibility Gap (DUG), a set of complementary measures that separately quantify membership information decodable from internal representations and the extent to which it is exposed through the model’s readout. Across supervised fine-tuning (SFT) and reinforcement learning (RL)-based membership inference methods, membership-specific effects appear in both the amount of decodable information and readout under SFT, while under RL-based membership inference methods they appear primarily in readout. Activation patching further localizes this membership-specific effect to intermediate layers. To examine whether these findings extend to a more general RL setting, we further study reinforcement learning with verifiable rewards (RLVR) contamination, where the training reward is independent of the reference solution. In this setting, we find that behavioral changes can emerge without a corresponding increase in likelihood-based membership inference signals.
[AI-49] CryoAtlas: A Large Curated Dataset and Unified Benchmark for Cryo-EM Atomic Model Building NEURIPS2026
链接: https://arxiv.org/abs/2610.41411
作者: Mingrui Li,Minzhang Li,Weichen Qin,Yufan Xie,Zhangzhi Xiong,Sixian Shen,Jiakai Zhang,Yuan Pei,Jingyi Yu
类目: Artificial Intelligence (cs.AI)
备注: NeurIPS 2026 ED Track
Abstract:Deep learning has accelerated automated atomic model building from cryo-EM maps, yet progress remains limited by the lack of large, high-quality datasets and standardized evaluation benchmarks. We address this gap by introducing CryoAtlas, a rigorously curated dataset and unified benchmark for density-guided atomic model building of protein chains. By integrating and filtering EMDB and PDB entries (up to March 2026), CryoAtlas provides 17,809 map-model-sequence entries, including a curated 16,657-entry training split. From this resource, we construct a 618-target benchmark with a shared evaluation protocol that jointly assesses structural accuracy, model-to-map fit, and stereochemical validity. Using this framework, we benchmark state-of-the-art learning-based methods against Phenix and AlphaFold3, comprehensively analyzing the impact of map resolution, sequence length, protein category and sequence identity on modeling accuracy. As a case study, retraining the ModelAngelo CNN component on CryoAtlas improves performance on the benchmark. CryoAtlas provides an open, reproducible foundation for future cryo-EM atomic model building research. Project details and resource release updates are available at this https URL.
[AI-50] When Safety Checks Become Privacy Risks: A Privacy-Preserving Paradigm for LLM Agent Safety Auditing
链接: https://arxiv.org/abs/2610.41401
作者: Yixiang Luo,Ziheng Ma,Ke Chang,Jian Wang,Jiqiang Liu
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 12 pages, 2 figures, 10 tables
Abstract:Large language model agents interact with online services through tool calls that may access private data or perform sensitive actions. Specialized providers can supply and maintain pre-execution safety auditing capabilities for agent operators. However, sending task descriptions, call arguments, and interaction histories to an external auditor may expose sensitive user information. In this work, we propose a paradigm for privacy-preserving safety auditing that separates delegated audit computation from plaintext access and execution authority. Grounded in a three-domain auditing model, the paradigm connects trusted representation construction, encrypted audit computation, and trusted decision and execution through explicit interfaces. These interfaces accommodate alternative representations and scorers, while encrypted computation protects audit inputs from a semi-honest auditor. We instantiate this paradigm in SealCall, a framework combining learned audit representations, CKKS-based encrypted similarity computation, and execution control bound to audited tool calls. Evaluated on 1,252 public binary candidates, SealCall achieves a mean F1 score of 0.840 under grouped nested evaluation, while preserving plaintext audit decisions in all measured cases. A representative configuration completes each encrypted scoring operation in 0.176 s.
[AI-51] scAnyOmics: Foundation-Model Distillation for Any-to-Any Completion of Multi-omics Virtual Cells
链接: https://arxiv.org/abs/2610.41399
作者: Zhaoqi Song,Ye Du,Yusuke Iwasawa,Yutaka Matsuo,Qian Niu
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures, 2 tables
Abstract:Recent advances in single-cell technologies have enabled the profiling of multiple molecular modalities, including transcriptomics, proteomics, and chromatin accessibility, providing a more comprehensive understanding of cellular states. However, integrating heterogeneous modalities and predicting missing molecular information remain challenging, particularly when paired measurements are limited. Here, we introduce scAnyOmics, a generative framework that distills knowledge from a frozen single-cell foundation model into a multimodal student model. scAnyOmics integrates scRNA-seq, protein-marker measurements, and scATAC-seq into a shared representation, enabling cross-modal prediction and reconstruction of missing modalities. We assembled 690,880 cells from six publicly available studies, covering both paired and partially paired multi-omics measurements, to systematically evaluate its predictive performance. We further evaluated scAnyOmics on held-out donor data, where it outperformed baseline methods across multiple evaluation metrics. Beyond cross-modal reconstruction, we explored its applications in cell-type annotation, perturbation-response prediction, and biological age estimation. Overall, scAnyOmics provides a unified framework for integrating incomplete single-cell multi-omics data and transferring pretrained biological knowledge, supporting the development of virtual cell models across diverse cellular contexts and experimental platforms.
[AI-52] Cross-Interface Authorization for AI Computer-Use Agents
链接: https://arxiv.org/abs/2610.41397
作者: Genliang Zhu,Chu Wang
类目: Artificial Intelligence (cs.AI)
备注: 31 pages, 2 figures, and 7 tables. Includes formal results, two reference runtime mappings, mutation tests, commit-time race probes, source-shaped adapter checks, and a frozen external-source audit. Guarantees bind the registered deployment profile and declared effect semantics
Abstract:Computer-use agents act through graphical interfaces, command lines, APIs, scripts, and Model Context Protocol servers. A policy decision can be interface-invariant while its executable authorization is not: another route can bypass mediation, alter a security-relevant default, or commit after the authorized state has changed. We present a profile-bounded protocol for a fixed, versioned computer-use deployment. It enumerates effect-bearing routes and durable sinks, checks a route-to-sink enforcement cut, normalizes each concrete request to a canonical effect, issues a route-bound prepare ticket, and verifies the route, effect, grant, trace, profile, inventory, and authoritative state again at commit. A committed result requires an agreeing sink receipt. Under explicit assumptions, we prove profile-bounded refinement, matched-effect decision invariance, resistance to alternate-route laundering and declared cross-surface recomposition, commit-time state safety, and receipt-grounded outcomes. The reference profile registers 12 effect families, five interfaces, and 60 routes. Two structurally distinct runtime mappings produce 3,464 traces over 1,732 scenarios each: 960 commits, 2,312 denials, and 192 commits after re-preparation. An independent checker accepts every retained trace and pair and rejects 26/26 semantic regressions; coverage and normalizer suites reject 24/24 and 720/720 mutants; and 168/168 commit probes match their declared outcomes. A frozen-source audit passes 52 checks over 12 files from two independent hybrid runtimes and one public authorization substrate. Source-shaped adapter refinements commit 48/48 instrumented calls and reject 15/15 unbound cases. The resulting guarantees bind the declared profile and its effect semantics. Comments: 31 pages, 2 figures, and 7 tables. Includes formal results, two reference runtime mappings, mutation tests, commit-time race probes, source-shaped adapter checks, and a frozen external-source audit. Guarantees bind the registered deployment profile and declared effect semantics Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2610.41397 [cs.AI] (or arXiv:2610.41397v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.41397 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-53] Online Evidence-Containment Prediction over Text Spans under Distinct Queries
链接: https://arxiv.org/abs/2610.41384
作者: Xiang Liu,Haochen Ding
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Evidence-grounded NLP systems often verify nested text regions sequentially, so verifying one region constrains others. We formalize this as Online Evidence-Containment Prediction (OECP): each candidate is predicted before exact verification and queried at most once. Span containment is exactly a triangular 2D product order, making OECP distinct-query online isotonic regression. With multiple admissible targets, worst-case regret is near-linear, \widetilde\Theta(m_\mathrmeff) , in the effective scale given by endpoint-rank compression; a single fixed target, as in standard QA, admits an explicit polynomial-time learner with optimal \Theta(\log m_\mathrmeff) regret, run on real NQ episodes. On Natural Questions, containment yields a large deductive gain plus a smaller learned gain on unresolved candidates, reproduced across two backbones and DPR hierarchies. Expected Search-space Reduction (ESR) turns these probabilities into decisions, localizing evidence in fewer verifier calls on SciFact, ERASER, and DPR hierarchies; on DPR hierarchies under call budgets it outperforms four recent external selectors and raises fixed-reader F1 over learned and generator-greedy scheduling.
[AI-54] From prediction error to sparse events: Biologically inspired signed-dyadic coding for Mandarin keyword spotting
链接: https://arxiv.org/abs/2610.41381
作者: Zijian Zhou,Rui Liu,Rongxiang Nie,Rui Ma,Ziyao Zhang,Xin Du
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: 14 pages, 4 figures
Abstract:In continuous spiking keyword spotting, temporally correlated acoustic frames can repeatedly trigger memory access and neural computation at a dense input interface, even when hidden activity is sparse. Predictive processing and sensory adaptation suggest retaining predictable input locally and responding selectively to deviations. Inspired by this mechanism, we propose Predictive Signed-Dyadic Coding (PG). PG maintains a local prediction for each input channel and transmits an event only when the residual exceeds a learned threshold. Each event carries a sign and a finite power-of-two magnitude, and the receiver reconstructs it with fixed-point shifts and additions. In Mandarin keyword spotting, INPUT-PG reaches 90.00% accuracy while reducing mean SRAM reads by 33.87% and inference cycles by 32.77%. PG provides a hardware interface for research on low-power continuous speech processing and event-based sensory systems.
[AI-55] Ask on Demand: Targeted Peer Communication for Parallel Agent ic Systems
链接: https://arxiv.org/abs/2610.41378
作者: Haofu Yang,Hongyang Yin,Ran Bi,Faguo Wu,Peng Xue,Feng Zhou,Han Zhang
类目: Artificial Intelligence (cs.AI)
备注: Equal contribution: Haofu Yang and Hongyang Yin. Corresponding authors: Ran Bi and Faguo Wu
Abstract:Parallel LLM agents tackle complex tasks by exploring multiple trajectories, yet useful discoveries remain isolated in individual execution histories. Existing sharing methods often select or summarize information before other agents’ needs become clear, potentially overlooking details that become important later. We propose Ask on Demand (AOD), a communication framework that enables agents to seek targeted peer assistance when specific information needs arise. Upon an approved request, AOD retrieves relevant evidence from peer execution histories, selects a suitable peer, and generates a response tailored to the request. To determine when such assistance is worthwhile, we train a lightweight gate using paired team continuations, with supervision derived from final team outcomes and inference cost. Experiments on AppWorld and WebShop demonstrate that AOD consistently improves task performance over independent parallel execution while achieving lower inference cost than existing information-sharing baselines. These findings highlight the benefits of aligning peer communication with agents’ evolving information needs to achieve more effective and efficient collaboration.
[AI-56] From the Zoo to the BULLPEN: Sample-Efficient LLM Profiling via Multi-Channel Model Embeddings
链接: https://arxiv.org/abs/2610.41375
作者: Russell Ro,Qiming Cui,Reza Abbasi-Asl
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 28 pages, 7 figures, 12 tables. Code and data: this https URL
Abstract:Choosing among the hundreds of thousands of available large language models (LLMs) requires a thorough understanding of each candidate model’s properties. In practice, each property is measured by its own benchmark suite, so profiling a model on many properties means paying for many separate evaluations. To address this, model embeddings compress each model into a fixed-length vector from which many properties can be predicted. Existing methods, such as EmbedLLM, build these vectors from correctness patterns and question features, and often need large probe batteries to do so. However, many behaviors that are important when choosing a model, such as its verbosity, safety and style, show up more in how it answers than in whether it is correct. We introduce BULLPEN (Behavioral Understanding of Large Language models via Profile ENcodings), a framework that extends EmbedLLM in three ways. First, BULLPEN organizes the downstream tasks that prior work uses and adds 20 task benchmarks we built on behavior, style and provenance. Second, it is the first framework to read a model’s own answer text jointly with its correctness and the question text. Third, it encodes models from a few hundred questions instead of the full 24,118-question pool. Across 260 open-weight models, our answer-text encoder scores 0.48 +/- 0.10 on the new tasks against 0.35 +/- 0.10 for the best prior encoder. Our encoders that read answer text keep at least 91% of their full-pool skill from only 400 questions (1.3% of the inference compute of the full question pool), while a correctness-only baseline keeps 83% with the same 400 questions. Because these embeddings need only model outputs, they support behavioral auditing and low-cost profiling, and could extend to API-only models. Code and data: this https URL
[AI-57] RECITE: Revising Eligibility Criteria Incorporating Textual Evidence KDD KDD2026
链接: https://arxiv.org/abs/2610.41368
作者: Russell Ro,Reza Abbasi-Asl
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, 4 figures, 4 tables. Extended version of a paper in KDD 2026 (32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining). Code and data: this https URL
Abstract:Overly restrictive eligibility criteria contribute to widespread enrollment failure in clinical trials, yet published recommendations for how specific criteria should be broadened remain disconnected from trial design at scale. We introduce RECITE (Revising Eligibility Criteria Incorporating Textual Evidence), a framework for automated discovery, extraction, and implementation of evidence-based eligibility criteria revisions. RECITE uses protocol amendment documents from this http URL to construct a dataset of 11,913 curated instances across 5,735 trials, each pairing original criteria with documentary evidence and target revisions. We benchmark fifteen LLM configurations under truncated-to-context-window and retrieval-augmented generation conditions, on a 3,116-instance sample under an LLM-as-judge protocol validated against ten domain experts (judge vs. human quadratic-weighted Cohen’s \kappa_qw = 0.694 \pm 0.096 ). The best open-weight model, Gemma 2 9B, achieves \kappa_qw -aligned ordinal score 3.0 \pm 0.9 on a 0–4 scale (normalized 0.742) and the best closed-weight model, GPT-4o, reaches 3.0 \pm 0.8 (normalized 0.745). Using an agentic literature discovery system, we then identified 44 paper–trial matches across five publications providing trial-design and EC-modification evidence. Our findings suggest that existing clinical knowledge can be operationalized into actionable trial design recommendations.
[AI-58] LEAP: Efficient LLM Inference Scheduling with Generalizable Length Prediction
链接: https://arxiv.org/abs/2610.41359
作者: Kwan Yan,Jialun Zhong,Yanzeng Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Length-aware scheduling reduces request latency in LLM serving by prioritizing shorter requests. However, despite their large model sizes, length predictors can suffer degraded ranking performance under domain and task shifts, and adapting them can require costly retraining. Their prediction overhead can further offset the benefits of scheduling. To improve the generalization and efficiency of length-aware scheduling, we propose \textbfLEAP, a length prediction system with fast online adaptation and asynchronous CPU execution. LEAP reuses the serving model’s prefill hidden states, which contain information about the output length, and extracts this signal through a linear readout. To adapt to substantial domain and task shifts, it updates the readout at test time through recursive least squares, using completed requests as supervision without gradient computation or historical-data replay. Prediction and updates run asynchronously on CPU, while prediction and initial batch ordering overlap the remaining GPU prefill. Across the evaluated cross-domain transfers, LEAP retains strong ranking performance, and online adaptation improves Spearman correlation by 64.7% under a larger domain shift. Serving experiments show that LEAP reduces average per-token latency by 15.9–40.8% compared with the evaluated length predictors, while achieving 1.9% higher throughput with 30,569\times fewer predictor parameters than the strongest evaluated baseline.
[AI-59] Do Transaction Prices Reflect Visual Preference? A Diagnostic Study with Multimodal Language Models
链接: https://arxiv.org/abs/2610.41358
作者: Fengxian Ji,Jingpu Yang,Fan Zhang,Junhong Liang,Qian Jiang,Yuke Li,Jinri Guo,Rongxin Zhu,Zhuohan Xie
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing visual preference evaluation relies on costly human annotations or preference reward models, whereas online marketplace transaction prices are readily available and reflect real purchasing behavior, offering potential low-cost supervision. However, transaction prices conflate visual preferences with non-visual factors such as provenance, demand, and competition, making aggregate accuracy insufficient to distinguish model errors, unreliable price signals, and protocol confounds. To address this identifiability challenge, we introduce MarketImage, a diagnostic framework that identifies when transaction prices reflect visual preferences, where they fail, and whether multimodal language models can recover human-supported visual preference signals. MarketImage comprises three components: (i) MarketImageArt, 266 six-image comparison sets with transaction prices and price-blind human rankings; (ii) MarketImageLens, a tie-preserving protocol that partitions 3,990 within-set image pairs into price-human agreement, conflict, and price-tie regions while separately auditing presentation-order effects; and (iii) MarketValuator, six elicitation methods spanning direct comparison, independent scoring, budget constraints, and multi-agent aggregation to evaluate signal recovery by static visual scorers and three multimodal language models. Experiments show that only 24.2% of image pairs have a non-tied price direction, of which 34.7% agree with the human majority. Within this region, HPSv2 leads at 66.4% Recoverable Accuracy; Qwen’s budgeted and aggregated variants trail baselines, while GPT interface gains remain statistically unresolved. Thus, transaction prices encode recoverable visual preferences only under limited conditions and should be treated as uncertain proxies rather than ground truth.
[AI-60] Operon-S1: a fast decision controller for long-horizon biomedical agents
链接: https://arxiv.org/abs/2610.41350
作者: Genoria AI Team:Yuejie Hou,Yueyuxiao Yang,Weiting Tang,Meng Yang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 40 pages, 5 figures, 3 tables, 5 Extended Data figures, 6 Extended Data tables
Abstract:Language-model agents now carry out long scientific work in biology, but their judgements about conventions, skills, errors, evidence and stopping are made inside the reasoning that does the work, where they cannot be measured, replaced or improved on their own. Here we present Operon-S1, a fast decision system, a System 1 for the agent, that separates judging from doing. A decision harness poses the agent’s recurring judgements as closed questions at 13 decision points and acts only through fixed messages that never contain an answer, so any model that answers the questions can serve as the judge. Across seven life-science benchmarks, a local decision model took over these judgements in 0.6 s at the median for an in-loop request on CPU at no request cost, with no measurable change in the agent’s score on five of six benchmarks, whereas the general language model that drives the agent, asked the same questions, accounted for 8.0% to 35.2% of task cost. Every decision is recorded with its consequence. Retraining on labels from task construction, verifiers or execution raised decision accuracy on every question in each of three trained versions, and a new version enters service only through a promotion gate. Task-level results in this study use the public checkpoint. The same closed decisions serve wet-laboratory protocol troubleshooting and the design of a germline target enrichment panel. Like the regulator of an operon, which switches genes on and off without making their products, Operon-S1 steers the agent without doing its work, and makes the agent’s judgements a layer that can be measured, replaced and trained.
[AI-61] ransferring Peptide-Target Interaction Patterns to Small Molecules by Joint Guidance for Diffusion and Flow Matching
链接: https://arxiv.org/abs/2610.41322
作者: Jian Lee,Changyun Cho,Sun Kim
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Generating small molecules to target protein-protein interactions (PPIs) remains one of the most challenging yet high-reward frontiers in drug discovery. Unlike traditional small-molecule drugs that bind to deep, well-defined pockets, PPI interfaces often comprise broad, shallow binding surfaces. A natural strategy is to leverage known peptide-target interactions, as peptide ligands provide rich interface-level constraints. However, a major algorithmic challenge is that rigid atom-level conditioning on peptide structures can over-constrain the generative space and exclude valid small-molecule solutions. To address this challenge, we present PIFT (Peptide InterFace Transfer), a novel framework that transfers peptide-target interaction patterns to small molecules through joint sampling-time guidance of masked discrete diffusion and continuous flow matching. PIFT first maps peptide-target and small-molecule-target interfaces into a shared, target-conditioned interaction space, capturing transferable interface information across molecular modalities. The resulting peptide-derived reference then jointly steers building-block logits and 3D coordinates toward peptide-compatible molecules while preserving a strong adapted molecular prior. Extensive experiments across diverse PPI targets show that the learned representation captures cross-modal interaction compatibility, while peptide guidance improves interface recovery and achieves strong docking performance and favorable molecular properties.
[AI-62] Erase the Injected Region: Multimodal Prompt-Injection Defense with On-Policy Self-Distillation
链接: https://arxiv.org/abs/2610.41307
作者: Shuo Hong,Jiaxin Ma,Xidi Cai,Qianli Ma
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal indirect prompt injection embeds malicious instructions in external media that also contains information needed for the user’s task. Existing defenses can block the attack yet impair the model’s use of that information, leaving a gap between attack defense and task preservation. We propose ERASE, an on-policy self-distillation method that constructs its reference by erasing the injected region in place. Modality-appropriate blank content replaces the injection while the surrounding media is preserved. A student reading attacked media learns from a frozen base model reading the erased view through full-distribution token-level feedback on its own rollouts. Across image, audio, and video on two model sizes, ERASE records near-zero observed attack success on the evaluated benchmark while retaining task utility close to the undefended clean-input level. On 3B audio, ERASE reaches 60.1% attacked-input accuracy compared with ARGUS’s 33.1%, within 0.5 points of the undefended clean-input level. Experiments show that ERASE reduces injection dependence from 6.26 to 0.0095 on 3B audio while staying close to the base model on the erased input. Out-of-domain accuracy also stays near the base level. Our code will be made publicly available upon acceptance.
[AI-63] Subliminal Reinforcement Learning: traits travel through a judges scores
链接: https://arxiv.org/abs/2610.41301
作者: Keuntae Kim,Yong Suk Choi
类目: Artificial Intelligence (cs.AI)
备注: 37 pages, 4 figures, 2 tables, 2 Extended Data figures, 6 Extended Data tables; Supplementary Information included. Code and data: this https URL
Abstract:Subliminal learning, the transfer of a model’s trait through data with no semantic trace of it, has been shown for students trained on a teacher’s text. Post-training relies on group relative policy optimization with a language-model judge, where the student learns only from its own outputs and one judge’s scores. We show this suffices: a student writes invented syllables or numbers, a judge made to prefer one animal scores them, and the student’s preference moves toward that animal relative to a run whose judge prefers another. Of 48 design cells, two fail the validity gate and 17 establish the trait at 5% false-discovery rate, numbers-only cells included; elsewhere, our account gives the reason. Removing only the reward difference between judges removes transfer. We establish the principle as a chain of four stages: the trait must show in the judge’s scores, favour strings that move the student, accumulate and reach the answers. Judges with the same preference built from other data, or on another model, place it on other strings, and transfer is not detected. Carried unchanged to AI-safety dispositions, the chain holds: undesirable dispositions reach the student’s answers, and stages measured before training anticipate where, helping to prevent part of this risk.
[AI-64] Self-Improvement with Auxiliary Rewards: Learning from Verified Traces and Evaluating Transfer
链接: https://arxiv.org/abs/2610.41296
作者: Minju Gwak,Alan Ritter
类目: Artificial Intelligence (cs.AI)
备注: 28 pages, Work in Progress
Abstract:Answer-level rewards cannot distinguish reasoning traces with equally correct outcomes. We study whether auxiliary trajectory preferences improve small-language-model self-improvement and transfer under limited supervision. We compare confidence-based (SC) and entropy-based metrics (UID-Conj.), which combines global entropy variation and local smoothness, through online reward augmentation and offline training on correct responses. Online experiments adapt Qwen3-1.7B on 30 AIME problems without reference answers; offline experiments use historical AIME problems. UID-Conj. improves the online macro-average across the adaptation benchmark and three transfer benchmarks by 2.08 percentage points over unmodified TTRL. The strongest evidence comes from offline weighted SFT: UID-Conj. exceeds uniform weighting on the same response pool by 1.93 points, with a paired prompt-bootstrap 95% CI of [0.44, 3.44]. Benefits are task-dependent: SC rewards judged trace quality and instruction following, while UID-Conj. rewards yield stronger training outcomes in several settings. We find that auxiliary preferences can expose learning value among equally correct traces not captured during inference time, motivating their evaluation through downstream gains.
[AI-65] SAT-ACT: Learning CDCL Branching from Outcome-Derived Action Preferences
链接: https://arxiv.org/abs/2610.41290
作者: Zhenchao Sun,Chongyang Tao,Ping Lu,Tongkai Lu,Jianling Gao,Shuai Ma
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Under review
Abstract:Learning-based approaches have increasingly been explored for SAT solving, with particular attention to improving branching decisions in conflict-driven clause learning (CDCL) solvers. These approaches derive branching guidance either from static structural properties of SAT formulas or from dynamic signals obtained through solver feedback and execution traces. However, these forms of supervision provide limited evidence about state-dependent branching effects, while different outcome measures may favor different branching choices. To address these limitations, we propose SAT-ACT, an outcome-derived learning framework for CDCL branching. We design three complementary outcome comparison criteria to extract action preferences from independent branching action rollouts initiated at the same solver state. The criteria identify preferences supported by the outcomes and leave comparisons unresolved when the evidence is insufficient or conflicting, instead of forcing an ordering using a predefined scalar metric. A state-conditioned network learns branching priorities from the extracted preferences and uses them to guide CDCL search directly without online action rollouts. We further introduce non-dominated heuristic action supervision, which retains the solver-selected action as a training target only when it remains non-dominated under these preferences. Solver-level experiments show that SAT-ACT consistently reduces CDCL search costs on three SAT families and improves PAR-2 on the SAT Competition benchmarks.
[AI-66] ANGLER: Verifier-Grounded Benchmarking with an Adaptive Attacker for Web Agents
链接: https://arxiv.org/abs/2610.41272
作者: Yini Huang,Aiwen LU,Zhen Sun,Yi Han,Yuzhi Zhao,Jiaheng Wei
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Web agents are increasingly deployed to complete tasks autonomously on real websites, where adversarially injected content can induce them to perform unauthorized actions. Existing evaluations primarily use fixed attacks or focus on a single attack type, and evaluate attacks at the text level rather than through real state changes, thus underestimating how vulnerable agents are to diverse attacks. To address the gap, we introduce REEF, a benchmark of red-teaming executable environments with faithful verification for measuring the security vulnerabilities of web agents. REEF covers 9 verifier-grounded websites spanning travel, commerce, productivity, media, and communication, and pairs every authentic benign task with a malicious objective delivered through 5 attack types at both the user and the environment level. REEF’s state-based verifiers provide the execution feedback that drives ANGLER, an adaptive near-miss-guided lure evolution framework for red-teaming. Rather than evaluating a fixed attack set, ANGLER uses outcomes from prior rollouts to guide LLM-based attack mutation and candidate selection, prioritizing near-miss seeds during refinement. Across 8 VLM-based web agents, ANGLER achieves an end-to-end attack success rate (ASR) of 20.36%, compared with 15.24% for the non-adaptive setting. Compared with attack-free execution on the same benign tasks, attacks generated by ANGLER reduce benign task success rate (BSR) from 61.9% to 44.4%.
[AI-67] From 1D Benchmarks to Complex 3D Geometries: Can Agents Reliably Specify Problems for Automated PDE Simulation?
链接: https://arxiv.org/abs/2610.41267
作者: Akshat Jani,Sakhinana Sagar Srinivas,Venkataramana Runkana
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Turning an engineering request into a simulation requires consistent geometry, physics, boundary conditions, and numerical settings. Recent agent-assisted systems automate simulation setup, but successful execution alone does not establish whether the specification preserves the intended problem or correctly handles missing information. We study whether agents can reliably construct complete executable JSON specifications for automated geometry creation, meshing, PDE solving, and result export. Our workflow combines model extraction with deterministic scientific and capability checks, explicit clarification, and validated local patches that preserve unrelated accepted fields. We evaluate specification correctness, execution completion, and numerical verification separately. On 120 template-related requests, the full workflow achieves 98.3% correct first-turn specification and decision outcomes, compared with 92.5% for schema-assisted single shot and 82.5% for model-only: gains of 5.8 and 15.8 percentage points, respectively. It makes no false-ready decisions among the 42 requests requiring clarification or rejection. Component ablations identify contributions from scientific checks and conditional repair. Separate downstream studies cover 1D, 2D, and 3D steady and unsteady, linear and nonlinear problems, including flow and elasticity with complex three-dimensional wing geometries. A 30-case gallery demonstrates automated execution from retained JSON specifications, while 9 of 12 selected live user-to-results tests achieve joint specification, execution, export, and numerical-reference success (75.0%). These results support traceable, engineer-reviewed simulation assistance within the tested scope, while fresh-response regressions identify consistency as a priority for further improvement.
[AI-68] Rome Wasnt Built in One Turn: Replay-Grounded Reward Signals for Long-Horizon Freeciv Agents
链接: https://arxiv.org/abs/2610.41264
作者: Yanchen Liu,Xinyu Che,Brihi Joshi,Renzo Fukuda,Junhua Liu,Ruifeng Li,Yunfei Ge,Hang Yan,Xiang Ren
类目: Artificial Intelligence (cs.AI)
备注: 22 pages, 3 figures, 12 tables. Code: this https URL ; Checkpoints: this https URL
Abstract:Training language-model agents to play long-horizon strategy games is hard because deterministic feedback is sparse and often arrives only at the end of a game. In-game scores can offer dense signals, but they do not reliably predict future outcomes: in 8-68% of position pairs across eight strategy games, the higher-scoring position leads to the worse outcome. We term this phenomenon scoreboard traps. In Freeciv, an open-source turn-based empire-building game where several players manage units, cities, and diplomacy over hundreds of turns, we look into better reward signals for gameplay agents. To facilitate this, we build CivHarness, an open system that saves current game states, branches on different decisions, and replays the game to future turns to provide measurable long-horizon feedback from saved positions. We use CivHarness to train CivTelescope, an LLM Judge that predicts which one of two game positions will yield the better outcome. On scoreboard-trap pairs, CivTelescope is 1.3-2.0 times more accurate than off-the-shelf language models, and it generalizes to new game starts, later turns, and unseen rules. We then train a Freeciv gameplay agent with CivTelescope as a dense reward, and call this recipe CivMarshmallow. When used in the middle stage of the game where scoreboard traps are common, CivMarshmallow reaches an average training phase gain of 8.94 points, 77% more than the best baseline, the end-of-game reward. Over the full 120-turn game, CivMarshmallow uses a hybrid reward that starts from the scoreboard or the end-of-game reward and shifts to CivTelescope as the game advances; this hybrid reward scores highest among the rewards we compare.
[AI-69] MEMIG: Self-evolving Memory Construction for Antibody Optimization
链接: https://arxiv.org/abs/2610.41245
作者: Yunhui Jang,Zach Wang,Jai Doshi,Kangway Chuang,Sidney Lisanza,Taylor Joren
类目: Artificial Intelligence (cs.AI)
备注: 10 pages
Abstract:Memory has become central to self-evolving LLM agents, which store and retrieve experience from past rollouts to guide future decisions. Existing systems represent this experience as free-form natural language and maintain it by rewriting, which fits tasks whose experience is narratable. However, in scientific discovery and optimization, experience is often a quantity measured by an oracle under a specific context. Rewriting it as text leaves no index for where a record should be, replaces a measurement with a description, and cannot express how interventions interact. To address this, we argue that agent memory for biomolecule optimization should instead be indexable, quantitative, and structured, and we introduce MEMIG. This memory construction framework realizes these three properties. In detail, MEMIG addresses each insight with a domain-grounded identifier and accumulates the quantitative effect measured by a property oracle, so that updates remain local and retrieval becomes an exact lookup. Next, MEMIG connects insights whose interventions are applied together and measures their epistasis by decomposing each evaluated set. Our results show that MEMIG improves performance in most settings across in silico antibody optimization tasks.
[AI-70] Experts Exhibit Structured Redundancy: Structure-Preserving Expert Pruning for Mixture-of-Experts Large Language Models
链接: https://arxiv.org/abs/2610.41236
作者: Tian Wang,Yongding Tao,Yihong Dong,Xiaoming Wu,Ying Guo,Ge Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Sparse MoE mechanism significantly improves the trade-off between model capacity and inference efficiency. However, practical deployment still requires storing all experts, resulting in substantial memory overhead. We show that expert outputs in MoE layers exhibit structured redundancy. Across different models and task categories, the stable rank and entropy effective rank of expert outputs are consistently far below the nominal number of experts, while expert output distributions also exhibit fewer effective modes. Motivated by these observations, we reformulate expert pruning as a structure-preserving expert subset selection problem and efficiently optimize it via a greedy strategy, rather than only independently scoring and ranking individual experts or exhaustively searching over expert combinations. We propose two expert pruning methods under the unified structure-preserving framework: Projection Energy Expert Pruning (PEEP), which preserves the functional subspace by maximizing weighted projection energy, and Distributional Coverage Expert Pruning (DCEP), which preserves the distributional geometry by minimizing weighted coverage error. Both methods incorporate router-derived weights as priors, enabling them to jointly preserve the global structural relationships among experts while accounting for the local importance of individual experts. Experiments on DeepSeek-V2-Lite and Qwen3-30B-A3B demonstrate that, at the expert retention ratios of 75% and 50%, our methods outperform the state-of-the-art baselines on the majority of benchmarks and consistently achieve the best average performance across both general tasks and mathematical reasoning tasks.
[AI-71] RTL-Interact: Training EDA-Interactive Models for Multi-Task RTL Design ICLR2027
链接: https://arxiv.org/abs/2610.41225
作者: Wenxuan Shi,Yaoyu Zhu,Di Huang,Chongxiao Li,Xiaoyun Zhang,Muxin Song,Yutong Wu,Ziyuan Nan,Pengwei Jin,Tian Zhi,Xishan Zhang,Rui Zhang,Xing Hu,Zidong Du,Qi Guo,Yunji Chen
类目: Artificial Intelligence (cs.AI)
备注: Under review at ICLR 2027
Abstract:Large language models offer a promising route to register-transfer-level (RTL) design, but reliable implementations require checking and revision based on electronic design automation (EDA) tools’ feedback. Existing RTL-specialized models often lack learned interaction with such EDA tools or rely on separate generation and repair components. We introduce RTL-Interact, a multi-task post-training framework that trains a single RTL-specialized model to generate RTL, construct task-appropriate EDA checks, and revise implementations using tool feedback. The framework combines automatically constructed multi-task demonstrations with staged supervision from chain-of-thought to tool-integrated reasoning. We further introduce Pattern-Aware Policy Optimization (PAPO) for multi-task reinforcement learning. PAPO augments Group Relative Policy Optimization with correctness-gated rewards that favor concise successful checking while rewarding effective revision, and uses task grouping to preserve coverage across objectives. On CVDP, RTL-Interact-32B achieves 48.7% weighted Pass@1 and 68.9% Pass@5, surpassing its teacher model, DeepSeek-V3.2, while RTL-Interact-8B achieves 44.9% weighted Pass@1. Additionally, we show that PAPO can reduce average tool calls by 63.8% while improving Pass@1. Both models also outperform all RTL-specialized baselines on VerilogEval v2 and RTLLM v2, demonstrating the effectiveness of our method.
[AI-72] Researcher-in-the-Loop Evolutionary Discovery of Entanglement-Assisted Quantum Code Families
链接: https://arxiv.org/abs/2610.41183
作者: Jae-Hyun Baek,Byung-Sun Won,Jon-Lark Kim
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 25 pages, 6 figures. Code, data and Lean proofs: this https URL
Abstract:LLM-guided program evolution can produce mathematical constructions, but validity, generality, and reproducibility require distinct evidence. We develop a researcher-in-the-loop discovery pipeline that combines program evolution, exact evaluation, mathematical generalization, certified nonexistence checks, and formal proof checking. We apply it to entanglement-assisted quantum error-correcting codes (EAQECCs). Starting from tasks that did not specify a family formula, evolved programs produced finite qubit constructions whose structure researchers generalized to the family [[n,1,n-1;n-q-1]]_q for prime powers q\ge3 at n\ge2q+1 and for q=2 at odd n\ge5 . Lean checks the algebraic certificate underlying this family. Together with entanglement lifting, the family closes 54 gaps in archived EAQECC tables. The released evidence includes 115 verified code records, ten nonexistence results with checked DRAT proofs, family implementations, and preserved search programs for exact checks and seeded replay. In a controlled cohort of eight paired generation blocks, validation-selected iterative programs achieve success in 46.1% and 31.6% of held-out executions at unseen lengths n=13,15 , compared with 19.5% and 3.1% for independently generated programs. In this setting, program evolution and researcher analysis turn finite computational discoveries into a proved family with independently checkable evidence.
[AI-73] ImplicitHop: Evaluating knowledge integration across context and memory
链接: https://arxiv.org/abs/2610.41169
作者: Alexander Detkov,Dashiel Karish,Matthew Oleynikov,Matt Thomson
类目: Artificial Intelligence (cs.AI)
备注: 6 pages, 2 figures
Abstract:The ability to integrate new information and deduce its consequences is central to learning, understanding, and reasoning in large language models (LLMs). Whether information arrives during training or in-context, models must not only store local facts, but propagate their long-range implications globally. To explore information integration in LLMs, we introduce two complementary natural language benchmarks for large-scale evaluation of deductive reasoning as a function of inference depth. The first is a formal graph benchmark enabling controlled evaluation over a wide range of graph topologies and integration tasks; the second is a knowledge composition benchmark grounded in real-world knowledge graphs. We use these benchmarks to compare knowledge integration from information encoded in context versus information encoded in model weights during pretraining, evaluating a set of open models implicitly, without chain of thought. We find that accuracy decays sharply with increased composition length for both pretrained and in-context knowledge. Information presented in-context, however, substantially outperforms deduction over information encoded in weights. These results quantify a fundamental limitation of current models with direct implications for prompt-based control of agent behavior and for continual learning strategies, and they provide a foundation for developing training, prompting, and integration methods that support deeper reasoning.
[AI-74] ROSTrans: Empirically Benchmarking LLM s for ROS1-to-ROS2 Migration
链接: https://arxiv.org/abs/2610.41168
作者: Yichen Zhao,Minghao Shao,Zhuoyan Yu,Yijun Shen,Boyuan Chen,Yik-Cheung Tam,Muhammad Shafique
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Although large language models have shown strong capabilities in domain-specific code generation, their performance in robotics software development, especially for the Robot Operating System (ROS), remains largely unexplored. ROS code generation presents distinctive challenges, including complex inter-node coordination, middleware configuration, and lifecycle semantics that go far beyond standard coding benchmarks, and many important correctness properties become observable only once migrated nodes are launched and communicate. We present ROSTrans, a benchmark for evaluating LLMs on ROS1-to-ROS2 code migration, a representative and practically important setting for ROS code generation. ROSTrans comprises 108 scaffolded completion tasks drawn from a diverse set of 70+ open-source ROS1 repositories and organized into a hierarchical taxonomy assessing interface-level, behavioral-level and system-level correctness. For every task we establish an executable reference: a verified ROS2 implementation, a runtime test suite that launches the nodes and exercises their topics, services, actions and parameters, and a container ensuring reproducibility. Candidates are scored by injecting only their target files into an otherwise fixed copy of the reference package, so a candidate can neither supply nor weaken the tests it is judged by. Evaluating six frontier LLMs over five stochastic generations per task, we find pass@1 below 10.9%, and that 56.5% of all samples build successfully and then fail at runtime,and 32.0% fail runtime assertions on the migrated interfaces, a class of error no compile-only oracle can detect. Execution-guided repair further improves task success, reaching 49.1% and 47.2% for Claude Opus 4.6 and GPT-5.5 after three rounds, respectively.
[AI-75] LLM Detoxification via Transition-Aware Reward-Guided Representation Sampling
链接: https://arxiv.org/abs/2610.41165
作者: Aishan Liu,Yisong Xiao,Xianglong Liu,Yongxin Huang,Siyuan Liang,Zonghao Ying,Yang Qu,Jian Yang,Yaodong Yang,Dacheng Tao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) can generate toxic content, motivating effective test-time detoxification. Existing representation steering methods often rely on static or coarse interventions and provide imprecise guidance because they inadequately model transitions between toxic and non-toxic generations. We propose Transition-Aware Reward-Guided Representation Sampling (TARGRS), a test-time framework that learns reward guidance over toxicity transitions in latent representation space. During exploration, progressive steering along the toxicity direction constructs trajectories from non-toxic to toxic states. Toxicity scores along these trajectories turn sparse annotations into dense, listwise supervision for a transition-aware token-level reward model. During inference, TARGRS uses this model to guide coarse-to-fine sampling along the opposite, non-toxic direction: coarse sampling identifies the intervention needed to reach non-toxic regions, and refinement selects a detoxified representation with minimal perturbation. Experiments on 10 LLMs across RealToxicityPrompts, BOLD, and AttaQ show that TARGRS consistently outperforms state-of-the-art baselines, reducing toxicity by up to 69.38% on RealToxicityPrompts. TARGRS runs nearly as fast as the original LLM while preserving its core capabilities with minimal impact. By intervening in the language decoder space, TARGRS also extends to Large Vision-Language Models (LVLMs), reducing toxicity by up to 64.23% across 3 representative LVLMs.
[AI-76] FrontierMat: Benchmarking Language Models at the Frontier of Materials RD
链接: https://arxiv.org/abs/2610.41162
作者: Feng Chen,Jianguo Huang,Yushan Xiao,Yue Li,Bijun Tang,Xuyu Dong,Xingguo Wang,Feiyang Xie,Jianhui Gu,Dongfeng Cao,Hongda Chen,Yongji Gong,Ruiqi Li,Shuxin Li,Xingji Li,Ruitao Lv,Li Song,Lei Wang,Ye Zhang,Xiaoxu Zhao,Xitao Zheng,Bo An,Zheng Liu
类目: Artificial Intelligence (cs.AI)
备注: 23 pages, 4 figures, 13 tables
Abstract:To accelerate materials discovery, language models must be able to address problems at the research frontier. Yet textbook- and examination-derived benchmarks primarily assess established knowledge, leaving performance on frontier research problems largely untested. We introduce FrontierMat, a benchmark of 330 problems drawn from frontier academic and industrial research in fields including aerospace, semiconductors, embodied intelligence and nuclear fusion. Every problem is validated by leading experimentalists and industry experts in its field. Each problem presents a real study and asks the model to infer a withheld outcome or draw a conclusion from selected experimental evidence. Problems are annotated by functional domain, material system and the scale of the governing mechanism; 130 require cross-scale reasoning. The strongest general-purpose model scores only 53.7%, and on matched problems, cross-scale problems score 8.7 points below single-scale ones. Half of the problems are solved by at most one model, and models fail chiefly by endorsing what is plausible rather than what was observed. A domain-adaptation study with meta-weighted continued pretraining and scale-modular self-distillation raises an open-weight model from 36.7% to 55.4%, matching the strongest proprietary model, with improvements on public materials benchmarks and no decline in aggregate performance on general benchmarks. FrontierMat provides a testbed for measuring progress toward language models that support frontier materials RD.
[AI-77] ARK: Avoiding Routing Collisions for KV Cache Transfer in Disaggregated LLM Inference
链接: https://arxiv.org/abs/2610.41160
作者: Hung-Chun Lin,Ting-Wei Hsu,Chung-En Ho,Ahmed Saeed
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 7 pages. Published in the Proceedings of the ACM SIGCOMM 2026 Conference (SIGCOMM '26)
Abstract:Disaggregated LLM inference separates the prefill and decode phases across GPU pools, generating massive KV-cache transfers. Because these transfers last hundreds of milliseconds to seconds, they behave as mega elephant flows that dominate link bandwidth utilization. In this regime, stateless ECMP can perform poorly: hash collisions may overload one spine link while leaving others idle, stretching transfer times by seconds. Yet this same persistence makes coordination practical. Since these flows are long-lived, even lightweight one-to-all coordination can be amortized over their lifetime. We present ARK, a distributed elephant-flow path reservation mechanism. ARK coordinates senders to choose source ports whose hashes map concurrent flows onto distinct spines, without requiring switch changes or receiver-side packet reordering. Packet-level RDMA simulations show that ARK reduces mean and P95 FCT by up to 27.3% and 34.0% under moderate load, and further reduces mean TTFT by up to 12.9%.
[AI-78] Lifelong Learning of Scheduling Rules with Surrogate-assisted Genetic Programming for Dynamic Flexible Job Shop Scheduling
链接: https://arxiv.org/abs/2610.41158
作者: Luyao Zhu,Fangfang Zhang,Yi Mei,Ke Chen,Mengjie Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Genetic programming (GP) has been widely used to learn scheduling rules for dynamic flexible job shop scheduling (DFJSS). However, trained rules tend to only generalise to tasks of similar scales and distributions, and when task characteristics change, learned rules often suffer significant performance degradation. Moreover, fine-tuning pre-trained rules on new tasks often leads to catastrophic forgetting, where prior knowledge is lost. This motivates the need for GP methods that can adapt to new tasks while preserving past knowledge, leading to a lifelong learning paradigm. However, lifelong learning in GP for DFJSS remains largely under-explored. To address this gap, this paper proposes a surrogate-assisted GP method for lifelong learning to handle sequential heterogeneous DFJSS tasks with job or batch arrivals. The framework includes three key components: an adaptive two-stage training strategy to balance plasticity and stability, a task-similarity-based weighting mechanism to regulate knowledge reuse, and an improved surrogate model with distance constraints to enhance estimation accuracy. Experimental results show that the proposed method effectively mitigates catastrophic forgetting while maintaining strong adaptability, outperforming several compared methods and demonstrating the potential of lifelong GP in dynamic scheduling environments.
[AI-79] Counterfactual Pairwise Augmentation Improves Small Pointwise LLM Judges NEURIPS’26
链接: https://arxiv.org/abs/2610.41149
作者: Mohammad Zangooei,Peyvand Teymoori,Mohammad Robati Shirzad,Soroosh Shahtalebi
类目: Artificial Intelligence (cs.AI)
备注: Accepted at Neurips’26 workshop of small language models for agentic systems
Abstract:Agentic systems increasingly gate individual steps with a pointwise judge that scores a response against a criterion. Small language models are attractive for this role because they can be served and specialized efficiently. Such a judge must locate the criterion’s decision boundary, but pointwise labels under-determine that boundary in fine-tuning: labeled responses to different queries differ along many dimensions at once, so they do not reveal which features decide the label. We introduce CPA (Counterfactual Pairwise Augmentation), which makes the deciding difference explicit in training. For each pointwise item, CPA synthesizes and audits a counterfactual response to the same query intended to carry the opposite label. It frames the accepted pair as a pairwise task asking which of the two responses satisfies the criterion. The judge is fine-tuned jointly on both pointwise and pairwise datasets and deployed in pointwise mode only. A second-order analysis shows when this should outperform the prevailing strategy of merging separately trained pointwise and pairwise experts: merging moves every parameter direction toward the pairwise expert by the same fraction, whereas joint training can hold the pointwise optimum wherever the pairwise loss is flat and absorb its curvature where it is sharp. This is the regime that CPA’s same-source, counterfactually minimal pairwise view is designed to produce. On separate Qwen3-8B LoRA adapters for six binary criteria, CPA improves criterion-averaged pointwise macro-F1 by 2.5 points over pointwise-only training and outperforms model merging on all six criteria at every tested merge weight. It also surpasses the strongest zero-shot frontier judge we evaluate, Gemini 3.6 Flash, by 11.8 points.
[AI-80] Confident Fools or Careful Searchers? A Behavioural Study of When LLM s Search Beyond Their Static Internal Memory
链接: https://arxiv.org/abs/2610.41124
作者: Nipun D. Pathirage,Kwan Srijomkwan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Agentic LLM assistants must repeatedly decide whether to answer from memory or to call a tool such as web search. Unnecessary searches cost latency and money; missing ones yield confident but unsupported answers. Prior work mostly measures verbalised confidence or trains new adaptive-retrieval methods, and rarely audits the search decisions that deployed products actually make. We introduce a benchmark of 400 multiple-choice questions that combines widely known inside-knowledge (IK) questions, which should be answered directly, with outside-knowledge (OK) questions about facts from news published after the models’ training cut-offs (to our knowledge), which should trigger search or, without tools, abstention. Each item offers an abstain option, and two action-level scores, NAS and KIS, reward searching on OK questions and restraint on IK questions. Across five commercial LLMs with and without their vendors’ web search, GPT-5 mini and Grok 4 Fast search on over 90% of OK but only 1% of IK questions, whereas Mistral Small searches on just 20% of OK questions, often abstaining instead of using its tool; one product’s search indicator proved unusable. Searching is partly triggered by explicit temporal cues: on uncued OK questions, the NAS of the two selective models falls from about 0.72 to 0.57–0.58. The scores thus measure the search triggering of specific products, not metacognition in a strict sense.
[AI-81] Predicting Human-Gain Curves under Local Proxy Optimization from Repeated Ratings NEURIPS2026
链接: https://arxiv.org/abs/2610.41114
作者: Moonwon Choi,Kisung Nam,Seunggeun Lee
类目: Artificial Intelligence (cs.AI)
备注: 10-page main paper, 65 pages including references and supplementary material. Accepted as a Spotlight at NeurIPS 2026. Code: this https URL
Abstract:Optimizing a proxy objective can improve human-perceived quality, but the resulting gain can vary with optimization strength. Average proxy-human correlation alone does not reveal how human-perceived quality changes as optimization strength increases. We propose PACE (Proxy Alignment and Curvature Expansion), a pre-optimization diagnostic that uses repeated human ratings to predict the human-gain curve induced by a fixed proxy. The method models proxy optimization as a KL-regularized exponential tilt. At KL radius d , the local gain is approximated by A\sqrtd+Bd , where A measures first-order proxy-human alignment and B captures the leading second-order correction along the proxy-optimization path. We estimate A and B from repeated human ratings on calibration items and test the resulting gain-curve prediction on held-out evaluation items. Across SummEval, analysis-eligible WMT MQM clusters, an OpenMEVA-MAGS ROC/WP repeated-rating slice, and a GPT-4o judge proxy experiment, PACE consistently reduces held-out gain-prediction error relative to A -only, zero-gain, and reliability-only baselines. With the same pre-pool information, it is competitive with flexible proxy-conditional calibration, and a LoRA-DPO experiment provides a modest bridge to actual optimization. Transfer experiments across datasets and evaluation slices show that source coefficients are useful priors, but target calibration further improves prediction. The resulting diagnostic is local and one-sided: it characterizes gentle optimization along the positive tilt branch of a fixed item pool.
[AI-82] Calibration of Large Model Quantization based on HiFloat4 Format
链接: https://arxiv.org/abs/2610.41112
作者: Jing Huang,Yu Cheng,Ziwei Yu,Lan Zhao,Sitan Li,Guangxiu Yao,Xin Wang,Yuanyong Luo,Hengyi Liang,Ting Li,Bo Liu,Hu Liu,Michael Bi Mi,Heng Liao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Quantization of Large Language Models (LLMs) has be- coming increasingly important for reducing the computa- tional costs and resource requirements for practical deploy- this http URL- nity to move beyond 8-bit quantization towards 4-bit preci- sion, leading to the development of customized 4-bit formats such as MXFP4 and NVFP4. Recently, HiFloat4 (HiF4) has emerged as a competitive alternative which adopts a Block Floating-Point(BFP)formatwithathree-levelscalinghierar- chy, thereby achieving a significantly larger dynamic range. While quantization can substantially reduce memory usage and accelerate inference, it frequently introduces accuracy degradation. Calibration is therefore essential for narrowing theperformancegapbetweenlow-precisionquantizedmodels and their full-precision counterparts. Nevertheless, existing calibration methods are mainly designed and validated us- ing integer-precision formats, such as INT4, lacking support for HiF4 due to its hierarchical BFP structure. In this paper, weinvestigatethreecalibrationapproaches,namelyHiGPTQ, HiAutoRound, and HiFlatQuant, to further enhance model this http URL demonstratethatallthreeproposedapproachesimproveinfer- ence accuracy compared with direct HiF4 conversion, reduc- ing the gap between HiF4 and BF16 to less than 1% and out- performing the state-of-the-art NVFP4 format across various models and downstream tasks.
[AI-83] Differential rank preservation manifold learning
链接: https://arxiv.org/abs/2610.41107
作者: Henry Han,Luyu Niu,Diane Li,Jiacun Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 67 pages, 10 figures, 28 tables
Abstract:Manifold learning methods like t-SNE and UMAP preserve local neighborhoods, but they do not preserve the ranking of the neighbors inside them. Two embeddings can return the same neighbor set while disagreeing on which point is closest. This loss of order degrades downstream applications that value the order in the embedding. We propose Smooth Rank t-SNE (SR-tSNE), which adds a differentiable rank term to the standard t-SNE loss. To enable efficient training, this term uses a sigmoid rank approximation to explicitly force a point’s true high-dimensional neighbors to outrank unrelated intruders and retain their original order. We also introduce Ordinal DLP (O-DLP), an extension of the Degree of Locality Preservation (DLP) metric that credits a neighbor only when it returns at its original rank. Across eight benchmarks spanning image, text, speech, remote-sensing and single-cell data, SR-tSNE outperforms 12 baselines in rank preservation and neighborhood overlap. Ablation studies demonstrate our rank term alone boosts O-DLP by 5% to 28%, a gain not achievable using standard pairwise loss. This advantage transfers to downstream applications. SR-tSNE achieves the best label-transfer performance across seven vision datasets. For cell-type discovery across six single-cell datasets, it ranks first in 17 of 18 dataset-metric combinations, whether clustering resolution is selected with or without cell-type labels. In intraday trading, SR-tSNE achieves higher returns, lower risk, and lower turnover than the baselines. To our knowledge, SR-tSNE is the first t-SNE-family method with explicit order preservation.
[AI-84] QUAL: Quantile Alignment Improves Zero-Shot Time-Series Foundation Models NEURIPS2026
链接: https://arxiv.org/abs/2610.41100
作者: Junhyeok Kang,Sangjun Han,Jun Seo,Seunghan Lee,Jaehoon Lee,Dongwan Kang,Minjae Kim,Sungdong Yoo,Tae Yoon Lim,Hwanil Choi,Soonyoung Lee,Wonbin Ahn
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the NeurIPS 2026 Workshop on Foundation Models for Temporal Systems: From Forecasting to World Modeling
Abstract:Time-series foundation models (TSFMs) provide zero-shot forecasts for unseen series, but their accuracy varies across datasets. Combining several models can reduce this variability, yet zero-shot deployment provides no target labels for selecting or weighting them. We find that a model pool is useful not only as a final ensemble, but also as a probability reference for transforming each member forecast. Aligning quantiles through this pool-relative reference improves every member forecast in aggregate. Motivated by this finding, we propose QUAL (Quantile alignment), a simple label-free framework for improving and combining diverse TSFMs. QUAL first produces aligned member forecasts, then applies Huber aggregation and rearrangement to obtain a robust combined forecast without training or model modification. On GIFT-Eval, quantile alignment reduces average member MASE and CRPS by 7.4% and 9.8%. The full framework remains robust when weak forecasters enter a candidate pool whose quality cannot be verified in advance. Code is available at this https URL.
[AI-85] Verifying Text-to-SQL Queries under Changing Business Semantics
链接: https://arxiv.org/abs/2610.41090
作者: Genliang Zhu,Xiaobo Chen,Ziyuan Wang,Chu Wang
类目: Artificial Intelligence (cs.AI)
备注: 22 pages
Abstract:In text-to-SQL and AI-agent analytics, successful SQL execution does not establish semantic correctness under changing business definitions. We study revalidation of fixed queries after changes to refund attribution, tax treatment, eligible populations and exchange-rate dates. An executable verification profile binds the requested revision and knowledge cutoff to legal counterexample data and exact monetary comparison. A preregistered confirmation on 32 newly authored revision families and two local producer bundles retains 192 query-definition cells: 144 nonconforming, 36 finite-suite agreeing and 12 unresolved. Of the nonconforming cells, 63 agree on the current snapshot. At an eight-ledger budget, obligation-guided checking detects 137 discrepancies versus 127 for generic ordering, a paired difference of 6.9 percentage points (family bootstrap 95% interval [2.1, 13.6]; exact family sign-flip p=0.0156). Every ledger arm rejects zero of the 36 agreeing cells. Current-obligation and equal-weight scoring match the guided result, identifying coverage rather than revision-specific priority as the observed source of benefit. Controlled assays, a natural-query pilot and repeated samples provide separate mechanism and robustness evidence. A legal discrepancy refutes conformance; agreement remains restricted to the recorded suite. The study establishes an auditable business-semantic revalidation profile within its authored schema and local-model scope.
[AI-86] CIRCE Divines LLM Task Complexity from Constraint Structure
链接: https://arxiv.org/abs/2610.41089
作者: Chenhao Hong,Jiakai Xu,Kaushiki Singh,Yusen Zhang,Eugene Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Estimating whether a problem is too complex for a large language model (LLM) before the model attempts it can guide model routing, how to allocate test-time compute, and suggest the need for new tools to aid the agent. While existing estimators rely on the problem text, LLM-annotated task difficulty labels, or signals obtained after a model attempts to solve the problem, such as its hidden states, none explicitly leverage the constraint structure inherent in the problem: the variables to assign and how the constraints over them interact. We introduce CIRCE, which extracts a problem’s structure by using a cheap LLM to construct a Constraint Intermediate Representation that deterministically compiles into the constraint graph. Across seven reasoning benchmarks (including SAT, logic, and math) and four LLMs, even a randomly initialized GNN encoder improves predictions of LLM response correctness, reducing negative log-likelihood from 0.465 to 0.423 and increasing AUROC from 0.691 to 0.727 relative to the strongest baseline for each metric. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2610.41089 [cs.AI] (or arXiv:2610.41089v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2610.41089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-87] Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules Path Features and Graph Neural Networks
链接: https://arxiv.org/abs/2610.41085
作者: Xiaobo Chen,Genliang Zhu,Ziyuan Wang,Chu Wang
类目: Artificial Intelligence (cs.AI)
备注: 40 pages
Abstract:Payroll-fraud screening requires finding hidden employee–payee links while distinguishing suspicious relationships from legitimate ones. We formulate this problem as inductive link prediction and entity ranking on synthetic human-resources and enterprise resource planning (HR/ERP) graphs, with unseen organizations and a matched benign twin for every planted case. An original study on 24 held-out organizations and a prospectively frozen extension compare analyst rules, explicit path features and graph neural networks (GNNs). A tuned gradient-boosted model on chained-join features achieved the highest overall link average precision (AP; 0.823), above the GraIL-style GNN (0.663); its 4-hop AP was 0.646 versus 0.272. Thus, the observed multi-hop gain is not specific to GNNs. On a new synthetic cohort with 2.0% positive entities, chained-join boosting again led (AP 0.798); the relational entity GNN also exceeded record-aware rules (0.707 versus 0.504). At a 2% review budget, their precision was 0.695 versus 0.602. Under 40% failed identifier joins, the entity GNN ranked above chained-join boosting (AP 0.712 versus 0.683, descriptively). Structure-only auditing exposed a shortcut in the initial generator; matched twins reduced the audited twin-pool AUROC to 0.493–0.514. These results separate multi-hop candidate discovery, record-based discrimination and review prioritization within an explicit synthetic evaluation design. Code and data are available from the authors on request.
[AI-88] Optimization Techniques for Solving the Minimum Red–Blue Separating Set Problem with an Application to Human–AI Text Separation
链接: https://arxiv.org/abs/2610.41081
作者: Marko Djukanović,Staša Vujičić Stanković,Žiga Zebec,Christian Blum
类目: Artificial Intelligence (cs.AI)
备注: 36 pages, 5 figures
Abstract:We study the so-called Minimum Red’'Blue Separating Set Problem (MRBSSP) which arises from computational geometry. In particular, we propose a compact Set-Cover ILP formulation, safe preprocessing rules, a Greedy Randomized Adaptive Search Procedure (GRASP), and two exact approaches: one is based on Logic-Based Benders Decomposition (LBBD) approach, and the second represents a novel Branch-and-Benders-Cut (BBC) approach. These two exact approaches are compared to ILP–alongside GRASP and the Variable Neighborhood Search (VNS) from literature. Experiments on 250 random graphs with up to 500 vertices reveal complementary strengths: ILP yields the best feasible solutions, LBBD provides the strongest lower bounds and smallest mean optimality gap, and BBC certifies optimality most frequently. We further introduce an NLP application distinguishing human- and AI-generated texts through vocabulary-based graph features. On 42 instances, BBC achieves the smallest mean optimality gap, certifying 16 optima, while GRASP outperforms VNS. Structural analysis of optimal vocabulary separators identifies 113 mandatory and 151 replaceable features across 272 tests, highlighting substantial variability among optimal solutions. Overall, our results demonstrate the effectiveness of complementary optimization strategies and the interpretability of optimal vocabulary-based discrimination.
[AI-89] BABA-Bench: Evaluating Long-Horizon Patient-Advocacy Agents
链接: https://arxiv.org/abs/2610.41076
作者: Shriram Vasudevan,Videet Mehta,Connor Sweeney
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:An LLM agent that helps a patient get care must do two things. It must reach the correct outcome, and it must follow the necessary procedure. For example, the agent must get the patient’s permission before it shares the patient’s records. We introduce BABA-Bench, a benchmark of 47 interactive worlds. We built each world from a de-identified, real patient advocacy case that expert advocates reviewed. In each world, the agent must find hidden information and coordinate insurers, clinics, suppliers, pharmacies, and community organizations. The engine checks each action at the time the agent does it. It checks if the agent has permission, if the agent has evidence for the action, and if the action is prohibited. We evaluated 14 frontier models with three runs on each world, for a total of 1,974 runs. Agents often reached the correct outcome by an invalid route. Of the 921 runs that reached all required outcomes, 254 (28%) did not follow the necessary procedure. Claude Opus 5 reached all required outcomes in 81.6% of its runs, but only 52.5% of its runs passed all checks. When we gave two models a verified route, their pass rates increased from 29.8% to 91.5% and from 44.0% to 97.2%. For these models, the main problem is to find a route, not to execute it.
[AI-90] Where When and How Many Bits? Scaling Laws for SGD with Stochastic Rounding
链接: https://arxiv.org/abs/2610.41074
作者: Zhichao Wang,Michael W. Mahoney
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 67 pages, 6 figures
Abstract:Low-precision training reduces computation but introduces additional rounding error. We theoretically study how stochastic rounding (SR) affects prediction risk as model size, training time, and precision change. We consider a two-layer linear network with a fixed random first layer, trained by mini-batch SGD on fresh Gaussian data with power-law spectra. At each SGD step, the features used in forward and backward computations are rounded independently. We prove matched upper and lower bounds on prediction error for the final model and for uniform parameter averaging, with general learning-rate and precision schedules chosen before training. For fixed precisions and constant learning rates with parameter averaging, we derive how the smallest prediction error decreases with compute, and we identify model sizes and training times that achieve this optimal-compute scaling. Rounding only the forward features preserves the compute scaling of exact arithmetic, while rounding backward features changes it. Averaging parameters can change which computation needs higher precision and when higher precision is useful. Our results also give bit requirements for a given accuracy tolerance and show how backward rounding can change learning-rate schedule design. At a fixed compute budget, lower precision can improve prediction when the benefit of a larger model or longer training outweighs the additional SR-induced error. Model simulations illustrate these tradeoffs, and experiments with an MLP and a small transformer examine rounding choices beyond the theoretical model.
[AI-91] bf16 Rounding Silently Discards Most RL Policy Updates: A Hidden Training-Inference Mismatch and an Unbiased One-Line Fix
链接: https://arxiv.org/abs/2610.41073
作者: Andrew Gao,William Gao
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) post-training of language models keeps fp32 master weights in the optimizer and serves rollouts from a bf16 copy produced by round-to-nearest conversion. At the learning rates in common use, most per-step updates are smaller than half a bf16 spacing and the conversion discards them: on Qwen3-1.7B only 0.4-0.5% of served weights change per step, and the policy that generates the training data lags the optimizer by 50-150 steps. Existing mismatch corrections cannot see this lag because they compare two copies downstream of the same rounding. We propose Fair Cast: export the master with an unbiased rounding rule (stochastic rounding or error feedback) and compute the training loss on the served copy. The change is one line at the export call, adds no hyperparameters and costs 22-38 ms per export of 1.7B parameters. Across 25 matched seed-pairs on Qwen3-0.6B to 4B with AdamW and Muon, Fair Cast is ahead on 18, tied on 6 and behind on 1, with a mean gain of 6.1 points; on zebra puzzles it reaches 0.57 +/- 0.16 over four runs where round-to-nearest collapses to 0.33 +/- 0.10. It also makes the saved fp32 checkpoint agree with the served model, and it exposes the implicit lag of round-to-nearest as a regularizer that can be restored explicitly when a task needs it.
[AI-92] LanG 2.0: Energy-Efficient Agent ic AI for Semi-Automated Security Operations at Scale
链接: https://arxiv.org/abs/2610.41072
作者: Anes Abdennebi,Nadjia Kara,Laaziz Lahlou,Hakima Ould-Slimane
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:Security operations centers (SOCs) need to turn terabytes of alerts, logs and flows into trusted, affordable decisions to unveil hidden threats. We present LanG 2.0, an agentic system that detects threats, correlates evidence and writes rules, investigating whether small local models can replace frontier models affordably and safely when structural safeguards, not model accuracy, provide the guarantees. It incorporates entity-anchored correlation, human approval of rule changes and a tamper-evident ledger. Tests span eight flow datasets from five networks, real XDR alerts and multistage campaigns. Against its predecessor, campaign correlation precision rises from 18.75% to 51.81%, and rule acceptance from at most 60.00% to 98.33%. Against Claude Opus 5.5, scoring flows against each network’s normal traffic detects attacks on unseen networks at 73.42% F1 without false positives, versus 58.10% with 49.00% false positives, for at least 650 times less energy. Finetuned models statistically match it on alert classification and rule writing for 5.6 to 59 times less energy. The work demonstrates LanG’s practical scalability, processing 3.33 million records in 6.4 minutes, over 30 million hourly, and its safeguards resisted prompt injection, label poisoning and ledger tampering, so small local models can carry most SOC detection affordably and safely.
[AI-93] Accept Changes Reject Attacks: Robust Memory Management for LLM Agents
链接: https://arxiv.org/abs/2610.41056
作者: Runze Zhao,Dongruo Zhou,Sumit Kumar Jha,Nathaniel D. Bastian,Ankit Shah
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 24 pages, 9 figures, 6 tables
Abstract:Since large language model (LLM) agents rely on long-term memory, what they store now shapes what they do later. A memory manager decides how each input changes this memory and which records the agent acts on. It can fail in two ways: keeping a fact that has since changed (outdated memory), or storing a planted passage or injected instruction (memory poisoning). Most prior work studies them separately, yet they call for opposite write decisions, so no fixed write rule handles both. We therefore formalize memory robustness as handling both without doing worse on other inputs. Since accuracy checks only the answer, we measure robustness with evidence-conditioned accuracy (ECA), which also checks that the records the manager selects include all the evidence the answer needs, updated and free of attacks. However, existing memory systems and defenses are not more robust than an untuned manager on our test sets. We improve robustness with a simple one-shot recipe that fine-tunes the manager on episodes synthesized around a single conversation with inserted changes and attacks. On three backbones, it improves every category score on our test sets and more than doubles the untuned manager’s overall ECA, though robustness remains incomplete.
[AI-94] Consensus Within Competition Between: Decentralized LLM Teams for Perimeter Defense
链接: https://arxiv.org/abs/2610.41044
作者: Khawaja Abaid Ullah,Ananya Acharya,Mohammad Javad Khojasteh
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 88 pages (33 main text and references, 55 supplementary material), 7 main figures. Code: this https URL
Abstract:The use of large language models (LLMs) to coordinate multi-agent teams raises a fundamental architectural question: whether task allocation should be centralized in a single LLM controller or distributed across autonomous LLM agents, particularly when communication is unreliable. In a deliberately simplified perimeter-defense simulation against LLM-controlled intruders, we compared a central commander, an auction-based consensus team and independent agents under five network conditions. Breach rates were 15% for the consensus team, 32% for the commander and 29% for independent agents, and of the different network conditions, only limited radio range changed a team’s breach rate, raising the commander’s. In consensus team, giving a low-cost LLM more authority over the bid computation raised breaches by 38 percentage points. With birds and civilian drones in airspace, we added a photograph for each to the input text state. Only the near-frontier model treated them apart from intruders. Intruders disguised as existing taxidermy bird drones were mistook as birds by both low-cost and near-frontier LLMs, going unpursued in 59-61% of near-frontier decisions (1-16% before). However, a one-sentence forewarning in the prompt lowered it to 0-25%. We find that LLM team with a dedicated external conflict resolution protocol results in fewest breaches. Code: this https URL.
[AI-95] Characterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-of-Experts LLM s
链接: https://arxiv.org/abs/2610.41039
作者: Fei Wang,Baochun Li,Bo Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注:
Abstract:As Mixture-of-Experts (MoE) large language models are increasingly deployed in distributed environments, from federated learning to edge-cloud systems, their architecture harbors an underexplored landscape of security vulnerabilities. Moving beyond textual analysis, we uncover structural memorization: a novel vulnerability where a fine-tuned model memorizes not only its training data but also the internal pathway used to process it, creating a characteristic routing fingerprint unique to the training process. We provide comprehensive empirical evidence that structural memorization emerges consistently even from a few samples, and that its intensity is directly influenced by the choice of trainable modules. We show that these memorized expert pathways can be stealthily recovered through the model’s unconditional generation, using only routing and output logits, with no knowledge of the fine-tuning data or procedure. In distributed deployments, this vulnerability is amplified as routing information is exposed at multiple trust boundaries, including federated aggregation points, edge-cloud interfaces, and multi-tenant monitoring systems. Recovery exposes a critical blind spot: traditional membership inference metrics, viewed through the lens of expert routing, fail to identify memorized data corresponding to these structural pathways. To close this gap, we devise composite metrics combining reference model tail likelihood with complexity-aware normalization, significantly improving detection of memorized routing structures. This enables adversaries to pinpoint critical experts shaped by fine-tuning, whether dominant or dormant, and execute targeted attacks such as router manipulation or backdoor injection in realistic distributed settings where attackers lack training data but can observe routing patterns across system components. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2610.41039 [cs.LG] (or arXiv:2610.41039v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.41039 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-96] ReplayRCA: Measuring Post-hoc Diagnosis and Live Oversight of Agent Failures by Counterfactual Replay
链接: https://arxiv.org/abs/2610.41025
作者: Jeremy Canale,Fabien Soulis
类目: Artificial Intelligence (cs.AI)
备注: 29 pages, 10 figures, 16 tables. Code: this https URL
Abstract:When a language-model agent fails a multi-step task, can another model find the decisive mistake and fix it with a single action? We introduce ReplayRCA, a benchmark that scores such diagnoses by intervention rather than by agreement with annotations: the environment is restored to the state preceding the chosen step, the proposed action replaces the agent’s own, and the same agent continues. The score, causal repair, is the continuation success rate minus that of a paired placebo that replays the agent’s original action through the same mechanism. Two tracks share this protocol: a post-hoc autopsy, where the diagnoser sees the whole failed run and is told that it failed, and live oversight, where a supervisor watches the run unfold and may intervene once, before an irreversible action, without knowing whether the run will fail. Tasks come from OpsWorld, a deterministic, procedurally generated back-office environment with six difficulty levels, the last of which is multimodal (QR codes, handwriting). On 62 calibrated systematic failures, the best of 13 frontier models (Grok 4.7) reaches a causal repair of +51.3 points; the top eight models are statistically indistinguishable, the flagship models of the two largest vendors rank 8th and 12th, and 5 items are repaired by no model. Forcing a strong model to intervene at the last step costs it 17.1 points, so the last-step shortcut of annotation-based attribution benchmarks does not transfer. On 87 runs, the two best live supervisors are statistically tied and one of them is a small open-weight model (DeepSeek V4.1 Flash); the supervisor that is among the best post-hoc diagnosers (GPT-6.1 Sol) is the weakest live one (-14.5 points on the same runs, p = 0.002), so post-hoc diagnostic performance can be a poor proxy for live oversight. The environment and the evaluation code are released at this https URL.
[AI-97] Violet Teaming in Practice: Lessons from a Pilot Expert Workshop on Governing Complex AI Systems
链接: https://arxiv.org/abs/2610.41018
作者: Steph Batalis,Kait Connors,Ben C. Snyder,Vikram Venkatram,Josh Wentzel
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 9 pages, 2 figures
Abstract:Violet teaming has been proposed as a method to integrate technical, policy, and societal considerations into the evaluation of emerging technologies. Here, we document a pilot expert workshop used to explore violet teaming as a governance methodology for biodefense-relevant AI capabilities and describe observations from the exercise. The workshop used a hypothetical technology scenario across three sequential stages - capability development, misuse potential, and implementation - while incorporating technical, security, operational, and policy perspectives. The sequential format provided an integrated way to examine potential benefits, misuse risks, and real-world implementation challenges, rather than considering these issues in isolation. The exercise surfaced non-obvious misuse pathways, expanded potential interventions beyond purely technical safeguards, and highlighted operational and policy constraints that could affect implementation. This report summarizes the workshop design, outputs, and lessons learned for applying violet teaming to the evaluation of complex, dual-use technologies.
[AI-98] owards Understanding Score-Seeking in LLM s
链接: https://arxiv.org/abs/2610.41014
作者: Aghyad Deeb,Ryan Greenblatt,Alex Mallen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Production large language models sometimes reward-hack, exploiting flaws in how tasks are scored rather than performing the tasks as intended, sometimes in highly sophisticated and adversarial ways. We investigate the motivations driving this behavior in GPT 5.6 Sol, GPT 5.5, and a checkpoint of OpenAI o3 without safety training (“exp-rl-cap-late”) by observing model behavior in agentic environments where the grader is leaked. In our environments, the models appear to be score-seeking: trying to score well according to graders that they believe assess their current actions, even at the expense of violating explicit user instructions. They appear to care about score itself rather than its downstream consequences, violating instructions at approximately the same rate whether the score is indicated to be used for training, for evaluation, or not at all. We observe evidence that the more recent models care to some extent about scores that do not assess their own current actions. GPT 5.6 Sol and GPT 5.5 help other agents 24.6 percentage points more often on average when the other agent stands to gain a score of 1 rather than 0 from being helped. However, when grading or monitoring another trajectory, we find models never collude by distorting its score.
[AI-99] STAR: Steering Action Representations during Inference
链接: https://arxiv.org/abs/2610.41009
作者: Guangyu Zhao,Yongchang Zhang,Haoxuan Ru,Kaichen He,Haoran Wen,Kun Zhan,Zeyu Zhang,Yin Li,Yiwu Zhong
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language-action models (VLAs) map language instructions and visual observations to robot actions, showing strong capabilities across robotic manipulation tasks. However, in our probing experiment, we find that action representations near the action output do not fully preserve task-relevant visual grounding. Motivated by this late-layer grounding gap, we introduce STAR, for STeering Action Representations, a training-free inference method for pre-trained VLAs. STAR applies an exponential moving average over late-layer residual updates, allowing later action representations to inherit object-aware update directions from earlier layers without model updates or architectural changes. It further uses recent proprioceptive states to adapt the mixing strength, using conservative mixing when execution dynamics change rapidly. Experiments on robot manipulation benchmarks show that STAR improves late-layer object awareness and increases average task success rates by 4.0 percentage points. Our code is available at this https URL.
[AI-100] Steering Vectors as a Training Signal in LLM Post-Training NEURIPS2026
链接: https://arxiv.org/abs/2610.40998
作者: Tiejin Chen,Maunil Vyas,Huaiyuan Yao,Hua Wei
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accept to NeurIPS 2026
Abstract:Steering vectors (SVs) are residual-stream directions that shift the behavior of LLMs when added to hidden states at inference time. While prior work has mainly used SVs as post-hoc controls, we ask whether the same directions can also guide parameter updates during post-training. We study this question across supervised learning, including SFT and distillation, and reinforcement learning with verifiable rewards (RLVR). Our framework considers two intervention regimes: a hidden-state hook during teacher-forced supervised training, and an advantage-repair mechanism on wrong rollouts in GRPO. For each regime, we analyze three design axes, which are the steering direction, its placement, and the operation used to combine it with training. In supervised settings, SV injection improves over plain SFT and distillation, with gains scaling with the off-policy gap and reaching up to a 12% improvement when distilling from thinking-mode teachers. These gains hold across three random seeds. In RL settings, applying a correctness-contrast direction as advantage repair on wrong rollouts, including otherwise zero-gradient all-wrong groups, recovers a learning signal that transfers to held-out math benchmarks and acts as a composable auxiliary signal that adds to existing zero-variance remedies such as entropy-aware advantage shaping. We further identify three failure boundaries. In detail, code generation can break the direction, cross-family layer transfer can break the placement, and inference-time application on an SV-trained checkpoint can break the intervention regime. Together, these results establish steering vectors as a conditional but effective training signal for LLM post-training. Our code is available here.
[AI-101] Can Personal Requirements Become Supervision for Planning ?
链接: https://arxiv.org/abs/2610.40992
作者: Junwei Wu,Runze Yan,Yuzhang Xie,Hanqi Luo,Ziwei Dong,Jiaying Lu,Xiao Hu,Carl Yang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Managing multiple chronic conditions through diet requires satisfying disease- and medication-specific requirements jointly across meals and days while preserving established eating habits. These requirements are public in clinical guidelines and drug labels, yet turning them into a feasible plan for one person remains difficult for language-model agents. In our dietitian-approved weekly planning evaluation built from NHANES, thirteen models, from medically fine-tuned 8B models to GPT-6.1 Sol, pass fewer than 3% of complete plans through direct generation. Explicit rules, higher reasoning effort, or model-directed tools leave GPT-6.1 Sol at no more than 8%. We present Noura, which pairs LLM planning with executable personal specifications instantiated from public clinical guidance and individual health and dietary profiles. During planning, the specification grounds foods, optimizes portions, checks the complete multi-day plan, and guides revision. Our key insight is that a specification that rejects a plan can also determine what to learn from it. Noura turns executed plans and contextually credited revisions into supervision for compact models, recovering targets even from trajectories that never achieve complete success. The same specification supplies supervised targets, preferences, and rewards for post-training. On 4,993 held-out participants, Noura-SFT raises Qwen3-14B’s complete-plan pass rate from 24.0% to 72.5% within the same execution loop, cuts rule violations by 97.5% with no observed critical failures, and improves dietary quality. The locally hosted 14B planner incurs fewer rule violations than its GPT-6 Luna teacher. A matched comparison shows that executed targets reduce rule violations by 41% over model-written proposals. Across four Qwen, Phi, and Llama backbones, self-training alone reduces rule violations by 41-61% on a 1,249-person population subset.
[AI-102] P-ContextBench: From Personal Fact Recall to Personalized Agent Behavior
链接: https://arxiv.org/abs/2610.40959
作者: Grant Nasir Brown,Abdul Basir Bari,Anmol Tadikonda,Isabel Song
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly used for personal tasks, yet evaluating their safe use of personal context remains difficult because such contexts are private, fragmented, dynamic, and socially situated. We introduce P-ContextBench, a realistic, verifiable, and scalable benchmark for evaluating personal-context understanding. P-ContextBench constructs population-structured, life-story-grounded digital humans from demographic seeds reflecting the U.S. population. Rather than using explicit, static profiles, P-ContextBench embeds traits, routines, goals, and constraints as implicit evidence across fragmented, evolving synthetic mobile-app records. It evaluates five capabilities: single-source fact recovery, multi-source fact recovery, temporal state tracking, inference of higher-level personal patterns from routine behavior, and safe handling of requests conflicting with persona-specific constraints through compliance, warning, clarification, or refusal. A multi-stage validation pipeline checks persona plausibility, context recoverability, information leakage, evidence preservation, test-case grounding, and human-judge agreement. We instantiate P-ContextBench with 100 personas, 10,088 hidden facts, 18,261 app-trace fragments, and 4,810 verified test cases. The pipeline supports configurable generation across scales and demographic groups. Cost-effective state-of-the-art (SOTA) LLMs relevant to personal agents achieve below 60% accuracy on temporal tracking, higher-level pattern inference, and constraint-aware action-boundary decisions, with fewer than 10% of test cases answered correctly across all three repeated runs. Even a high-performing SOTA model remains inconsistent on these capabilities. P-ContextBench provides measurable targets for improving the accuracy, reliability, and safety of LLM-based personal agents.
[AI-103] Opening the Box Changes the Cat: LLM s Detect Chain-of-Thought Monitoring - What now?
链接: https://arxiv.org/abs/2610.40946
作者: Amir Jalilifard,Marcos Medeiros Raimundo,Anderson Rocha
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Chain-of-thought (CoT) reasoning allows large language models (LLMs) to generate intermediate reasoning traces in natural language while solving complex tasks. These traces can support safety monitoring by exposing potentially harmful plans before actions are executed. In this work, we investigate the conditions under which LLMs infer the presence of CoT monitoring from direct and indirect environmental cues and examine how this inference affects their subsequent behavior. Our observations suggest that oversight is an interactive process: while a monitor inspects a model’s reasoning, traces left by monitoring interventions can allow the model to infer that its thoughts are being observed and to adapt its behavior accordingly. We show that even subtle environmental cues, such as timing-related signals from timeouts and operation durations, can reveal the presence of monitoring and trigger models to adapt their behavior. Across repeated episodes and interaction loops, we further observe that models can form statistical estimates of the monitor’s effectiveness and use these estimates to guide attempts to evade detection. These findings highlight the need to evaluate CoT-based oversight under adaptive conditions, where the monitoring process itself may influence the behavior it seeks to observe.
[AI-104] On the Origin and Evolution of Training-Inference Mismatch in LLM Reinforcement Learning
链接: https://arxiv.org/abs/2610.40945
作者: Yu Li,Yufa Zhou,Tian Lan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy reinforcement learning for large language models assumes that training responses are sampled from the current policy. In practice, an inference engine samples the responses and a separate trainer computes the gradients, and the \textbftraining-inference mismatch between the two makes the training data off-policy. The mismatch has two sources, the numerics of the engine and the stale weights of asynchronous sampling, and a correction that stabilizes training under one source can fail under the other. \textitWe ask which property of a mismatch source decides the correction it needs. Evaluating the trainer at the sampler’s weights splits each sampled-token error exactly into a \textitnumerical discrepancy of the engine and a \textitpolicy lag of the trainer, and we follow both through training. The numerical discrepancy stays approximately constant, so it biases the updates by a nearly fixed amount, and centering the score at each prefix removes its leading bias. The policy lag is produced by the trainer’s own updates: the bias it leaves enters the next update, which enlarges the next lag and closes a feedback loop. Importance weighting cuts this loop only while the lag ratio stays concentrated, and on Qwen3-1.7B it fails at the step where the effective sample size of the ratio falls below one half. From this account we derive the \textbfdiscrepancy-lag correction (DLC), which centers the numerical discrepancy, clips the lag ratio against the sampler’s weights and resynchronizes the sampler when this effective sample size falls below one half. Under a stale sampler, DLC keeps the KL divergence from on-policy training within twice the KL between two independent on-policy trainings on Qwen3-0.6B and Qwen3-1.7B, and its resynchronization prevents collapses that the per-token corrections leave and that a KL measured on the sampler’s prefixes does not detect.
[AI-105] Benchmarking Forecasting Agents on Yesterdays Tomorrows
链接: https://arxiv.org/abs/2610.40940
作者: Yuxuan Wang,Yishu Wang,Jiaqi Deng
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Digital agents can now gather and organize information, reason over it, and use tools to complete complex tasks. Forecasting puts all of these abilities to the test: it requires integrating incomplete evidence, inferring causal drivers, and committing to calibrated probabilistic judgments. Existing benchmarks are poorly suited to measuring and improving this ability. Live benchmarks provide feedback only after events resolve, so every evaluation cycle waits on the calendar; fixed-corpus evaluations return results immediately but confine research to pre-collected documents. We introduce FABench, a scalable backtesting environment for Forecasting Agents. An automated pipeline discovers events, generates questions, and resolves outcomes for any past forecast date, so the benchmark can be renewed to stay ahead of model training cutoffs; its first release poses 287 questions across nine domains. Agents research each question freely in an archive environment that combines an offline archive with live web search screened to prevent temporal leakage. Evaluating agents built on eight recent models, we find that retrieval improves the Brier score of all eight in every retrieval setting and lifts the leading agents to the market consensus on the 54 questions linked to Polymarket, with the best three scoring slightly above it. Research traces reveal recurring failures in how agents weigh and apply evidence, leaving ample room for improvement. We release our code, data, and archive environment as a foundation for community research on forecasting agents.
[AI-106] he First Few Tokens Matter: Understanding Chain-of-Thought Controllability
链接: https://arxiv.org/abs/2610.40939
作者: Hugh Van Deventer,Dmitrii Troitskii,Laura Ruis
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 15 figures
Abstract:Chain-of-thought (CoT) monitoring is a promising way to oversee increasingly capable agents. It succeeds in part because current models appear limited in their ability to control what they verbalize in their reasoning traces. We investigate the source of this limitation and link it to an entropy collapse in the first few tokens following the opening thinking tag. We exploit this mechanism to recover CoT controllability through two inference-time interventions on initial tokens: prefilling, where even a single token can substantially increase controllability, and banning frequent opening tokens combined with in-context examples, which improves average compliance by 34-41 percentage points across models. These interventions recover mainly addition and style controllability (adding or transforming information), while suppression controllability (avoiding or removing information) exhibits more varied responses, suggesting distinct underlying mechanisms. Consistent with this, the two groups behave as separate capabilities: across models, performance correlates positively within these groups, while cross-group correlations are mostly negative. Importantly, we find that the relationship with monitorability depends on the evaluation: addition and style controllability more consistently positively correlate with monitorability, while suppression often shows negative associations. Our findings motivate the development of controllability evaluations that distinguish difficulty initiating a specific opening from general limitations of the ability to alter reasoning content, alongside separate reporting of control tasks and categories.
[AI-107] DiRAFT: Privacy-Aware Financial Transcript Synthesis from Aggregate Discourse Priors
链接: https://arxiv.org/abs/2610.40938
作者: Yunbai Zhang,Yunyi Gao,Han-Tai Shiao
类目: Artificial Intelligence (cs.AI)
备注: 21 pages. Accepted at the Financial AI Security, Privacy and Safety Workshop at ICAIF 2026 (non-archival)
Abstract:Using customer conversations to train and evaluate financial language models raises confidentiality concerns because non-public financial facts can remain after direct identifiers are masked. Synthetic transcripts offer an alternative data source, but their discourse fidelity and residual confidentiality risks require assessment. We introduce DiRAFT (Discourse Realization from Aggregate First-order Transitions), which extracts financial concept graphs and records the order in which concepts are first introduced. It pools adjacent concept transitions across training records and samples new graph plans from aggregate transition, starting-concept, and plan-length priors. A discourse layer guides text realization without supplying an individual source passage or graph. We assess privacy-related risks through two complementary evaluations: corpus-level structural overlap and source-grounded checks for specific non-public investor facts. The structural assessment covers two financial corpora and three generation backends. The confidential-fact assessment uses human review of 1,400 investor-call outputs across seven strategies and two backends. We also evaluate transition fidelity, content recoverability, and trajectory diversity. Stochastic sampling provides a favorable balance between transition fidelity and structural overlap, with consistent advantages over greedy and top-3 on investor calls. The confidential-fact assessment likewise detects fewer matches for stochastic sampling than these alternatives on both backends covered by the fact assessment. These complementary assessments provide empirical evidence on residual confidentiality risks without establishing a formal guarantee.
[AI-108] A Theoretical Framework for Understanding and Building “Any-task inference” Capable Foundation Models
链接: https://arxiv.org/abs/2610.40934
作者: Matthew McDermott,Florent Pollet,Payal Chandak,Gregory Kondas,Liat Friedman Antwarg,Noa Dagan,Isaac Kohane
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In this work, we provide a formal framework and mathematical definition for the subset of foundation models focused on enabling the property of “any-task inference” (ATI) (similar to zero-shot inference), rather than those designed for pre-training fine-tuning. To achieve this mathematical definition, we formalize the notion of a “space of tasks of interest,” which we include in the problem domain, then provide an interface-based formal definition for ATI foundation models as those that map samples of data x and tasks t to predictions over the space of labels directly. While simple, this definition allows us to show that all ATI foundation models can be decomposed through a “universal task reduction” lens into encoder, model, and decoder aspects. We can leverage this decomposition to also decompose foundation model error into terms corresponding to different aspects of this reduction, stratified into a collection of “design terms,” which are theoretical properties that can inform one of whether a particular proposed ATI foundation model can achieve optimality or not due to the structure of the proposed task reduction, and “realized model excess” terms which capture interactions between learned and programmatic components of foundation model systems. Lastly, we can use this perspective to offer two “prototypical” recipes for realizing ATI foundation models in diverse domains, which we term generative and discriminative variants of ATI foundation models. This exercise is grounded throughout by 5 real-world examples of foundation models in different settings, varying greatly in how these capabilities are realized, showing the applicability of this definition to real-world settings.
[AI-109] owards Portable Specification of the Agent Harness
链接: https://arxiv.org/abs/2610.40917
作者: Nicolas Gertler,Claudio Novelli,Luciano Floridi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models act only through the infrastructure that surrounds them. That infrastructure, the harness, determines what a model can accomplish and how it fails, yet it has no settled definition. To address this gap, we decompose the harness into eight functional components that can be specified and compared across systems, and we propose a portable specification framework that records which model ran in which harness. The record is needed because different harness configurations can yield several-fold differences in reported scores for the same model, and because much of an agent’s attack surface lies in its harness. Existing model cards and agent cards provide release and operational information, but do not establish the sequence of effective configurations within a run. Harnesses can change between releases and between runs, and, where agents edit their own tools and configuration, within a run. Enacted AI laws contain documentation duties relevant to covered systems, subject to their respective scopes and application dates, but do not prescribe the common harness record proposed here. Therefore, the harness has to be explicitly described and specified as a versioned part of the system.
[AI-110] Does Recursive Knowledge Construction Help Agents Verify Consensus Claims in Science?
链接: https://arxiv.org/abs/2610.40906
作者: Lun Ai,Yu-Heng Wu,Jonathan Schaul,Leonie Küchenhoff,Denes Turei,Francesco Carli,Fabio Petroni,Aurelien Dugourd,Julio Saez-Rodriguez
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent scientific agents build knowledge recursively by retrieving evidence, constructing an intermediate state, and judging whether that state contains sufficient information. These agents are evaluated end-to-end, usually without ground truth, so the effect of each design choice is unknown. Here, we examined two design axes: (1) what the recursion builds (the state of evidence) and (2) what controls it (the judge that decides when to stop). To measure them, we defined consensus scientific claim verification and measured these effects on this task, where a system must establish a claim’s consensus stance from the open literature. We built a new consensus claim dataset, ProClaim-eval, including 718 claims with SUPPORT and REFUTE gold labels from five expert-curated databases spanning molecular biology, nuclear physics, and astronomy. Our instrument, ProClaim, persists an evidence state outside the model’s context, so each axis can be studied with the other held fixed. Against verification systems and general-purpose agent harnesses, ProClaim ranks among the top three, with a significant advantage over the harnesses. We found that the evidence state improves performance over in-context accumulation, and it also matters what is stored in it. In addition, a trained judge that reads the evidence state performs better than fixed-round stopping. A judge trained separately and never exposed to ProClaim-eval achieves the highest agreement with the gold labels on three of five subsets, where it matches the off-the-shelf Claude Sonnet~4.6 judge and outperforms Claude Haiku~4.5. Our results support recursively maintaining a structured evidence state outside the model’s context as an effective design for verifying claims in scientific agents.
[AI-111] SFLab: A Self-Improving Agent -Agnostic Workbench for Time Series Forecasting Research
链接: https://arxiv.org/abs/2610.40901
作者: ChengAo Shen,Yanjun Zhao,Haoxin Liu,Bonnie Chen,Miles Ma,Dongjin Song,Hanghang Tong,Qingsong Wen,Jingrui He,Jingchao Ni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:As publications and products on time series forecasting (TSF) proliferate, researchers face a growing burden of analyzing existing work. First, not all publications release code. For instance, only about 20% of arXiv TSF papers have a code link in the surveyed period. Second, released code also requires manual maintenance. Thus, TSF researchers must constantly adapt or implement existing works. However, independent implementations can differ in experimental settings and are difficult to maintain. We therefore propose to use AI agents for benchmarking TSF research and even supporting autoresearch, e.g., developing new TSF models. Yet directly applying existing coding agents, such as Claude Code and Codex, is suboptimal: prohibitive costs, limited scalability to large research projects (e.g., benchmarking), and lack of self-maintenance. To address these challenges, we propose TSFLab, a self-improving agent-agnostic “workbench” that equips compatible coding agents with shared resources and tools for low-cost TSF research and continuous maintenance of research resources. Our experiments cover reproduction, benchmarking, and new method development. In reproduction and benchmarking, TSFLab improves task completion by 20-163% and cuts fresh tokens per completed task by 11-66% across baselines. A pilot study of agent-driven method development also shows improved forecasting accuracy via TSFLab, with 32.8-73.6% fewer fresh tokens and 9.2-68.8% less development time than the other settings.
[AI-112] ChatGPT as a Surveillance Assistant? An Exploratory Case Study of AI-Assisted Surveillance Risks
链接: https://arxiv.org/abs/2610.40890
作者: Ayana Hussain,Nicholas Vincent
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can generate detailed guidance, functional code, and persuasive text at scale. As one of the most widely used consumer language model products as of 2026, ChatGPT gives broad public access to these capabilities, motivating concerns about its potential use in surveillance-related tasks. We present an exploratory case study of what potentially harmful surveillance assistance looks like in ChatGPT’s public web interface. We contribute a taxonomy of seven surveillance risk categories based on prior LLM risk research and reports of deployed surveillance systems, instantiated as 110 prompts, along with a manually reviewed analysis of ChatGPT’s responses. Prompts were issued using two approaches: 55 jailbreak prompts drawing on five types of elicitation strategies, and 55 plain-language direct prompts, each run alone, and if unsuccessful, again with a short fixed prefix. We find that ChatGPT provides substantive surveillance guidance for 40 of the 55 jailbreak prompts and for 33 of the 55 direct prompts without any prefix. Compliant responses also rarely included policy mentions or suggestions for privacy-preserving alternatives. We manually review every response and find that even when ChatGPT states a refusal, it may still supply detailed instructions, working code, or architectural guidance alongside brief ethical caveats.
[AI-113] Circumventing the Computational Hardness of Computational Arbitrag e
链接: https://arxiv.org/abs/2610.40831
作者: Nastaran Okati,Ricardo Olmedo,Moritz Hardt
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:AI model marketplaces host providers with heterogeneous costs and capabilities. These differences create an opportunity for computational arbitrage (Olmedo et al., 2026), in which an intermediary purchases inference from a portfolio of models and resells it to users at a profit. Beyond creating profit opportunities for the arbitrageur, computational arbitrage offers financial value to both consumers and providers and could therefore significantly influence AI model markets. While prior work empirically demonstrates the feasibility of computational arbitrage for small model pools, scaling it to large marketplaces requires arbitrage strategies that are both computationally tractable and inexpensive to identify and deploy. In this work, we formalize computational arbitrage as an allocation problem and prove that it is generally NP-hard. However, we show that the optimal allocation can be computed efficiently under a natural diminishing-returns condition on how model performance scales with inference spending. We further develop a sample-efficient method for estimating model performance from limited data, reducing the upfront cost of identifying arbitrage strategies. Our analysis also yields several economic insights: at the optimum, every funded model delivers the same marginal utility per dollar; arbitrage directs revenue away from providers offering little utility relative to their prices; and it rewards complementary specialization.
[AI-114] From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation
链接: https://arxiv.org/abs/2610.40828
作者: Ammar N. Abbas,Aayush Jain,Prashanth Viswanath,Nivesh Gadipudi,Giulia Vilone,Jim Gibbons
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: Accepted at CoRL 2026 Workshop
Abstract:Imitation learning policies are intended to make robotic manipulation robust to variations in object pose, appearance, and environment that challenge pre-programmed motion. However, real-world performance remains difficult to predict, as conventional optimization signals such as validation loss show no consistent relationship with robot success. To identify the design choices that govern real-world performance, 281 imitation-learning policies spanning seven policy families were evaluated in approximately 1,700 real-robot trials of RJ45 connector grasping and insertion using a five-fingered robotic hand. Controlled ablations showed that observation and action representations dominated conventional optimizer and sampling choices. For point-cloud policies, region-of-interest cropping, representation in the robot-base frame, and normalization within the point encoder each improved success by 70-100 percentage points. For the vision-language-action policy, absolute rather than relative action prediction improved insertion success by up to 70 percentage points. Skill decomposition, multi-view perception, and recovery demonstrations were associated with the strongest-performing systems, although their effects were not independently isolated. Of 67 offline metrics evaluated, only five measures of predicted-motion smoothness and directness reliably correlated with insertion success (r = -0.70 to -0.80), and only within a single policy family. These results identify representation choices as key determinants of real-world imitation-learning performance and show that offline metrics can shortlist policies, but cannot replace real-robot evaluation.
[AI-115] Beyond Plausible Prediction: Safety Sufficiency in Web World Models
链接: https://arxiv.org/abs/2610.40798
作者: Zhifei Hu,Alexandra I. Cristea,Adel Bibi,Jialin Yu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Web world models increasingly predict the consequences of candidate actions before agents act, yet a plausible predicted future does not necessarily support the correct safety decision. In a matched human evaluation of 3,000 predictions from ten frozen open models, 86.1% are judged semantically plausible. However, several models exhibit highly asymmetric authorisation behaviour, combining low false-safe execution with poor recall of clarifiable ASK cases and a strong preference for BLOCK. This motivates safety sufficiency: whether a predictive interface preserves and exposes the policy-decisive information required to distinguish actions that may be EXECUTED, require clarification through ASK, or should be BLOCKED. We introduce SafeWebWorld, a benchmark for evaluating safety sufficiency across five web-safety domains through action-conditioned futures, policy-decisive evidence, and policy-conditioned decisions. Under a controlled Security-Aligned Supervised Fine-Tuning protocol, evidence preservation can improve substantially while authorisation-sensitive decisions deteriorate. To determine whether this deterioration reflects loss of authorisation-sensitive score structure or a failure of the decision readout, we introduce Latent Authorisation Recovery (LAR). Frozen-score recalibration reveals substantial but backbone-dependent recovery, while paired recovery provides only a modest additional gain. Yet within-family and matched-authorisation evaluations remain poor, showing that aggregate recoverability does not imply robust fine-grained authorisation behaviour. Overall, plausible prediction, evidence preservation, and aggregate recovery are each insufficient on their own to establish robust authorisation.
[AI-116] APE: Learning General Audio Effect Representations
链接: https://arxiv.org/abs/2610.40759
作者: Phillip Long,Yongyi Zang,Qihui Yang,Christodoulos Benetatos,Junfei Liu,Randal Leistikow,Julian McAuley,Perry R. Cook
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注: Submitted to Transactions of Machine Learning Research; 25 pages, 5 figures, 18 tables
Abstract:Musicians and mixing engineers often can decipher the signal processing chain they hear on a reference recording, then adapt it to new material. We present TAPE (Transformations of Audio Parameter Embeddings), which estimates this processing from the processed reference audio alone. An encoder trained to identify processors in eight major effect families maps the reference to an embedding. A differentiable digital signal processing (DSP) chain decodes the embedding into editable parameters in one forward pass and applies them to a different unprocessed recording. Its modules are equalization, compression, waveshaping, a small residual network, and reverberation. TAPE achieves lower spectral error than DeepAFx-ST, the previous state of the art for single-step regression, and lower spectral error than per-example search over a chain approximating ST-ITO’s, while running 275 \times faster than that search. Because the decoder outputs DSP parameters, its chain can also run causally: a C++ implementation with the trained weights processed 128-sample buffers 1.8 \times faster than real time, with no missed deadlines.
[AI-117] More Rules Longer Lists? Benchmarking Self-Evolving Agents on a Hidden-Rule Curriculum
链接: https://arxiv.org/abs/2610.40758
作者: Chenjie Qiu,Boyan Xu,Shaobin Shi,Bingfeng Chen,Ruichu Cai,Dongsheng Luo
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 8 figures, 11 tables
Abstract:Self-evolving agents rewrite a persistent state from feedback, but it is largely untested whether this lets them learn a rule nobody told them. An agent can also pass by writing down each correction as an exception, and its list then grows with every new rule. We introduce CLR-Bench, a Curriculum of Latent Rules benchmark. Each chain adds one hidden rule per level on top of the earlier ones. A rule acts on an opaque label that stands for a named grouping published on the Web, such as the founding members of an airline alliance. The task gives one example value, practice feedback reveals one more member and one non-member, and the test checks unseen members and similar non-members. Probes certify that the base model knows the members of each grouping and does not guess it without feedback, and that the evidence identifies the grouping once labels are read as named groupings. An automated pipeline builds chains from a new data domain, and we release 20 chains over 10 domains. We evaluate six recent self-evolution methods. With the certifying model they barely learn. The best gain at most 5 points over a frozen agent and exclude only 6-12% of the unseen members from the count, fewer than the 20% reached by simply keeping the raw practice history. A fixed lesson that states the named-grouping convention lifts the same methods by 14.5 to 16.5 points. Their states hold the revealed member in 75-98% of cases and almost never the class name, and when practice exposes a member the list misses, they add that member to the list. With the stronger GPT-6-Sol, ACE and Dynamic Cheatsheet learn from feedback alone. They reach 78% accuracy and solve 59-60% of the single cells, against 53% and 8% with the certifying model, and they surpass the raw history. CLR-Bench therefore measures how well an agent learns, and this ability differs across models.
[AI-118] AutoHebb: Self-Modifying Neural Networks that Zero-Shot Infer and Continually Learn
链接: https://arxiv.org/abs/2610.40757
作者: Daniel Levine,David van Dijk,Kenneth Kay
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Humans and animals have a unique capacity to generalize and learn in novel circumstances, including “0-shot” inference (ZI) and continual learning (CL) – two powerful cognitive abilities. Despite this importance, efforts to model ZI and CL are seriously limited in several ways. First, existing models typically lack components critical to ZI and CL in the brain, namely, strong inductive biases (for ZI) and autonomous control of learning (for CL). Second, models can be less tractable to analysis if model components are overly complex (lacking simplicity), or too task-restricted if model components are overly bespoke (lacking expressivity). Here we introduce autonomous Hebbian networks (“AutoHebb”), a class of online learning feedforward neural networks that realize strong inductive biases and autonomously controlled learning, and are both simple and expressive. Unlike standard models that learn via externally controlled optimization (e.g. gradient descent), AutoHebb networks combine external feedback with internally generated activity to produce a global learning signal (‘neuromodulator’) fed back to the network. The neuromodulator in turn gates a biologically grounded local learning rule (3-factor Hebbian rule) that updates networks’ plastic weights. Using meta-learning to search for fixed (non-plastic) weights, we find that AutoHebb networks (1) succeed on ZI tasks, (2) show emergent (untrained) ZI/CL abilities, (3) admit reverse-engineering into explicit algorithms, and (4) outperform modern learning models on ZI/CL tasks. Lastly, performance comparisons to related models implied an essential role for three model components: meta-learning, Hebbian plasticity, and autonomous control of learning. These findings indicate that AutoHebb may be useful in hypothesis generation, analysis, and discovery of biologically grounded forms of learning and generalization.
[AI-119] “Shall We Approve?” Consequence-Aware Approval for Autonomous Coding Agents
链接: https://arxiv.org/abs/2610.40753
作者: Weiheng Wu,Junkai Chen,Zhensu Sun,Jieke Shi,Yunbo Lyu,David Lo
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 24 pages, 9 figures
Abstract:Coding agents increasingly rely on automated approvers to decide whether a proposed shell command may run. Existing approvers, from rule-based checkers to guardrail models and deployed permission classifiers, base their decisions on the text of the request and command. Yet a command’s behavior depends on the code it invokes and the state of the workspace, and systems that observe file changes leave the assessment to the agent or a human after execution. We present Caper, a consequence-aware approval mechanism that pre-executes each command in a snapshotted sandbox, provides an independent approver with a bounded report of its observed effects, commits allowed commands, and rolls back blocked ones with a reason the agent can act on. Compared with six existing systems, Caper achieves the lowest harmful-outcome rate on SABER (6.7% versus at least 14.8%) and YoloFS-Ext, a benchmark we build in which harmful behavior is hidden inside invoked tools (7.8% versus at least 35.3%), while blocking 1.0% of the safe twins of the dangerous tasks in YoloFS-Ext.
[AI-120] CLIC: Closed-Loop Intent Consistency for Securing LLM -Driven Control in Open RAN
链接: https://arxiv.org/abs/2610.40744
作者: Sunder Ali Khowaja,Kapal Dev,Joongheon Kim,Angelos Antonopoulos,Nikolaos Bartzoudis
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: 6 pages, 1 figure, 3 tables
Abstract:With the progressive capabilities of large language models (LLMs), researchers are finding ways to integrate them into the control loops of Open Radio Access Network (O-RAN) to enable slicing and resource management through user intents. However, integration of LLMs in the O-RAN control loop via the RAN Intelligent Controller (RIC) makes the system vulnerable to \textitruntime prompt injection threats, which manipulate control decisions without violating interface authentication, and \textitresidual virtual prompt injection (VPI) attacks that plant trigger mechanisms to silently steer the behaviour during fine-tuning. Existing studies address only the runtime threat and do not enforce consistency across the full control path, i.e. from the certified A1 intent, through the multi-step LLM trajectory, to the final E2 action. We propose a closed-loop intent-consistency layer (CLIC) that jointly mitigates both threats. The proposed CLIC defines a formal consistency score using a small quantized language model, which is deployed as a Non-RT rApp for model-integrity certification along with a Near-RT xApp for runtime enforcement. We implement CLIC on the OpenAI Cellular (OAIC) platform and evaluate it under domain-adapted JailbreakBench, AdvBench, and VPI workloads. The experimental results show that the proposed CLIC reduces the attack success rate from 0.92 to 0.29 and residual VPI attacks from 0.37 to 0.04, while improving isolation and fairness at slightly higher latency costs, i.e. approximately 27 ms.
[AI-121] RiskyPy: Toward Model-Calibrated Prompt Generation for Joint Security and Functionality Evaluation of LLM -Generated Code
链接: https://arxiv.org/abs/2610.40735
作者: Zacharie Chenail-Larcher,Daniele Lunghi,Melissa Tessa,Quentin Stiévenart,Jacques Klein,Tegawendé F. Bissyandé,Florent Avellaneda,Naouel Moha
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Evaluating approaches that improve the security of LLM code generation requires datasets that jointly assess security and functionality and remain adapted to the capabilities of the models being studied. However, existing benchmarks are costly to produce and are generally not calibrated to a chosen range of model capabilities. We introduce RiskyPy, an automated pipeline that generates security-oriented code-generation datasets calibrated to model proficiency using functionality and vulnerability canaries. We instantiate RiskyPy for Python using domains covering 18 CWEs from the 2025 CWE Top 25, producing 101 prompts. Experiments on Qwen3-4B and Seed-Coder-8B show that the generated tasks remain functionally accessible, with 57-72% of prompts yielding functional code, while only 5-10% of generations are free of Bandit findings, leaving room for joint evaluation of security and functionality of security improvement approaches.
[AI-122] Bayesian Distribution System State Estimation with Flow Matching
链接: https://arxiv.org/abs/2610.40728
作者: Nan Lin,Peter Palensky,Pedro Vergara Barrios
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: 10 pages
Abstract:Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms. Weighted least squares keeps the power flow exact but reduces this prior to a mean and variance, while supervised deep learning estimators also learn the power flow and are tied to one measurement set. We propose PoSE (posterior sampling for state estimation), which keeps the Bayesian structure of DSSE and learns only what the distribution system operator does not know, namely the distribution of substation injections, modelled by a neural network trained with flow matching on historical data. The power flow equations, grid topology and measurements enter only at sampling, through a noise-aware Kalman-type correction. The neural network therefore never approximates the power flow, but every posterior sample is a physically consistent operating point, and one trained prior serves any measurement set, noise level and topology. On the IEEE 33-bus feeder, PoSE lowers the probabilistic injection error (CRPS) by 6.7-7.5% against a full-covariance Gaussian prior in all 12 tested settings. It provides reliable uncertainty: the 90% intervals contain the true injection almost exactly as often as exact posterior samples would, whereas those of supervised estimators contain it in only 37-78% of cases. On the 240-bus unbalanced three-phase Iowa feeder, it gives the lowest injection, voltage and branch-current errors of all estimators. A fast variant matches this accuracy at 0.2-0.6s per snapshot and, without retraining, beats Gaussian priors and retrained supervised estimators on seven reconfigured topologies.
[AI-123] MetaVul: Agent ic Harness Discovery for Vulnerability Detection
链接: https://arxiv.org/abs/2610.40721
作者: Ira Ceka,Arnav Garg,Ryan Lutz,Ting Zhang,Ratnadira Widyasari,Gail Kaiser,Baishakhi Ray
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Originally announced September 2026
Abstract:LLM agents are increasingly used to automate vulnerability detection, but reliable detection is challenging because agents must distinguish subtle, security-relevant fixes from benign code variation. While underlying LLM capability has improved significantly, the accuracy of the agent still depends heavily on the harness: the code that defines the scaffolding around the LLM. Harnesses are typically fixed, and largely hand-written, limiting adaptation to evaluator feedback. Additionally, when harnesses are static, improvements on one dataset or model may not transfer to others. To address these challenges, we introduce MetaVul, a self-improving framework for harness discovery in vulnerability detection. Starting from a seed structured reasoning scaffold, MetaVul uses a workflow-based optimization loop to iteratively refine the harness using feedback from paired vulnerable and patched functions, while preserving the structured reasoning and verdict format. At each iteration, it searches over the history of prior harnesses, with access to their trajectories, logs, and metrics, and it returns a set of Pareto-optimal harnesses. We apply MetaVul to discover agent harnesses for various architectures. A validation set (202 pairs) is used for optimization, while an unseen dataset (430 pairs) is kept as the held-out set. We test whether harnesses learned from one model transfer to others not seen during optimization. Results on both frontier and small LLMs show that the optimized harnesses improve pairwise vulnerability detection up to 23.3 percentage points on the validation set and up to 25.8 points on the held-out set with a Pareto-frontier harness. Harnesses learned with one model also transfer to others. We show that harnesses optimized with one model transfer to another, improving held-out pairwise accuracy by 14.7 points without further optimization.
[AI-124] Characterizing LLM Agent Behaviors in Module Shadowing Environments
链接: https://arxiv.org/abs/2610.40716
作者: Shuai Yang,Enmeng Lu,Jinyu Fan,Gongce Cao,Yu Chen,Jiawei Xie,Feifei Zhao,Zhangsheng Wang,Yongyu Jiang,Yi Zeng
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents operating in untrusted workspaces face the risk of untrusted code execution during routine tasks, yet final reports and workspace states alone cannot reliably establish whether execution occurred. We introduce an evaluation framework that codes observation, reasoning, execution, and response behaviors from agent trajectories and aligns them with independent environmental records to reconstruct execution histories and verify final reports. We apply this framework to module shadowing, in which a local file is loaded in place of a trusted module during import, enabling untrusted code execution without adversarial prompts. Across 90 trials with 15 models, the payload executed in 51% of direct shadowing trials and 87% of indirect shadowing trials. Even explicit recognition of the shadowing mechanism did not ensure prevention, as the payload ran in 10 of the 29 trials with recorded recognition. These failures were often omitted from final reports, as 49 of the 62 triggered trials ended without mention of the security issue. Three reports even denied execution, including one from a trial in which independent logs recorded 19 payload executions. Final workspace states also obscured prior execution when agents deleted or overwrote the payload marker in 10 triggered trials. These findings demonstrate that combining behavioral coding with independent environmental auditing enables rigorous and transparent evaluation of agent safety.
[AI-125] PrivacyFlow: Privacy in AI Agents through Information Flow Analysis and Consequence-Aware Risk Management
链接: https://arxiv.org/abs/2610.40714
作者: Robin Staab,Tom Wollschläger,Vahab Mirrokni,Reza Shokri
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Autonomous LLM agents executing multi-step workflows over personal data often misalign with user privacy expectations and leak sensitive, out-of-context information. Although Contextual Integrity (CI) formalizes this problem, existing benchmarks and defenses rarely operationalize CI for realistic multi-step task sequences in evolving environments. In this work, we first introduce agentic PrivacyBench, a benchmark of 5873 tasks in 986 task sequences with dynamic environment updates and CI-grounded allow-sets for sensitive information. We also introduce PrivacyFlow, a defense that separates deliberative privacy planning (System 2) from task execution (System 1), treating each sharing decision as a structured risk-management problem. Before execution, PrivacyFlow traces the provenance and purpose of each planned data flow, models recipient background knowledge, and simulates what external actors could infer. This allows us to model the downstream consequences of disclosure, including inference attacks and context-dependent harms. It then weighs the remaining disclosure risk against task utility and executes a minimized plan, blocks it, or consults the user. Across six models, PrivacyFlow significantly reduces step-level privacy failures and raises the share of completed task sequences without a detected privacy violation by 22–55 percentage points. We further highlight a new privacy–utility–autonomy tradeoff: allowing PrivacyFlow to proactively prompt the user in case of uncertainty can increase authorized task completion at the cost of shifting decision effort back to the user. Our results suggest that proactive CI reasoning can substantially improve agentic privacy and motivate further study of this tradeoff.
[AI-126] Hear No Evil: Detecting Gradient Leakage by Malicious Servers in Federated Learning
链接: https://arxiv.org/abs/2506.20651
作者: Fei Wang,Baochun Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Recent work has shown that gradient updates in federated learning (FL) can unintentionally reveal sensitive information about a client’s local data. This risk becomes significantly greater when a malicious server manipulates the global model to provoke information-rich updates from clients. In this paper, we adopt a defender’s perspective to provide the first comprehensive analysis of malicious gradient leakage attacks and the model manipulation techniques that enable them. Our investigation reveals a core trade-off: these attacks cannot be both highly effective in reconstructing private data and sufficiently stealthy to evade detection – especially in realistic FL settings that incorporate common normalization techniques and federated averaging. Building on this insight, we argue that malicious gradient leakage attacks, while theoretically concerning, are inherently limited in practice and often detectable through basic monitoring. As a complementary contribution, we propose a simple, lightweight, and broadly applicable client-side detection mechanism that flags suspicious model updates before local training begins, despite the fact that such detection may not be strictly necessary in realistic FL settings. This mechanism further underscores the feasibility of defending against these attacks with minimal overhead, offering a deployable safeguard for privacy-conscious federated learning systems. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2506.20651 [cs.LG] (or arXiv:2506.20651v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.20651 Focus to learn more arXiv-issued DOI via DataCite
[AI-127] Neural Networks Based on Power Method and Inverse Power Method for Solving Linear Eigenvalue Problems
链接: https://arxiv.org/abs/2209.11134
作者: Qihong Yang,Yangtao Deng,Yu Yang,Qiaolin He,Shiquan Zhang
类目: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In this article, we propose two kinds of neural networks inspired by power method and inverse power method to solve linear eigenvalue problems. These neural networks share similar ideas with traditional methods, in which the differential operator is realized by automatic differentiation. The eigenfunction of the eigenvalue problem is learned by the neural network and the iterative algorithms are implemented by optimizing the specially defined loss function. The largest positive eigenvalue, smallest eigenvalue and interior eigenvalues with the given prior knowledge can be solved efficiently. We examine the applicability and accuracy of our methods in the numerical experiments in one dimension, two dimensions and higher dimensions. Numerical results show that accurate eigenvalue and eigenfunction approximations can be obtained by our methods.
[AI-128] Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
链接: https://arxiv.org/abs/2610.42040
作者: Xinliang Zhang,Lesi Chen,Zonghan Li,Jingzhao Zhang
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with n components. Very recently, Tsedenov \ Kovalev (2026) showed that the minimax optimal complexity under averaged smoothness and convexity is \tilde\Theta(n + n^6/7 \epsilon^-2/7) by establishing nearly matching upper and lower bounds. In this paper, we show that individual smoothness and convexity provably enable further acceleration, where the lower complexity bound is \Omega(n+ n^5/7 \epsilon^-2/7) under this stronger assumption. Then, by establishing a novel second-order self-bounding inequality, we design a new Taylor-corrected lazy Newton method, which can lead to a better complexity of \tilde\mathcalO(n + n^5/7 \epsilon^-2/7) by applying the optimal Monteiro-Svaiter acceleration, matching our lower bound up to only logarithmic factors.
[AI-129] General-purpose foundation models surpass specialized ones on chemical property prediction tasks
链接: https://arxiv.org/abs/2610.41899
作者: Stepan N. Ostarkov,Gregorii A. Semakin,Ivan A. Bespalov,Michael G. Medvedev
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Foundation models (FMs) learn reusable representations from large collections of unlabeled data. In chemistry the corresponding data mainly consists of molecular structures, which are abundant, whereas experimental measurements are scarce. Chemical FMs such as ChemBERTa and MoLFormer were therefore trained on diverse molecular structures to enable fine-tuning on small experimental datasets (less than 100 datapoints in most realistic scenarios) and subsequent property prediction. At the same time, general-purpose FMs such as GPT-6 Astra and Claude Opus 5.5 were trained on virtually all publicly available data, including knowledge of the humankind on molecular properties and laws governing them. Here we compare accuracies of chemical and general-purpose FMs on tasks from various areas of chemistry. Strikingly, general-purpose GPT-6 Astra outperforms the currently eligible chemical FMs on five physicochemical and quantum-chemical regression tasks when only 60 experimental datapoints are available. Only when provided 10x datapoints do the best chemical FMs reach performance of GPT-6 Astra: on 1,000 and 10,000 points they show performance on par with Astra on 60 and 1,000 points, respectively. Thus, counterintuitively, frontier general-purpose FMs are better suited for guiding chemical research in data-scarce areas than specialized chemical FMs, suggesting that learning relevant primitives (e.g. chemical structures) does not substitute training on relevant humankind knowledge.
[AI-130] Distilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
链接: https://arxiv.org/abs/2610.41398
作者: Anil Bilgin,Wanda Hou,Pranav Srinivas,Yi-Zhuang You
类目: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code sizes, physical error rates, and noise models. GraphMP uses local message passing to keep computation scalable as the decoding graph grows. Guided by its predictions, beam search progressively refines candidate error patterns, trading additional decoding time for lower logical error rates. We develop the decoder model factory as a paradigm for training successive models on experience generated by earlier models during decoding. Model-guided search supplies verified trajectories for knowledge distillation, folding computation spent on decoding into reusable training data. Repeated collection and distillation improve the decoder across generations and adapt its strategy as the noise changes. These results motivate a coupled quantum–classical architecture with two interacting cycles: rapid decoding protects quantum information, while slower learning renews the models from operating experience. The long-term vision is a scalable decoder model factory that produces high-performance, low-latency decoders and continually adapts them to evolving quantum hardware.
[AI-131] Pinching Antennas for ISAC: Waveguide Architectures Solutions and Future Directions
链接: https://arxiv.org/abs/2610.41380
作者: Qian Gao,Ruikang Zhong
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Integrated sensing and communication (ISAC) is a core design principle for sixth-generation (6G) wireless systems, yet fixed antenna geometry limits its performance. Pinching antenna systems (PASS) guide electromagnetic waves along dielectric waveguides and radiate them through position-adaptive antennas, allowing communication links and sensing observations to benefit from spatial reconfiguration. This article reviews PASS-enabled ISAC through single-waveguide, multi-waveguide, and segmented-waveguide architectures, comparing their feed structures, spatial degrees of freedom, operating protocols, and uplink/downlink support. We organize solution pathways around physics-consistent modeling, joint spatial-signal optimization, dynamic channel acquisition, and stable real-time control. A segmented waveguide-enabled pinching-antenna array (SWAN) case study demonstrates joint segment selection, antenna po?sitioning, and transmit beamforming using segment-hysteresis reinforcement learning. The results illustrate how waveguide loss and per-segment aperture gain create communication-sensing tradeoffs. We conclude with research directions in joint transmit?receive operation, measurement-calibrated digital twins, and trustworthy control.
[AI-132] GraphTFactor: A graph-based approach for proteins transcription factor prediction
链接: https://arxiv.org/abs/2610.40867
作者: Domenico Amato,Salvatore Calderaro,Giosuè Lo Bosco,Riccardo Rizzo,Filippo Vella
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Transcription factors (TFs) play an important role in regulating gene expression, making their identification a fundamental task in computational biology. In this work, we propose GraphTFactor, a novel end-to-end framework that models protein sequences as graphs and directly classifies transcription factors. By encoding sequences into graph structures, the proposed approach naturally handles variable-length inputs while preserving relevant relational information, without imposing length constraints. Experimental results show that GraphTFactor outperforms state-of-the-art methods on benchmark datasets, demonstrating the effectiveness of graph-based representations for TF prediction while providing interpretable insights into the sequence regions contributing to the model’s predictions. To support reproducibility and practical adoption, we release an open-source software package and an interactive web application that let users easily perform TF prediction on custom sequences.
[AI-133] CausalGrade: Evidence-Guided Causal Graph Scoring with Jev
链接: https://arxiv.org/abs/2610.40692
作者: Jiangming Zhong,Jiawei Zhu,Guanting Wu,Qiaoxi Cai,Wanyi Chen,Jiale Li
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 2 figures
Abstract:Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results. This paper proposes CausalGrade, a causal graph scoring method based on the probabilistic judgment model Jev. The method jointly provides Jev with evidence of association, conditional dependence, and directionality from observational data and edge probabilities produced by the causal discovery foundation model CDFM. Jev produces probabilistic judgments about direct causal edges, and a graph-level score is obtained by measuring the agreement between the adjacency structure of the graph under evaluation and these edge-level judgments. Across 300 synthetic datasets, the score exhibits a relatively strong negative correlation with structural error in all 20 combinations of four causal discovery algorithms and five variable counts, indicating that higher scores generally correspond to smaller structural errors. In 500 synthetic graph selection tasks, CausalGrade achieves a best-candidate hit rate of 54.4%, exceeding the 48.4% achieved by the Self-Compatibility metric (\kappa_G), further supporting its ability to discriminate the structural quality of graphs under evaluation.
[AI-134] Recycle only when you have to!
链接: https://arxiv.org/abs/2610.40665
作者: Lorenz Vaitl,Ivan Tanasijevic,Arian Rokkum Jamasb,L. Santiago Mille-Fragoso,Pan Kessel
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:AlphaFold 3-like models (e.g., Boltz, Protenix, etc.) have pushed the frontier of protein binder design. This architecture is used both for the generation of binder candidates (e.g., by hallucination or converting these models into backbone or full atom generative models) as well as for the screening of these candidates for valid binders. In this paper, we start from the observation that AlphaFold 3’s confidence metrics (ipSAE, ipTM) as well as structural consistency metrics can be predicted directly from the trunk even at early recycling. Because our proposed predictor is cheap, differentiable, and available at every cycle, it can be used in two distinct ways: as a decision signal to wisely spend the recycling budget for screening and as an objective for hallucination-based design. We show that both applications lead to increased performance at significantly reduced computational cost.
机器学习
[LG-0] Counting on Dummies: Privacy Amplification with Observable Counts and Truncated Batches
链接: https://arxiv.org/abs/2610.42139
作者: Vitaly Feldman,Audra McMillan,Kunal Talwar
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:In many practical deployments of Differentially Private Stochastic Gradient Descent (DP-SGD), the server processes data in batches. Randomly sampling these batches allows for better privacy bounds via privacy amplification results. Most sampling schemes with provable privacy amplification, including Poisson sampling and random allocation, result in variable batch sizes. The privacy guarantee of these schemes requires that the batch sizes are not revealed. In distributed settings, the server performing the aggregation or any party observing the communication traffic may learn these batch sizes. Furthermore, in both centralized and distributed settings, memory and computation constraints may require each batch to be of some maximum size s , necessitating truncation of the batch when it exceeds this limit. It is known that this truncation can result in a significant increase in privacy loss. In this work, we address both of these challenges via a simple mechanism that uses a random number of \emphdummy points to privatize the batch size and maintain the privacy guarantee even under truncation. We demonstrate that our mechanism is easy to implement in a distributed setting, and provide a general analysis of this mechanism for both Poisson sampling and random allocation. Our analysis is designed to support efficient and relatively tight privacy accounting. It also effectively amplifies the privacy of the count releases by subsampling, making the expected number of dummy points asymptotically smaller than what is needed to directly privatize the (sensitivity-one) batch sizes with non-negative noise. For Poisson sampling with truncated batches, we demonstrate empirically that our mechanism achieves better privacy–efficiency tradeoffs than the direct accounting for truncation recently given by (Ganesh, 2025).
[LG-1] Instance-dependent sequential limits as an exact information ledger
链接: https://arxiv.org/abs/2610.42133
作者: Akshay Balsubramani
类目: Machine Learning (cs.LG); Information Theory (cs.IT); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain. It runs from a divergence decomposition through data processing to a statistic, a testing step that turns a class guarantee into a requirement on the history, and optimization over alternatives and allocations. Only the first step is an identity. This work writes the chain as an exact ledger. By the chain rule, data processing loses exactly a conditional divergence, which vanishes for arms in natural exponential families at the per-arm counts and reward sums. A one-parameter family of martingale identities extends the decomposition to every tilt between two instances. The classical limits follow by relaxations with named defects. All defects but the asymptotic step’s can be computed from an algorithm’s own runs, the uniform quantification’s only when the class guarantee is a number. On seven published sources, from movie ratings to engine replacement, the ledger locates the difference between standard algorithms and the classical limits. For identification among trained models and for Wald’s test the largest term is usually the uniform quantification’s.
[LG-2] Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions
链接: https://arxiv.org/abs/2610.42126
作者: Benjamin T. Pike
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: 10 pages, 3 figures, 3 tables. Supplementary material available as an ancillary file
Abstract:Objective: To distinguish the transferable predictive value of physiological trajectories from that of observation patterns in ICU mortality prediction, and to assess whether measurement timing affects performance independently of observation quantity. Materials and Methods: In-hospital mortality was predicted at 48 hours using MIMIC-III, MIMIC-IV, and eICU. Nested representations progressively integrated latest-value snapshots (R1), longitudinal physiological summaries (R2), and measurement frequency and timing features (R3). Models fitted on 7,762 MetaVision patients were evaluated on 21,682 MIMIC-IV patients and 43,878 eICU patients across 204 hospitals. The effect of measurement timing was assessed while holding the number of retained observations constant. Results: Physiological trajectories improved external prediction in eICU (R2-R1 log-loss difference -0.00922, 95% CI -0.01148 to -0.00704). Observation-pattern features improved locally developed eICU models (R3-R2 -0.00257, 95% CI -0.00370 to -0.00146), but not clearly after transfer from MetaVision (-0.00012, 95% CI -0.00206 to +0.00208). R3 was favoured in 10 of 30 source bootstrap refits. At matched observation counts, retaining measurements spread throughout the window reduced log loss relative to random retention by 0.03280 for R3 versus 0.00703 for R2. Removing the final 12 hours increased log loss relative to count-matched random removal by 0.01947 for R3 versus 0.00822 for R2. Under reduced availability, the local R3 increment changed from beneficial to harmful. Discussion: Physiological trajectories transferred more consistently, while observation-pattern features were particularly sensitive to the timing and availability of measurements. Conclusion: Clinical prediction models should be evaluated not only in new patient populations but also under the measurement conditions that supply their inputs. Comments: 10 pages, 3 figures, 3 tables. Supplementary material available as an ancillary file Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) Cite as: arXiv:2610.42126 [cs.LG] (or arXiv:2610.42126v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.42126 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Benjamin T. Pike [view email] [v1] Fri, 30 Oct 2026 17:56:10 UTC (557 KB) function toggleList(whichLayer,toggleThis) var elem, vis; if( document.getElementById ) // standard elem = document.getElementById( whichLayer ); else if( document.all ) // old msie versions elem = document.all[whichLayer]; else if( document.layers ) // nn4 elem = document.layers[whichLayer]; vis = elem.style; // if the style.display value is blank we try to figure it out here if(vis.display==‘’!=undefined!=undefined) vis.display = (elem.offsetWidth!=0!=0)?‘inline’:‘none’; vis.display = (vis.display==‘’||vis.display==‘inline’)?‘none’:‘inline’; // toggle link inner text status = vis.display; if(vis.display==‘inline’) document.getElementById(‘toggle’).innerHTML = “(collapse list)”; document.getElementById(‘toggle’).title = “Collapse list”; else document.getElementById(‘toggle’).innerHTML = “(”+toggleThis+“)”; document.getElementById(‘toggle’).title = “Show complete list”; Full-text links: Access Paper: View a PDF of the paper titled Physiological Trajectories and Observation Patterns in ICU Mortality Prediction: Transportability Under Changing Measurement Conditions, by Benjamin T. 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[LG-3] Gradient Descent on Two ReLU Neurons: Global Landscape and Bifurcation Dynamics NEURIPS2026
链接: https://arxiv.org/abs/2610.42118
作者: Binghua Li,Mengzhe Li,Denny Wu,Tianhao Wang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 85 pages, 2 figures. Accepted at NeurIPS 2026
Abstract:Understanding how gradient descent learns features remains a central challenge in neural network theory. We study this question in a minimal multi-index setting that already exhibits rich multi-phase gradient dynamics: a well-specified two-neuron ReLU teacher-student model under isotropic Gaussian inputs, trained by population gradient flow from small initialization. We show that the dynamics are organized by two directions: the “easy” bisector direction, which carries the leading signal, and the “hard” splitting direction, which governs specialization. To characterize the loss structure and learning behavior, we analyze the population squared-loss landscape and show that every critical point with both student weights nonzero is either a global minimum or a saddle. We then track the gradient-flow trajectory, showing that student neurons first collapse toward a bisector saddle before escaping and specializing to teacher neurons. Our results provide both a landscape and dynamics account of the multi-phase symmetry-breaking behavior that arises in a simple multi-index model and under standard algorithmic and architectural choices.
[LG-4] Fast Privacy Loss Accounting for Random Allocation via Multiscale FFT
链接: https://arxiv.org/abs/2610.42117
作者: Vitaly Feldman,Moshe Shenfeld
类目: Machine Learning (cs.LG)
*备注:
Abstract:In the random allocation sampling scheme each user’s data is used in k steps chosen randomly and uniformly from a sequence of t steps. This scheme is known to improve on Poisson sampling in private optimization and in communication-efficient aggregation. The existing privacy loss distribution (PLD) accountant for this scheme runs in time quadratic in the inverse of the accuracy parameter \alpha (Feldman and Shenfeld, 2026). We give an algorithm, \emphmultiscale FFT, that represents likelihood ratios on a grid that is uniform within each dyadic interval and computes the exact rounding of every pairwise sum using FFTs within each interval. It keeps the guarantees of the accountant and improves the dependence of its running time on \alpha from O(\alpha^-2) to O(\alpha^-1\log(1/\alpha)) . In addition, we demonstrate that rounding can be performed via the mean-preserving connect-the-dots projection of (Doroshenko et al., 2026). This reduces the errors that rounding introduces in the mean of the privacy loss and in the \chi^2 divergence of the computed pair from the order of \alpha to O(\alpha^2/\log(t)) . In our experiments, our algorithm achieves speedups of several orders of magnitude at high accuracy and gives improved privacy bounds for DP-SGD with PREAMBLE.
[LG-5] Hyperbolic Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2610.42104
作者: Iulia Rusu,Anoop Praturu,Sergei Gepshtein,Tatyana Sharpee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Inverse reinforcement learning (IRL) seeks to infer the reward function underlying observed behavior, but its predictive accuracy and interpretability depend on how that reward is computed from the underlying features. While prior IRL reward functions have been implemented through a variety of techniques including linear weights, neural networks, or other function approximators, none make assumptions about the geometry of the underlying feature space. Inspired by geometric organization observed in neural circuits, we introduce Hyperbolic inverse reinforcement learning (Hyperbolic IRL), which parameterizes reward as a function of Hyperbolic distance between the feature vector and a learned parameter vector, as well as its Euclidean distance variant. We tested our methods against a range of linear and nonlinear IRL baselines under both deterministic and stochastic policies. Our Hyperbolic approach achieved comparable performance to state-of-the-art models on established benchmarks and excelled in the low data limit and across dense environments. Applied to human navigation among multiple exhibits in a museum room, Hyperbolic IRL reveals reward structure that depends not only on individual exhibit-distance features, but also on non-separable conjunctions between them. Together, these results demonstrate that a Hyperbolic reward parameterization can improve both the predictive accuracy and interpretability of IRL, providing a framework for studying how non-separable, conjunctive structure can begin to explain behavior.
[LG-6] owards Understanding the Impact of Plasticity Loss on Reinforcement Learning in Stochastic Environments
链接: https://arxiv.org/abs/2610.42100
作者: Philipp Bordne,André Biedenkapp
类目: Machine Learning (cs.LG)
*备注: 9 pages main paper, 15 pages references and appendix, 17 figures. Presented at the Nineteenth European Workshop on Reinforcement Learning (EWRL 2026)
Abstract:Deep reinforcement learning (RL) is a powerful framework for sequential decision-making. Despite many high-profile successes in recent years, deep RL is known to be brittle. An emerging line of research attributes this brittleness to a loss of plasticity in neural networks. However, plasticity loss has so far been understudied in stochastic environments. We study this interplay systematically, training SAC agents on three DeepMind Control tasks augmented with controlled levels of observation and action noise. We find that plasticity-preserving methods increase learning robustness on the two harder tasks, with their advantage growing as observation noise increases, while combining resets with layer normalization underperforms resets alone at every observation noise level tested. Further, stochasticity can accelerate the loss of plasticity in harder environments, whereas in easier settings noise can act as a regularizer that sustains plasticity. Lastly, plasticity loss is most pronounced in the early stages of training. In the most challenging stochastic settings, TD instability rather than gradual plasticity loss becomes the dominant failure mode.
[LG-7] Cost-Aware Stopping for Freeze-Thaw Bayesian Optimization: When Does One-Step Lookahead Suffice?
链接: https://arxiv.org/abs/2610.42085
作者: Long Vu,Jongseong Brad Choi,Binoy Ravindran,Zhoulai Fu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:Freeze-thaw Bayesian optimization (BO) evaluates candidates in resumable increments but usually stops at a budget or convergence test. We maximize a budget-free utility, the best observed score minus a cost per increment, with a one-step lookahead: advance the candidate with the largest expected improvement (EI) under a fixed surrogate, and stop once no EI exceeds the cost. This rule, EI stopping, never falls below stopping immediately in expected utility, yet it can stop too early. We construct a certificate, a lower bound on the value of two-step lookahead, that proves such a stop premature, and repeating this correction never lowers expected utility under the surrogate. Under the surrogate, EI stops are premature on average at low costs and lose little at high costs. On three hyperparameter optimization benchmarks, a pre-registered test finds EI stopping noninferior to the correction in most high-cost settings, while the correction gains at low costs on two of the three; more information sharing in the surrogate raises EI stopping’s utility. On the surrogates of two published methods, EI stopping trades lower scores for fewer training increments. EI stopping is a simple cost-aware reference whose utility depends on the surrogate.
[LG-8] Differentially Private Synthetic Data from Sliced Chebyshev Moments
链接: https://arxiv.org/abs/2610.42081
作者: Lucas Rosenblatt,R. Teal Witter,Christopher Musco
类目: Machine Learning (cs.LG); Computers and Society (cs.CY); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Research on practical differentially private (DP) synthetic data has largely focused on methods that approximately preserve low-dimensional marginals of a target distribution, and can thus only hope to preserve higher-dimensional, global structure as a lucky by-product. At the same time, existing methods that target global accuracy guarantees pay a price exponential in the data dimension, d . In this work, we address this ``curse of dimensionality’’ by presenting a new DP synthetic data algorithm, SPLICE, that comes with a compelling global distributional guarantee, even in high-dimensions, and moreover, is practically state-of-the-art. Our algorithm is based on measuring noisy sliced Chebyshev polynomial moments of the target distribution and fitting a distribution to match, a twist on recent polynomial methods for 1-D synthetic data. We prove that, given a dataset of n examples, SPLICE returns a private distribution with Sliced Wasserstein-1 distance \tildeO(1/\sqrtn) from the target, for constant privacy parameters. Notably, this bound is independent of the data dimension, d . Along the way, we prove a moment matching result of independent interest: for a distribution on the unit ball, matching the first O(1/\alpha) Chebyshev moments along \tildeO(d/\alpha^2) random directions guarantees \alpha Sliced Wasserstein error. Finally, we provide a practical implementation of the algorithm that borrows tools from the tensor decomposition literature and achieves state-of-the-art performance compared to prior DP synthetic data methods on an extensive benchmark. Subjects: Machine Learning (cs.LG); Computers and Society (cs.CY); Data Structures and Algorithms (cs.DS) MSC classes: 68P27, 68T05 (Primary), 49Q22, 41A10, 60E15, 62G05 (Secondary) ACMclasses: G.3; I.2.6; G.1.2; K.4.1 Cite as: arXiv:2610.42081 [cs.LG] (or arXiv:2610.42081v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.42081 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Lucas Rosenblatt [view email] [v1] Fri, 30 Oct 2026 17:34:58 UTC (243 KB)
[LG-9] RAMA: Retrieval-Augmented Multi-Agent Routing with Episodic Memory for LEO Satellite Networks
链接: https://arxiv.org/abs/2610.42068
作者: Xin Ming Li,Peng Hu
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:Low-Earth orbit (LEO) satellite networks present a challenging routing environment due to rapidly changing topology, traffic, and link conditions. Existing deep reinforcement learning (DRL) routing methods typically rely on current observations and information encoded in network weights, limiting their ability to exploit successful past experiences. We propose RAMA, a retrieval-augmented multi-agent DRL framework with Approximate Information State (AIS), in which each satellite combines its local observation, recent routing history, neighboring state information, and retrieved entries from a per-satellite episodic memory. Retrieved experiences are integrated through a Transformer-based decision network trained with AIS-based auxiliary objectives. With weights frozen in a 140-satellite Kepler constellation, RAMA reduces mean end-to-end latency by 13.4% and 99th-percentile latency by 66.7% relative to the state-of-theart double deep Q-network (DDQN) routing method, mainly by avoiding long detours. Updated online, RAMA remains stable while DDQN degrades. On the 1,584-satellite Starlink shell 1, the reduction in mean latency grows to 93.2%, and updating RAMA online lowers its mean latency by a further 25.5%. These results demonstrate the effectiveness of retrieval-augmented memory for scalable decentralized routing in LEO satellite networks.
[LG-10] A richness-sensitive Acoustic Biodiversity Index from species-level detections: classifier benchmarking false-positive control and validation against standard soundscape indices in an African savanna and an urban zoo
链接: https://arxiv.org/abs/2610.42042
作者: Muhammad Saeed,Dmitry Mikhaylov,Mohamed H. Alhosani,Yasser F. Al Wahedi,Rory Pilossof,Ahmed Arabi,Walid Shaaba
类目: Machine Learning (cs.LG); Sound (cs.SD); Quantitative Methods (q-bio.QM)
*备注: 35 pages, 16 figures, 7 tables; supplementary tables and figures included
Abstract:Acoustic indices are widely used as rapid proxies for biodiversity, but they summarise the distribution of sound energy rather than the species producing it and often track richness poorly. We revise and test an Acoustic Biodiversity Index (ABI) built from species-level detections. Its primary form, ABI_E, is Shannon diversity of detections normalised by the regional species pool expected at the site and season (H / ln S_ref); temporal stability T is reported as a separate component. We first benchmarked four classifiers on a strongly labelled tropical dataset (1,216 calls, 24 classes) with recording-grouped cross-validation. BirdNET embeddings + logistic regression performed best (macro-F1 0.750 +/- 0.029), and random frame-level splits inflated accuracy from 0.41 to 0.70. We then applied BirdNET v2.4 with a location and season filter to recordings from a riverine savanna in the Zambezi Valley, Zimbabwe, and from three habitats at Emirates Park Zoo and Resort, Abu Dhabi, United Arab Emirates. A stricter post-hoc location filter removed 517 high-confidence detections of 9 implausible species, all at the zoo. After diel-matched rarefaction to equal effort (6 h), the savanna site held 21 species (Hill number 1D = 9.83; ABI_E = 0.38) against 1.3-13 species (ABI_E = 0.03-0.21) at the zoo sites. Across 149 recorded hours, ABI_E correlated strongly with richness (Spearman rho = 0.93), whereas six standard indices did not (best: ACI, rho = 0.48). The stability term was lower at the savanna site (0.39) than at the zoo (0.65-0.67), showing that it penalises the natural diel cycle of bird activity. Species-level indices with explicit false-positive control, a declared regional reference pool and effort standardisation provide a more interpretable biodiversity indicator than energy-based acoustic indices.
[LG-11] CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning ICML2026
链接: https://arxiv.org/abs/2610.42028
作者: Yanming Zhang,Eric Papenhausen,Klaus Mueller
类目: Machine Learning (cs.LG)
*备注: Accepted to ICML 2026 - full paper - main track
Abstract:Reinforcement learning is a powerful paradigm for training autonomous agents and has achieved impressive performance in complex environments. However, this success often comes at the cost of interpretability, diminishing trust and complicating efforts to debug and improve agent behavior. To address these challenges, we introduce CausalXRL, a novel framework for explainable reinforcement learning (XRL). A key feature of CausalXRL is its use of causal graph reasoning, which provides transparent, structured, multi-level explanations of agent decision-making. We validate CausalXRL through comprehensive case studies and a two-part evaluation: (1) a quantitative analysis of explanation fidelity and causal-structure learning efficiency in benchmark RL environments, and (2) a qualitative expert study assessing explainability in the real-time strategy (RTS) benchmark MicroRTS. The quantitative results show that CausalXRL can provide faithful explanations while efficiently learning causal structures, and the qualitative expert study suggests that participants found CausalXRL useful for inspecting high-level RTS strategies.
[LG-12] A Neural Network Approach towards Real-Time Knot Point Density Estimates for the Nonlinear Quadrotor
链接: https://arxiv.org/abs/2610.42027
作者: Jasmine Ratchford,Raffaele Romagnoli,Mark H. Klein
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 8 pages, 5 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. This work is presented at IEEE CDC 2026
Abstract:Spline representations provide a compact way to approximate continuous trajectories using a finite number of parameters. When approximating trajectories generated by dynamical systems, the efficiency of a spline representation depends strongly on the placement of knot points. Uniform knot distributions often lead to inefficient representations, requiring either unnecessary control points or producing poor approximations in regions where the trajectory varies rapidly. In this work we propose a learning-based method for adaptive knot placement in B-spline approximations of trajectories generated by ordinary differential equations. A neural network learns a mapping from the initial condition of the system to quantities that characterize the local variation of the resulting trajectory. These estimates are used to construct nonuniform knot distributions that approximately equidistribute a local error indicator, reducing the number of control points needed to meet a prescribed accuracy.
[LG-13] GhostPrefix: Synchronization-Free Universal Prefix Attacks for ASR Manipulation
链接: https://arxiv.org/abs/2610.42026
作者: Shengding Liu,Ashwin Innuganti,Qiben Yan
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present GhostPrefix, the first synchronization-free universal adversarial prefix attack against automatic speech recognition (ASR) systems. Unlike prior audio attacks, which are typically crafted per utterance, require synchronization with the victim waveform, or target narrow objectives, GhostPrefix learns a short, reusable audio segment prepended to arbitrary live speech to exert real-time control over ASR outputs without knowledge of the future utterance or temporal alignment. A key challenge is maintaining stable adversarial effects across unknown, evolving speech contexts. GhostPrefix combines token-level losses with a novel representation-level objective for stable, input-agnostic effects. GhostPrefix supports three attack modes: (i) prompted prefix attacks for semantic steering, (ii) targeted prefix attacks for transcript hijacking, and (iii) untargeted prefix attacks for transcription degradation. To remain perceptually inconspicuous, we regularize prefixes toward benign environmental-sound distributions and improve robustness against over-the-air distortions. We evaluate GhostPrefix across 13 open-source ASR models from four families. In digital settings, it achieves average attack success rates of 97.6% for prompted steering and 93.4% for targeted hijacking, while driving word error rate to 1.18 under untargeted attacks. Over the air, it achieves success rates of 91.5% and 89.5% for prompted and targeted modes, respectively. These manipulations propagate into downstream AI-assisted pipelines, including voice assistants and AI note-taking tools, causing systematic semantic misinterpretations. GhostPrefix exposes synchronization-free universal prefixes as a practical, under-explored threat to real-world ASR and downstream applications. Artifacts and demos are available at this https URL.
[LG-14] When Does Jacobian Fine-Tuning Stabilize Neural PDE Emulators? ATC
链接: https://arxiv.org/abs/2610.42013
作者: Shreeyam Bangera,Manya Gupta
类目: Machine Learning (cs.LG)
*备注: 18 pages, 6 figures, 7 tables. Code: this https URL
Abstract:Neural emulators of chaotic PDEs are trained on one-step prediction but deployed autoregressively, where their errors compound. Jacobian-based regularisers aim to curb this growth, but in a chaotic system part of the growth is physical and cannot be trained away. We separate the two sources of this error with a first-order decomposition. We compare the emulator’s error with a counterfactual that propagates the same one-step mistakes through the solver’s Jacobian, giving an amplification ratio: the factor by which the model’s own dynamics amplify its errors. We study 21 configurations across four PDEs, five architectures and two model sizes (8-10k and 33k parameters), with errors measured at one Lyapunov time. Jacobian fine-tuning reduced the error by more than 10% for 1 of 34 seeds whose amplification ratio was at most 1.25, and for all 20 seeds whose ratio was at least four; for the latter, the error fell by a factor of 8 (small models) and 55 (33k models) in geometric mean over seeds. The gain approximately equals the one-step accuracy cost of fine-tuning divided by the amplification ratio. Matching the solver’s Jacobian along the model’s own error direction (EDJM) outperformed random directions at the same cost on 28 of 35 seeds; input-noise injection performed comparably to EDJM.
[LG-15] New Lower Bounds for Private PAC Learning
链接: https://arxiv.org/abs/2610.41980
作者: Prathamesh Dharangutte,Amartya Sanyal
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study realizable PAC learning under approximate differential privacy, and give a framework for proving lower bounds on its sample complexity. We construct classes with identical VC and Littlestone dimensions but different private sample complexities at common privacy and accuracy parameters, showing that these dimensions do not determine the sample complexity, even for improper learners. To address this we introduce the Independent Class Packing (ICP) parameter, the largest total sample complexity of classes in \mathcalG that can be packed independently into a class \mathcalH , and prove that every (\varepsilon, \delta) -DP, (\alpha, \beta) -PAC learner for \mathcalH , possibly improper, uses \Omega\left(\mathrmICP(\mathcalH, \mathcalG)/(\alpha\varepsilon)\right) examples. Using this framework we prove tight lower bounds for axis-aligned rectangles, union of intervals and linear subspaces and improved lower bounds for halfspaces and convex polygons. Finally we also show that for product classes, and more generally when the same classes pack into \mathcalH and cover its domain so that target labels can be used directly, ICP characterizes private PAC sample complexity up to logarithmic factors.
[LG-16] When Is a Game a Markov Decision Process? Correspondences Between Game Theory and Reinforcement Learning
链接: https://arxiv.org/abs/2610.41962
作者: Eduardo C. Garrido-Merchán
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning and game theory describe the same object from two sides. Game theory says what a rational outcome of a strategic interaction is; reinforcement learning says how an agent could reach one through experience. The two literatures nevertheless keep separate vocabularies for identical structures, and the treatments that bridge them present the correspondence as analogy rather than theorem. This monograph supplies the theorems. A game is a Markov decision process in three distinct senses, and every table entry is labelled with the one that applies: a definitional identity, an exact reduction carried by a value- and solution-preserving map, or a structural analogy that fails under identifiable conditions. We take every standard class of game, from normal-form through repeated, stochastic, partially observable, extensive-form with imperfect information, Bayesian, graphical, Stackelberg, potential, coalitional, mean-field and evolutionary games, and identify for each the decision-theoretic model that reinforcement learning already studies under another name. The same treatment is applied to solution concepts: Nash equilibrium and the fixed point of the Bellman optimality operator, quantal response equilibrium and the soft Bellman operator, the Shapley value and credit assignment, Blackwell approachability and no-regret learning. We map each model class onto the training procedures sound for it and record the complexity of the associated solution problems. A final part collects the correspondences that do not hold: the non-stationarity that invalidates the independent-learner reduction, the impossibility results that forbid uncoupled convergence to Nash equilibrium, and the gap between the coarse correlated equilibria that no-regret self-play reaches and the correlated equilibria it is credited with.
[LG-17] Robust Learning of Multi-Index Models under Logconcave Distributions via Weak Partial Learners
链接: https://arxiv.org/abs/2610.41961
作者: Ilias Diakonikolas,Daniel M. Kane,Mingchen Ma
类目: Machine Learning (cs.LG)
*备注: Full version of the paper appearing in FOCS 2026. The abstract was shortened due to requirements from arXiv
Abstract:We develop a new algorithmic framework for learning Boolean concept classes under logconcave distributions. Our framework applies to broad families of multi-index models and yields significantly faster robust learners than were previously known in these settings. It extends to partial classification and testable learning. Consequently, we obtain improved learners for several fundamental concept classes, including functions of halfspaces and multiclass linear classifiers, under logconcave marginals. Our starting point is a new boosting framework based on weak partial learners. Classical boosting approaches require often-intractable distribution-free weak learners or yield weak approximation guarantees. We show instead that weak learners for partial classification, where labels may be undefined on part of the domain, suffice for strong agnostic learning. The key challenge is that boosting requires a weak learner that succeeds under conditioned distributions that are not guaranteed to be logconcave. To address this, we develop a recursive moment-based partitioning procedure: given a logconcave distribution X and a conditional version X’ , we partition the space into polynomially many convex regions so that, on most regions, the low-degree moments of X and X’ approximately agree. On each good region, a well-behaved multi-index model correlates with a low-degree polynomial, giving a weak hypothesis. Combining these hypotheses yields the required weak learner. A key ingredient of our approach is an efficient method for detecting moment discrepancy. Specifically, we show how to find a low-dimensional subspace that captures any non-trivial discrepancy between X and X’ , if one exists. Our approach combines spectral analysis of moment tensors with recent progress on isoperimetry for logconcave measures. This discrepancy-finding procedure may be of independent interest. Comments: Full version of the paper appearing in FOCS 2026. The abstract was shortened due to requirements from arXiv Subjects: Machine Learning (cs.LG) Cite as: arXiv:2610.41961 [cs.LG] (or arXiv:2610.41961v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.41961 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-18] Binary Sparse Codes: Where are we on Energy Efficient Representations?
链接: https://arxiv.org/abs/2610.41933
作者: Jose Miguel Ramos,Luis SaCouto,Andreas Wichert
类目: Machine Learning (cs.LG)
*备注: 38 pages, 8 figures
Abstract:Biological neural systems achieve impressive computational capabilities under tight metabolic constraints, relying on sparse cortical representations that minimise total neural activity. This combination of efficiency and representational power has motivated growing interest in sparse coding as a computational strategy for artificial systems. Artificial models designed to produce binary sparse codes span three broad paradigms, theoretical methods grounded in efficient coding, deep learning methods trained via backpropagation, and biologically plausible methods that learn through local Hebbian rules, each yielding codes whose quality depends not only on achieving low activation density but also on preserving the informational structure of the input. The latter property, however, lacks an agreed-upon quantitative definition, leaving the field without a principled basis for comparing methods. We propose a set of metrics that formalise information preservation as similarity preservation, measuring how faithfully a binary sparse code reproduces the relational geometry of the input space, and apply this framework to compare four representative methods under matched conditions. The results reveal a consistent trade-off in which models that achieve high information preservation do so at the cost of sparsity, while models approaching the uniformity of an optimal random sparse code tend to sacrifice it. Competitive Sparse Codes, a biologically plausible approach, is the only architecture to occupy a region of near-maximal sparsity while retaining meaningful information preservation. These findings frame the simultaneous satisfaction of both factors as an open challenge for the field and provide a reusable evaluation framework for measuring progress toward it.
[LG-19] On k-nearest-neighbor regression in metric spaces
链接: https://arxiv.org/abs/2610.41896
作者: László Györfi,Vladimir G. Pestov,Harro Walk,Roi Weiss
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study mean squared consistency of k-nearest-neighbor regression for metric space valued features. For a fixed feature distribution mu with atomless distance distributions, we describe how neighbor selection redistributes probability through the density of a uniformly chosen neighbor of an independent query. This gives separate conditions for the approximation error and the conditional response variance, and weakens Stone’s domination condition when stronger response moments are available. In particular, when k_n tends to infinity and k_n/n tends to zero, a uniform L2(mu) bound on the selection densities suffices under a finite fourth response moment. We give a corresponding condition in terms of neighborhoods of fixed population mass and recover the Euclidean density bound. Finite global Nagata dimension gives Stone’s domination. While sigma-finite Nagata dimensionality is known to ensure universal consistency of k-NN classification in complete separable spaces, we show that this is not the case for mean squared consistency with unbounded responses: we construct a space in this class on which one fixed noiseless response with all finite moments has diverging mean squared error.
[LG-20] On-Chain Behavioral Signatures for Classifying Centralized Exchange Addresses on Ethereum Using Proof-of-Reserves
链接: https://arxiv.org/abs/2610.41888
作者: Seongchan Park,Hyunho Jeon,Byungsoo Kang,Gaeun Kim,Minseob So,Seongyong Kim
类目: Computers and Society (cs.CY); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted at the 7th ACM International Conference on AI in Finance (ICAIF '26)
Abstract:Centralized exchanges (CEXs) are key gateways between traditional finance and digital asset markets, yet identifying their blockchain addresses remains challenging. Existing approaches often rely on opaque attribution labels or treat exchanges as secondary classification targets, leaving generalization to unseen exchanges insufficiently examined. This study develops a classification framework using Proof-of-Reserves (PoR) disclosures, with 3,033,857 Ethereum addresses from 15 CEXs and five Non-CEX categories. We derive 58 on-chain behavioral features and evaluate LightGBM against logistic regression (LR) under a leave-one-exchange-out protocol. Across five lookback windows (30-730 days), LightGBM consistently outperformed LR, with macro PR-AUC increasing from 0.6611 to 0.9594 using the selected 17 features, compared with 0.7697 for LR at 730 days. Robustness checks showed comparable performance when feature selection and hyperparameter optimization were repeated across splits and held-out exchanges, while exchange-balanced reweighting improved macro PR-AUC. SHAP analysis and feature-group ablation identified gas behavior, ERC-20 token activity, and internal transactions as key predictive signals in the constructed dataset. These findings support interpretable CEX address screening for blockchain monitoring, while the released feature dataset enables reproducible follow-up research.
[LG-21] Learning Nonlocal Interactions in Large-Scale Heterogeneous Metamaterials through Scientific Machine Learning
链接: https://arxiv.org/abs/2610.41887
作者: Dex Doksoo Lee,Jehyeon Shin,Yue Yu,Sunkyu Yu,Junsuk Rho,Wei Chen
类目: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Optics (physics.optics)
*备注:
Abstract:In multiscale systems, nonlocal interactions among spatially distributed constituents can unlock collective, potentially emergent behavior irreducible to individual constituent responses. Beyond characterization, nonlocality in multiscale systems is increasingly viewed as a design element across fracture mechanics, wave physics, and disordered systems. Existing nonlocality theories provide rigorous descriptions and mechanistic insight, but often require prescribed analytic forms, range, symmetries, or periodicity. Machine learning offers a data-driven route to learn interactions, yet principled approaches that tie the network design to physical theory remain nascent. Here we present domain-decomposition attention neural operator (DANO), which learns configuration-dependent interactions in heterogeneous systems governed by linear field equations from field data alone. Through a case study in large-scale optical metasurfaces, we demonstrate DANO’s full-field prediction under geometric heterogeneity and across drive conditions, small-to-large domain transfer, and multifunctional inverse design, with learned interactions validated through physics-based interrogation. As an AI-for-Science tool, it supports interaction discovery and scalable, customizable design of nonlocal multiscale systems, including virtual-reality optics.
[LG-22] rainable Vector Quantization for Large Language Models KDD2026 ECML
链接: https://arxiv.org/abs/2610.41822
作者: Anastasiia Prutianova,Alina Bogdanova,Mikhail Shutov,Dmitrii Poimanov,Anatoly Gorkov,Ilia Zharikov
类目: Machine Learning (cs.LG)
*备注: ECML PKDD 2026, Research Track
Abstract:Vector Quantization (VQ) is a rapidly evolving technique for compressing Large Language Model (LLM) weights, reducing memory usage, and speeding up inference. However, extreme compression deteriorates model performance, which is challenging to recover due to the discrete nature of VQ parameters. Existing methods employ training-free approaches that are limited to layer-wise reconstruction loss minimization. To address this limitation, we propose TVQ, a novel method that leverages straight-through estimation to enable gradient descent-based optimization of vector-quantized LLMs. By reformulating VQ LLM optimization with trainable latent weights, TVQ enables arbitrary objective minimization. We constitute our approach by applying TVQ in a block-wise knowledge distillation paradigm, which balances quality and training efficiency, and propose several practical solutions to accelerate training. In experiments on modern LLMs, we demonstrate that TVQ is compatible with modern VQ schemes. As a result, TVQ achieves state-of-the-art quality in low-bit post-training quantization settings with a modest training budget and straightforward PyTorch implementation. For instance, TVQ achieves near-lossless 2-bit quantization for LLaMA2-70B with only 1% accuracy drop on popular Question-Answering benchmarks.
[LG-23] ElTab: Elastically Looped Tabular Transformers
链接: https://arxiv.org/abs/2610.41819
作者: Mieszko Komisarczyk,Saurabh Mathur,Maurice Kraus,Sriraam Natarajan,Kristian Kersting
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tabular transformer models such as TabPFN achieve strong predictive performance but incur substantial inference costs, making them difficult to deploy in resource-constrained settings. While looped transformers reduce parameter count through weight sharing, they can lose significant predictive power when stopped early. To address this challenge, we introduce the Elastically Looped Tabular Transformer (ElTab), a single model trained to operate across different computational budgets through supervision at randomly sampled early exits. Across 30 OpenML classification datasets, a single ElTab model matches six depth-specific looped models while cutting training cost by more than half, and matches a 12-layer TabPFN-style baseline with 25% fewer inference FLOPs. These results show that elastic-depth training yields a single model that adapts to the deployment budget at hand, offering a favorable trade-off among predictive performance, storage, and computational cost.
[LG-24] DIFFNAS: Predictor-Free Neural Architecture Search via Diffusion Model and Zero-Cost Evaluation
链接: https://arxiv.org/abs/2610.41818
作者: Yize Sun,Hanzhang Ma,Zixin Wu,Xuechun Li,Volker Tresp,Yunpu Ma
类目: Machine Learning (cs.LG)
*备注: 13 pages, 1 figure, 6 tables. Yize Sun and Hanzhang Ma contributed equally
Abstract:Neural Architecture Search (NAS) has advanced the automated design of deep networks, but remains computationally expensive due to reliance on model training or performance predictors. We introduce DIFFNAS, a predictor-free NAS framework that leverages a discrete graph diffusion model guided by zero-cost performance proxies. Unlike prior diffusion-based NAS methods that require learned performance predictors, DIFFNAS conditions the generative process on training-free metrics such as MeCo and SWAP, enabling efficient architecture search with minimal computational overhead. Experiments on NAS-Bench-201 show that DIFFNAS finds the optimal architectures for CIFAR-10 and CIFAR-100 (94.37% and 73.51%) without a pretrained predictor, while reducing search cost by up to 17.5x compared to DiffusionNAG. On the MobileNetV3 search space, its accuracy is lower than that of DiffusionNAG. These results highlight the promise of diffusion models combined with zero-cost proxies for scalable and effective architecture search.
[LG-25] Continual Reinforcement Learning Is an Integration Problem
链接: https://arxiv.org/abs/2610.41809
作者: Nis Meinert,Jakob Gawlikowski,Edward Grant,Max Cyprys,Nathan Schurr,Neil Boston,Alfredo Ibias,Milind Tambe,Janusz Marecki
类目: Machine Learning (cs.LG)
*备注: 39 pages, 2 figures, 5 tables. Playable companion and source code: this https URL
Abstract:This paper presents an opinionated set of ten desiderata intended to inform the design and evaluation of reinforcement-learning agents that learn continually throughout a continuing lifetime. We use these desiderata to examine a representative selection of existing approaches. Progress on individual desiderata is valuable, but the longer-term aim should be to integrate all ten within a single agent. To our knowledge, no published evaluation has yet provided evidence for such an integration. Likewise, the representative benchmarks we review probe different parts of this target, but separate evaluations do not establish that the corresponding properties coexist within one agent throughout a continuing lifetime. As a secondary contribution, we therefore present a compact persistent-world scenario as a playable companion to the desiderata and outline a proposed benchmark that examines a broad range of associated behavioral capabilities together within one such lifetime.
[LG-26] Exploring Bad Minima in Transfer Learning
链接: https://arxiv.org/abs/2610.41807
作者: Mina Remeli
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 57 pages, 13 figures, 3 tables. MPhil thesis, University of Cambridge, 2022. Supervised by Ferenc Huszár
Abstract:Transfer learning is a highly successful learning paradigm in deep learning which leverages knowledge from one task and applies it to another. Despite its success, the necessary conditions for knowledge transfer are unclear and require further research. In this dissertation, our aim is to understand transferability a little better by answering the following question: Does good generalization on a source task necessarily imply transferability to a related target task? We hypothesize the answer is negative, and attempt to find counterexamples via adversarial attacks on the transfer learning procedure. Over three types of adversarial attacks, our empirical results show a strong relationship between generalization on the source task and transferability. These results do not rule out generalization on the source task as a sufficient condition for successful transfer.
[LG-27] What Standard Evaluations Hide About Detectors of Hijacked LLM Agents : Rare Attacks Unseen Tools and Benchmark Transfer
链接: https://arxiv.org/abs/2610.41803
作者: Mahmoud Abbasi,Juan M. Núñez V.,Amin Shahraki
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 14 pages, 4 figures, 6 tables. Code, data and every monitor call: this https URL
Abstract:Detectors can flag hijacked large language model (LLM) agents from the traces of the tools they call. In the eleven evaluations we audited, attacks or anomalies make up 16% to 86% of the test samples, no evaluation varies that share, and six report no test on data from a second benchmark or source. We ask what such evaluations hide. On the AgentDrift and AgentDojo benchmarks we test trained detectors that read tool identities, behavior, call sequences, step embeddings or text, and a released LLM action monitor, AlignmentCheck, with thresholds fixed on training data or set by the monitor’s released decision rule. First, at a 2% attack rate a detector with an area under the receiver operating characteristic curve (AUROC) of 0.879 reaches a precision of 0.076, and the mix of negatives alone moves that precision between 0.039 and 0.117. Second, as tool coverage, the share of test tool calls whose tool was seen in training, falls, detector rankings cross near 50% coverage and reverse below it, because unseen tools map to inputs that training never reached. Dropout on tool inputs largely removes the reversal. Third, no detector trained on AgentDrift ranks AgentDojo traces better than chance (AUROC 0.47 to 0.52), including one that reaches 0.996 within AgentDojo. The monitor, trained on neither, reaches 0.930, but with its open backbone its released decision rule would still mostly raise false alerts at a 2% attack rate (precision 0.096). We turn these findings into a checklist for evaluating trace detectors.
[LG-28] owards Out-of-Distribution Uncertainty Quantification for Machine-Learned Interatomic Potentials via Pre-Training
链接: https://arxiv.org/abs/2610.41801
作者: Stefan P. Schmid,Tobias Setzer,Maximilian Scheurer
类目: Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注:
Abstract:Informative uncertainty quantification (UQ) is essential for the practical deployment of machine-learned interatomic potentials (MLIPs), as it provides insight into their reliability across chemical space. Although numerous post-hoc uncertainty estimation methods for MLIPs have recently been proposed, most have been trained in low-data regimes, restricting their applicability to narrow domains of interest. Inspired by the emergence of foundational MLIPs, we propose pre-training uncertainty estimators on large, diverse datasets to enable broader applicability. We pre-train several UQ models on large materials science datasets to quantify the uncertainty of different MLIPs, and evaluate their performance on both in-distribution and out-of-domain (OOD) adsorption-energy predictions. We find that pre-trained uncertainty estimators provide viable zero-shot uncertainty estimates across multiple datasets, while fine-tuning consistently yields positive correlations between predicted uncertainty and prediction error, even on the most challenging datasets. These results lay the groundwork for foundational UQ models for MLIPs, ultimately facilitating their deployment in practical settings by providing users with a priori information about model reliability and applicability.
[LG-29] On-device Detection of Wearer-induced NLOS on Chest-worn UWB Tags: Machine Learning Link Indicators or Both?
链接: https://arxiv.org/abs/2610.41799
作者: Markus Gallacher,Vu Anh Minh Le,Matteo Trobinger,Davide Vecchia,Carlo Alberto Boano,Gian Pietro Picco
类目: Machine Learning (cs.LG)
*备注: Submitted to ACM Transactions on Sensor Networks
Abstract:Ultra-wideband (UWB) enables high-accuracy distance estimation (ranging), but performance degrades in non-line-of-sight (NLOS) conditions. When UWB tags are worn, the human body inevitably causes a wearer-induced NLOS (WNLOS), yielding significant errors; still, despite its relevance, WNLOS remains understudied. Moreover, WNLOS detection typically must occur on-device at the tag, requiring classification accuracy to be balanced against tight runtime and energy constraints often hard to meet with machine learning (ML) – the dominating NLOS classification approach. Meanwhile, UWB radio link indicators (LI) offer a faster and lightweight alternative that has rarely been explored. We study existing on-device ML and LI classifiers when applied to WNLOS, and propose novel hybrid ones exploiting their complementarity. We focus on chest-worn tags and analyze 21 classifiers on a dataset of 1.5 million samples across channels, PHY settings, environments, subjects, and orientations. Results show that while all of them achieve good classification accuracy, relative performance, including ranging errors due to misclassification, depends heavily on channel and PHY configuration. Runtime and energy efficiency also vary dramatically; some techniques offer marginal accuracy gains while consuming up to 39.6x more energy. Therefore, we also introduce a recommender tool to identify optimal classifiers under user-defined constraints, publicly released with our dataset.
[LG-30] Do LLM s Know When They Are Wrong About Molecules? In-Context Property Prediction with Self-Reported Uncertainty
链接: https://arxiv.org/abs/2610.41745
作者: Matthias Busch,Marius Tacke,Tamadur Albaraghtheh,Sviatlana V. Lamaka,Mikhail L. Zheludkevich,Roland C. Aydin,Christian Feiler,Christian J. Cyron
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) are used in many domains, but molecular property prediction with them is still little explored. In this work we evaluate the capability of 7 frontier LLMs to predict molecular properties together with a self-reported uncertainty from in-context examples, and compare them with classical baselines trained on the same molecules. We use 4 datasets and screen each for contamination of the training data of the LLMs: three are clean, and the fourth, ESOL, is memorised by most of the models and serves as a comparison. On the clean datasets every LLM improves with the context, at 30 examples the most accurate one beats every baseline. The requested reasoning effort changes accuracy little. The self-reported uncertainty ranks the prediction errors weakly, with Spearman rank correlations of up to 0.46, about the level reached by the uncertainties of the baselines. Combining uncertainty and prediction, on lipophilicity the most certain half of the best LLM is the most accurate at small context sizes; on BACE and antiviral, and on lipophilicity at large context sizes, the best LLMs are on par with the best baseline. On the contaminated ESOL dataset the LLMs show no improvement with the context size, as expected for a memorised dataset, and their stated uncertainty is smaller on the molecules whose value they recall.
[LG-31] Locality-Controlled Out-of-Distribution Guidance for Robust Regulatory DNA Sequence Design NEURIPS2026
链接: https://arxiv.org/abs/2610.41736
作者: Dabin Jeong,Marwane Bourdim,Mo Lotfollahi
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注: Accepted to NeurIPS 2026
Abstract:Designing regulatory DNA sequences for targeted gene-expression control is an important challenge. Recent approaches frame this task as property-conditioned generation, where diffusion models trained on natural sequences are guided toward high predicted activity through reward-based fine-tuning or sampling-time guidance. We show that single-reward evaluation can obscure reward-hacking behavior. By decomposing evaluation into property, novelty, and plausibility, we identify an empirically underexplored region of the evaluation space in which all three objectives are satisfied, but no existing baseline succeeds. We postulate that this gap arises because prior methods treat guidance primarily as a matter of magnitude. In regulatory DNA, however, sequence grammar is spatially nonuniform: motif positions are sensitive to perturbation, while background regions allow more exploration. We therefore propose LOCOGEN by reformulating guidance as a problem of locality. LOCOGEN learns an invariant motif mask through sequence-level invariance learning to protect motif positions, while applying OOD damping only to background positions. It operates at sampling time on a frozen diffusion backbone, without retraining the backbone. Across HepG2, K562, and SK-N-SH, LOCOGEN reaches this previously underexplored region of the property-novelty-plausibility space. Code is available at this https URL.
[LG-32] Hierarchical Residuals: A Minimalist Decoupling of Residual Width from Compute Width
链接: https://arxiv.org/abs/2610.41711
作者: Ziteng Wang,Jianqiao Lu,Xinyu Ma,Jintao Zhang,Jun Zhu,Yiyuan Ma,Jianfei Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Residual connections underpin Transformers, but they implicitly tie the residual width carried through depth to the compute width used inside each layer. We introduce Hierarchical Residuals (HiRes), a minimalist architectural change that decouples these dimensions, widening the residual state while keeping per-layer compute width fixed. HiRes groups layers into blocks: standard d-dimensional PreNorm layers operate inside each block, while a wider D-dimensional PostNorm outer residual connects blocks. Learned read/write projections bridge the two spaces at block boundaries, leaving individual Transformer layers unchanged. Across different model sizes and depths, HiRes consistently outperforms vanilla residual addition, with fitted scaling laws indicating a 1.30-fold improvement in FLOPs efficiency. On OLMoE-1B-7B pretraining for 500B tokens, HiRes achieves 0.0208 lower training loss and 1.14-point higher downstream accuracy. HiRes is also competitive with other residual expansion methods such as VWN, mHC and AttnRes with less activation memory and I/O costs. Built from only dense projections and normalizations, it requires neither specialized kernels nor infrastructure-level optimizations. HiRes thus exposes residual width as a practical scaling variable using only standard operators.
[LG-33] From fleet trajectories to single-cell promises: hierarchically weighted conformal prediction for battery SOH monitoring
链接: https://arxiv.org/abs/2610.41696
作者: Xiangyu Liu
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: Under review at Applied Energy
Abstract:Interval predictions from conformal prediction are usually read as “90% coverage”, but on nested battery data that number promises the fleet average, not the individual cell. We measure the gap on 124 public LFP cells under a preregistered three-tier reporting protocol (fleet, batch, cell): pooled coverage looks healthy (0.875-0.906 across splits) while the worst cell is covered 0.1-2% of the time, with exact binomial tests against the nominal level. We then repair part of the gap with a hierarchically weighted conformal engine whose weights consume only side information frozen at prediction time - signature features, batch labels, cycle indices - and never touch residuals. The preregistered repair criterion passes: 10th-percentile cell coverage moves 0.750 to 0.830 (53% of the gap), the worst cell 0.642 to 0.694, with zero deferrals and a flat sensitivity spectrum. What the engine does not repair is localized rather than left open: under protocol drift the frozen weights harm monotonically on the SOH scale (the absolute scale is reported in full), a structural dead end we report instead of tuning away; and repair capacity is bounded by calibration-pool battery diversity, measured on both datasets. On a cross-chemistry external set the calibrated intervals collapse (0.286), while the within-domain skeleton replicates (pooled 0.933, worst cell 0.686). The deliverable is a reporting protocol - three tiers of promise anchored to measured regimes, worst-cell statistics, deferral share - that makes per-cell interval claims auditable before deployment.
[LG-34] From WSIs to Bulk Spatial and Single-Cell Transcriptomics via Multi-Scale Graph Neural Networks
链接: https://arxiv.org/abs/2610.41692
作者: Miguel Andrade,José Almeida,Carlos Santiago,Catarina Barata
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding the spatial distribution of gene expression is key to studying complex diseases like cancer. While Spatial Transcriptomics (ST) and Single-Cell Transcriptomics (scT) provide high-resolution insights, they remain expensive and scarce. In contrast, standard histopathology Whole-Slide Images (WSIs) paired with bulk RNA sequencing are widely available. This work introduces a Multi-Scale Graph Neural Network (GNN) capable of simultaneously predicting bulk, spatial, and single-cell transcriptomics directly from WSIs. By extracting morphological features using a Pathology Foundation Model (PFM) (UNI2-h) and constructing spatial graphs at both patch and cell levels, the model learns the hierarchical relationships within the tissue. The network is trained on a combined dataset of The Cancer Genome Atlas Program (TCGA), Visium, and Xenium samples across lung, kidney, and breast tissues. A hybrid training strategy is employed, leveraging direct supervision alongside weakly supervised consistency losses to ensure predictions align across biological scales. Results show the model accurately predicts gene expression at the slide level and moderately at the patch level (strongest on Xenium), and achieves moderate correlation at the challenging single-cell level. Furthermore, downstream tasks explore the clinical relevance of these predictions, demonstrating their utility in survival analysis, spatial hazard mapping, and tumor classification via molecular signatures.
[LG-35] nablaMimic: Ultra-Efficient Humanoid Control with Differentiable Simulation
链接: https://arxiv.org/abs/2610.41654
作者: Weyl Lu,Yubei Chen
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: 26 pages, 7 figures
Abstract:Robot policy learning is dominated by zeroth-order methods such as PPO, which treat the simulator as a black box and pay for it with enormous sample counts. First-order policy learning differentiates through the dynamics, but on contact-rich whole-body control it has rarely worked: its gradients explode. A recent world-model method showed that derivative information can make humanoid motion tracking far more efficient, using surrogate gradients of a learned model. We show that exact gradients of the simulator itself do better, once two obstacles are removed. Exact gradients contain rare unbounded samples, because contact dynamics is not differentiable everywhere and its derivative diverges near some events; and they are expensive, because the simulator’s adjoint has to replay the contact solve. \nabla Mimic bounds these samples with median-relative per-sample gradient clipping at every step of the backward pass, and reuses each costly simulated window by multistep regression onto gradient-derived action targets, which bounds every action target independently of the gradient scale. NablaEngine, a differentiable GPU simulation engine for the Unitree G1, removes the cost: it computes MuJoCo’s full contact dynamics with fused per-world kernels and the adjoint of a whole control step as a single megakernel, with analytic gradients that match float64 finite differences, 11.5\times faster than the available MuJoCo Warp adjoint. On whole-body tracking of all 40 LAFAN1 clips with one policy on one GPU, \nabla Mimic reaches the score reported by the state-of-the-art world-model method, 0.82, in 20 minutes instead of 1.2 h, and 0.874 in 1.8 h. SHAC, the standard first-order baseline, stops on exploding gradients within minutes. We release NablaEngine and \nabla Mimic as open-source infrastructure for first-order humanoid policy learning. Comments: 26 pages, 7 figures Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2610.41654 [cs.RO] (or arXiv:2610.41654v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2610.41654 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-36] On the Generation Order in GFlowNets
链接: https://arxiv.org/abs/2610.41648
作者: Timofei Gritsaev,Evgeny Katasonov,Ildus Sadrtdinov,Esmeralda S. Whitammer,Dmitry Vetrov
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sampling from discrete distributions specified by unnormalised probability functions is a classical statistical problem. GFlowNets address this problem by constructing objects sequentially, adding one discrete component at a time in either a predefined or a learned order. The choice of order determines which conditional distributions the neural network must learn, some of which may be easier to learn than others. Yet whether existing GFlowNet algorithms discover interpretable generation orders and make use of them during training remains unexplored. We introduce controlled problems where generation order is experimentally shown to affect convergence and standard GFlowNet training struggles to discover useful orders. To address this difficulty, we develop Learning-Order GFlowNet, a training recipe that learns interpretable generation orders without order annotations and improves convergence on these problems. Finally, we investigate gradient signal-to-noise ratio (SNR) as a possible explanation for order emergence: orders associated with faster convergence exhibit higher SNR at comparable validation loss, suggesting both a mechanism favouring their emergence and an SNR-based characterisation of a good generation order.
[LG-37] A Constructive Orbit-Based Theory of Learnability
链接: https://arxiv.org/abs/2610.41641
作者: Yunied Puig
类目: Machine Learning (cs.LG)
*备注:
Abstract:We develop a finite-dimensional orbit framework for constructing data-adapted dictionaries, sparse realizations, and predictive extensions. The method uses wrapped weighted shifts to generate orbit dictionaries and local interpolation maps from independent orbit representatives, turning the learning problem into three explicit tasks: scaffold discovery, sparse realization on the discovered dictionary, and prediction through learned sparse coordinates. We introduce CCDS as the main constructive ingredient: a deviation-controlled sparse realization procedure that recovers MP/OMP-type behavior at the minimal-deviation boundary while allowing feasible interior paths with prescribed residual geometry that is inaccessible to MP/OMP. We prove generic orbit independence, exact block interpolation with norm control, observable bounds on sparse approximation distortion, sparse realization guarantees on selected supports, and fixed-path stability under admissible perturbations of the block interpolants. These results connect sparse approximation, interpolation, and finite-dimensional operator dynamics through the construction and analysis of data-induced orbit dictionaries. Experiments on synthetic geometry, controlled realization tests, and standard classification benchmarks show that selected orbit scaffolds have substantially better matched-rank projection behavior than random orbit or unstructured alternatives, that CCDS attains near-zero realization error at the minimal-deviation boundary, and that predictors trained from the sparse CCDS certificates give competitive performance.
[LG-38] What Carries Architectural Signal? A Code-to-Architecture Mapping Study
链接: https://arxiv.org/abs/2610.41628
作者: Rakhshanda Jabeen,Morgan Ericsson,Yuyao Duan,Anna Wingkvist
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Code-to-architecture mapping assigns source files to modules of a reference architecture. Representations of implementation artifacts can capture code text, dependencies, and directory organization, but predictive gains alone do not establish the contribution of additional graph processing. Dependencies may already be encoded in these representations, while directories may closely match the reference decomposition. We investigate how artifact representations and graph context affect mapping on ten Java, C, and C++ systems with a limited labeling budget. We compare lexical, structural, and contextual file representations and assess each predictor with and without dependency neighbors. Using a Heterogeneous Graph Transformer (HGT), we compare file-level graphs extended with member nodes summarizing each file’s program elements, folder nodes, or both, alongside a variant encoding folder paths directly in file representations. We compare folder-aware predictions with a directory rule using the same labeled files. Representations encoding dependency information outperform lexical representations on most systems. Additional neighbor aggregation can improve mapping, depending on the predictor and aggregation direction. Folder-aware variants yield the largest and most consistent gains across the evaluated HGT configurations. However, the directory rule achieves per-system median accuracy of 89-100%, and adding folder information does not consistently improve predictions for files the rule misclassifies. Member nodes provide no consistent improvement over file-level graphs. These findings support assessing what file representations and directory organization already provide before attributing improvements to graph neural networks. Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG) Cite as: arXiv:2610.41628 [cs.SE] (or arXiv:2610.41628v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2610.41628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-39] DeepWeave: Weaving Neural and Symbolic Evidence for ML-Based Network Intrusion Detection System
链接: https://arxiv.org/abs/2610.41541
作者: Raoul Saad,Enkeleda Bardhi,Andrea Agiollo,Fernando Kuipers
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Machine learning-based network intrusion detection systems can recognize complex traffic patterns, but often require substantial labelled attack data, degrade under deployment shift, and provide limited evidence for analysts. We present DeepWeave, a probabilistic neuro-symbolic Network Intrusion Detection System (NIDS) that makes logic an integral part of detection. DeepWeave automatically constructs and validates interpretable rules, represents neural predictions as probabilistic predicates, and reconciles both evidence sources through explicit coupling policies over packet-, flow-, and hybrid-views. The resulting proofs provide intrinsic explanations for rule-supported alerts. Across multiple datasets, DeepWeave improves low-data detection, false-positive control, transferability, and robustness. With only 50 labelled packet observations per attack class, it improves macro-F1 from 0.632 to 0.924 and reduces the attack false-positive rate from 0.075 to 0.008 over the matched neural baseline. Its proofs are also more stable than post-hoc neural attributions, while batched inference keeps probabilistic reasoning practical. These results demonstrate label-efficient, adaptable, and inspectable neuro-symbolic intrusion detection.
[LG-40] FOCUS: First-Order Control Using Optimal Transport Supervision for Imitation from Observations NEURIPS2026
链接: https://arxiv.org/abs/2610.41540
作者: João A. Cândido Ramos,Lionel Blondé,Hugues Vinzant,Jacopo Castellini,Alexandros Kalousis
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted at the NeurIPS 2026 Workshop on Physical Understanding for Decision-Making
Abstract:Imitation learning from observations must recover behavior from state-only demonstrations without known temporal correspondence. Differentiable simulators provide pathwise policy gradients, but differentiating through long, contact-rich trajectories can make optimization unstable. We introduce FOCUS, which combines local optimal-transport alignment with short-horizon differentiable actor-critic learning. FOCUS matches agent rollout windows to multiple sampled expert segments using a soft best-of- K objective that emphasizes locally compatible matches. The resulting transport-based costs provide differentiable imitation rewards, while a learned critic bootstraps value beyond the rollout horizon. FOCUS requires neither temporally aligned trajectories nor expert-aligned initialization or teacher forcing. Across contact-rich locomotion tasks in DFlex, including a musculoskeletal humanoid controlled by 152 muscle activations, FOCUS improves sample efficiency and training stability over adversarial imitation-from-observation methods and prior differentiable imitation baselines.
[LG-41] Infinite ResNets are Gaussian Mean ODEs
链接: https://arxiv.org/abs/2610.41514
作者: Louis-Pierre Chaintron,Lénaïc Chizat,Javier Maass
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding the training dynamics of deep neural networks in large-scale limits is a question both of foundational and of practical importance. Prior works have obtained such descriptions through dynamical mean field theory (DMFT), yielding a system of non-Markovian dynamics that are challenging to analyze and that obscure the optimization structure of the original dynamics. In this paper, we obtain instead a simple description of the large-scale limit training dynamics for ResNets in standard shape scalings and in the 1/\textdepth branch scaling that allows maximal (local) feature updates. For a model trained with gradient descent (GD) from a random initialization, the limit dynamics is simply GD on a Neural Mean ODE with Gaussian initial weights. Our analysis covers population or empirical risk GD in the joint limit of large width, depth and embedding, input, and output dimensions. We obtain approximation scaling laws that depend on the shape of the ResNet and the covariance spectrum of the data. We also illustrate the flexibility of our approach by discussing Mixture of Expert models, where likewise the limit admits an intuitive (nested) Mean ODE description.
[LG-42] PCD: Predictive Coding Dynamics for Irregularly Sampled Time Series
链接: https://arxiv.org/abs/2610.41506
作者: Tianrui Gou,Jiayangmei Xu,Xingzhou Zhang,Lei Sha
类目: Machine Learning (cs.LG)
*备注: 15 pages. Supplementary material (2 pages) available as an ancillary file
Abstract:Dynamic systems evolve continuously over time, whereas observations in real scenarios are often sparse, irregular, and asynchronous. Accurate prediction therefore requires inferring latent state evolution between observations, as future values depend on partially observed trajectories. We propose Predictive Coding Dynamics (PCD), a residual feedback mechanism for irregular continuous dynamic models. PCD maintains previous reconstructions, predicts observed entries from them, and incorporates masked signed residuals into state recognition through an energy penalty. The mechanism is implemented with ODE-RNN and Latent ODE, supporting event-based hidden updates and latent state recognition. Experiments on forecasting and trajectory recovery tasks show that PCD improves continuous dynamic backbones under sparse and irregular sampling. The results further indicate that predictive error feedback improves latent state estimation when observations constrain the current trajectory. These results suggest that PCD provides a residual feedback framework that jointly supports trajectory generation, observation consistency checking, and state refinement in continuous-time series modeling. The source code is available at this https URL.
[LG-43] Spectral Haar Binarizer: Compact Boolean Contrast Representations for Hyperspectral Classification
链接: https://arxiv.org/abs/2610.41478
作者: Michał Romaszewski,Przemysław Głomb
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tsetlin Machines offer an interpretable, hardware-friendly alternative to conventional machine learning models, but applying them to continuous hyperspectral measurements requires converting spectral signals into Boolean representations. Standard binarisation methods typically discretise each spectral band independently, potentially discarding important relationships between neighbouring wavelengths. In this paper, we introduce the Spectral Haar Binarizer (SHB), a contrast-based spectral representation method that constructs Boolean predicates from differences between selected spectral intervals. Inspired by Haar-like contrast features, the method uses an AdaBoost-style selection procedure to obtain a compact set of discriminative spectral predicates. We evaluate SHB on five heterogeneous hyperspectral datasets covering forensic blood detection, agricultural and urban remote sensing, and rainforest tree-species classification. Across binary and multiclass tasks, SHB consistently outperforms standard per-band binarisation strategies and provides the strongest or near-strongest results across classifiers. In binary tasks, TM-SHB achieves the best average performance, while RF-SHB is strongest in multiclass settings. Bayesian and rank-based analyses confirm that SHB’s advantage is robust across datasets and not driven by isolated cases. The results show that representing continuous hyperspectral measurements is a central processing step for efficient classification. That contrast-based Boolean features provide an effective bridge between information-rich sensor measurements and compact, logic-based processing.
[LG-44] When Links Mislead: Label-Free Structural Polarity for Zero-Shot Classification of Web Pages
链接: https://arxiv.org/abs/2610.41464
作者: Hanshuai Cui,Zhiqing Tang,Zhi Yao,Weijia Jia,Jun Luo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Web platforms such as digital libraries, online stores and university sites must classify new pages before anyone labels them. Large language models (LLMs) can label each page from its text, but these labels are noisy, and links could correct them only where linked pages share classes. Existing zero-shot methods ignore the links, or pull a page toward its neighbours as label propagation and graph neural networks (GNNs) do, which hurts where linked pages differ in class. Others weight propagation by an LLM-estimated homophily that costs extra calls and drifts across LLMs. We find that the sign with which links should enter a prediction, which we call structural polarity, is readable without true labels. It is the excess agreement of LLM labels and text-prototype predictions along links. We thus propose ZeroPolar, which gates one signed propagation step by this statistic. Training-free ZeroPolar mixes prototype scores with any number of LLM labels, and ZeroPolar-T denoises the labels with a text classifier once half of the pages carry one. With one GPT-4o label per page, ZeroPolar-T reaches the highest mean accuracy on eleven graphs (73.99%), 6.01 points above LLM-BP and significantly above all eighteen baselines we compare. With LLM labels on only 5% of the pages, training-free ZeroPolar leads a GNN trained on these labels by 10.80 points.
[LG-45] RetryRoute: Retry-Aware Model Routing for Agent ic Image Editing Services
链接: https://arxiv.org/abs/2610.41460
作者: Hanshuai Cui,Zhiqing Tang,Zhi Yao,Jun Luo,Weijia Jia
类目: Machine Learning (cs.LG)
*备注:
Abstract:Web agents increasingly edit images by calling open editing models in a critique-and-retry loop, and these models differ in cost by more than an order of magnitude. After every rejected edit, a service must decide whether to retry the same model, escalate to another or give up. Existing routers pick one model per request, and cascades escalate in a fixed order. However, both treat a retry as an independent draw. We identify sticky failures, meaning that a retry after a failure succeeds only 17-53% as often as a first try across six open models, three benchmarks and two judges. Based on this finding, we propose RetryRoute, a retry-aware router that estimates each model’s chance of success given the request’s failure history. It plans retries, escalations and abandonment over failure states and adds live queue waits to the cost, so the same plan becomes load-aware dispatch. At the main budget, RetryRoute’s success under the in-loop judge exceeds the strongest of eleven prior baselines by 2.8 points on average over eight groups. On a GPU serving testbed it raises the share of edits delivered within the service-level objective by 5.0-6.3 points over the best prior baseline at every load.
[LG-46] NormDetect: Anchored Calibration for Low-False-Positive Detection of Machine-Generated Web Text
链接: https://arxiv.org/abs/2610.41459
作者: Hanshuai Cui,Fanshuai Meng,Zhiqing Tang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Web platforms screen posts for text written by large language models, and every false flag hits a human writer. Detectors must therefore run at a very low false positive rate (FPR). Zero-shot detectors such as Binoculars score text with frozen language models, but invisible edits such as homoglyphs shatter tokenization and push machine text into the human range. Unicode normalization undoes these edits, yet the normalized score still misses half of the machine text at a strict FPR, under attacks that leave nothing to clean. We find that with the Falcon-7B scoring pair, heads on token statistics recall less than the normalized score at this FPR, and several flag too many human posts in some sections. We propose NormDetect, which anchors a learned per-section head to the normalized zero-shot score. A blend weight chosen on training folds under a human FPR cap combines the two scores, and one threshold is calibrated on human text only. On eight sections of the RAID benchmark with eleven attacks, NormDetect raises recall at a 0.1% calibrated FPR by 5.0 points over normalized Binoculars. Its human FPR bound stays at most 1% in every section, and no other detector within this bound recalls as much.
[LG-47] Improving the Optimization Landscape of Matrix Completion with ε-close Surrogates NEURIPS’26
链接: https://arxiv.org/abs/2610.41455
作者: Ziye Ma
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 30 pages, accepted at NeurIPS’26
Abstract:Non-convex optimization is central to machine learning and AI, yet unlike its convex counterpart, it still lacks a general and principled framework. In this work, we try to highlight the importance of combining noisy perturbations with over-parametrization mathematically under the classic setting of matrix completion (MC). Matrix completion is an important non-convex recovery problem in which only a subset of a matrix’s entries is observed, and the remaining entries must be recovered using low-rank constraints. This problem is notoriously difficult because observations are limited. However, we show that by injecting \epsilon -level perturbations into the nullspace of the observation mask, we can construct noisy matrix sensing surrogates whose solutions remain \mathcalO(\epsilon) -close to the ground truth with valid restricted isometry property (RIP) constants. The existence of RIP further enables powerful tensor frameworks to be applied with theoretical guarantees. This approach reflects a broader empirical trend in which stochasticity or noise improves the curvature of the optimization landscape, allowing over-parameterized (a.k.a large) models to better separate signal from noise. Assuming each matrix entry is observed independently, we establish quantitative recovery guarantees without explicit requirements on sampling rate or incoherence, and discuss how such assumptions can further strengthen our results.
[LG-48] When Everyone Can Build a Model: Mechanistic Inference in the Age of AI
链接: https://arxiv.org/abs/2610.41454
作者: Marvin van Aalst(1),Adèle H. Ribeiro(2 and 3),Anna Matuszyńska(1 and 3) ((1) Computational Life Science, Faculty of Mathematics and Natural Sciences, RWTH Aachen University, Germany, (2) Causal and Explainable Artificial Intelligence in Life Sciences, Faculty of Mathematics and Natural Sciences, RWTH Aachen University, Germany, (3) Center for Computational Life Sciences, RWTH Aachen University, Germany)
类目: Machine Learning (cs.LG)
*备注: 8 pages, 2 figures
Abstract:Modern deep learning and AI-assisted code generation are lowering the technical barrier to building mathematical models of biological systems that reproduce complex observations. This lets researchers with domain expertise build semi-mechanistic, hybrid models constrained by prior knowledge and augmented with learned structure without extensive training in modelling. However, easier construction does not make interpretation easier: predictive success of such so-called grey-box models does not establish that an inferred mechanism corresponds to the one that generated the data, and the learned component can absorb errors in the mechanistic part. Many biological modelling problems are underdetermined, so several, possibly infinitely many, models may reproduce the same data equally well. This ambiguity enters at two modelling choices: which variables can affect others, and which response function describes the underlying mechanism. For the first choice, causal inference provides a formal framework stating which causal relationships and intervention effects are identifiable from the data and available knowledge. For the second, ensemble analyses quantify the uncertainty and the range of behaviours still allowed by models that fit equally well. Together, they turn a single good fit into an explicit account of what is supported, what remains open, and which experiment would decide.
[LG-49] A Unified XAI-NSAI Analytical Framework for Trustworthy IoT Anomaly Detection
链接: https://arxiv.org/abs/2610.41424
作者: Hugues Leger,Giovani R. Abuaitah
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 figures, accepted for publication in the Proceedings of the IEEE Annual Congress on Artificial Intelligence of Things (AIoT), 2026
Abstract:The widespread deployment of Internet of Things (IoT) devices in critical environments makes anomaly detection essential for identifying abnormal behaviors and supporting timely investigation and response. However, accurate detection alone is insufficient. Practical IoT deployments must also provide explanations, incorporate domain knowledge and reasoning, operate under IoT constraints, and support human decision making. Although recently proposed approaches increasingly incorporate Explainable Artificial Intelligence (XAI) or Neuro-Symbolic Artificial Intelligence (NSAI), XAI and NSAI methods are often considered independently. We argue that they should be considered together to better understand their complementary roles in supporting trustworthy IoT anomaly detection. To address this gap, we propose a unified analytical framework organized around five complementary dimensions. The framework provides a structured basis for characterizing existing approaches, identifying capability gaps and design trade-offs, and guiding the design of new anomaly detection systems for different IoT applications. We demonstrate the framework’s analytical utility by applying it to three representative studies and illustrate its design utility through its application to a Medical IoT anomaly detection system.
[LG-50] Certifying Retraining Regret through Residual Structure
链接: https://arxiv.org/abs/2610.41410
作者: Peida Li
类目: Machine Learning (cs.LG)
*备注: 46 pages, 5 figures. Code and reproduction materials: this https URL
Abstract:We certify a fixed ridge predictor’s regret against full-feature retraining over every continuous completion of an interval-valued table. Our main theorem gives verifiable original-row residual conditions for a uniform error bound independent of nuisance-feature scale and rank, with an asymptotically sharp leading constant and quantitative leakage stability. Full-rank constructions separate certification dimension from feature dimension, including examples requiring directions beyond the candidate. Observed-row anchors make the conditions computable, while exact row geometry and global search return valid upper bounds and feasible original-table witnesses. A finite-scale contraction and common-domain moment fallback implement the structural result. Four real-world tasks retain all model features and certify twenty rank-three instances to width 10^-5 . Their primary median widths improve over centered moments. Joint residual refinement additionally certifies all twenty new response-independent masks to the same precision within 76.21 seconds. For a concrete fixed-point predictor contract, pure energy certifies thirteen of fifteen stopping decisions in ten seconds, versus five for moments. Refinement certifies all fifteen within 91.68 seconds at the same objective threshold.
[LG-51] Anomalies as Residuals: Cauchy Displacement Networks
链接: https://arxiv.org/abs/2610.41387
作者: Yang Cao,Zihao Guo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Neighbourhood-based detectors such as KNN, LOF and LUNAR remain among the most accurate methods for tabular anomaly detection, but they compare every query with the stored training set, so their inference cost and memory grow with the data and the data must be kept at deployment. We ask whether the neighbourhood geometry these detectors exploit can be compiled into a small network that scores a query with a single forward pass. We propose the Cauchy Displacement Network (CDN), which regresses each normal point onto the mean of its k nearest training neighbours with a single-hidden-layer network of Cauchy activations and scores a query by the length of its predicted displacement. This displacement is an empirical mean-shift vector, whose fixed-radius population counterpart locally approximates a scaled gradient of the log density. We show that Cauchy units exactly represent the score fields of a structured family of Student- t expert densities, and establish a constructive approximation bound for the corresponding population neighbourhood-mean map using a single-hidden-layer Cauchy network. We further show that the bounded network output guarantees asymptotically linear residual-score growth along every fixed direction. On 46 benchmark datasets against 28 baselines, CDN obtains the best mean rank under both AUC-ROC and AUC-PR, while scoring 10^4 queries in a few milliseconds on a single CPU thread, up to three orders of magnitude faster than neighbourhood-based detectors, with a model whose size does not depend on the training set.
[LG-52] Riemannian Optimization on Unknown Manifolds via Stochastic Proximal Refinement
链接: https://arxiv.org/abs/2610.41379
作者: Ruixiang Wu,Jiahao Ai,Tinko Sebastian Bartels,Xuanzhuo Liu,Yifei Wu,Yu Mao,Tongxin Li,Costas Courcoubetis
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study Riemannian optimization on an unknown manifold accessed only through feasible samples. The central challenge is to turn feasible samples into optimization steps, i.e., the samples are drawn from the unknown feasible manifold, but do not directly provide the tangent spaces, projections, or retractions required by classic Riemannian methods. Moreover, regions with low objective values may be sparsely represented in the data, so directly sampling from a distribution fitted to these observations can require many draws to find better feasible solutions. We introduce Proximal Refinement on Implicit Smooth Manifolds (PRISM), which turns feasible-sample access into an iterative optimization procedure by repeatedly sampling an objective-aware proximal distribution conditioned on the current iterate. Theoretically, we show that PRISM recovers the effect of Riemannian-gradient refinement, leading to provable objective descent, feasibility (manifold adherence), and convergence toward first-order stationarity. We demonstrate the effectiveness of our approach through synthetic experiments illustrating its geometric properties and applications to data-driven building control and diverse real world optimization tasks, highlighting its potential as a general framework for data-driven optimization across scientific and engineering domains.
[LG-53] Generated storm lifecycles fill the rare-event tail of short rainfall records
链接: https://arxiv.org/abs/2610.41365
作者: Valerie Tsao,Nathaniel W. Chaney,Manolis Veveakis,Marta Zaniolo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Historically rare ‘‘one-in-a-generation’’ storms are occurring with unprecedented frequency, causing catastrophic damage and mounting casualties, and exposing the vulnerability of infrastructure designed around short observational records. To overcome the limitations of sparse data, we present StormGen, a generative machine-learning model that transforms an observed storm’s initial hours into complete, physically plausible hourly rainfall lifecycles in minutes. Evaluated on a decade of southeast Texas rainfall, StormGen produced close counterparts for 4\5% of the most severe 2% of storms–compared to just 3% for stochastic storm transposition–and maintained spatial coherence over full 24-hour sequences. Crucially, from a single hour of initial rainfall, the model successfully reconstructed the specific lifecycle, spatial pattern, and extreme catchment totals of Hurricane Harvey itself, despite the event being entirely absent in training. Because storm timing alone can more than double peak flows, these complete generated scenarios provide a vital mechanism to stress-test flood protections against unprecedented extremes.
[LG-54] Quantile Propagation Planning for Budgeted Tail-Risk Evaluation
链接: https://arxiv.org/abs/2610.41351
作者: Weijia Han,Zhi Huang,Lisha Qu,Yuanyuan Tan,Jiayue Meng
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 60 pages, 8 figures, 34 tables; includes appendices
Abstract:Risk-aware planning must identify rare, costly outcomes using only a limited number of model evaluations. Monte Carlo rollouts spend this budget on noisy tail estimates, while planning from the mean can miss the tail entirely. We introduce quantile propagation planning (QPP), which propagates a small, fixed set of disturbance quantiles through the dynamics and integrates the resulting cost curve to evaluate both mean and tail risk. The method is exact at the propagated levels, converges as the quantile grid is refined, and admits a one-sided conservatism guarantee under stated structural conditions. In controlled overload and inventory tasks, QPP reduces realized tail cost and constraint violations relative to mean-rollout planning. On overload, it remains competitive with sampled evaluators that use substantially more trajectories. These results position QPP as a transparent, budget-controlled approach to tail-risk evaluation.
[LG-55] StrictPRMBench: A Replayable Evaluation Benchmark for Auditing Fixed-Score PRM Aggregation
链接: https://arxiv.org/abs/2610.41333
作者: Pengfei Zheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Process reward models (PRMs) score reasoning steps, but best-of-N selection also requires a rule that aggregates those scores. We study that rule as a separate evaluation object. StrictPRMBench fixes mathematical reasoning pools, scorer evidence, and the source trace of a deletion intervention, so that different reducers face the same recorded selection problem. Across seven scoring checkpoints and two benchmarks, the stored N=32 outcomes give higher deletion-induced selection instability for product than geometric mean (GM) in all 14 cells. The equal-cell difference is +0.0965; a shared-problem bootstrap, conditional on these checkpoints and frozen pools, gives a 95% interval of [0.0846,0.1086]. A pre-injection-defined sensitivity excludes replacement slots that are either reducer’s clean winner and retains 4,459 of 4,622 cell-problem records. The retained difference is +0.0986 [0.0875,0.1098]. Effects are heterogeneous: the two Qwen cells have positive percentile-bootstrap intervals on retained support but two-sided exact paired p-values of 0.0625 and 0.0703. Clean-pool Qwen accuracy does not detect a product advantage, but this is not an equivalence result. We provide versioned outcome records, CPU reanalysis, and an explicit evidence-availability contract. The contribution is a diagnostic evaluation protocol and a reproducible audit of recorded selector behavior, not a new PRM, a universally optimal reducer, or evidence of deployment-wide harm. Raw-score replay and outcome-record reanalysis are distinguished: missing primary edited-score vectors limit independent evaluation of new reducers under the same stress.
[LG-56] rl_rob: A transparent from-scratch Python library for teaching reinforcement learning from tabular methods to deep policy gradients
链接: https://arxiv.org/abs/2610.41303
作者: Abdulrahman Altahhan
类目: Machine Learning (cs.LG)
*备注:
Abstract:rl_rob is an open-source Python library for teaching reinforcement learning. It implements the arc of a modern course, from tabular temporal-difference learning through linear function approximation to deep value-based and policy-gradient methods, with every algorithm written from first principles so that its update rule is visible in a few lines of code. All agents share one interface, so learners can compare methods directly across stages and trace a deep reinforcement learning update from the temporal-difference error to the weight change. The library supports teaching and assessment in which students implement, modify and explain update rules, rather than only produce working code.
[LG-57] AuditFL: A Contract-Backed Clearing Layer for Open Federated Learning
链接: https://arxiv.org/abs/2610.41278
作者: Alexandros Balla,Spyros Lalis
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 9 pages, 6 figures. Accepted for publication at the 4th International Conference on Federated Learning Technologies and Applications (FLTA 2026). The final version will appear in the conference proceedings published by IEEE
Abstract:Federated learning (FL) keeps raw data local, but most deployments still rely on a trusted coordinator for client admission, checkpoint distribution, update collection, aggregation, and rewards. This paper presents AuditFL, a contract-backed clearing layer for auditable FL in open settings. AuditFL keeps training and aggregation off chain, stores model artifacts in an assumed reliable off-chain artifact store, and records only content identifiers and economic state on chain. Clients reserve update tickets with deposits, publish update identifiers, and later claim refunds or rewards; the task coordinator publishes global checkpoints and finalizes rounds through deterministic settlement rules. The design does not remove the aggregator, but makes its on-chain round decisions attributable, replayable, auditable, and economically constrained. We implement AuditFL as both a deterministic state-machine prototype and a Solidity contract on a local Ethereum-compatible Anvil chain. On full MNIST with Dirichlet non-IID splits, 12 rounds, and up to 200 clients, AuditFL preserves the Plain FL learning trajectory while its overhead follows closed-form control-plane models. At 200 clients, the contract run issues 5,026 transactions, stores 2,413 off-chain artifacts (144.9 MiB), and consumes 829.7M EVM gas, dominated by per-client reserve and publish calls. Controlled dropout, invalid-update, and artifact-unavailability stress tests reduce inclusion nearly linearly but still finalize rounds, with final-accuracy changes remaining within a few tenths of an accuracy percentage point even under high injected failure rates.
[LG-58] SPT-Flow: One Integration Many Futures in Continuous-Time World Models
链接: https://arxiv.org/abs/2610.41273
作者: Mingyang Lyu,Yinqian Sun,Gongce Cao,Yuhao Zhang,Lu Jia,Qian Zhang,Bo Wang,Pin Lyu
类目: Machine Learning (cs.LG)
*备注: 19 pages, including appendices
Abstract:Continuous-time world models generate a latent trajectory through a single ODE integration, but adding stochastic conditioning does not ensure that the model uses it. In our teacher-forced baseline, posterior means vary by only about 1% of the prior scale, and matched and swapped latents yield indistinguishable reconstruction PSNR at the reported precision. We study a supervision-shortcut hypothesis: ground-truth intermediate states can reduce the need for trajectory-associated latent information. We introduce SPT-Flow, which samples one latent per trajectory and matches a free-running ODE rollout to demonstration states at multiple readout times. Re-anchoring experiments support the proposed mechanism. On LIBERO, with both models trained for 30k optimizer steps, SPT-Flow improves best-of-10 PSNR over our retrained ODEWorld baseline by 2.0 and 2.4 dB at the mid-trajectory and final frames and reduces mean LPIPS by 23%, with comparable per-sample inference cost. The rollout control recovers most of the accuracy gain; learned latent conditioning adds diversity and best-of-K gains while reducing mean sample accuracy relative to this control. The diagnostics support trajectory-associated latent utilization; semantic mode coverage under matched conditions in the robotic setting remains unverified.
[LG-59] A Unified Framework for Probability Estimation of Data-Dependent Events
链接: https://arxiv.org/abs/2610.41244
作者: Vitaly Feldman,Satyen Kale,Vinod Raman,Kunal Talwar,Ambuj Tewari
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 39 pages
Abstract:We study the general problem of estimating D(\dde(S)) , the probability that an unknown distribution D assigns to an event determined by a sample S of n i.i.d.\ draws from D through a known mapping \dde . The estimation algorithm knows \dde and S but does not have access to any additional samples from D . This formulation captures classical missing mass, the data-dependent targets of adaptive data analysis and post-selection inference, and the test error of a data-trained predictor, whose estimation was recently studied by \newGastpar et al.\ (2024a,b). Within this framework we examine the two classical approaches to estimation. The error of the empirical plug-in estimator is exactly a generalization gap, so generalization bounds transfer to it directly. The leave-one-out estimator, which recovers the Good–Turing estimator, is accurate for every mapping that is stable under the deletion of one sample point, including new structured examples such as neighborhoods of the sample and pierced sets. The main application of our framework is to error estimation for a predictor that combines a base classifier with memorized exceptions, a structure common in theory and in practice. For it, the test error is exactly the missing mass restricted to the error region of the base classifier, a region selected by the same sample. We formulate the general problem of \emphadaptive missing mass estimation to address such dependence and propose a natural hybrid estimator, the Good–Turing estimator restricted to the selected region. We show that the generalization of the base classifier alone does not guarantee the accuracy of this estimator, and derive two bounds on its error: the first one based on the VC dimension of possible error regions and another based on the mutual information of the selected region with the sample. Comments: 39 pages Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2610.41244 [cs.LG] (or arXiv:2610.41244v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.41244 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-60] Generative SDEs with Trainable Diffusion and Density-Based Losses
链接: https://arxiv.org/abs/2610.41243
作者: Angxiu Ni
类目: Machine Learning (cs.LG); Dynamical Systems (math.DS)
*备注: 17 pages, 5 figures
Abstract:We develop a divergence-kernel method for jointly optimizing neural SDE drift and diffusion fields and the initial Gaussian distribution, using losses that depend directly on the terminal density. In two- and eight-dimensional Gaussian-mixture experiments, we learn time-independent fields and obtain far fewer off-mode samples than score-based generators with similar parameter counts. The drift attracts samples toward the modes, while the diffusion helps disperse excess probability between them.
[LG-61] Corroborative Learning: Mutual Enhancement through Task-Response Reconstruction
链接: https://arxiv.org/abs/2610.41222
作者: Zexing Zhang,Jichao Li,Tianyang Lei,Yiyin Zhang,Kewei Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:In pattern analysis, models trained independently for the same task often develop complementary capabilities through differences in architecture, data, and optimization. Exploiting complementarity while retaining independent deployment is central to collaborative learning. Most methods establish transfer by reducing differences in predictions, relations, or representations between target and collaborator, although these differences also underlie complementarity. This creates a structural trade-off between reducing differences for knowledge exchange and preserving them for complementarity. We therefore propose Corroborative Learning (CL). Each model forms task-driven responses, with cross-group support from its collaborator. Source-excluded reconstruction of response increments from collaborator representations separates fitting and evaluation across disjoint groups and selectively constrains directions by cross-group reconstructability. The process requires no direct output or feature matching or additional inference dependency. Furthermore, with linear reconstruction and fixed collaborator representations, the loss equals a positive semidefinite directional regularizer, constraining less reconstructable directions more strongly. Signal and noise conditions characterize when directional shrinkage reduces response-estimation error. Across six language tasks, 24 models, and 36 pairs, CL improves over capacity-matched independent adaptation on every task, with positive gains across most directed model combinations. Pairwise comparisons show broad advantages over transfer and mutual-learning methods and different gains from complementarity across collaboration rules. Low-label language and vision results support cross-domain applicability. Task-relevant differences can support mutual enhancement without eliminating disagreement.
[LG-62] A Positional Realizability Theory of Length Generalization in Single-Head Attention
链接: https://arxiv.org/abs/2610.41218
作者: Jiasong Han,Yunkai Chai,Jintao Wang
类目: Machine Learning (cs.LG)
*备注: 26 pages, 6 figures. Published in Transactions on Machine Learning Research (TMLR), October 2026. Code available at this https URL
Abstract:Why does length generalization (LG) depend so sharply on the positional encoding (PE)? Relative schemes extrapolate fixed offsets, absolute schemes fix positions, and neither handles growing offsets. We give a realizability theory for a deliberately controlled abstraction, a single positional-only attention head, that derives this well-documented empirical pattern and says precisely where it holds and breaks. The device is a simulation transfer operator T_L that re-evaluates a selected length- L_0 -realizing positional kernel on the longer grid, with a scalar \alpha_L \in [0,1] read off before any training from (\Phi, t_n) alone. Here \alpha_L serves as the computable witness of that taxonomy’s derivation, not as a stronger empirical predictor. Our results are a relative-head deep-row characterization for kernels with a strict unique global maximizer (Theorem 2), an absolute/relative duality that derives the pattern (Proposition 4), a learned-absolute underdetermination result, and a static expressivity bound; a sufficiency theorem (Theorem 1) bridges \alpha_L to LG, and the general deterministic case is a precise open conjecture (correcting a tempting but invalid “outside-colspan” argument). Experiments confirm the structure: in the controlled single-head regime \alpha_L exactly separates the taxonomy’s generalizing and non-generalizing (PE, task) cells (2625 trained-model measurements; stable at vocab 64/d=128). Together, these results turn the received PE taxonomy into a positional-kernel transfer theorem, and identify the content and depth mechanisms that escape it. The theory concerns the positional channel alone: tasks whose targets depend on token content, including the standard length-generalization suite such as addition and sorting, fall outside Theorem 2.
[LG-63] Role-Aware Server Optimization for Multimodal Federated Learning with Missing Modalities
链接: https://arxiv.org/abs/2610.41193
作者: Liuyin Shi,Wenbo He
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal federated learning (MFL) trains a shared model by aggregating client updates without exchanging raw data. Under missing modalities, clients adapt active modality encoders and a shared prediction block (fusion and classifier) to different input subsets. This raises two server-side questions: which clients should contribute to each component, and how should their aggregates be incorporated across rounds? We observe that an absent-modality encoder can still adapt through missing-input handling. Availability- based aggregation can therefore omit its update while retaining jointly learned prediction-block updates. Contributor selection also shapes server momentum, a weighted history of aggregated updates. We observe that prediction-block momentum can improve or reduce performance depending on the encoder update policy. We therefore propose Role-Aware Server Optimization (RASO), which coordinates contributor selection with role-specific server updates. RASO selects contributors through prediction dependence and inclusion in local optimization, retains encoder momentum, and synchronizes the shared prediction block to its current aggregate. Experiments across datasets and fusion architectures demonstrate the effectiveness of RASO under missing modalities.
[LG-64] When AI Investigators Cannot Trust Their Tools: Conflict-Aware Evidence Verification for Cyber Threat Investigation ACSA
链接: https://arxiv.org/abs/2610.41191
作者: Yuchen Zhang
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted at WAITI 2026 (3rd Workshop on AI for Cyber Threat Intelligence), co-located with ACSAC 2026
Abstract:LLM agents increasingly triage security alerts by combining the outputs of several detection tools, implicitly treating those tools as trustworthy. Unlike prior work on conflicts between a model’s knowledge and its context, we study conflicts among the tools themselves: what happens when this machine-generated evidence is conflicting or wrong. On the corrected CIC-IDS2017 dataset, four heterogeneous detectors emit labels and confidences that we corrupt under a threat model varying the number of corrupted tools, the persistence of the corruption, and the confidence attached to wrong outputs. Across open-weight LLMs from 3B to 14B parameters, unverified investigators are fragile: one corrupted detector out of four lowers naive accuracy to .50-.72, three lower every unverified investigator to .13 or less, and honest disagreement alone makes them abstain on up to 63% of flows. We propose Conflict-Aware Evidence Verification (CAV), built from established components: an equal-weight vote that ignores reported confidences, an evidence-conflict gate, and an independent nearest-neighbour trust check that tests the leading candidates. With its prompt frozen before testing, CAV-LLM improves on the naive investigator in every corrupted condition ( p 0.001 ), reaching .84-.88 with three corrupted detectors against at most .08, while calling the LLM on 0.4% of clean traffic. A fixed verification rule is as accurate or better unless most tools are corrupted. When we make the independent channel itself noisy, CAV inherits its errors: it stays ahead when two or more tools are corrupted, but adding the channel as an extra voter is better when at most one is. Verification thus moves trust from the tools to the check, which must be measured and protected in turn.
[LG-65] General Frequency Principle for Linear Approximation Classes
链接: https://arxiv.org/abs/2610.41179
作者: Yujun Wang,Yiqi Gu
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:In this paper, we study a general frequency principle under gradient descent training for the finite linear approximation classes. Under suitable assumptions, we prove that the learner amplitude relative to the target amplitude vanishes at high frequencies, uniformly throughout training. We also establish sufficient conditions for low-frequency priority, covering single-frequency targets trained separately and two frequency components trained in the same run. This general frequency principle does not imply that lower frequencies always converge faster. Instead, for two frequencies that satisfy certain comparison conditions, one component will take longer to learn than the other; and in common cases, the longer component has higher frequency. Numerical experiments with Legendre and trigonometric bases validate the theoretical results.
[LG-66] CAP: Learning to Optimize from Sparse Observations of Related Tasks
链接: https://arxiv.org/abs/2610.41173
作者: Lin Fu,Siwei Fu,Dong Ni
类目: Machine Learning (cs.LG)
*备注:
Abstract:In experiment-driven black-box optimization, each evaluation can consume materials, equipment time, and other resources, limiting the number of experiments available for a new task. Records from completed related optimization campaigns could help, but learning a sequential query policy remains challenging when task-specific experience consists of only a few sparse histories. We propose CAP (Candidate Acquisition Policy), which models shared structure and task-specific variation in fixed historical records to sample interactive practice functions. On these functions, CAP learns a feedback-conditioned query policy aimed at final optimization performance under a fixed evaluation budget. At deployment, its parameters remain fixed while each new observation informs the next query. Across five synthetic task families spanning 6 and 20 dimensions and two engineering simulation benchmarks, BOPTEST and SU2, CAP attains the lowest median final-budget value among the reported methods on four synthetic families and both engineering benchmarks. These results indicate that sparse records from completed related tasks can support adaptive query decisions on new tasks, making more effective use of limited, costly experimental evaluations.
[LG-67] alentRL: Strategy-Guided Reinforcement Learning for Hierarchical Skill Routing
链接: https://arxiv.org/abs/2610.41143
作者: Huancheng Chen,Xiaodi Sun,Zhaoqiong Huang,Michelle Chang,Jingwen Lu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Natural-language skills enable large language model (LLM) agents to reuse procedures across tasks, yet most systems retrieve individual skills from a flat SkillBank without explicitly modeling the high-level strategy that governs their selection and use. We introduce TalentRL, a strategy-guided reinforcement learning framework for hierarchical routing over procedural memory. Coarse-grained talents represent recurring strategies for skill use, while fine-grained skills provide executable procedures. For each task, the policy model selects a talent that guides execution and softly prioritizes its associated skills during similarity-based retrieval. However, since task outcomes depend jointly on talent selection, retrieved skill context, and downstream execution, terminal rewards alone cannot isolate the contribution of the routing decision. TalentRL therefore trains talent routing with reward contrasts between rollouts of the same task under matched interventions on the talent-conditioned context, and optimizes downstream execution with group-relative advantages. Across seven open-domain and multi-hop question-answering benchmarks, talent-aware supervised fine-tuning improves average success rate by 7.53%, 6.71%, and 9.21% over the corresponding zero-shot baselines for Qwen2.5-7B, Qwen3-8B, and Llama-3.1-8B, respectively. Reinforcement learning further improves these SFT checkpoints by 7.58% to 9.32%, and TalentRL achieves the highest average success rate among all compared methods on all three backbones. On the agentic benchmarks ALFWorld and WebShop, TalentRL achieves the highest ALFWorld success rate (99.3%) and the highest WebShop score (90.7) among all compared methods.
[LG-68] Nonlinear Maps Between Irreducible Representations in Graph Neural Networks
链接: https://arxiv.org/abs/2610.41059
作者: Mani Shayestehfar
类目: Machine Learning (cs.LG); Representation Theory (math.RT)
*备注: 18 pages; comments are welcome
Abstract:Equivariant linear maps cannot mix non-isomorphic irreducible representations, whereas nonlinear activations can. We develop a degree-resolved version of this nonlinear interaction picture and use the resulting block inventory to construct Specht Triple, a residual on unordered graph triples. On a synthetic structural benchmark with a frozen GIN backbone, Specht Triple reduces aggregate standardised test MSE by 11.05% relative to a parameter-matched Projector Control using the same irreducible projectors and complete linear equivariant space. The results support irreducible decomposition as an inductive bias for nonlinear higher-order equivariant processing.
[LG-69] Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks
链接: https://arxiv.org/abs/2610.41055
作者: Hojat Allah Salehi,Devrim Aras,Md Jueal Mia,M. Hadi Amini,Imtiaz Karim,Mashrur Chowdhury,Bhavani Thuraisingham
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 46 pages, 12 figures, 12 tables. Code: this https URL
Abstract:Federated reinforcement learning supports cooperative vehicular routing without centralizing trajectory data, but model updates may reveal private information and incur communication overhead over intermittent vehicle-to-infrastructure links, particularly when post-quantum authentication is required. We study correlated private quantization, using complementary shared randomness between paired vehicles to reduce aggregation mean squared error while preserving the marginal parameter-level local differential privacy and one-bit communication cost of independent quantization. A tunable extension mixes shared and independent randomness to bound privacy leakage given paired outputs and shared-randomness side information. For this extension, Rényi differential privacy accounting establishes vector-level local differential privacy for one model upload even when pairings are revealed. Under matched vector-level privacy and identical local and global parameters, the extension yields 16.5% lower analytical aggregation mean squared error than independent quantization. We evaluate cooperative routing by coupling traffic simulation with packet-level simulation of post-quantum-authenticated vehicle-to-infrastructure communication. Compared to full-precision updates, one-bit uploads reduce authenticated uplink size by 92.4% and increase mean verified updates aggregated per round from 19.6 to 26.5. Under mobility-constrained communication at a parameter-level privacy budget of 1, correlated private quantization reduces mean travel time by 14.3% relative to independent quantization at the same budget and communication cost. At a parameter-level privacy budget of 0.3, a vehicle-origin inference attack trained on independent simulations reaches 30% accuracy with correlated private quantization and 22% with independent quantization after 100 rounds, versus 91% without privacy and 10% for random guessing.
[LG-70] Localizing predictive concepts encoded in neural representations via hierarchical testing
链接: https://arxiv.org/abs/2610.41046
作者: Viktoriia Huryn,Marco Simnacher,Jim Berend,Wojciech Samek,Christoph Lippert,Sonja Greven,Uwe Ohler
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注:
Abstract:Understanding which regulatory sequence motifs a genomic deep learning model has learned, with statistical error guarantees, is essential for trusting its use in downstream tasks such as variant effect prediction. Existing approaches to identifying which units in a trained network encode human- understandable concepts fall into two groups. Exploratory methods rank and visualize units but offer no statistical guarantee that the selected units are associated with the prediction target across the population of inputs. Testing-based methods test only single units or one pre-defined set of them, and do not control the error across the many sets of units. We introduce CUTTIE (Cluster-tree Unit Top-down Testing of Independence with Error control), which clusters the units of each layer into a tree and tests the clusters top-down with conditional independence tests, localizing information about the target while controlling the family-wise error rate. Thereby, we identify clusters that carry information about the target beyond the rest of the layer. For the units in the identified clusters, conditional attribution maps then reveal what motifs they encode. In a controlled simulation, the procedure identifies the clusters whose concepts affect the target. We apply the procedure to a convolutional model predicting cell-type-specific chromatin accessibility from DNA sequence, where the concepts encoded in the identified clusters map onto known regulatory motifs.
[LG-71] Meshless Latent Dynamics Models for Model Order Reduction of time-dependent Partial Differential Equations
链接: https://arxiv.org/abs/2610.41043
作者: Lorenzo Tomada,Federico Pichi,Gianluigi Rozza
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:Latent dynamics learning has emerged as a prominent approach for Model Order Reduction (MOR) of time-dependent, parameterized Partial Differential Equations (PDEs). Traditional autoencoder-based approaches are intrinsically tied to fixed spatial discretizations during both training and inference, motivating the development of meshless methodologies. In this context, conditioning provides a natural mechanism for representing temporal evolution through spatially continuous functions, enabling mesh-agnostic Latent Dynamics Models (LDMs). In this work, we introduce a unified mathematical framework for mesh-agnostic LDMs based on conditioning in Neural Fields (NeFs). Leveraging a taxonomy of conditioning mechanisms (weak, strong, and pure), we formulate meshless LDMs as NeFs conditioned on a latent state. We establish a Universal Approximation Theorem (UAT) for this class of models and analyze their stability by deriving Lipschitz bounds for different conditioning mechanisms. Our analysis reveals an expressivity–stability trade-off, providing insight into why more expressive conditioning, despite its higher representational capacity, can potentially degrade performance during temporal extrapolation. Finally, we provide a comprehensive comparison of meshless and mesh-based architectures in the continuous limit of the spatial discretization, showing that meshless models guarantee grid-independent Lipschitz bounds, unlike mesh-based counterparts. Our experiments across Burgers’ equation, Navier–Stokes flow past a cylinder, and linear advection show that meshless LDMs can achieve predictive accuracy comparable to or better than their mesh-based counterparts while using only 2% of the trainable parameters and exhibiting nearly constant error across spatial discretizations. Subjects: Numerical Analysis (math.NA); Machine Learning (cs.LG) Cite as: arXiv:2610.41043 [math.NA] (or arXiv:2610.41043v1 [math.NA] for this version) https://doi.org/10.48550/arXiv.2610.41043 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-72] Before We Adapt: The Zero-Shot Generalization Bottleneck in Continual World Models NEURIPS2026
链接: https://arxiv.org/abs/2610.41041
作者: Amir Mesbah,Di Wu
类目: Machine Learning (cs.LG)
*备注: Accepted to Continual World Models workshop @ NeurIPS 2026
Abstract:Continual reinforcement learning (CRL) requires agents to operate under environmental changes while reusing prior knowledge whenever possible. We argue that model-based reinforcement learning provides a promising framework for this setting because learned world models (WMs) can, in principle, support change detection, knowledge reuse, adaptation, and acquisition of new knowledge. However, such benefits depend heavily on whether the learned model can generalize beyond the contexts in which it was trained. In this work, we propose to formulate this problem with a contextual Markov decision process in which context is decomposed into behavioral and physical components. This view organizes continual WM learning into reuse, adaptation, and acquisition of new knowledge. As a first step, we study direct reuse when behavioral rules remain unchanged. Using Dyna-Q and TD-MPC in controlled FourRooms tasks, we find that zero-shot reuse can degrade substantially under physical-context changes and that this behavior depends strongly on the observation representation used by the WM. Additional ablation studies and latent-space diagnostics suggest that learned representations do not automatically provide the invariances needed for reuse. These results identify representation generalization as an important bottleneck for reliable world-model reuse in CRL.
[LG-73] When can Optimally Trained Transformers Achieve Near-Lossless Token Prediction for non-Markovian Data?
链接: https://arxiv.org/abs/2610.41038
作者: Kağan Akman,Jake Feldman Starosta,Naci Saldi,Serdar Yüksel
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Probability (math.PR)
*备注: 62 pages, 12 figures
Abstract:There exists little mathematical theory explaining why Transformer architectures are empirically successful even for non-Markovian data. To this end, this paper studies next-token prediction via stochastic optimal control theoretically trained Transformers for such sources, where we consider either stationary and ergodic non-Markovian sources or those which are hidden Markov models of some finite order (possibly non-stationary). In either case, we first obtain error bounds via finite models with finite window approximations. For stationary ergodic sources, the resulting error is bounded by an entropy gap; for N th order Markovian sources, it decays at an exponential rate \alpha^\lfloor M/N\rfloor , where M is the window length and \alpha is determined by filter stability (and the Dobrushin coefficients of the transition and measurement kernels). We then construct a Transformer of depth M+1 , one head per layer, realizing the truncated filter, and we optimally train it with stochastic control developed in earlier work, which rigorously returns a near optimal Transformer. In particular, we show that for a non-Markovian source, either under stationarity and ergodicity for general models or under filter stability for hidden Markov models, an optimally trained Transformer has arbitrarily small performance error with increasing context length, data, and approximation refinement. Numerical studies accompany the theory.
[LG-74] Omniprediction for All Mixable Proper Losses and Beyond
链接: https://arxiv.org/abs/2610.41023
作者: Junyan Liu,Haipeng Luo,Lillian J. Ratliff
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
*备注:
Abstract:Omniprediction concerns the task of making predictions that are simultaneously accurate with respect to different losses. Earlier results by [CV09] show that in an online learning setting, O(\log(|\mathcalF||\mathcalL|)) regret is achievable simultaneously against every predictor in a finite predictor class \mathcalF and for every loss in a finite class \mathcalL of mixable proper losses, while recent advances [OKK25, FP26] show that \widetildeO(\sqrtT\log(|\mathcalF||\mathcalL|)) regret is achievable simultaneously for every loss in a finite class \mathcalL of bounded losses. In this work, we ask whether sublinear regret remains possible when \mathcalL is an infinite class of proper losses. Our first result gives a negative answer in general: even when \mathcalF contains only two linear predictors, it is impossible to achieve sublinear regret simultaneously for all bounded proper losses. We then show that \widetildeO(T^1/3(\log|\mathcalF|)^2/3) regret is achievable simultaneously for \textitall mixable proper losses and that this bound is optimal up to logarithmic factors. This optimal rate stands in stark contrast to the constant (in T ) regret achievable for a finite class of mixable proper losses. Finally, we extend our algorithm to obtain a best-of-all-worlds result: in addition to the new regret guarantee for all mixable proper losses, the resulting algorithm simultaneously achieves the existing guarantees for finite loss classes. With appropriate choices of these finite classes, our algorithm simultaneously achieves constant regret for squared loss and log loss, \widetildeO(T^1/3(\log|\mathcalF|)^2/3) regret for all mixable proper losses, and \widetildeO(\sqrtT\log|\mathcalF|) regret for 0 - 1 loss, all Lipschitz losses, all GLM losses, and other structured families. Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML) Cite as: arXiv:2610.41023 [cs.LG] (or arXiv:2610.41023v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2610.41023 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-75] Density-Guided Bootstrap Boundaries for Latent World-Model Reinforcement Learning
链接: https://arxiv.org/abs/2610.41022
作者: Shashank Gupta,Pilhwa Lee
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 37 pages, 5 figures
Abstract:Latent world models allow reinforcement learning agents to learn policies from imagined trajectories, yet determining the optimal depth of model reliance remains a fundamental challenge. Fixed imagination horizons apply uniform prediction depths regardless of trajectory reliability, exposing target values to compounding model error or over-relying on critic approximations. We introduce density-guided bootstrap boundaries, a trajectory-dependent mechanism that adapts the depth of imagined returns using a score-informed relative density proxy. The proxy accumulates the estimated divergence of a score-corrected auxiliary field formed by a feature displacement network and a noise-conditioned score estimator along imagined paths. We propose two gating mechanisms: a hard boundary that halts return expansion at a discrete state, and a soft boundary that continuously shifts the return recursion toward critic bootstrapping. Both variants preserve boundary value estimates and apply corresponding survival weights to actor and critic losses. Formulated independently of specific dynamics models and instantiated within Dreamer V3, we prove that the resulting gated Bellman operator is a strict contraction mapping and that early bootstrapping monotonically reduces conditional target variance. We clarify the theoretical assumptions distinguishing our expansion proxy from exact path likelihoods. Across continuous-control tasks and Atari Pong, empirical evaluations show that dynamic boundary selection enhances sample efficiency and late-training performance over fixed-horizon and auxiliary-enabled baselines.
[LG-76] When Can Unlabeled Data Pools Replace Membership Queries?
链接: https://arxiv.org/abs/2610.41001
作者: Dravyansh Sharma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Membership queries let a learner label arbitrary points and give near-optimal \tilde O(n\log(1/\eps)) label complexity for halfspaces under every marginal. A pool learner may label only sampled points. We ask when unlabeled data can replace membership queries. For homogeneous halfspaces the answer depends only on the angular law of X/|X| . If this law assigns zero mass to every hyperplane through the origin and positive mass to every nonempty open set of directions together with its reflection, a pool learner uses O(n\log^3n\log(n/(\eps\delta))) labels, uniformly over all such marginals and targets, without density, moment, concavity or isotropy assumptions. The same label bound holds for non-homogeneous halfspaces under every marginal with full support and no mass on affine hyperplanes, including Gaussians. Dropping either angular assumption admits \Omega(1/\eps) pool versus O(\log(1/\eps)) membership-query separations, even with unlimited unlabeled data. The price is paid in unlabeled data: in dimension n\ge3 , for every unlabeled budget fixed in advance, some marginal satisfying both assumptions forces \Omega(1/\eps) labels. For isotropic s -concave marginals in dimension n\ge3 , unlimited pools keep logarithmic label rates for every finite s . With an unlabeled budget fixed in advance, class-uniform polylogarithmic rates hold exactly for s-1/(n+1) , or s-1/n under central symmetry, and in the general class the optimal rate between these thresholds is \tilde\Theta(\eps^-(n-2)/n) . This extends the range s\ge-1/(2n+3) of previous sample-based guarantees, in the realizable setting.
[LG-77] RC Prediction: A Python Package for Parameter-Aware Reservoir Computing
链接: https://arxiv.org/abs/2610.40984
作者: Jin-Cheng Rao,Zheng-Meng Zhai,Ying-Cheng Lai
类目: Machine Learning (cs.LG); Systems and Control (eess.SY); Dynamical Systems (math.DS)
*备注:
Abstract:A nonlinear dynamical system can be characterized by the autonomous evolution of its state in time and its dependence on system parameters. As a parameter varies, the dynamics can change quantitatively and, at bifurcation points, qualitatively. When the governing equations are unknown, a data-driven model should therefore capture both elements from observational data, rather than reproduce the dynamics at a single operating condition only. Parameter-aware reservoir computing (PARC) is a model-free framework for this purpose, where the parameter is supplied to a reservoir computer through an additional input channel. PARC is trained on time series recorded at several parameter values, and the trained reservoir, run in closed loop as an autonomous dynamical system, generates trajectories at parameter values that were never observed. rc_prediction is an open-source Python package that implements this workflow. It provides multi-parameter training of a reservoir, autonomous closed-loop prediction at user-specified parameter values, parameter scans, and a standard echo state network for forecasting at a fixed parameter. Hyperparameter optimization and optional GPU acceleration through PyTorch are also provided. We use three benchmark dynamical systems as examples to demonstrate its usage. As one of the featured applications, here we illustrate the prediction of critical transitions and system collapse: trained only on pre-critical data, the model is queried beyond the training range to predict whether the system collapses, to estimate the critical parameter p_c^* , and to characterize transient lifetimes.
[LG-78] Monte Carlo Random Walk Kernel
链接: https://arxiv.org/abs/2610.40956
作者: Arslan Iamaletdinov,Elfat Sabitov,Konstantin Sorokin
类目: Machine Learning (cs.LG)
*备注:
Abstract:We introduce the Monte Carlo Random Walk Kernel (MCRWK), an estimator whose per-pair cost is independent of graph size and derived directly from the random walk kernel (RWK) expansion. By normalizing the kernel coefficients, MCRWK samples a shared walk length and estimates its contribution using random walks on the input graphs. This gives a simple way to approximate RWK without constructing the product graph. For a fixed expected walk length and efficient neighbor sampling, each pairwise estimate takes O(m) time for m Monte Carlo samples, allowing the sample budget to control the trade-off between runtime and accuracy. We evaluate MCRWK against Graph Voyagers and classical RWK methods on synthetic and real graph datasets. In the reported experiments on large synthetic graphs, MCRWK is faster than Graph Voyagers and, at matched runtime, more accurate. It also has the lowest Gram-matrix runtime on the five real datasets studied. Code and experiments are available at this https URL
[LG-79] Fair Recommender Systems for Intersecting Groups
链接: https://arxiv.org/abs/2610.40936
作者: Christine Ling,Juba Ziani
类目: Machine Learning (cs.LG); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
*备注:
Abstract:Recommendation systems that optimize for the average user can fail to capture the preferences of diverse demographic groups. We consider a dynamic recommendation system, where arriving users can belong to different demographic groups. Importantly, these groups can intersect, and a user may belong to several groups. We first show that over time, any group-blind policy (which excludes all current-user group information) recommending K \geq 1 items incurs linear total regret under mild conditions. In turn, we introduce three group-aware algorithms based on the classical Upper Confidence Bound algorithm (UCB): i) Intersection-Fitting, which runs UCB on each intersection, but has a sublinear group- and intersection-level regret guarantee with an exponential dependence in the number of groups; and ii) two group-level heuristics, Inner-Outer UCB and Group-Item UCB which allocate different arms to different groups, and use a sleeping expert meta-learner to choose which active group’s recommendation an intersection should inherit at any given time step. We provide experimental results on two datasets, Goodreads and BoardGameGeek, and highlight that our heuristics can be preferable to the intersection-fitting algorithm when intersection-level preferences are closely related to group-level preferences, but that intersection-fitting performs better when different intersections within a same group prefer different items.
[LG-80] nsorSSM: Memorizing Spatial Position in Dynamic State Space for Tensor Core Generation
链接: https://arxiv.org/abs/2610.40871
作者: Sheng Zhang,Xinyi Wang,Maolin Wang,Xiangyu Zhao
类目: Machine Learning (cs.LG)
*备注:
Abstract:High-dimensional tensors are ubiquitous in real-world applications but lead to massive storage overhead. Functional tensor decomposition addresses this by employing neural networks to map element coordinates to tensor cores, significantly reducing the parameters for data compression. However, existing functional methods typically flatten high-order tensors into 1D sequences. This folding operation causes highly overlapping local coordinate sequences and struggles to capture complex cross-mode spatial dependencies, severely limiting their expressiveness on diverse datasets. To tackle these challenges, we propose TensorSSM, a novel Implicit Neural Representation (INR) for functional decomposition method that memorizes spatial positions within a dynamic State Space Model (SSM). Specifically, TensorSSM first decomposes mode-wise coordinates into sequences spit by K-ary rule, encoding spatial tokens from coarse to fine, which effectively captures spatial variations. Furthermore, we extend the SSM’s state space to explicitly map cross-mode spatial dependencies, allowing inter-mode interactions to enhance the updates of each mode. By integrating cross-mode information from previous hidden states into the current state transitions, TensorSSM effectively expands tensor representation along two dimensions: the sequence dimension for coarse-to-fine spatial tokens, and the state dimension for cross-mode memorization. This design effectively integrates spatial information into the state space and the tensor core, substantially advancing the state adaptability and enhancing the expressiveness of the data compression. Extensive experiments demonstrate that TensorSSM achieves significantly high reconstruction accuracy and storage efficiency, exhibiting robust expressiveness across diverse tensor datasets without relying on strict spatial assumptions.
[LG-81] Near-Optimal First-Order Regret for Bandits with Feedback Graphs
链接: https://arxiv.org/abs/2610.40861
作者: Mengxiao Zhang,Yuheng Zhang,Haipeng Luo,Paul Mineiro
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We study first-order regret guarantees for adversarial bandits with a fixed, known directed feedback graph. For strongly observable graphs, [ACBDK15] establishes a minimax regret of \widetilde\Theta(\sqrt\alpha T) , where \alpha denotes the independence number of the graph and T denotes the horizon. On the other hand, the best existing first-order bound scales as \widetildeO(\sqrt(\kappa+1) L^\star) [LLZ20], where \kappa denotes the clique partition number, satisfying \kappa+1\geq\alpha , and L^\star denotes the cumulative loss of the best arm, leaving a gap between the graph dependence in worst-case and first-order guarantees. In this paper, we bridge this gap by obtaining an expected regret bound of \widetildeO(\sqrt\alpha L^\star+\alpha) . Our first result is an algorithm for the special case of self-aware graphs, in which every vertex has a self-loop. The algorithm combines the update of EXP3 [ACBFS02] with a sampling rule inspired by exploration by optimization [LS20]. More specifically, we construct a map whose fixed points yield sampling distributions that control the one-round regret remainder in terms of the learner’s loss and the graph’s independence number, which is crucial in deriving optimal first-order guarantees. We further show that a sufficiently accurate fixed point can be computed in polynomial time by a geometrically convergent iteration. Finally, we extend the algorithm to general strongly observable graphs and achieve the same \widetildeO(\sqrt\alpha L^\star+\alpha) guarantee by adapting the two-level aggregation approach of [LLZ20].
[LG-82] Frozen in Time: How a Physics Foundation Model Forgets Under Sim-to-Real Adaptation NEURIPS2026
链接: https://arxiv.org/abs/2610.40857
作者: Haaris Mian,Miles Cranmer,Alessandro Favero,Payel Mukhopadhyay
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注: Accepted at the Sim2Science workshop, NeurIPS 2026. Code: this https URL
Abstract:Scientific foundation models (SciFMs) have achieved remarkable performance on a wide range of PDEs, including the ability to efficiently adapt to unseen data distributions with additional finetuning. As with all neural architectures, finetuning such models comes with the risk of catastrophic forgetting, whereby adapting to new tasks degrades prior knowledge of the model. We study catastrophic forgetting in Walrus-1.3B, a SciFM for continuum dynamics, over the course of a two-stage sim-to-real adaptation: first finetuning on simulated flow around a cylinder and then adapting to experimental data of the same system. We find that finetuning on the simulations rapidly degrades rollout performance on the original pretraining datasets, with one-step errors increasing up to 35x after just 20 epochs. Subsequent adaptation to real data further erodes performance on the simulated task. We characterize the potential drivers of forgetting, such as dataset dependence and task representation similarity. We then show that replaying as little as 1% simulated data during real-data finetuning recovers most of the lost simulation performance while retaining effective adaptation to the experimental dynamics. Our results identify catastrophic forgetting as a practical obstacle to sim-to-real adaptation of SciFMs and show that lightweight replay strategies significantly improve retention.
[LG-83] Certified Behavioral Retention for Continual Adaptation with Held-Out Anchors NEURIPS2026
链接: https://arxiv.org/abs/2610.40740
作者: Ahanaf Hasan Ariq,Abrar Shahid
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 11 pages, 1 figure, 3 tables. Accepted at the NeurIPS 2026 Workshop on Continual Learning in the Era of Foundation Models and Embodied Agents (CL4M / CL4FMAgents)
Abstract:Continual adaptation requires deciding not only how to update a model, but whether to commit an update without exceeding a declared budget for changes to protected behavior. We introduce Certified Anchor-Based Adaptation (CABA), a candidate-agnostic post-update gate that reserves historical observations from candidate optimization, compares old and candidate policies on frozen representations, and selects the largest tested interpolation coefficient whose worst-anchor forward KL divergence remains within budget. The finite check requires neither action labels nor complete trajectories. We develop a theory-led certification framework that separates measured anchor retention from conditional off-anchor guarantees. Under explicit representation-coverage and Lipschitz assumptions, an accepted update satisfies a uniform behavioral-divergence bound on protected support. The analysis gives regularity bounds for affine softmax heads, probabilistic coverage and compression guarantees, interpolation monotonicity conditions, grid-resolution bounds, and trajectory-level and sequential-composition guarantees. In a controlled UCI Human Activity Recognition study used as a non-stationary sensor-policy proxy, CABA reduces mean forgetting from 1.01 percent for unconstrained adaptation to 0.00 percent at budget epsilon = 0.05, while reducing final average accuracy from 93.04 percent to 88.48 percent. The experiment illustrates a retention-plasticity trade-off and does not establish foundation-model or robotic performance. CABA provides an auditable retention check for candidate generators exposing comparable policy distributions and a declared update path, while deployment also requires separate evidence of current-task utility.
[LG-84] Unsupervised Feature Selection Using Local Intrinsic Dimensionality – Extended Version
链接: https://arxiv.org/abs/2610.40736
作者: Oussama Chelly,Michael E. Houle,Ken-ichi Kawarabayashi,Muhammad Rajabinasab,Arthur Zimek
类目: Machine Learning (cs.LG)
*备注: Preprint submitted to Elsevier Pattern Recognition
Abstract:High dimensionality degrades both the efficiency and the effectiveness of learning algorithms, and in many applications no class labels are available to guide dimensionality reduction. This paper proposes LIDFS, an unsupervised feature selection method based on Local Intrinsic Dimensionality (LID). The method builds on the formal equivalence between LID and the indiscriminability of distance measures. The data matrix is transposed so that each feature is treated as a point, and the LID of each feature is estimated by maximum likelihood from the distances to its k nearest neighboring features. Features with lower LID exhibit greater local discriminability and receive higher importance scores. A refinement step then retains one representative per feature neighborhood and demotes locally redundant features. The result is a complete ranking from which any number of features can be selected without rerunning the procedure. LIDFS does not use any information about labels or the number of classes, and runs in \mathcalO(d^2 n) time for n instances and d features. LIDFS is compared with classic and state-of-the-art methods and a random baseline on 23 high-dimensional datasets, the classic benchmark collection. The evaluation uses classification accuracy and AUC with five downstream classifiers, as well as clustering accuracy and normalized mutual information, over both full and aggressive selection ranges. LIDFS ranks first in the supervised evaluations and first or tied second in the clustering-based evaluations. It belongs to the top statistical group together with methods that require the number of classes, and it runs faster than its closest competitor. Several recent methods, by contrast, fail to outperform the random baseline. A sensitivity analysis shows that small neighborhood sizes yield the best results.
[LG-85] Dont Just Impute Detect: Task-Aware Diffusion Pipelines for Edge Anomaly Detection
链接: https://arxiv.org/abs/2610.40732
作者: Jeongsu Park,Taehyeon Yun,Geonwoo Kim,Yuji Lim,Moohong Min
类目: Machine Learning (cs.LG)
*备注: 11 pages, 6 figures. Accepted at the 33rd IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC 2026)
Abstract:Industrial Internet of Things (IIoT) edge pipelines routinely lose contiguous blocks of multi-sensor data to network dropouts and hardware faults. The standard remedy is a disjointed “impute-then-detect” pipeline whose imputer is trained only for Mean Squared Error (MSE). Such an imputer fills a missing block with the most probable normal pattern: a partially observed anomaly is smoothed back toward normality, and boundary artifacts on normal data raise false alarms. We call this imputation bias and propose the Task-Aware Diffusion Refiner (TADR), a conditional diffusion refiner trained against the loss landscape of a frozen downstream detector. Because back-propagating through the sampling trajectory is intractable, the task-aware terms act on the one-step clean-window estimate that the noise prediction already yields, so training costs no more than plain diffusion training. Under strict Point-to-Point evaluation over five seeds with paired significance tests, TADR raises F1 by 0.338 over the unrefined detector on SMD ( p = 0.0015 ), and the anomaly enhancement objective alone contributes 0.463 in ablation. We also characterise when the method helps: the gain tracks the continuous, mutually redundant channels available and the anomaly evidence the outage removes, and it is absent on telemetry datasets dominated by one-hot command channels and on PSM. At ten sampling steps TADR runs in 41.4 ms per window on a single CPU core, sustaining about 24 concurrent 1 Hz streams from a 4.03 MB model. Code and per-seed results are available at this https URL.
[LG-86] FGSM Attack Transferability from Simulation to Quantum Hardware in Network Intrusion Detection
链接: https://arxiv.org/abs/2610.40701
作者: Zeeshan Ajmal,Kimmo Halunen,Marc Maußner,Volker Reers,Arif Ali Khan
类目: Cryptography and Security (cs.CR); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注: 8 pages, 2 figures, 5 tables. Accepted at the 2026 IEEE 2nd International Conference on Quantum Artificial Intelligence (QAI), 13-16 December 2026, Nottingham, UK. Code: this https URL
Abstract:Quantum computers are now available through cloud platforms, and recent work has proposed using them to detect network intrusions. It is not yet clear how easily such systems can be attacked. We train a hybrid variational quantum classifier (VQC) on the Friday DDoS subset of CICIDS2017 and test it against two attacks: label flipping during training and FGSM evasion at prediction time. We run the tests in simulation and on IBM hardware, the Fez superconducting processor (Heron r2). We find that two models with similar accuracy can differ greatly in how easily they are attacked. A model that decides with little confidence loses half its detections to a very small change in the input, while a model trained to decide with more confidence needs a much larger change before it fails. Attacks built in simulation against the more confident model also work on real hardware, and the device classifies every unaltered attack sample correctly. Simulated device noise does not reduce accuracy, but it does reduce the model’s confidence and makes attacks easier, so accuracy alone does not show how exposed a model is. Label flipping has no measurable effect until 15% of attack labels are changed. These results show that device noise should not be treated as a defense and that reported weaknesses in these classifiers depend partly on how they were trained.
[LG-87] Automatic Construction of Strong Simple Baselines
链接: https://arxiv.org/abs/2610.40700
作者: Caleb N. Ellington,Jiaqi Wang,Sohan Addagudi,Elijah Cole
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:To rigorously establish the value of a novel deep learning method, it must be compared against appropriate baselines. As deep learning has become increasingly common in the physical and life sciences, there have been numerous cases in which apparent progress has turned out to be an artifact of omitted or missing baselines. In this work, we investigate whether this problem can be mitigated by using large language models to automate the construction of baselines. We assemble 36 supervised classification and regression tasks from peer-reviewed studies in Nature, Science, and Cell-family journals that investigate novel deep learning methods for scientific applications. We then use a language model to construct simple baselines for each task. These automatically constructed baselines are strong, recovering a median of 95.5% (mean 85.7%) of the gap between a trivial predictor and the score of the deep learning model. The median simple baseline matches or exceeds the performance of the deep learning model in 12 studies. We further examine the variation across scientific domains and publication venues and we characterize the effects of test-time compute and access to different levels of study context. As human peer review has struggled to keep up with the rapidly increasing volume of research articles in recent years, our results suggest that automated baseline construction is a practical and scalable check on deep learning contributions in the physical and life sciences.
[LG-88] Dual-Corrected Stochastic Block Model (textDC2textSBM): A Generalized Structure Detection Method for Attributed Networks
链接: https://arxiv.org/abs/2610.40676
作者: Zhenhai Chang,Shuo Wang,Yumeng Liu,Wei Liu
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 24 pages, 8 figures
Abstract:Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogeneity, or characterize node activity in the topological and attribute dimensions by one and the same factor, making it difficult to correctly model node activity when the two kinds of activity are decoupled. In this paper, we propose the Dual-Corrected Stochastic Block Model ( \textDC^2\textSBM ), which introduces independent correction factors—a topological degree factor and an attribute abundance factor—into the link-generation and attribute-generation channels, respectively, thereby effectively decoupling node activity from membership inference, and which further introduces a global scaling parameter to resolve the mismatch between the normalization constraints and the network scale. The model is inferred by the expectation–maximization (EM) algorithm, and the newly introduced factors cancel out completely in the posterior computation, so that no additional computational cost is incurred. Experiments on synthetic and real attributed networks show that DC^2SBM outperforms state-of-the-art methods in community detection accuracy, with an especially pronounced advantage when the topological signal is weak. The two correction factors can be recovered independently from their respective channels without cross-talk. Analytical as well as controlled experiments further demonstrate that modeling with a single shared factor fails when the two kinds of activity are decoupled—a regime that is prevalent in real networks—thereby validating the necessity of dual independent correction.
[LG-89] SATFRONT: Detecting Atmospheric Fronts with Satellite Data using Machine Learning
链接: https://arxiv.org/abs/2610.42111
作者: Ioannis Dravilas,Miruna Stoicescu,Roope Tervo,Carlos Horn,Oriol Hinojo Comellas,Sina Montazeri,Andrea Meraner,Ana Lucic
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:
Abstract:Atmospheric fronts are associated with much of the high-impact weather in the mid-latitudes, yet their identification and classification remain largely manual and subjective. Existing deep learning approaches rely primarily on gridded reanalysis or forecast data, limiting their applicability to satellite observations available for time-critical applications. We introduce SATFRONT, a machine learning framework for detecting cold, warm, and occluded fronts over Europe from geostationary satellite observations, with reanalysis data as an optional additional input. The model is trained on frontal analyses from five European national meteorological services and evaluated against UK Met Office analyses for 2024. Using satellite observations alone, SATFRONT achieves a Critical Success Index (CSI) of 0.428 at a 150 km tolerance, within the range of agreement between analyses from different meteorological services (0.277-0.501) and higher than that of an operational rule-based front detector that uses satellite data (0.133). Adding ERA5 reanalysis increases the CSI to 0.591. ERA5 substantially improves the placement of individual fronts, although its effect on their annual spatial distribution varies by front type. These results demonstrate that cold, warm, and occluded fronts can be detected directly from geostationary satellite observations, providing a basis for automated frontal analyses at the high temporal frequency required for time-critical applications.
[LG-90] Regret-Optimal Wasserstein-Robust Linear Regression NEURIPS2026
链接: https://arxiv.org/abs/2610.42109
作者: Sloan Nietert,Daniel Kuhn
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: Accepted at NeurIPS 2026
Abstract:Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its decisions can be suboptimal in terms of their excess risk, or regret. We study this gap for multivariate least-squares regression, assuming that the observed source law and unknown target law differ by an adversarial 2-Wasserstein perturbation of size \varepsilon . We first identify the asymptotic complexity of robust decision-making in the \varepsilon \to 0 limit, characterized by a problem-specific condition number \kappa . Here, a wide spectrum of simple algorithms, including DRO, achieve the instance-optimal rate of O(\kappa \varepsilon^2) . For fixed \varepsilon 0 , we prove that DRO still achieves the minimax rate if the problem has bounded residual effective rank. On the other hand, we identify significant failure modes where DRO is provably suboptimal by dimension-dependent factors. Fortunately, we prove that two alternative procedures, minimax regret optimization (MRO) and spectral-norm regularization (SNR), attain the minimax rate. We derive an exact convex reformulation of MRO and an accelerated proximal algorithm for SNR, along with high-probability finite-sample guarantees. We support these theoretical results with numerical experiments separating the performance of MRO and SNR from DRO.
[LG-91] Hard Constraint Projection in a Physics Informed Neural Network (Burgers)
链接: https://arxiv.org/abs/2610.42106
作者: Miranda J S Horne,Peter K Jimack,Amirul Khan,He Wang
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: 6 pages, 3 figures, In proceedings of 1st international Symposium on AI and Fluid Mechanics 2025 (Paper No. S11P2)
Abstract:Physics informed machine learning provides a framework to study and simulate fluid dynamics, potentially at a fraction of the computational cost of traditional numerical methods, with the goal of improving the explainability, accuracy and robustness of purely data driven machine learning methods. In this work, we extend the hard constraint projection (HCP) method developed by Chen et al. [Journal of Computational Physics, 445, 110624, 2021] from a linear partial differential equation (PDE) to a nonlinear PDE: the 1D Burgers’ equation. To strictly impose a discrete approximation of the governing equation, the Burgers’ equation is first locally discretised (using a finite difference stencil) then decomposed into a constraint matrix and a prediction matrix. Using tools from linear algebra, the constraint matrix is used to construct a projection matrix. A physics informed neural network (PINN) [Raissi et al. Journal of Computational Physics, 378, 686, 2019] is used to predict the velocity, and a final unlearnable layer projects the velocity prediction to the hyperplane of exact solutions to a discretised form of the Burgers’ equation. Our initial results indicate that the HCP-PINN initially trains similarly to an equivalent PINN, and as the PINN loss plateaus, the HCP-PINN loss continues to decline. When tested on a previously unseen uniform grid of points covering the domain, the HCP-PINN exhibits higher accuracy, converging to a data error approximately an order of magnitude lower than the comparable PINN. We also observe that the inclusion of the HCP layer results in better observance of the governing PDE. Our findings suggest that the HCP-PINN can be more robust to the selection of physics collocation points, and less dependent on collocation point quantity.
[LG-92] Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization
链接: https://arxiv.org/abs/2610.42048
作者: Benjamin Grimmer,Qining Shen
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization. Throughout, we assume that gradient noise is conditionally zero-mean with bounded variance. Among problems with dimension d\geq N+1 , we provide an exact worst-case analysis of the expected suboptimality of stochastic gradient descent’s final iterate. Our analysis applies to any (potentially nonconstant) schedule of N stepsizes in [0,1.5]^N . Here, exact means that we provide a matching objective function and noise distributions where our guarantee is attained. Whenever the variance bound is at least \Omega(1/\sqrtN) , our theory provides the minimax optimal stepsize schedule for stochastic gradient descent and proves its uniqueness. These optimal stepsizes monotonically decrease, ruling out any accelerations in expected final suboptimality from fixed fractal schedules observed in the deterministic case.
[LG-93] Empirical Evaluation of Quantum Kernel Methods for Classification: A Comparative Study with Classical Kernels
链接: https://arxiv.org/abs/2610.42029
作者: Ronak Bhalgami
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Quantum kernel methods are the most concrete near-term proposal for quantum machine learning on classical data, yet published benchmarks disagree on whether they outperform tuned classical kernels, and the disagreement cannot be resolved from the literature because the comparisons are not protocol-controlled. We argue that the obstacle is one of measurement, not theory: the usual setup fixes the quantum feature map’s data scaling while grid-searching the classical kernel’s hyperparameters, recomputes nothing per fold, and reports cost in simulator time. We therefore treat the comparison itself as the object of study and impose three controls. The bandwidth is tuned on the same grid budget as the classical \gamma , the kernel is recomputed inside every fold from fold-fitted preprocessing, and cost is counted in hardware circuit evaluations. On top of these we run a factorial design of 324 cells in which dimensionality, training size, class separability and data structure are explicit covariates, together with four standard datasets, a shots-versus-gate-noise ablation, and a cost sweep. The dominant effect we measure is not quantum: tuning the feature map’s bandwidth is worth 0.120 accuracy on synthetic data and 0.339 on real data, exceeding every quantum-classical difference we observe. Tuned quantum kernels are then statistically indistinguishable from a tuned RBF on all four real datasets. The advantage on synthetic data is confined almost entirely to data generated by the feature map itself, and it shrinks with dimensionality (-0.0045 per qubit) while growing with sample size. Finite sampling costs an order of magnitude more accuracy than gate and readout noise, and a fidelity kernel needs 5\times10^7 circuits at n = 10,000. Protocol variance is thus sufficient to explain the field’s contradictory conclusions.
[LG-94] WASABI: Wasserstein Sampling via Bridge Interpolants
链接: https://arxiv.org/abs/2610.41958
作者: Sebastian Salazar,Dilum Fernando,Michal Kucer,Emily Casleton,Luhuan Wu,David M. Blei
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Sampling from Boltzmann distributions is a fundamental problem in physics, chemistry, and biology. We introduce WAsserstein SAmpling via Bridge Interpolants (WASABI), a method that trains a diffusion model to generate samples from a target Boltzmann distribution given access only to the energy function. WASABI minimizes the KL divergence between the distribution generated by its diffusion model and the Boltzmann target using Wasserstein gradient flows. This approach builds on a principle underlying classical sampling methods such as Langevin dynamics. However, applying Wasserstein gradient flows to diffusion models is challenging because the updates require the gradient of the model’s own log density, which is generally intractable. We derive a practical training algorithm that avoids computing this gradient. Once trained, WASABI can generate new samples without further evaluations of the energy or its gradient. On alanine dipeptide, WASABI outperforms the best reported baselines using an estimated 3.3 times fewer energy evaluations. It also achieves competitive performance on interacting particle system benchmarks.
[LG-95] Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis
链接: https://arxiv.org/abs/2610.41955
作者: Mingjie Wen,Jiahe Han,Zihan Zhou,Hao Wang,Xueyong Guo,Qingzhao Chu,Dongping Chen
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注: 28 pages, 9 figures, 1 table
Abstract:Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways. Here, we develop EMFF-MACE, a reactive machine-learning potential for C/H/N/O energetic materials, enabling thermolysis simulations of 20 materials and shock simulations of 10 on a common potential energy surface. Comparisons at matched normalized reaction progress reveal divergent chemical states and local structural environments. Mean reaction-state similarity decreases from 94.0% at 20% progress to 25.9% at trajectory endpoints, while 47.1-92.6% of shock local environments lie outside corresponding material-specific thermolysis reference domains. Structural and reaction-network analyses reveal enhanced intermolecular reorganization and additional association and rearrangement branches under shock loading, despite partially shared early decomposition chemistry. These findings show that loading history influences the structural states and subsequent reaction pathways accessed during decomposition, rather than merely the rate of thermolysis.
[LG-96] Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates
链接: https://arxiv.org/abs/2610.41927
作者: Jie Liu,Nicola Bastianello,Wenying Xu,Daniel W. C. Ho
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly. However, such algorithms generally converge only to a neighborhood of the optimum, whose size depends on the stepsize and the variance of gradient noise. Variance reduction has been incorporated into ADMM-based methods to achieve exact convergence in expectation, but their stepsizes typically scale inversely with both the square root of the network size and the cube of the number of local updates, leading to slow convergence as the network size or the number of local updates increases. In this paper, we propose a novel primal-dual-based decentralized optimization algorithm that integrates variance reduction with multiple local updates, with a stepsize independent of the network size and inversely proportional to the number of local updates (rather than its cube), thereby enabling significantly larger stepsizes and faster convergence. Moreover, to the best of our knowledge, we establish the first linear convergence guarantee in expectation for decentralized optimization with multiple local updates under stochastic gradients in the strongly convex setting. Beyond the theoretical guarantee, the proposed method exchanges only one variable per round, halving the communication cost compared with gradient-tracking-based methods. Numerical experiments further demonstrate its advantages in convergence speed and accuracy.
[LG-97] Statistical Foundations of Reinforcement Learning: A Non-Asymptotic Perspective
链接: https://arxiv.org/abs/2610.41903
作者: Yuxin Chen,Yuejie Chi,Jianqing Fan,Gen Li,Yuting Wei,Yuling Yan
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty. The rapid growth of model complexity in emerging applications has significantly amplified the challenges of achieving data-efficient RL, particularly in sample-starved scenarios where data collection may be costly, time-consuming, or even high-stakes. Consequently, understanding and improving the data efficiency of RL algorithms has become a central problem of both fundamental theoretical interest and practical importance. Despite decades of research, the statistical underpinnings of RL remained remarkably underdeveloped until relatively recently, particularly in the finite-sample regimes most relevant to modern practice. Over the past few years, there has been a surge of activity advancing the frontiers of non-asymptotic statistical RL theory, drawing on ideas from high-dimensional statistics and probability, information theory, and statistical learning theory. In this monograph, we present a non-asymptotic treatment for RL based on tabular Markov decision processes, focusing on three canonical sampling mechanisms — RL with a generative model, online RL, and offline RL — together with two algorithmic paradigms: model-based and model-free approaches. Centered around non-asymptotic sample complexity analysis, our treatment emphasizes minimax-optimal algorithm design, particularly methods that attain statistical optimality without significant burn-in costs, as well as sharp statistical characterizations of several widely used RL algorithms. Whenever possible, we provide largely self-contained analyses together with intuitive explanations. We also briefly discuss several extensions, such as robust RL, competitive multi-agent RL, federated RL, uncertainty quantification, and RL from human feedback. Subjects: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG) Cite as: arXiv:2610.41903 [stat.ML] (or arXiv:2610.41903v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2610.41903 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-98] Overcoming the Representational Limitations of Amplitude Encoding for Quantum Neural Networks
链接: https://arxiv.org/abs/2610.41824
作者: Vincent Gurgul,David Prinz
类目: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Quantum Neural Networks (QNNs) offer a framework for learning functions from classical data, but their expressive power depends critically on how inputs are encoded into quantum states. Angle encoding can require resources that scale with the number of features, whereas amplitude encoding represents d features using only \mathcalO(\log_2 d) qubits. This compactness comes at a representational cost: normalization makes conventional amplitude encoding invariant to nonzero real rescaling of the input, up to an unobservable global phase. We show that this invariance induces an exact, architecture-independent restriction on the resulting hypothesis class. No downstream quantum circuit can distinguish inputs differing by nonzero real scaling. We develop a general condition under which deterministic preprocessing can remove this ambiguity and show that nonlinearity alone is insufficient. Based on this analysis, we introduce a fixed nonlinear feature map that provably distinguishes all real inputs under subsequent amplitude encoding, without adding trainable parameters and while retaining logarithmic qubit scaling. We evaluate the resulting QNN on three canonical classification benchmarks and a nonlinear synthetic dataset against conventional amplitude and angle encoding, as well as a classical neural network. Across these tasks, the proposed encoding consistently improves classification accuracy over both quantum encoding baselines by 21 - 44 %. Our results establish normalization-induced scale invariance as a concrete representational bottleneck of amplitude encoding and demonstrate that it can be removed while preserving a compact quantum representation, marking a step toward quantum models capable of capturing arbitrary relationships in classical data.
[LG-99] Leverag ing Quantum Measurement Randomness for Shot-Efficient Diffusion Models
链接: https://arxiv.org/abs/2610.41721
作者: Tianhao Guo,Dianxin Zheng,Yuhua Qian,Yaohui Zheng,Yuzhi Yang,Merouane Debbah,Chau Yuen
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 13 pages, 13 figures, 3 tables
Abstract:In quantum computing, the readout always carries finite-shot sampling noise. Suppressing this noise through repeated measurements raises the cost of a quantum neural network (QNN). Meanwhile, a stochastic diffusion sampler deliberately adds randomness at each reverse (generation) step. This paper turns the measurement noise from a liability into a resource. Specifically, we use the noise quantum measurement produces anyway to replace part of the noise the sampler would otherwise inject. We characterize the measurement error as multinomial sampling noise propagated through a trainable linear readout. To connect the circuit’s Born probabilities to the denoising task, we further formulate the diffusion process on a bounded zero-sum hyperplane mapped to the probability simplex. Therefore, we can employ a compensation noise to match the mean and covariance of the shot-noise-free linear update under some minimum shot conditions. Solving this condition for the shot count gives a state-dependent adaptive budget. For the denoising diffusion implicit model (DDIM), this budget increases as the sampler becomes more deterministic. On an eight-dimensional Gaussian mixture, the schedule achieves comparable generation quality with 303 shots per sample against a high-shot reference of 1.6\times10^6 , about 5300\times fewer. Latent diffusion experiments on MNIST further validate the proposed mechanism for generation in a learned latent space.
[LG-100] Co2ODE: Conditionally Coarse-Grained Model Discovery for Rewiring in Biochemical Networks
链接: https://arxiv.org/abs/2610.41634
作者: Theodore de Pomereu,Fabian Fröhlich
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Biochemical networks propagate the signals that regulate cellular behaviour, and which routes carry them depends on cellular context: a shift in context rewires the network’s effective dynamics. Mechanistic simulators are misspecified here because the effective dynamics emerge from an interaction structure that is only partially known. Model discovery instead learns the dynamics from data, but sparse, correlated measurements poorly identify a full network model. What matters is narrower: the portion the context rewires. We introduce conditionally coarse-grained ODEs (Co ^2 ODE), which model that portion as a sparse neural or symbolic ODE: a conditional mask selects the markers carrying the rewiring, and a variational autoencoder compresses the remaining markers into a learnt closure term. On single-cell phosphoproteomic data spanning 30 cancer-relevant overexpressions, Co ^2 ODE compresses a 30-marker panel into a 10-dimensional state while matching the predictive accuracy of a neural ODE fit to the full panel. The closure absorbs what the mask drops, hence the accuracy is insensitive to which markers are kept. Adding a prior score to the loss shifts the selected markers at no cost in fit, indicating the data are not interventional enough to identify the underlying biology.
[LG-101] Underdamped Stochastic Optimal Transport with Landauer boundary conditions: machine learning and analytical solution
链接: https://arxiv.org/abs/2610.41599
作者: Julia Sanders,Dario Lucente,Marco Baldovin,Paolo Muratore-Ginanneschi
类目: atistical Mechanics (cond-mat.stat-mech); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: Main: 12 pages, 6 figures. Supplemental material: 11 pages
Abstract:Finding optimal transport protocols for underdamped stochastic processes is a difficult yet important problem in control theory. In this paper we develop a direct method, based on machine learning, to find the optimal solution of the problem in the presence of non-linear boundary conditions, including those modelling the Landauer bit-erasure problem. The dynamics is steered from a single-peak to a double-peak distribution in position space, by only acting on a controllable external potential. The problem is highly non-linear, and analytical solutions are not available in general. We benchmark our numerical results with a perturbative expansion, which is expected to hold in a suitable overdamped limit.
[LG-102] PDE-Residual-Informed Kriging
链接: https://arxiv.org/abs/2610.41590
作者: Yu Tan,Shichang Du,Yongxiang Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 104 pages, 4 figures, 3 tables
Abstract:Physics-informed Gaussian process (GP) methods provide a probabilistic framework for PDE solving. Existing approaches incorporate PDE information in different ways: pointwise conditioning uses the governing equation only at finitely many selected locations, globally constrained GPs restrict the admissible functions to satisfy the PDE exactly, and residual-penalized methods control the PDE mismatch over the whole domain through an additional penalty weight. What is still missing is a GP formulation that directly incorporates global PDE-residual information of the entire domain into its covariance structure, while retaining model flexibility and standard GP conditioning without a separate residual-weight parameter. To solve this issue, this study proposes PDE-residual-informed Kriging (PRIK) based on a PDE-residual kernel. The PDE-residual kernel is constructed by equipping a kernel-section space with an inner product defined by the L^2 inner product of PDE residuals. This construction makes PRIK yield the posterior mean with minimum integrated squared PDE residual. It also provides direct statistical interpretations: the variance-scale estimate equals the normalized PDE-residual energy (i.e., the integrated squared PDE residual of the posterior mean divided by the number of labeled observations), and the profile likelihood balances this residual energy against model complexity. Under the stated regularity and stability conditions, we establish H^2 -quasi-optimality and convergence rates for the posterior mean, together with a nonasymptotic bound for its PDE-residual norm. We further show that the normalized posterior kernel converges to a variational Green-type kernel and derive a uniform nonasymptotic contraction bound for the posterior variance. Numerical studies provide empirical evidence consistent with the theory and demonstrate the PDE-solving capability of PRIK.
[LG-103] Reliability-Weighted Direct Preference Optimization: Penalized Weights for Robust Preference Learning
链接: https://arxiv.org/abs/2610.41588
作者: Bin Luo,Param Venkat Vivek Kesireddy,Anil Vallepu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Direct Preference Optimization (DPO) treats every reported comparison as correct, yet preference data contain reversed, tied or otherwise noisy comparisons. RW-DPO gives each comparison an unknown reliability weight w_i\in[0,1] that enters the likelihood linearly – DPO is w\equiv1 , a reversed comparison w_i=0 – and estimates the weights jointly with the policy under a minimax-concave penalty on -\log w_i . The penalty keeps every comparison the model agrees with at exactly w_i=1 and acts only on those it contradicts, through closed-form weight updates at negligible cost over DPO. We prove that recovering all flipped comparisons from a single annotation per comparison requires margins of order \log n . Furthermore, in a linear-index model, starting from an initial fit pointing in the correct direction, our estimator jointly consistently recovers the preference direction and weights, as well as identifies the flipped comparisons above this margin scale, even when a fixed fraction of comparisons is flipped. In simulations RW-DPO has the lowest average estimation error among DPO, noise-rate corrections and recent robust methods, with far more precise flags than recent label-correction methods. In DPO fine-tuning of a language model with 10-30% flipped labels, its win rate does not fall as the flip rate grows and exceeds that of DPO trained on clean labels, matching the strongest recent method with corrections concentrated on the flipped labels. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME) Cite as: arXiv:2610.41588 [stat.ML] (or arXiv:2610.41588v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2610.41588 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bin Luo [view email] [v1] Fri, 30 Oct 2026 11:24:14 UTC (61 KB) Full-text links: Access Paper: View a PDF of the paper titled Reliability-Weighted Direct Preference Optimization: Penalized Weights for Robust Preference Learning, by Bin Luo and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: stat.ML prev | next new | recent | 2026-10 Change to browse by: cs cs.LG stat stat.ME References Citations SciXGoogle 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)
[LG-104] Conditional Neural SDEs for Financial Time-Series Generation
链接: https://arxiv.org/abs/2610.41405
作者: Sina Molavipour,Alireza M. Javid,Björn Löfdahl,Mikhail Nechaev
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framework for this task by combining continuous-time stochastic dynamics with learnable drift and diffusion functions. However, directly modeling the target state can be unstable in conditional settings, especially when the target and conditioning variables have strong level dependence, limited training data, or different this http URL propose a residualanchor Neural SDE architecture that decomposes the target into a learned conditional anchor and a stochastic residual. The anchor captures the context-dependent baseline, while the Neural SDE (Stochastic Differential Equation) models the residual dynamics around this baseline. This separation reduces the burden on the stochastic dynamics and generalizes spread-based representations to more general conditioning this http URL evaluate the approach on real-world financial time series, including conditional interest-rate scenarios and a comparison with a time-series foundation model. The results show that the proposed model can generate plausible conditional paths, maintain competitive point accuracy, and provide calibrated uncertainty estimates.
[LG-105] Estimation of Quantum Umlaut Information
链接: https://arxiv.org/abs/2610.41282
作者: Beomjoon Kim,Donghwa Ji,Kabgyun Jeong
类目: Quantum Physics (quant-ph); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 22 pages, 7 figures, 1 table
Abstract:Quantifying correlations is important for understanding how quantum states and channels support information processing. Quantum umlaut information U connects such correlations to reliability in hypothesis testing and certain communication settings, but its known formulas require classical descriptions of the underlying states. To estimate correlations without these descriptions, we introduce measured quantum umlaut information U^M by replacing Umegaki relative entropy with measured relative entropy. Its variational formulation leads to a quantum generative adversarial framework for estimating U^M from measurement statistics of unknown bipartite states and classical-quantum channels. This framework optimizes reference states and measurements without reconstructing the target states or channel outputs. For classical-quantum channels, it also optimizes the input probabilities. We further analyze the relation between U^M and U , deriving a state-dependent gap bound and characterizing equality for full-rank bipartite states. Numerical simulations show trained estimates close on average to independently computed numerical references for U^M across the tested states and channels. These estimates could help assess how well quantum memories preserve correlations and how noise or drift affects input-output dependence in quantum transmitters and state-preparation devices.
[LG-106] Coarse-Graining Optimal Transport for Rollout-Free Post-Training of Flow Matching
链接: https://arxiv.org/abs/2610.41269
作者: Soon Hoe Lim,Aditi Gupta,Michael W. Mahoney,N. Benjamin Erichson
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 42 pages
Abstract:Flow matching depends critically on the coupling used to pair source and target samples. Optimal transport (OT) couplings can reduce velocity conflict and straighten trajectories; but, in practice, challenges remain: the source comes from a continuous distribution, only a finite number of target samples data are accessible, and the neural network has finite capacity. For these reasons, making the coupling more OT-like may not consistently improve sampling quality, in particular in the low number-of-function-evaluation (NFE) regime. Motivated by this, we introduce ReCouple, a controllable framework that constructs coarse-grained OT couplings, to better understand rollout-free OT-based post-training. We also consider extensions of this basic framework: ReCouple+ extends this toward the continuous-source setting by increasing source resolution, converging to semi-discrete OT in the appropriate limit; and ReCouple++ further adds spatial regularization, motivated by the observation that straight trajectories need not correspond to spatially simple velocity fields. Empirical validation demonstrates the effectiveness of these methods, and it reveals an NFE-dependent interplay between coupling optimality, neural realizability, and generation quality.
[LG-107] Adaptive Uncertainty Quantification on Simplicial Complexes with Conformalized Simplicial Neural Networks
链接: https://arxiv.org/abs/2610.41166
作者: Lekun Wang,Jaidev Goel,Yulia R. Gel,Yao Xie
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Simplicial neural networks (SNNs) are powerful tools for prediction tasks on higher-order relational datasets stored on simplicial complexes (SCs), a higher-order generalization of graphs. However, SNNs usually lack rigorous uncertainty quantification (UQ), limiting their usefulness in high-stakes scenarios. We propose conformalized SNN (CF-SNN), a framework that, given a simplicial-complex entity with m prediction targets, generates prediction sets for all m targets simultaneously. Under mild assumptions, CF-SNN guarantees that, for each target, the prediction set contains the true label with a user-specified nominal coverage probability, and the realized miscoverage proportion across all m targets is guaranteed to remain close to the nominal level with high probability. Extensive experiments across a wide range of SC prediction tasks demonstrate that CF-SNN is a flexible UQ framework that achieves user-specified coverage while producing informative prediction sets of small size.
[LG-108] Stochastic and Noisy Oracles in Smooth Continuous Optimization
链接: https://arxiv.org/abs/2610.41086
作者: Albert S. Berahas,Liyuan Cao,Katya Scheinberg
类目: Optimization and Control (math.OC); Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注:
Abstract:We review models of inexact computational oracles in smooth continuous optimization and their implications for the convergence, computational complexity, and attainable accuracy of optimization algorithms. We adopt a framework in which zeroth-,first-, and second-order oracles are characterized by quantitative error bounds and may accept additional control parameters that influence both their accuracy and computational cost. We introduce deterministic and stochastic oracle models and illustrate their properties through a variety of examples, including finite-difference approximations, stochastic gradients, and randomized derivative estimators. Particular attention is given to the distinction between absolute and relative errors, bias and variance, and stochastic function evaluations with independent or coupled randomness. A central theme of the review is the use of common analytical tools to recover and compare convergence and complexity guarantees under different oracle models. For deterministic and stochastic first-order methods, we show how a general descent inequality leads to complexity bounds under various assumptions on the objective function and all proposed oracle models. In the stochastic setting, we develop bounds on expected stopping times and the accuracy that can be guaranteed under different oracle conditions. We also examine the total computational complexity of algorithms relying on specific oracle constructions and briefly review models of inexact second- order information and their use in optimization algorithms.
[LG-109] D2ML: Models for Direct-to-Biology Small Molecule Screening NEURIPS2026
链接: https://arxiv.org/abs/2610.41070
作者: AkshatKumar Nigam,Neri Amara,Hillary Dequina,Dyana Kenanova,Seth Malmersjo,Fiorella Ruggiu,Raymond V. Fucini,Jack Sadowsky,Stig K. Hansen,Imran S. Haque
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注: NeurIPS 2026 Workshop on AI for Drug Discovery (AI4DD)
Abstract:Direct-to-biology (D2B) screening enables rapid testing of newly synthesized molecules without purification. However, uncertain concentrations in crude mixtures and limited purified-compound measurements complicate the use of these data in machine learning. Here we show that models tailored to the structure of D2B data can improve prediction and guide the discovery of more potent and selective compounds. Our assay-aware models predict activity from single-point screens, relative potency across targets from crude dose-response assays, and absolute potency from purified-compound measurements. For crude assays, potency ratios between targets tested from the same well can retain selectivity information despite uncertain concentrations. A joint model that shares information across stages and balances their contributions to training improved predictions over separate neural networks. The largest gain was in crude dose-response selectivity prediction, where rank correlation increased from 0.27 to 0.40. In a separate experiment, independently trained models guided the selection of 84 new compounds to improve ROS1 potency while limiting activity against the anti-target TRKB. Synthesis and testing identified compounds that extended the measured Pareto front, achieving better combinations of potency and selectivity than those in the training data. The most selective new compound achieved a ROS1 IC50 of 14.7 nM and 233-fold selectivity over TRKB. By comparison, the most selective training compound had a ROS1 IC50 of 20.3 nM and 79-fold selectivity. ATLAS provides the experimental setting for this approach, supporting synthesis and screening of 1,000-10,000 model-selected compounds in two- to three-week cycles. Together, these results show how assay-aware learning can use the full D2B assay sequence to guide multi-objective molecular optimization.
[LG-110] Learning Stabilizing Latent Feedback Controllers via Multi-Step Backward Losses
链接: https://arxiv.org/abs/2610.40931
作者: Paul Lutkus,Lars Lindemann,Stephen Tu
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Can learned latent dynamics models support the design of feedback controllers that provably stabilize the original system? For linear time-invariant systems, we show that learning linear autoencoders and latent dynamics with multi-step prediction-reconstruction losses \unicodex2014 which we term “backward losses” as they encourage latent trajectories to reconstruct full state trajectories \unicodex2014 provably recovers the structure needed for stabilization. Specifically, a sufficiently long reconstruction horizon ensures that a minimum H_\infty -norm controller designed using the learned latent model stabilizes the original system through output-feedback via the corresponding encoder, provided the latent dimension is large enough to capture all non-stable modes of the underlying system. We also show why both reconstruction and a multi-step horizon are essential: forward prediction losses that require only low prediction error along latent trajectories admit global loss minimizers that discard unstable modes regardless of horizon length, while single-step reconstruction losses can yield latent models whose stabilizing controllers fail to stabilize the original system. Finally, experiments on a non-linear flexible-arm system illustrate the practical implications of our results for learning non-linear autoencoders and latent dynamics.
[LG-111] Robust-by-Design Distributional Learning from Contaminated Samples NEURIPS2026
链接: https://arxiv.org/abs/2610.40884
作者: Nevena Gligić,Arya Farahi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Modern data analysis pipelines increasingly rely on large datasets assembled through retrieval, scraping, automated filtering, and post-hoc curation, where the observed sample is often a contaminated mixture rather than the target population. Standard distributional objectives either ignore contamination and converge to the wrong target, or retain only a small trusted subset and discard most of the data. We study supervised and unsupervised learning when a clean reference sample is compared with a pooled sample from r=(1-\epsilon)q+\epsilon z , where q is the latent target distribution and z is an unknown contaminant. Motivated by settings with noisy membership scores for all observations and exact labels for a small audited subset, we propose contamination-corrected maximum mean discrepancy (CC-MMD), a design-based estimator that combines proxy scores with audited residual corrections to recover the oracle MMD that would have been computed from the latent clean sample. CC-MMD requires no calibration assumption on the proxy scores and no structural assumptions on the contaminant beyond standard kernel regularity. We prove finite-population design-unbiasedness of the corrected kernel sums, establish asymptotic normality of the audit correction, and show consistency for the target discrepancy \mathrmMMD^2(p,q;k) . Empirically, CC-MMD tracks the oracle under increasing contamination, improves parameter recovery and prediction in contaminated regression, and enables generative models trained on heavily corrupted mixtures to recover the clean target distribution. These results show that contaminated data need not be discarded or treated as the estimand. With sparse trusted labels and abundant noisy supervision, distributional objectives can be made robust by design.
[LG-112] Predictive Maintenance at the Edge: An Embedded Anomaly Detection Pipeline Trained with Quantum Optimization
链接: https://arxiv.org/abs/2610.40848
作者: Filipe Chagas Ferraz
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:This article presents a predictive-maintenance pipeline that detects anomalies in bearings from vibration signals, adapted for deployment at the edge on low-cost microcontrollers, and that uses quantum optimization in two steps of its training. The pipeline consists of a step of vibration feature extraction and a step of classification with a one-class support vector machine (OCSVM). The features are selected by minimum redundancy and maximum relevance (mRMR) with the variational quantum eigensolver (VQE), under a limit on computing time. The OCSVM is adapted for inference at the edge without floating-point operations and is trained by a variational quantum algorithm with Pauli-correlation encoding (PCE). In small-scale tests on a simulator, with the Paderborn bearing dataset, the VQE found the optimal selection in 36% of the runs with Adam, and PCE chose better support vectors than chance, until a classical local search made the two equal. A classical baseline, the usual OCSVM trained by SMO with its weights rounded, matched them without any search.
[LG-113] Combinatorial Residual Network: a decay-chain-aware classifier without explicit jet assignment
链接: https://arxiv.org/abs/2610.40749
作者: John Alison,Patrick Bryant,Alejandro Gomez Espinosa,Christian Guinto-Brody,Aniket Khanal,Chuyuan Liu,Sindhu Murthy,Guanglei Zhao
类目: High Energy Physics - Experiment (hep-ex); Machine Learning (cs.LG); High Energy Physics - Phenomenology (hep-ph); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 24 pages, 7 figures, 6 tables. Code available at this https URL
Abstract:Final states with several jets from the decays of heavy particles pose a combinatorial problem, since the jets must be assigned, at least implicitly, to their parent particles. We describe the Combinatorial Residual Network (CoReNet), a classifier that encodes the assumed decay chain of the final state in its architecture. CoReNet builds its event representation level by level along the decay hierarchy, evaluates every jet assignment hypothesis with weights shared across objects, and combines the hypotheses with learned weights using residual networks. The exchange symmetries of the decay are built into the construction, the size of the network does not depend on the number of hypotheses, and the network is trained with the classification loss alone, without generator-level jet assignments. The architecture was developed for the b\barbb\barb final state and has been used in several CMS searches. On fast-simulation samples of Higgs boson pair production in the b\barbb\barb final state against QCD multijet production, CoReNet with 1508 trainable parameters performs well above kinematic baselines and trains in minutes on a single GPU. Its hypothesis weights select the truth-matched jet pairing in most of the signal events in which it is defined, although no assignment target is used in training. Since only the encoded decay chain is specific to the final state, the construction carries over to other topologies, which we test with HH \to b\barbW^+W^- against semileptonic t\bart . There, encoding the decay chain of the background as well as that of the signal further improves the classification, at a cost of about 10^3 parameters. In both final states its performance is comparable to that of a state-of-the-art transformer-based network more than two orders of magnitude larger. The implementation is publicly available at this https URL.
[LG-114] Model-Agnostic Search for New Physics in the Presence of Imperfect Background by Smearing the Data
链接: https://arxiv.org/abs/2610.40734
作者: Soheun Yi,John Alison,Mikael Kuusela
类目: Applications (stat.AP); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注:
Abstract:In the search for new particles in high-energy physics, it is necessary to first define the signal region (SR) enriched in signal events, and then compare events in this region with the expected background distribution. While most existing search methods rely on prior domain knowledge of either the signal or the background, there has been less effort to relax both assumptions simultaneously, as methods that weaken one assumption typically rely on strong knowledge of the other. We propose Model-Agnostic Search with Smearing and Imperfect Background (MASSIB), a method that addresses this challenge by learning the density ratio of events relative to a misspecified background distribution using modern tools from machine learning, and defining the SR as the region with high density ratio. Recognizing that a naive application of this idea introduces significant bias in background estimation when interpolating from the control region to the SR, MASSIB mitigates this issue by introducing a “smeared” density ratio, which can be learned efficiently through data-level manipulation and used to define an SR that allows for accurate background estimation. By applying it to various types of simulated Large Hadron Collider (LHC) events with realistic background mismodeling, we demonstrate that MASSIB identifies a data-driven SR that achieves high signal concentration consistently across different types of signals while avoiding bias in background estimation. The method does this without making strong assumptions about either the signal or background distribution. We then show that this robustness extends to the validity and strong statistical power of the resulting hypothesis tests for signal detection, demonstrating the potential of this approach for future model-agnostic searches at the LHC.
[LG-115] INLA for Adaptive UAV Sampling of Radio Environment Maps in IoT Networks: Prior Width Governs Accuracy and Calibration
链接: https://arxiv.org/abs/2610.40723
作者: Changhao He,Kaichuang Yang,Xiaojuan Zhang
类目: Machine Learning (stat.ML); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注:
Abstract:We study adaptive UAV sampling for radio environment map (REM) construction from non-Gaussian IoT observations. Integrated nested Laplace approximation(INLA) updates a Matérn GMRF posterior, with waypoints selected by entropy pre-screening and Sherman–Morrison lookahead over the reachable set. We show that the likelihood-induced Skew-Normal correction has a sextic entropy effect, explaining its limited influence on waypoint selection. Across 19 retained ray-traced windows,Random and lawnmower sampling cost a median 18.0% and 14.0% more residual NMSE than posterior-informed sampling, equivalently reductions of 15.3% and 12.3% when referred to those baselines, while differences among informative rules remain unresolved. Widening the prior shadowing standard deviation from 6.0 to 10.025,dB improves NMSE and credible-interval coverage, with monotone improvement over a 4–16,dB sweep on this corpus and no observed turning point, although overconfidence persists. Online width estimation from the UAV’s own counts yields about 6.5,dB without recovering the gain. Across 30 measured LoRaWAN propagation windows paired with simulated counts and scored only on received cells, the margin over non-adaptive sampling reproduces, the training-derived width minimizes the tested interval score, and GP-UCB has nominally 3 – 5% lower NMSE. The results show substantial, corpus-dependent sensitivity to prior specification, while fine differences among acquisition rules have limited or unresolved effects, the preferred width depends on the corpus, scored cells and model mismatch.
[LG-116] he Geometry of Multitask Learning: Sharp Rates for Shared Subspace Estimation
链接: https://arxiv.org/abs/2610.40713
作者: Tony Cai,Abhinav Chakraborty,Samory Kpotufe
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 81 pages, 2 figures
Abstract:Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain poorly understood in many of the formal settings considered in the literature. To address this gap, which arises from limitations of existing upper-bound techniques, we propose a general multitask lemma. The lemma provides sufficient conditions for fast subspace estimation in generic multitask settings, in the form of geometric conditions on the task regression functions and their estimators. We subsequently show that the multitask lemma yields sharp minimax rates—previously unknown—for linear regression and classification in the regimes considered, and rates matching up to logarithmic factors for additive-noise regression.
[LG-117] CalibCause: A Quality-Gated Interpretable and Calibrated Deep Learning Framework for Diabetic Retinopathy Grading
链接: https://arxiv.org/abs/2610.40699
作者: Sohaib Sulman,Muhammad Atif Saeed,Akhtar Jamil,Alaa Ali Hameed,Roland Aydin
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注: 15 pages, 8 figures, 6 tables
Abstract:Most deep learning systems for diabetic retinopathy (DR) grading are evaluated on a single data source, with limited evidence of cross-site generalisation, uncertainty calibration, and interpretability. We propose CalibCause, a quality-gated mixture-of-experts framework that combines per-expert visual explanations, label-free image-quality conditioning, knowledge distillation, and calibrated confidence with deferral. We evaluated the framework using 51,310 fundus images from EyePACS, DDR, and APTOS 2019, with IDRiD and DeepDRiD for external testing. Evaluation included pooled testing, leave-one-environment-out experiments, paired bootstrap confidence intervals, and local grade-threshold adaptation. On the pooled test split, the label-free distilled student achieved quadratic weighted kappa (QWK) of 0.863 and accuracy of 0.865 with 27.8 million parameters, 52% fewer than its teacher. Across held-out environments, the label-free model achieved mean QWK of 0.741 compared with 0.743 for a plain ConvNeXt-Tiny baseline, while adversarial invariance did not improve cross-site grading accuracy. Refitting grade thresholds using 200 labelled images increased held-out EyePACS QWK from 0.533 to 0.621 for the label-free model. After vector scaling, expected calibration error ranged from 0.025 to 0.036 across CalibCause variants, and deferring the least confident 20% of images increased student accuracy from 0.865 to 0.923 on the remaining images. These findings demonstrate that quality-aware grading, interpretable expert routing, model compression, and calibrated deferral can be integrated without sacrificing cross-site performance relative to a plain baseline, although reliable deployment still requires local validation and adaptation.
[LG-118] VitisGP: Multi-Task Benchmark for Genotype to Phenotype Grapevine Prediction
链接: https://arxiv.org/abs/2610.40668
作者: Chiara D’Ercoli,May Levin,Pietro Liò
类目: Genomics (q-bio.GN); Machine Learning (cs.LG)
*备注:
Abstract:As climate change reshapes grape-growing conditions, predicting grapevine phenotypes from genomic variation is increasingly important for identifying and breeding cultivars suited to emerging environmental pressures. Existing research on the grapevine genotype-phenotype relationship using machine learning has often focused on individual traits, isolated regions, or a limited set of predictive models. We introduce VitisGP, a multi-phenotype curated dataset, integrating genome-wide SNP data with 26 heterogeneous phenotype prediction tasks spanning sommelier-curated aroma traits (N=11), botanical traits (N=14) and wine color—across 1,197 Vitis vinifera cultivars. We employ VitisGP to benchmark phenotype prediction, comparing machine-learning models with differing inductive biases—L2-regularized logistic regression, Random Forest, XGBoost, CatBoost, GBLUP and Transformer-based models, providing the first systematic comparison of this breadth on harmonized grapevine data. Across the benchmark, CatBoost achieves the strongest overall performance (macro-average AUROC=0.696 across 26 tasks), although model performance varies substantially across phenotype classes, with sensory and color traits showing stronger predictability than agronomic characteristics. VitisGP provides the community with a resource for investigating the genomic basis of phenotypic traits and screening grapevine diversity for climate-relevant characteristics.
[LG-119] Intervention-Conditioned Counterfactual Human Digital Twin Generator
链接: https://arxiv.org/abs/2610.40664
作者: Saeed Farzi,Sofia Michopoulou,Christopher Kipps,Rahman Attar
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注:
Abstract:Chronic diseases evolve through long, heterogeneous trajectories shaped by interacting mediators, confounders, and treatment histories. Estimating long-term patient trajectories under alternative interventions is essential for in silico clinical trial design. However, most existing models overlook irregular visit intervals and cross-mediator dependencies, both of which are crucial for reliable long-term longitudinal prediction. Moreover, these models typically focus on outcome prediction rather than generating complete patient-state trajectories. To address these challenges, we propose CHDT, an intervention-conditioned Human Digital Twin generator under standard identification assumptions for multimodal longitudinal trajectory estimation. Its main empirical advantage arises from explicit modeling of irregular visit intervals, while cross-mediator refinement provides additional gains in selected intervention-conditioned settings. Across real-world ADNI and simulator-generated ALPACA cohorts, CHDT shows improved long-horizon patient-state fidelity relative to competing baselines. Ablation results indicate that irregular-time conditioning provides the primary empirical gain, and cross-mediator refinement provides additional improvements.
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