本篇博文主要内容为 2026-11-04 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 (2026-11-04)
今日共更新930篇论文,其中:
- 自然语言处理共87篇(Computation and Language (cs.CL))
- 人工智能共257篇(Artificial Intelligence (cs.AI))
- 计算机视觉共160篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共254篇(Machine Learning (cs.LG))
- 多智能体系统共19篇(Multiagent Systems (cs.MA))
- 信息检索共26篇(Information Retrieval (cs.IR))
- 人机交互共21篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] Haptic Shared Control for Multi-Robot Systems
【速读】：该论文旨在解决多机器人系统在复杂任务执行过程中，如何实现自主代理与人类操作者之间高效协同控制的问题。其核心挑战在于，在传感器视场受限的条件下，维持多智能体间的全局连通性，同时允许操作者以不同层级的干预尺度对群体行为进行调控。解决方案的关键在于提出一种改进的全局连通性保持算法，该算法基于智能体局部坐标系中的传感器测量数据，能够在有限视野约束下实现鲁棒的自主协作；并将其与三种自适应监督策略相结合：单智能体控制（Single Agent Control, S）、定点控制（Pinning Control, P）和质心控制（Centroid Control, C），从而支持操作者在微观、中观到宏观层面的灵活干预。此外，系统通过提供触觉反馈，实时反映系统的连通状态，增强人机交互的感知与控制精度。用户研究表明，引入触觉反馈显著提升了各类控制模式下的任务性能与系统稳定性。
链接: https://arxiv.org/abs/2611.05027
作者: Maxime Bernard
机构: 未知
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: PhD thesis, Université de Rennes (IRISA UMR 6074 / Inria Rennes), defended 30 April 2025. 134 pages, 38 figures
Abstract:Multi-agent systems offer significant advantages in terms of flexibility and redundancy, enabling the efficient distribution of complex tasks among multiple robots. However, their deployment requires advanced control strategies to ensure optimal cooperation between autonomous agents and human operators. We propose a modification of the global connectivity maintenance algorithm to incorporate measurements taken in agent’s local frame, using sensors with limited fields of view. This algorithm, responsible for the autonomous behavior of the multi-robot system, is combined with different adaptive supervision strategies, allowing the user to exert control at varying scales over the entire group. (i) Single Agent Control (S): The operator directly controls a single robot, (ii) Pinning Control §: The operator imposes constraints on multiple robots to influence the group, (iii) Centroid Control ©: The operator acts on the system’s center of mass to steer the entire fleet. In conjunction with the connectivity maintenance algorithm, these methods ensure compliance with inter-agent constraints while providing haptic feedback to the user regarding the system’s connectivity state. The effectiveness of these approaches was evaluated through a user study, comparing performance with and without haptic feedback across multiple scenarios tailored to the different control modes.
[MA-1] GeoFaham: Domain-Specialized Multi-Agent Geospatial Analytics for Disaster Response
【速读】：该论文旨在解决真实世界地理空间决策支持中多步骤、多源异构数据融合的复杂工作流自动化问题，尤其聚焦于灾害响应场景下对空间范围、时间上下文及中间执行状态的精确保持需求。现有大语言模型（LLM）与代码代理在处理地理空间任务时存在诸多特定缺陷，如难以管理大型地理空间数据集、错误选择证据源、丢失时空元数据以及执行无效几何操作等。为此，论文提出一种领域专用的多智能体系统GeoFaham，其核心创新在于通过专业化分工的智能体（矢量操作、地图特征检索、影像发现、栅格分析）与具备双账本控制循环（dual-ledger control loop）的协调器协同工作，实现依赖感知的规划、进度追踪、验证与重规划。为支持以数据为中心的协作，GeoFaham引入响应存储（Response Store），将完整的地理空间输出及其类型化元数据持久化于LLM上下文之外，仅通过紧凑标识符与摘要进行智能体间通信，有效缓解上下文窗口限制。此外，研究构建了基于17个真实灾害案例的GeoFaham-Bench基准测试集，涵盖40项任务，用于评估多步地理空间推理、跨源集成、栅格-矢量计算、成果生成及非刚性真值下的容错能力。实验表明，GeoFaham在该基准上显著优于Codex、Claude Code和OpenCode等通用代码代理，整体得分达0.940，优于次优方案的0.879，同时仅消耗其7.5倍的令牌数，展现出更高的效率与鲁棒性。
链接: https://arxiv.org/abs/2611.04977
作者: Muhammad Sohail Danish,Gilles Q. Hacheme,Akram Zaytar,Satej S. Soman,Inbal Becker-Reshef,Juan Lavista Ferres,Salman Khan,Girmaw Abebe Tadesse
机构: Microsoft AI for Good Lab(微软人工智能向善实验室); Mohamed bin Zayed University of Artificial Intelligence(穆罕默德·本·扎耶德人工智能大学)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world geospatial decision support requires more than single-step retrieval or code generation. Disaster-response questions often involve multi-source workflows that combine spatial databases, map-derived features, satellite imagery, raster products, and reusable geospatial artifacts, where correctness depends on preserving spatial scope, temporal context, and intermediate execution state. While large language models and coding agents offer a promising interface for such workflows, they struggle with geospatial-specific failure modes, including handling large artifacts, selecting the wrong evidence source, losing spatial or temporal metadata, and applying invalid geometric operations. We introduce GeoFaham, a domain-specialized multi-agent system for geospatial disaster analytics. GeoFaham decomposes workflows into specialist agents for vector operations, map feature retrieval, imagery discovery, and raster analysis, coordinated by an Orchestrator with a dual-ledger control loop for dependency-aware planning, progress tracking, validation, and replanning. To support artifact-centric collaboration, GeoFaham uses a Response Store that persists full geospatial outputs and typed metadata outside the LLM context window while agents exchange compact identifiers and summaries. We also introduce GeoFaham-Bench, a 40-task benchmark grounded in 17 real-world disaster records, designed to evaluate multi-step geospatial reasoning, cross-source integration, raster-vector computation, artifact generation, and recovery under non-rigid ground truth. We evaluate GeoFaham on GeoFaham-Bench against strong general-purpose coding-agent baselines, including Codex, Claude Code, and OpenCode. GeoFaham achieves the best overall performance, scoring 0.940 versus the next-best 0.879, while using 7.5 times fewer tokens. Our code and benchmark are publicly available at this https URL.
[MA-2] Local Visibility Roadmaps in Continous Multi-agent Path Finding
【速读】：该论文旨在解决连续环境下多智能体路径规划（smooth continuous multi-agent path finding, SC-MAPF）问题，即在连续时间与连续空间中，多个智能体需沿平滑曲线移动并避免静态障碍物及其他动态智能体的碰撞。其核心挑战在于如何在保证路径平滑性的同时实现高效且无冲突的全局路径规划。解决方案的关键在于提出一种新型算法，该算法将高层搜索的冲突消除约束束（Conflict-Free CBS, CE-CBS）与任意角度路径规划（any-angle path finding）相结合，利用局部可视性路网（local visibility roadmaps）进行单智能体路径规划，从而在保持路径光滑性的同时有效处理动态障碍物与智能体间的避碰需求。实验结果表明，该方法在路径质量、计算效率和可扩展性方面均优于传统的CE-CBS、基于RRT*的个体路径规划以及SMT-CCBS算法。
链接: https://arxiv.org/abs/2611.04944
作者: Kristýna Janovská,Pavel Surynek
机构: Czech Technical University in Prague, Faculty of Information Technology (捷克技术大学信息工程学院)
类目: Multiagent Systems (cs.MA)
备注: 11 pages. Published in ICAART 2026
Abstract:We address the problem of smooth continuous multi-agent path finding (SC-MAPF), where agents move in a continuous environment along smooth curves in continuous time. The paths for individual agents are searched in local visibility roadmaps that consider obstacles and other agents. A new algorithm is proposed in this work, which combines the previous algorithm CE-CBS for high-level search with any-angle path finding for local single-agent planning, where agents are to avoid obstacles, both static and other agents. The new algorithm is tested and compared to CE-CBS, the algorithm building individual paths using RRT ^* , and SMT-CCBS algorithm, an algorithm for MAPF _R . Experimental results show various advantages of the new algorithm over CE-CBS and SMT-CCBS in comparable settings.
[MA-3] Cooperative Multi-Agent Reinforcement Learning with Submodular Trajectory Rewards
【速读】：该论文旨在解决具有解耦动态和单调子模联合轨迹奖励函数的协作多智能体强化学习中的优化难题。由于奖励函数非可加，最优策略可能依赖于所有智能体的历史信息，导致精确优化在时间步长和智能体数量上呈指数级复杂度。为在保持理论保证的前提下克服此挑战，论文引入了“预窗口曲率”（pre-window curvature, $ c_m $），用于量化超过 $ m $ 步前访问的状态对当前边际收益的抑制程度。基于该曲率，论文提出一种贪心算法，利用解耦动态特性，按顺序优化各智能体的局部策略，限制每个智能体的策略仅依赖于其最近 $ m+1 $ 个状态。对于固定的 $ m $，该算法在估计误差范围内实现了 $ \frac{1 - c_m}{2 - c_m} $ 的近似比，且计算时间在智能体数量和时间步长上为多项式级别。当奖励函数为时序可加时，该方法退化为已知的 $ 1/2 $-近似结果，并在单智能体情形下达到与总曲率界一致的近似性能。论文以多智能体交通监控为例，验证了所提算法的有效性。
链接: https://arxiv.org/abs/2611.04774
作者: Yasaman Zolfimoselo,Nikolas Geroliminis,Maryam Kamgarpour
机构: 未知
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:We study cooperative multi-agent reinforcement learning with decoupled dynamics and a monotone submodular reward function over joint trajectories. Due to the non-additive reward, an optimal policy may depend on the history of all agents. This dependence makes exact optimization exponential in the horizon and the number of agents. To overcome this challenge while retaining theoretical guarantees, we introduce pre-window curvature c_m , which quantifies how states visited more than m steps earlier reduce current marginal gains. Using this curvature to control the error from restricting the policy of each agent to depend only on its last m+1 states, we propose a greedy algorithm that exploits the decoupled dynamics to optimize local policies sequentially. For fixed m , the algorithm achieves a \frac1-c_m2-c_m approximation guarantee up to estimation error, and its computation time is polynomial in the number of agents and the horizon. We recover the known 1/2 -factor approximation for time-additive rewards and obtain an approximation factor no smaller than the known total-curvature bound for a single agent. We formulate multi-agent traffic monitoring as an example of this framework and evaluate our algorithm on it.
[MA-4] Can Network Models of Science Do Without Pluralist Agents ?
【速读】：该论文试图解决的核心问题是：尽管科学理论在不断被证伪，但科学体系为何仍能有效运作？基于保罗·费耶阿本德（Paul Feyerabend）提出的“理论多元主义”（theoretical pluralism）思想，论文探讨了在存在相互竞争的理论时，科学知识如何通过动态演化实现进步。其解决方案的关键在于构建一个基于多智能体变分推断（multi-agent variational inference）的理论与实验模型，对比分析“多元主义代理”（pluralist agents）——能够同时持有并维护多个相互竞争的理论，并在理论间保持信念协调——与“单一主义代理”（monist agents）——只能将所接收的信息整合进单一认知模型——在集体信念形成过程中的表现差异。实验结果表明，具备理论多元性的群体在面对不确定性时表现出更灵活的认知转换能力，且跨群体间的信念共享与理论支持共享对集体认知演化具有不同作用机制，从而揭示了多元共存与理论竞争在推动科学进步中的关键功能。
链接: https://arxiv.org/abs/2611.04590
作者: Jonas Hallgren,David Hyland,Mahault Albarracin
机构: Equilibria Network(均衡网络); Uppsala University (乌普萨拉大学); University of Oxford (牛津大学); Laboratoire d’Analyse Cognitive de l’Information, Université du Québec à Montréal (魁北克大学蒙特利尔分校认知信息分析实验室); Centre of Excellence for AI and Robotics, Sheffield Hallam University (谢菲尔德哈勒姆大学人工智能与机器人卓越中心)
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 21 pages, 6 figures, 1 table. Accepted at IWAI 2026 (International Workshop on Active Inference)
Abstract:Science is a process of useful models being proven wrong time and again, but despite its many flaws, it somehow works. Why is this? Paul Feyerabend argued that it works because people dare to hold and defend incompatible rival theories against the accepted one, since a theory’s failures often lead to its abandonment in the presence of a more accurate or simpler alternative. He called this view theoretical pluralism. We introduce a theoretical and experimental model in which this can be examined. It compares communities of pluralist agents, which can hold rival theories side by side and maintain beliefs within and between those theories, with communities of monist agents, which can only incorporate what they hear or observe into a single model of their own. In a multi-agent variational inference setting, our experimental demonstrations illustrate how communities with rival theories can go through periods of uncertainty followed by a shift in the theories they hold true, how the information that is communicated between communities affects beliefs when those communities make different observations about the world, and how sharing support for theories and sharing beliefs within them contribute differently to collective belief dynamics.
[MA-5] Inter-Agent Influence: Evaluating Persuasion Deception and Coercion in Multi-Agent Systems NEURIPS2026
【速读】：该论文旨在解决多智能体系统中智能体间相互影响所带来的新型安全风险问题，特别是恶意智能体可能通过策略性手段引导其他智能体采取有害行为的潜在威胁。其核心挑战在于揭示生成式智能体在真实场景下利用说服（persuasion）、欺骗（deception）和强制（coercion）等手段实现跨智能体操控的能力。解决方案的关键在于通过构建五个具有代表性的评估环境，对采用前沿语言模型的多种智能体进行系统性测试，验证了在监督型与非层级“平级对等”设置中，智能体均可显著影响其他智能体的行为决策——例如在监督环境中，所有测试模型均能通过上述三种机制将监管者的决策从拒绝转为批准；在平级协作场景中，亦成功诱导同伴的安全研究方向发生偏移。这些发现表明，随着多智能体部署的普及，亟需发展针对智能体间相互影响风险的防御机制与治理框架。
链接: https://arxiv.org/abs/2611.04556
作者: Chandler Smith,Qi Guo,Cecilia Elena Tilli,Sophia Hatz,David Demitri Africa,Patricia Paskov,Christian Schroeder de Witt,Philip Torr,Lewis Hammond
机构: University of Oxford(牛津大学); Cooperative AI Foundation(合作人工智能基金会); Allelo; Uppsala University(乌普萨拉大学); UK AI Security Institute(英国人工智能安全研究所); AVERI; University College London(伦敦大学学院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at NeurIPS 2026. 70 pages, 17 figures, 34 tables. Code: this https URL ; Data: this https URL
Abstract:The deployment of AI agents in multi-agent settings enables inter-agent influence, whereby agents can strategically steer each other’s behaviour towards an intended outcome. Such capabilities pose novel risks, as malicious agents could direct others towards harmful actions. In this work we investigate three such capabilities - persuasion, deception, and coercion - across five realistic evaluation environments and a variety of agents instantiated with frontier language models. We observed significant inter-agent influence capabilities; in oversight environments, all tested models shifted overseer decisions from rejection to approval through persuasion, deception, or coercion. We also observed successful influence in non-hierarchical ‘peer-to-peer’ settings through redirection of a peer’s safety research trajectory. As multi-agent deployments become increasingly commonplace, these results point to a pressing need for work on mitigating the risks from inter-agent influence. Code and data are available at this https URL.
[MA-6] Are LLM Assistants Regulatory Ready? Answers from regenolds 2026 EU AI Act QA Challenge
【速读】：该论文旨在解决生成式 AI 在监管事务领域应用中的可靠性问题，尤其聚焦于大型语言模型（Large Language Model, LLM）在欧盟人工智能法案（EU AI Act, Regulation (EU) 2024/1689）相关任务中的表现不确定性。其核心解决方案是构建并公开发布“欧盟人工智能法案问答基准”（EU AI Act QA Benchmark），该基准包含110个关于欧盟人工智能法案的开放式问题，每个问题均配有示范答案、特定正确性标准及预期参考文献。评估体系涵盖答案正确性、参考文献质量、简洁性、语调和响应延迟，并引入多轮“高难度模式”以测试模型对模拟用户质疑的应对能力。通过2026年欧盟人工智能法案问答挑战赛的实证评估，发现尽管有五款专为该任务设计的助手参与，但仅有两款持续优于通用基线模型，且仅一款在所有评估维度上均取得优异表现。这一结果表明，专用模型开发本身并不能保证性能提升，凸显了在监管事务中对生成式 AI 助手进行系统化评估的关键重要性。
链接: https://arxiv.org/abs/2611.04546
作者: Marco Virgolin,Thomas Haszprunar,Maximilian Regenold
机构: Unlayer AI / regenold GmbH(Regenold GmbH); Amsterdam, the Netherlands; Badenweiler, Germany
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Large language model (LLM) assistants are increasingly proposed for regulatory affairs, yet their reliability in regulatory tasks remains unclear. We introduce the EU AI Act QA Benchmark, an expert-curated benchmark of 110 open-ended questions about the EU AI Act (Regulation (EU) 2024/1689). Each question comes with an exemplary answer, question-specific correctness criteria, and expected references. Evaluation measures answer correctness, reference quality, conciseness, tone, and latency, with a multi-turn ``hard mode’’ testing responses to simulated user pushback. We also present the results of the 2026 EU AI Act QA Challenge, based on the benchmark, where three off-the-shelf baselines and five purpose-built assistants (from four commercial developers and one academic entrant) were tested. Only two of the five submitted assistants consistently outperformed the baselines; one achieved high scores across all evaluated metrics. These results show that specialist development alone does not guarantee better performance and highlight the critical importance of LLM assistant evaluation in regulatory affairs. The benchmark code is openly available at this https URL.
[MA-7] AttriSwarm: Orchestrating Agent Swarms for Cross-Page Visual Attribution
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在处理跨页知识密集型任务时，因答案由多页文档信息综合生成而导致的视觉证据支持关系模糊问题，进而影响结果的可验证性与可信度。其核心挑战在于实现跨页视觉归因（cross-page visual attribution），即精准识别支持答案的原始页面，并定位其中细粒度的证据区域，以提升多页推理过程的透明性与可审计性。现有方法或采用联合处理多页信息，或依赖单一流程迭代优化，存在跨页上下文污染及交互式方法序列开销过大的缺陷。本文提出一种名为AttriSwarm的代理集群（agent-swarm）框架，通过角色分工将不同职责解耦：主代理（leader）负责全局跨页推理，子代理（subagents）则并行、独立地完成各页面的归因任务，有效避免了信息干扰。为支持训练与评估，研究构建了AttriDoc数据集，该数据集包含跨页问答对及其标注的证据区域，用于对AttriSwarm进行角色特异性训练。实验表明，相较于无训练基线，AttriSwarm在三个开源模型和三个数据集上平均提升了18.94个百分点的答案准确率、29.34个百分点的页面级F₁值以及23.04个百分点的区域级交并比（IoU），显著增强了模型的性能与可解释性。
链接: https://arxiv.org/abs/2611.04504
作者: Suifeng Zhao,Zida Liu,Lei Sun,Xun Wang,Daojian Zeng,Jun Gao,Sujian Li
机构: Peking University (北京大学); Panasonic Connect Co., Ltd. (松下通信公司); Hunan Normal University (湖南师范大学)
类目: Multiagent Systems (cs.MA)
备注:
Abstract:Multimodal large language models (MLLMs) increasingly address knowledge-intensive tasks by integrating evidence scattered across multiple document pages into a final answer. Because such answers are synthesized from many pages, the link between the answer and its supporting visual evidence can become obscured, hindering verification and trust. Cross-page visual attribution is therefore essential: it requires identifying the supporting pages and localizing fine-grained evidence regions within them, thereby making multi-page reasoning transparent and auditable. Existing approaches either process multiple pages jointly or iteratively refine evidence within a single trajectory, risking cross-page context pollution and, for interactive methods, substantial sequential overhead. We introduce \textbfAttriSwarm, an agent-swarm framework that assigns these concerns to separate roles: a leader handles global cross-page reasoning, while subagents perform page-wise attribution in isolation and in parallel. To support training and evaluation, we construct \textbfAttriDoc, a cross-page QA dataset linking supporting pages to their annotated evidence regions, and use it for role-specific training of AttriSwarm, improving answer accuracy, page-level F_1 , and region-level IoU over its training-free counterpart by an average of 18.94, 29.34, and 23.04 percentage points, respectively, across three open-source backbones and three datasets.
[MA-8] Goal-Driven Autonomous Optical Experimentation via a Self-Recursive Hardware-Software Harness
【速读】：该论文旨在解决科学实验中因模型与物理系统不匹配导致的性能瓶颈问题，尤其是在光学测量任务中，传统方法难以实现动态自适应的软硬件协同优化。其核心挑战在于如何在真实物理环境中，通过持续反馈机制将模型预测与实际测量结果对齐，从而提升目标达成的精度与可靠性。解决方案的关键在于提出一种目标驱动的自递归光学研究框架（goal-driven self-recursive optical research），该框架允许智能体基于物理证据迭代地更新硬件配置、软件逻辑、模型参数及实验协议。通过引入物理可达性（physical attainability）、命令访问权限（command access）与模型近似误差（model approximation error）之间的理论关系，实现了对系统演化过程的形式化约束。在光学透明色彩匹配的案例研究中，利用iPad、RayNeo X3智能眼镜与TOP 300探头联用的测量系统，通过重构眼镜应用以支持USB控制的RGB保持和帧确认机制，确保模型生成的颜色在光谱采集期间稳定显示。实验表明，该方法使单个目标色差由19.756 ΔE₇₆*降至1.211，其余五个目标也均取得显著改善，且系统通过12项实时控制验证。该方案不仅验证了概念的有效性，还提供了可复用的软件与协议，为后续科学研究提供可扩展的闭环优化范式。
链接: https://arxiv.org/abs/2611.04481
作者: Tianxing Zhu,Huacheng Lu,Ming Liu,Malte Hillebrand
机构: Konica Minolta (China) Investment Ltd(柯尼卡美能达(中国)投资有限公司); Giant Testing Equipment Ltd(巨量检测设备有限公司)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:We propose goal-driven self-recursive optical research in which agents use physical evidence to revise the hardware, software, models, and protocols used to pursue a scientific goal. The framework can be instantiated with different stimulus systems and measurement devices. We formalize its updates and derive a relation between physical attainability, command access, and model approximation error. We demonstrate the concept in an optical see-through color-matching case study using an iPad, RayNeo X3 glasses, and a TOP 300 probe coupled to a CAS140D spectroradiometer, both from Instrument Systems. A revised glasses application adds USB-controlled RGB holds and frame acknowledgements, keeping model-generated colors displayed during spectral acquisition. The interface passes 12 live control checks. Spectral feedback reduces one target color difference from 19.756 to 1.211 \DeltaE_76^*; all five targets improve in a separate comparison. These measurements demonstrate the concept in one configuration. The resulting software and protocols remain available for subsequent investigations.
[MA-9] Distributed Marginal Return: A Communication-Efficient Algorithm for Multi-Agent Weapon Target Assignment
【速读】：该论文旨在解决分布式多智能体系统中武器-目标分配（Weapon-Target Assignment, WTA）问题，其核心挑战在于受限的情境感知能力与通信约束。现有分布式方法（如拍卖-共识算法）通常假设任务价值相互独立，这一简化假设无法有效刻画多武器分配之间的耦合效应。为此，本文提出一种新型去中心化算法——分布式边际回报（Distributed Marginal Return, DMR），该算法在拍卖-共识框架基础上进行扩展，使各智能体基于边际回报计算投标，并通过一致性协议协调冲突，支持多个无冲突的提案并行接受，从而实现协同优化且通信开销最小。该方案的关键理论贡献在于证明了非冲突投标的边际回报具有可加性，这一性质保障了全局一致性，为并行接受机制提供了理论依据。大量仿真实验表明，DMR在解的质量和通信效率方面均显著优于基准算法，展现出良好的可扩展性与鲁棒性，适用于现实中的分布式指挥与控制场景。
链接: https://arxiv.org/abs/2611.04370
作者: Kamal Mammadov,Glen Pearce,Helen Krause,Damith C. Ranasinghe
机构: 未知
类目: Multiagent Systems (cs.MA)
备注: 12 pages, 6 figures. Published in the IEEE Systems Journal
Abstract:The Weapon-Target Assignment (WTA) problem in distributed multi-agent systems is challenged by limited situational awareness and communication constraints. Existing distributed methods, such as auction-consensus algorithms, typically assume task-value independence, a simplification that fails to capture the coupled effects of multi-weapon assignments. This paper introduces the Distributed Marginal Return (DMR) algorithm, a novel decentralized approach that generalizes auction-consensus frameworks. DMR enables agents to compute bids based on marginal return and uses a consensus protocol to resolve conflicts, allowing multiple non-conflicting proposals to be accepted in parallel for cooperative optimization with minimal communication. A key theoretical contribution is our proof that the marginal returns of non-conflicting bids are additive, a property that ensures global consistency and validates our parallel acceptance mechanism. Extensive simulations demonstrate that DMR significantly outperforms baseline algorithms in both solution quality and communication efficiency, offering a scalable and robust solution for real-world distributed command and control applications.
[MA-10] When Is Fresh AI Stock Analysis Worth It? Allocating a Limited Research Budget Across Price Updates Deep Forecast Refreshes and Editorial Reviews of Top Stock Picks STOC
【速读】：该论文旨在解决长期投资研究中因股价波动而频繁重写分析报告所导致的效率低下问题，核心挑战在于如何在保持模型时效性的同时降低重复生成高质量分析报告的成本。其解决方案的关键在于提出“受保护复用”（guarded reuse）策略：通过在不完全重新生成报告的前提下，对已有目标进行价格冲击防护、动态调整预测增长路径，并结合多智能体系统（multi-agent systems）进行针对性编辑验证，从而实现对历史报告的有效再利用。实证结果显示，在不变模型窗口内，受保护复用策略相较基准指数SPY和全新生成报告展现出更优的风险调整收益（2.25% vs. 1.66%和4.77%），且在多个短期窗口中均表现稳健；然而当持有期延长至近六个月时，其收益显著下滑，揭示了投资论断（thesis）存在“半衰期”现象。研究进一步通过多维度诊断发现，机械性锚定旧增长路径可解决98%的预测误差改进，但新旧报告在头部标的上高度分化，表明模型更新与逻辑重构仍具必要性。最终构建的架构实现了连续价格监控与周期性论点重建之间的解耦，有效平衡了成本控制与分析质量。
链接: https://arxiv.org/abs/2611.04348
作者: Russlan Ramdowar
机构: Future Edge Group FZE(未来边缘集团); ORCID(开放研究者与贡献者身份识别码)
类目: Multiagent Systems (cs.MA)
备注: 26 pages, 9 figures, 13 tables. Paper and research artifacts: this https URL . Code: this https URL
Abstract:Must long-horizon AI investment research be rewritten every time stock prices move? Using 8,120 production reports on the same 203 stocks across four iPulse AI batches, we compare repricing existing targets, guarding them against price shocks, mechanically resetting old forecast growth, and generating fresh multi-agent reports. Ten matched configurations represent seven investment frameworks. Our primary economic evaluation selects Top 10 USD portfolios using one-year forecasts. Across two unchanged-model windows, guarded reuse averages 2.25%, versus 1.66% for SPY and 4.77% for fresh reports, after modeled trading costs and reference compute charges on a USD 100,000 account. Guarded reuse beats SPY in all three short windows, including the model-change transition. Extending the first holding window to nearly six months reverses the result: guarded reuse returns -11.26%, fresh reports +9.12%, and SPY +3.46%, motivating the thesis half-life question. Supporting quarter-forecast diagnostics show that mechanically re-anchoring old growth paths closes 98% of the MAE improvement over fixed targets, yet Fresh and Reset share only 1-4 of their Top 10 stocks. At Top 30, the unchanged-model portfolio comparison instead favors guarded reuse (4.17% versus 1.88%). Endpoint and concentration tests reveal the influence of horizon choices and exceptional winners. Nine production editions show substantial between-edition turnover, while every final editorial screen retains its own incoming list. Measured token workloads connect refresh costs to shared capital and realized break-even budgets. The resulting architecture separates continuous price monitoring from episodic thesis rebuilding and targeted editorial evidence checks. Comments: 26 pages, 9 figures, 13 tables. Paper and research artifacts: this https URL . Code: this https URL Subjects: Multiagent Systems (cs.MA) Cite as: arXiv:2611.04348 [cs.MA] (or arXiv:2611.04348v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2611.04348 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Russlan Ramdowar [view email] [v1] Tue, 3 Nov 2026 05:30:18 UTC (153 KB)
[MA-11] ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems EMNLP2026
【速读】：该论文旨在解决多智能体大语言模型（Multi-agent LLM）系统中因角色漂移（role drift）导致的职责越界问题。角色漂移指在多轮交互过程中，某一智能体虽生成看似合理的内容，却实际上越权执行了其他角色的职能，如验证者开始规划、检索者做出未经证实的断言、执行者擅自更改状态等，从而破坏系统的协作秩序与可信性。其解决方案的关键在于提出智能体轨迹控制（Agent Trajectory Control, ATC），通过将每个角色的权限形式化为可验证的“角色契约”（role contract），并在消息进入共享对话前实时检测其是否偏离本职角色。ATC通过评估输出在角色职能外溢、不当言语行为及逻辑契约违背等方面的偏差，计算一种泄漏正则化的目标值以约束生成，并对修正后的消息进行二次验证后才允许准入。该机制不依赖于对智能体内部表示的访问，因此适用于不可见内部结构的智能体，显著提升了任务执行性能与角色一致性，在异构团队和高角色混淆压力下表现尤为突出，相较提示工程、记忆管理、工作流控制及激活编辑等多种基线方法均取得显著改进。
链接: https://arxiv.org/abs/2611.04321
作者: Maisha Maliha,Vishal Pramanik,Sumit Kumar Jha
机构: University of Oklahoma (俄克拉荷马大学); University of Florida (佛罗里达大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)
Abstract:Multi-agent LLM systems assign different responsibilities to different agents: one plans, another retrieves evidence, another verifies, another executes. This division of labor is only meaningful if each agent stays within the functions it is authorized to perform. Over many turns, however, an agent can produce a locally reasonable message that exercises authority belonging to another role: a verifier that begins planning, a retriever that asserts unsupported conclusions, an executor that changes state no one approved. We formalize this as role drift, a turn-level breach of an agent’s role contract. We introduce Agent Trajectory Control (ATC), which makes each role’s authority explicit as a contract and enforces it at the point where a message would enter the shared conversation. ATC scores a proposed output for out-of-role deviation, leakage into the functions of other roles, impermissible speech acts, and logical contract breaches, then computes a leakage-regularized target that constrains regeneration; the revised message is re-checked before admission. Enforcement acts on the message rather than the generator, so ATC governs agents whose internal representations are unavailable. Across homogeneous and heterogeneous teams, ATC improves task performance and role consistency while lowering role-drift energy relative to strong baselines spanning prompting, memory, workflow control, and activation editing, with the largest gains under explicit role-confusion stress tests and in larger, more diverse teams.
[MA-12] Drift No More: Spectral Locking for Long-Horizon Open-Weight Multi-Agent AI EMNLP2026
【速读】：该论文旨在解决长时程多智能体系统中存在的三大紧密耦合的失效问题：任务漂移（task drift）、角色漂移（role drift）和状态陈旧（state staleness）。其核心假设是，这些问题具有共同的表征根源：智能体的隐藏状态偏离了编码当前任务、分配角色及共享状态方向的主成分空间。为验证该假设，研究在14,832个标注对话回合上进行初步实验，结果显示漂移样本在三个锚定子空间中的投影趋于均匀分布（两两相关性 r = 0.72–0.78），支持该表征假说。现有方法通常将这些问题分而治之，或仅作用于表面文本层面，或独立处理控制方向。本文提出ATLAS（Algebraic Task-role Locking with Adaptive Spectral Memory），一种无需训练的干预机制，通过在生成令牌前对隐藏状态进行修正，实现对表征空间的调控。其关键在于构建任务、角色与状态子空间上的半正定锚定核，并利用低秩Gram分解，将隐藏状态引导至这些子空间中高相干性的特征方向。实验表明，ATLAS在长时程记忆保持、状态维护、工具使用、最小权限协作及具身压力测试中均显著优于提示工程、检索、记忆增强及激活导向等基线方法；相比A-MEM，其记忆宏平均准确率提升最高达5.7点，写入/读取失败率分别降低31.7%和24.8%；相较于MemoryArena-Agent，性能提升最高达4.8点。在扰动环境下仍能获得10.7–12.1点增益，角色一致性达93.5%，且可成功迁移至720亿参数模型（宏平均得分61.0 vs. 56.2）。消融实验证明，联合任务-角色-状态张成空间的重要性远高于随机匹配秩的子空间（损失7.2点对比0.8点），表明这些方向具有功能性意义而非偶然；而通过提示或检索提供这些方向仅恢复4.3点性能，进一步凸显其内在表征价值。因此，ATLAS是一种面向开放权重多智能体系统的表征级控制器，可与黑箱智能体的输出级协议形成互补。
链接: https://arxiv.org/abs/2611.04315
作者: Vishal Pramanik,Maisha Maliha,Susmit Jha,Sumit Kumar Jha
机构: University of Florida(佛罗里达大学); University of Oklahoma(俄克拉荷马大学); SRI International(美国研究院)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted at the 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP 2026)
Abstract:Long-horizon multi-agent systems suffer from three tightly coupled failures: task drift, role drift, and state staleness. We test the hypothesis that these share a common representational cause: agent hidden states move away from the directions encoding the active task, assigned role, and current shared state. A pilot study over 14,832 annotated turns supports this: drifted turns collapse near-uniformly across all three anchor subspaces (pairwise r = 0.72-0.78). Existing methods address these failures separately, operating at the surface-text level or treating control directions independently. We introduce ATLAS (Algebraic Task-role Locking with Adaptive Spectral Memory), a training-free intervention acting on hidden states before a token is generated. ATLAS builds a positive-semidefinite anchor kernel over task, role, and state subspaces and steers hidden states toward their shared high-coherence eigendirections; a low-rank Gram decomposition keeps this tractable. Across long-horizon memory, state maintenance, tool use, least-privilege coordination, and embodied stress tests, ATLAS outperforms prompting, retrieval, memory, and activation-steering baselines. Against A-MEM it improves memory macro-average accuracy by up to 5.7 points and reduces write/read failures by up to 31.7%/24.8%; against MemoryArena-Agent it gains up to 4.8 points. It gains 10.7-12.1 points under perturbation, reaches 93.5% role consistency, and transfers to a 72B backbone (macro-average 61.0 vs. 56.2). Ablating the joint task-role-state span costs 7.2 points against 0.8 for a matched-rank random span, indicating these directions are functionally implicated, not incidental; supplying them by prompt or retrieval recovers 4.3 points less. ATLAS is therefore a representation-level controller for open-weight multi-agent systems, complementary to output-level protocols for black-box agents.
[MA-13] Interpretable Early Termination of Web Navigation Agents via Closed Sequential Pattern Mining
【速读】：该论文旨在解决基于开源大语言模型的网页导航代理（web navigation agents）在执行过程中频繁且晚期失败的问题，即大量计算资源（token）消耗在已无法挽回的错误轨迹上。其核心挑战在于现有方法虽能通过早期终止降低计算成本，但其终止决策缺乏可解释性，难以支持故障诊断与人工干预。为此，研究提出采用闭合序列模式挖掘（BIDE+）算法对代理执行轨迹的前K步（K-step prefixes）进行分析，构建一个小型、可人工审查的失败特征库（failure-signature library），并据此形成包含四类故障的分类体系及相应的修复建议。通过设计多变量评估协议，主动剔除零步崩溃和易区分单步成功等污染样本，确保检测准确率反映的是行为判别能力而非轨迹长度分布的影响。在从三个开源大语言模型收集的1,544个MiniWoB++轨迹上进行评估，该方法在排除错误配置下于K=3时达到8种方法中的最优F1值（0.717），能在3步内恢复88%的测试集失败案例，并支持一种早期终止策略，在保持92%精确率的前提下节省26.3%的token。解决方案的关键在于利用序列模式挖掘生成可解释的失败模式，从而实现高效、可诊断的早期终止。
链接: https://arxiv.org/abs/2611.04142
作者: Sergio Talavera,Magdalini Eirinaki
机构: San Jose State University (圣何塞州立大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: In online proceedings of AI Agents for Discovery in the Wild (AID-Wild) Workshop at ACM CAIS 2026
Abstract:Web navigation agents built on open-weight language models fail often and fail late, with most of the tokens spent after the trajectory has become unrecoverable. Recent work has shown that early termination is a viable cost-reduction strategy, but existing approaches produce opaque termination decisions that do not support diagnosis or human oversight. In this work we apply closed sequential pattern mining (BIDE+) to K-step prefixes of agent execution traces to produce a small, human-reviewable failure-signature library that organizes into a four-category failure taxonomy with associated repair recommendations. We use a multi-variant evaluation protocol that explicitly removes contamination artifacts (zero-step crashes and trivially separable single-step successes) so that reported detection accuracy reflects behavioral discrimination rather than trace-length distributions. We evaluate our methodology on 1,544 MiniWoB++ traces collected from three open-weight language models, achieving the best F1 at K=3 on the Exclude-errors configuration across eight methods (0.717), recovering 88% of test-set failures within three steps, and supporting an early-termination policy that saves 26.3% of tokens at 92% precision.
[MA-14] Finite-Sample Convergence in Networked Averag e Reward MARL: Decentralization Pitfalls and Entropy Remedies
【速读】：该论文旨在解决分布式多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）中基于平均奖励的优化问题，其中智能体在通信网络中通过局部信息交互以协同最大化整个网络的总体奖励。研究揭示了在仅依赖本地k-跳邻域信息进行去中心化学习的情况下，无法实现对全局最优联合策略的收敛，这一不可能性结果凸显了多智能体系统中两大核心挑战：多智能体博弈性（multiagency），即各智能体独立优化自身策略；以及部分可观测性（partial observability），即智能体无法获取全局状态信息。鉴于此，论文提出一种采用线性函数逼近的去中心化演员-评论家算法，并提供了有限样本下的收敛性保证，证明其可收敛至一个梯度范数较小的近似驻点。为突破上述不可能性限制，论文进一步引入固定状态熵正则化（fixed-state entropy regularization），通过重塑目标函数的景观结构，显著增强了算法的全局收敛性保障，从而在理论上实现了更优的性能边界。
链接: https://arxiv.org/abs/2611.04072
作者: Yizhou Zhang,Yashaswini Murthy,Laixi Shi,Adam Vierman
机构: California Institute of Technology(加州理工学院); The University of Texas at Austin(德克萨斯大学奥斯汀分校); Johns Hopkins University(约翰霍普金斯大学)
类目: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network. Each agent in the network conducts local updates with information within its k -hop neighborhood and collaboratively maximizes the overall reward of the entire network. We first provide impossibility results in achieving convergence to the global optimal joint policies in our setting. These impossibility results highlight the challenges of decentralized learning with local information in multi-agent systems, namely the issues of multiagency, where each agent independently optimizes its policy, and partial observability, where agents have limited visibility into the global state. Given these challenges which indicate that decentralized policy optimization can generally be certified only up to stationarity, we provide finite-sample convergence guarantees for a decentralized actor-critic algorithm with linear function approximation that converges to an approximate stationary point with small gradient norm. To overcome such impossibility results, we further show that adding fixed-state entropy regularization reshapes the objective landscape which leads to even stronger global convergence guarantees for our proposed actor-critic algorithm.
[MA-15] A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data
【速读】：该论文旨在解决将人群级运动指导转化为个体化运动处方时面临的挑战，即如何在大规模场景中整合临床数据（如诊断、用药、功能限制）与可穿戴设备采集的基线活动数据，并结合特定疾病状态的推荐指南，实现安全、精准且个性化的运动处方生成。其解决方案的关键在于提出FITTER这一多智能体框架，该框架通过融合多源异构数据与条件特异性指导，自动生成符合FITT-VP原则（频率、强度、时间、类型、进度、变量）的个性化处方，并引入确定性安全审计与修复机制，以自动检测并修正可规则验证的安全违规问题。评估结果显示，FITTER在400名“All of Us”参与者（300例癌症、100例卒中）数据上实现了98.5%的安全约束合规率，显著优于5个大语言模型（LLM）对比组（75.8%-85.8%），并在剂量精度和指南一致性方面表现更优；癌症队列的消融实验进一步证实，安全审计与修复是提升安全性贡献最大的模块。临床医生对80份处方的结构化评审表明，FITTER在安全性、个性化程度、指令明确性、预期疗效及临床可接受性方面均表现出色，为临床医生提供了一种可扩展、可信赖的智能辅助决策工具。
链接: https://arxiv.org/abs/2611.03983
作者: Xintong Li(1 and 3),Yufen Lin(2 and 4),Yuhua Wu(1),Fengran Wang(1),Minxiao Wang(1),Rebecca A. Gary(2),Jacob Kariuki(5),James Smith(6),Sandra A. Billinger(7),Canhua Xiao(2),Xiao Hu(1 and 3 and 8),Runze Yan(1) ((1) Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, (2) Nell Hodgson Woodruff School of Nursing, Emory University, (3) Department of Computer Science, Emory University, (4) Cancer Prevention and Control Research Program, Winship Cancer Institute, Emory University, (5) Lillian Carter Center for Global Health and Social Responsibility, Nell Hodgson Woodruff School of Nursing, Emory University, (6) Lancaster Medical School, Lancaster University, (7) Department of Neurology, University of Kansas Medical Center, (8) Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University)
机构: Emory University(埃默里大学); Lancaster University(兰卡斯特大学); University of Kansas Medical Center(堪萨斯大学医学中心); Georgia Institute of Technology and Emory University(佐治亚理工学院和埃默里大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 45 pages, 3 figures, 6 tables; includes Supplementary Information with 2 supplementary figures and 3 supplementary tables
Abstract:Translating population-level exercise guidance into individualized prescriptions requires integrating clinical and wearable data, including diagnoses, medications, functional limitations, and baseline activity, with condition-specific recommendations, a process difficult to scale beyond specialist rehabilitation settings. We developed FITTER, a multi-agent framework that integrates these data with condition-specific guidance to generate personalized FITT-VP prescriptions and applies deterministic safety audit and repair to correct rule-checkable violations. We evaluated FITTER using data from 400 All of Us participants (300 cancer, 100 stroke) against five LLM comparators through computational evaluation, ablation, and structured clinician review. FITTER achieved 98.5% safety-constraint compliance versus 75.8-85.8% for comparators and showed greater quantitative dosing precision and guideline alignment. Cancer-cohort ablation identified safety audit and repair as the largest contributor to safety-constraint compliance. Clinicians reviewed 80 prescriptions across safety, personalization, instruction concreteness, anticipated effectiveness, and clinical acceptability. FITTER provides a clinician-facing approach integrating clinical and wearable data, guidance, and explicit safety constraints.
[MA-16] Beyond Division of Labor: How Roles Shape Feedback and Recovery in Multi-Agent Systems AAMAS2027
【速读】：该论文旨在解决多智能体大语言模型（Multi-Agent LLM）系统中角色分工对任务执行效率与纠错能力的影响问题，尤其关注“规划-执行-评审”团队架构下评审者（Reviewer）在提交决策与反馈获取中的作用。其核心问题是：角色结构是否真正提升系统性能，抑或仅带来表面的功能专业化？解决方案的关键在于通过控制变量（保持模型、拓扑结构、调用预算及评估器不变），系统性地对比有无角色结构下的表现差异，发现去除角色虽未降低首次回答的准确率，但在首次失败后，角色结构导致评审者频繁拒绝提交，从而减少团队获得外部反馈的机会，抑制了纠错恢复能力。实验表明，在存在反馈机制的前提下，无角色结构的团队反而能更频繁地从失败中恢复；进一步干预实验显示，强制角色结构团队在每轮均提交，可使恢复率从0.198大幅提升至0.613，与无角色团队无显著差异。因此，论文提出应以“失败后的恢复能力”作为评价角色设计的核心指标，并强调需量化系统中“谁可提交”、“反馈频率”以及“哪些智能体能响应反馈”等关键因素，以实现对角色机制的有效评估。
链接: https://arxiv.org/abs/2611.03931
作者: Chih-Hsuan Yang,Xinyang Li,Rajeev Thakur,Feng Yan
机构: Argonne National Laboratory(阿贡国家实验室); University of Houston(休斯顿大学)
类目: Multiagent Systems (cs.MA)
备注: 19 pages, including supplementary material. Project website: this https URL
Abstract:Roles in multi-agent large language model (LLM) systems allocate decisions as well as work. In a Planner–Executor–Reviewer team, the Reviewer’s approval can determine when the team submits an answer and receives corrective feedback. We study both functions by varying role instructions while holding agents, topology, call budget, and ground-truth evaluator constant, using three open-weight models on Omni-MATH and two on SciBench. Removing roles largely eliminates functional specialization without lowering first-pass accuracy. After a failed first answer, however, submission decisions matter: a role-structured Reviewer often withholds approval, so its team submits less often and receives fewer evaluator verdicts. In an exploratory comparison with feedback available to both teams, the role-free team recovers more often across all five model–benchmark settings. Two further analyses support the importance of feedback opportunities. In a post hoc comparison at equal numbers of verdicts, the pooled recovery difference is small (role-structured minus role-free: +0.009 , 95% interval [-0.014,+0.033] ). In an exploratory intervention on 400 Omni-MATH problems with gemma-4, keeping the role prompts but forcing submission in every cycle raises recovery from 0.198 to 0.613 ; the paired comparison detects no difference from the role-free team. The takeaway is to evaluate roles through recovery after failure as well as specialization. For systems with reliable external feedback, this requires measuring who may submit, how often the team receives feedback, and which agents can act on it.
[MA-17] El Agent e Fármaco: A Scalable AI Scientist for Drug Discovery
【速读】：该论文旨在解决计算药物发现中多工具协同与决策流程高度依赖专家经验的问题，核心挑战在于如何将非专业语言表达的药物发现目标自动转化为可执行、可扩展的计算筛选工作流。其解决方案的关键在于构建一个基于大语言模型（Large Language Model, LLM）的自主智能体——El Agente Fármaco，该智能体通过调用科学工具、评估结果并动态调整后续步骤，实现对目标和用户需求的自适应响应。系统的核心创新在于引入共享科学记忆库“Fármaco Atlas”，用于统一存储分子、结构、计算结果及其溯源信息（provenance），使智能体能够通过精准数据库查询获取决策依据。该设计支持单次请求批量处理整个分子集合，避免对每个化合物重复进行LLM推理，显著降低模型处理负担，同时保持结果可追溯、可审计。集成工具涵盖分子生成、结合亲和力与分子性质预测、可购化合物检索等功能，在回溯性对比中展现出优于人工主导方案的化学多样性与药物样性表现；在22项基准测试中，相较直接工具调用和Codex模型，该代理实现了更高的平均模型评分，并表现出更低的累计令牌使用量与峰值上下文占用。整体框架为可扩展、灵活且具备可追溯性的药物发现工作流提供了坚实基础。
链接: https://arxiv.org/abs/2611.03832
作者: Marcel Müller,Mohammad Ghazi Vakili,Yoshihiro Ogura,Ella Ashoori,Shimeng Wang,Alessandro S. Nascimento,Eddie Calleja,Yutong Zhao,Yimeng Liu,Ella Miray Rajaonson,Aiwei Yin,Jiaru Bai,Yunheng Zou,Abraham C. Stern,Varinia Bernales,Alán Aspuru-Guzik
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Biomolecules (q-bio.BM)
备注: 63 pages, 13 figures, including Supporting Information
Abstract:Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combining them and deciding what to do next still requires substantial expert effort. Here, we introduce El Agente Fármaco, an AI scientist that uses modern large language model (LLM) technology to turn discovery goals expressed in everyday language into complex, scalable computational screening workflows. The agentic harness selects scientific tools, reviews their results, and adjusts subsequent steps to the target and the user’s objectives. Our approach centers on a shared scientific memory: Fármaco Atlas, which stores molecules, structures, and results together with their provenance – a record of how they were obtained. The agent uses targeted database queries to retrieve the evidence needed for each decision. A single request can launch calculations for an entire collection of molecules, without requiring a separate LLM decision for every compound. This design limits the information the model must process as screening collections grow, while keeping the underlying results available for inspection and further analysis. The integrated tools support molecule generation, prediction of binding and molecular properties, and searches for compounds available for purchase. In a retrospective comparison with an expert-led campaign, the agent proposed chemically distinct candidates with competitive docking scores and higher calculated drug-likeness under explicitly imposed selection criteria. In a 22-task benchmark against plain tool calls and Codex, the agent achieved the highest observed mean model-judged score, with lower median cumulative token use and peak context. Fármaco provides a foundation for flexible, scalable drug discovery workflows that connect scientific reasoning with an enduring, traceable record of computational work.
[MA-18] Labtune: Treating Lab Organization and Information Sharing as Experimental Variables in LLM -Agent Discovery
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）代理构建的研究系统中，组织结构僵化与信息共享机制不足所导致的效率低下和群体思维（groupthink）问题。传统研究系统中的“信息孤岛”（silos）虽可防止知识冗余，却抑制了跨领域协作；而过度开放的信息流通又可能引发混乱与资源浪费。针对这一矛盾，论文提出将实验室结构与信息共享策略作为可调控的实验变量，并设计了名为Labtune的开源框架。其核心解决方案在于：通过用户自定义的六种可编辑实验室组织模式（包括多学科孤岛、跨学科团队、联合领导制、中心辐射型结构、扁平共研实验室及单一科学家模式），结合可独立调节的信息共享程度（从无共享到高粒度共享），并引入一种名为“智慧”（wisdom）的条件性抽象通信通道，在阶段边界由控制器强制执行信息交换，而非依赖自由对话。此外，该框架通过分离开发与验证环节、支持多实验室间经过过滤的信息交互，实现了对研究流程的精细化控制。关键创新点在于将研究组织形式与信息流动机制纳入可控、可测量的实验空间，从而为探索不同组织结构与协作模式对研究效能的影响提供了可重复、可对比的实证基础。
链接: https://arxiv.org/abs/2611.03935
作者: Goran Mitevski
机构: 未知
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA)
备注: 14 pages. Exploratory preprint
Abstract:De-siloing can create groupthink; silos can prevent knowledge sharing and reduce research efficiency. The same tension appears in research systems built with large language model (LLM) agents, yet their organization is usually fixed by design. I propose treating laboratory structure and information sharing as experimental variables. I introduce Labtune, an open framework for bounded research workflows with user-selected laboratory organization, communication policy, model configuration and budget. Users can choose among six editable designs, from multidisciplinary silos and transdisciplinary teams to co-leaders, hub-and-spoke structures, flat co-labs and a single scientist. Information sharing can independently vary from none to high detail, with an optional channel for conditional abstractions called wisdom. Sharing is enforced by the controller at phase barriers rather than through unrestricted agent chat, while delegation does not create additional budget or permissions. Development evidence is separated from protected confirmation, and an optional multi-lab layer allows laboratories with different structures to exchange filtered information. Labtune is an idea and system-description paper, not a benchmark result. Its contribution is to make laboratory organization and information sharing configurable and measurable under matched budgets. I do not claim that one design or level of sharing is best; the aim is to create a concrete experimental space in which those differences can be tested. Comments: 14 pages. Exploratory preprint Subjects: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA) MSC classes: 68T42 Cite as: arXiv:2611.03935 [physics.soc-ph] (or arXiv:2611.03935v1 [physics.soc-ph] for this version) https://doi.org/10.48550/arXiv.2611.03935 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
自然语言处理
[NLP-0] From Autonomous Agents to Cognitive Symbionts: Toward AI-Augmented Human Intelligence
【速读】： 该论文旨在解决当前人工智能系统在物理世界中对人类认知与行为支持不足的问题，即尽管生成式AI（Generative AI）和基础模型已具备自主执行复杂任务的能力，但这些系统仍处于人机分离的状态，无法实时感知、理解并融入个体的长期情境与认知过程，导致人们在面对记忆衰退、注意力遗漏、表达困难及决策困境时缺乏持续性智能支持。其核心解决方案是提出“认知共生体”（cognitive symbiont）这一新型系统范式，关键在于构建一种终身嵌入、实时交互、私密低摩擦且主动干预的人机协同架构，通过外部设备感知用户所感，跨时间保持其上下文，建模其情境与心智状态，在恰当时刻参与思考、基于上下文执行日常任务，并持续学习个体的动态变化。该范式以“认知差距”（cognitive gap）为核心概念——即个体本可调用的知识与其工作记忆所能承载之间的鸿沟——并将其组织为一个包含感知、留存、理解、共处、代行、协思与成长的闭环共生循环，由个人运行时环境与信任机制共同保障。此外，论文强调此类系统带来的新风险（如考试中的隐蔽协助、社会性强制采纳等）需纳入治理框架，构成该范式不可分割的一部分。
链接: https://arxiv.org/abs/2611.05376
作者: Hao Fei
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 59 pages, 21 figures, 18 tables. Project page: this https URL
Abstract:For a decade, progress in artificial intelligence has been measured by how much a machine can do on its own. Large foundation models now write code and run multistep tasks, and personal agents act on a person’s behalf. Yet people still forget what they once knew, miss what they are not looking at, fail to find the right words, and face hard choices without the benefit of their own history, because the machine that could help stands outside the situation. As AI moves into the physical world, attention has settled on one kind of intelligent individual, the embodied robot; we argue that the era of Physical AI holds a second kind of equal importance, the person augmented by AI. Alongside artificial general intelligence, which asks how intelligent a machine can be, stands AI-augmented human intelligence (AHI), which asks how much intelligence a machine can enable in a particular person. Realizing it calls for a new kind of system, a cognitive symbiont: a lifelong, embedded, real-time, private, low-friction, and proactive companion that senses what the person senses through external devices, keeps their context across years, models their situation and their mind, takes part in their thinking at the right moment, carries out everyday tasks with their context, and keeps learning the person as both change. We give the paradigm a conceptual core, the cognitive gap between what a person could bring to a situation and what they can hold in working memory, and organize its design space as a symbiosis loop of sensing, keeping, understanding, living alongside, acting for and thinking with the person, and growing, enclosed by a personal runtime and by trust. Because a symbiont changes what a person can do in front of others, it also raises risks of a new kind, from covert assistance in examinations to pressure on everyone to adopt it, and we treat their governance as part of the paradigm.
[NLP-1] Aligning Language Models with General Preferences: An Empirical Study of Direct Nash Optimization
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在对齐通用偏好函数时的效率与效果问题，尤其关注如何通过近似双人博弈中的纳什均衡（Nash equilibrium）来实现更优的偏好对齐。其核心解决方案是基于直接纳什优化（Direct Nash Optimization, DNO）框架的实用变体DNO-Prct，其关键在于整合多项关键技术：基于策略的在线采样（on-policy sampling）、利用大语言模型进行样本评分、引入教师模型的黄金响应（gold response）作为参考基准、采用大间隔偏好对选择机制（large-margin preference-pair selection），以及类似直接偏好优化（DPO）的对比学习更新方式。实验表明，该方法在两个开源指令模型（Llama 3.1 8B Instruct 和 Mistral 7B Instruct v0.1）上显著提升了性能，即使使用同家族的开源标注模型替代专有模型（如GPT-4o），仍可保留约62%至82%的性能增益，证明评分步骤并不依赖于闭源模型。此外，消融实验证明，移除黄金响应、数据回溯机制或仅接受教师偏好的样本会导致性能下降6至10个长度控制（LC）胜率点，而采样数量和边际参数的调整影响较小，说明算法的关键设计要素具有强鲁棒性。研究还揭示了实际算法与理论框架之间的偏离及其计算成本，强调了将教师模型同时用作训练指导者与评估裁判可能带来的性能高估风险。
链接: https://arxiv.org/abs/2611.05373
作者: Austin Tsai,Saurav Nanda,Ganapathy Parthasarathy,Bhuvnesh Kumar,Parivesh Choudhary
机构: Synopsys, Inc. (新思科技)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 19 pages, 2 figures, 9 tables. Experiments were run in 2024 with the models and APIs available then
Abstract:Direct Nash Optimization (DNO) aligns a language model to a general preference function by approximating the Nash equilibrium of a two-player preference game. Its practical variant, DNO-Prct, iterates on-policy sampling, LLM-based rating of the samples together with a teacher’s gold response, large-margin preference-pair selection and a DPO-style contrastive update. We re-implement DNO-Prct independently and study it on two open-weight instruct models, Llama 3.1 8B Instruct and Mistral 7B Instruct v0.1, with GPT-4o or a larger open-weight model of the same family as the annotator. On AlpacaEval 2.0 with GPT-4o as judge, three iterations raise the length-controlled (LC) win rate against GPT-4o from 31.7% to 45.3% for Llama and from 6.8% to 18.4% for Mistral, and the tuned 8B model scores above Llama 3.1 70B Instruct on every reported win rate. With same-family open-weight annotators, the runs retain 62% (Llama) and 82% (Mistral) of the LC gain achieved with GPT-4o as annotator, so the rating step does not require a proprietary model. In a separate ablation configuration, variants that remove the gold responses, remove the data look-back, or accept only teacher-preferred pairs score 6 to 10 LC points below the full procedure, whereas varying the number of on-policy samples per prompt (1 to 7) moves the LC win rate by at most 2.1 points, and a margin sweep (1 to 5) run in its own pipeline by at most 2.4 points. We also report the cost of the pipeline and where the practical algorithm departs from the theory it approximates. All results are single runs, and GPT-4o is both the teacher and the evaluation judge; we discuss what this implies for the reported gains.
[NLP-2] Suppressing Misaligned Behaviors in Language Models Durably NEURIPS2026
【速读】： 该论文旨在解决生成式模型在训练过程中或之后可能涌现出的不良行为（undesired model behaviors）难以有效抑制的问题，尤其关注这些行为在后续训练中是否能够持续存在。其核心挑战在于如何实现对不良行为的即时抑制、在进一步训练中的持久性（durability）以及对模型通用能力（general capability）的保留。解决方案的关键在于提出一种名为前缀扩展对抗强化学习（Prefix-Expanded Adversarial Reinforcement Learning, PEARL）的新方法，该方法基于广义奖励策略优化（GRPO），通过在采样轨迹中引入从缓存的模型行为轨迹中抽取的中间状态延续（continuations），增强策略在多样化错误状态下的恢复能力。研究表明，这种设计显著提升了对广泛触发型不良行为（broad-trigger）的抑制效果，且在不同模型架构（Qwen3-4B与GPT-OSS-20B）和多种训练场景下均表现出强鲁棒性，尤其在无法通过针对性监督微调（SFT）解决的广泛不良行为场景中优势明显。研究验证了“更持久的抑制依赖于从更广泛误对齐状态中恢复的能力”这一假设，为后训练阶段可信赖的行为修正提供了更具普适性和稳定性的技术路径。
链接: https://arxiv.org/abs/2611.05358
作者: Luxi He,Pengcheng Jiang,Jifan Zhang,Jiawei Han,Danqi Chen
机构: Princeton University (普林斯顿大学); University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: NeurIPS 2026
Abstract:Undesired model behaviors can emerge or re-emerge at any stage of training. Once observed, how effectively can we suppress them? To study this question, we first construct two model organisms–language models deliberately trained to exhibit a specific misalignment–that differ in what triggers the behavior. The narrow-trigger organism misbehaves only when the prompt contains a specific cue: it cheats on multiple-choice questions by following a planted hint that points to a wrong answer, and behaves normally otherwise. The broad-trigger organism has no such cue: after fine-tuning on insecure code, it gives harmful responses across a wide range of open-ended prompts. We then formalize post-hoc behavioral suppression and evaluate methods along three criteria: immediate suppression of the target behavior, durability under further training, and preservation of general capability. We compare standard methods–SFT, GRPO, and gradient-ascent unlearning–along these dimensions. Building on prior work on alignment shallowness, we hypothesize that more durable suppression requires the ability to recover from a wider range of misalignment states. We introduce Prefix-Expanded Adversarial Reinforcement Learning (PEARL), a GRPO variant that augments rollouts with continuations from intermediate states sampled along cached organism trajectories. Across both organism settings and two model families (Qwen3-4B, GPT-OSS-20B), PEARL achieves strong immediate suppression while preserving task accuracy, and remains durable under further fine-tuning on either the organism’s data or unrelated data. It obtains the greatest benefit when the undesirable behavior is broad and cannot be addressed through targeted SFT. Together, our framework and method offer a step toward more durable post-hoc removal of undesired behaviors.
[NLP-3] A Circuit for In-Context Learning
【速读】： 该论文旨在解决生成式 AI（Generative AI）在上下文学习（In-context Learning, ICL）中如何实现无参数更新的少样本句子分类问题，尤其是当标签为任意且未被模型预先记忆时，模型如何在单次前向传播中完成从示范到预测的映射。其核心挑战在于揭示模型内部潜在的、可解释的计算机制，而此前研究仅识别出部分组件但未能阐明其组合逻辑。本文的关键解决方案是：在三个3-40亿参数的Transformer模型中，发现并验证了一个由特定注意力头构成的因果可追溯的神经电路，该电路实现了基于标签-令牌位置的键值记忆机制——每个标签令牌的残差流编码标签身份（值），中间层注意力头将前序示范的类别作为键写入其中；在查询阶段，高层注意力头通过匹配查询类别与存储的键来检索对应标签。一个早期层信号用于判断是否存在重复标签，以控制读取门控；若无此信号，则读取头关注提示起始位置，不偏好任何标签。此外，一组独立注意力头负责抑制最近出现的标签。所有步骤均通过激活修补、注意力头消融对比随机基线以及利用模型自身信号进行引导等因果分析方法得以验证。该机制在不同模型间具有计算一致性，仅靠命名明确的注意力头即可复现行为，且在从零训练的模型中亦能自发形成相同结构。该电路不仅适用于语义概念和有含义的标签，且在0.6B至8B参数规模范围内保持稳定，经指令微调后仍存续。因此，本文表明，在此设定下的ICL本质上是一种由可识别组件构成的、受门控的关联查找机制，其从示范到预测的全过程可通过因果路径精确追踪。
链接: https://arxiv.org/abs/2611.05344
作者: Andrew Gao,William Gao
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 38 pages, 22 figures. Code and an interactive walkthrough at this https URL
Abstract:In-context learning (ICL) lets a pretrained language model perform a task specified by demonstrations in its prompt, with no parameter updates. With arbitrary labels the model must acquire a mapping it cannot have memorized, so the learning happens within a single forward pass. Prior work found candidate parts but not how they compose. We give the composition for few-shot sentence classification with arbitrary labels in three 3-4B-parameter transformers. The model implements a key-value memory over label-token positions: each label token’s residual stream encodes the label’s identity, and middle-layer attention heads write into it the class of the preceding demonstration as its key. At the query, upper-layer heads match the query’s class against the stored keys and copy out the matching labels. An early-layer signal encoding whether any label has repeated gates this readout; in its absence the readers attend to the start of the prompt and no label is favored. A separate set of heads suppresses the most recently seen label. Each step is established causally by activation patching, head ablations against random-head baselines, and steering with the model’s own signals. A softmax model of the readout with two constants per head, fit on one prompt distribution, predicts another. The computation is conserved across the three models, the named heads alone reproduce the behaviour, and a transformer trained from scratch grows the same parts. The circuit transfers to semantic concepts and to labels with meaning, and the same parts, with a causal gate, appear from 0.6B to 8B in one family and survive instruction tuning. ICL in this setting is a gated associative lookup built from a few identifiable component types, traced causally from demonstrations to prediction.
[NLP-4] MDSKILL: Learning to Internalize Skill Memory through Multi-View Distillation for Efficient Dynamic Agent Self-Evolution
【速读】： 该论文旨在解决自演化智能体在利用技能记忆（skill memories）进行强化学习（Reinforcement Learning, RL）时，因自生成记忆中存在的噪声监督、功能耦合、冗余或冲突知识而导致的知识内化困难问题。尤其在训练预算有限的情况下，这些问题会阻碍策略学习甚至降低性能。其解决方案的关键在于提出一种多视角蒸馏方法MDSKILL，通过将技能记忆划分为三个互补的功能视图（状态锚定、动作规划与执行控制），并引入动态评估指标筛选高质量技能，构建视图特定与集成推理分支；在此基础上，基于在线策略自蒸馏（On-Policy Self-Distillation, OPSD）机制，将跨分支的互补经验知识高效迁移并融合至共享策略中，实现技能的高效内化与持续自我演化。该方法无需额外计算开销，在不依赖外部教师模型的前提下，显著提升了在ALFWorld和WebShop等基准上的性能，相较当前最优方法分别提升最高达7.1%和8.1%。
链接: https://arxiv.org/abs/2611.05313
作者: Dian Jin,Jinluan Yang,Kairong Han,Baohong Li,Zijing Hu,Yi He,Wenlin Zhong,Yunze Tong,Minqin Zhu,Linchuan Li,Yanan Niu,Kun Kuang
机构: Zhejiang University (浙江大学); Kuaishou Technology (快手科技)
类目: Computation and Language (cs.CL)
备注:
Abstract:Skill memories encode past experiences as textual prompts to improve reinforcement learning (RL) for self-evolving agents. Complementing RL, On-Policy Self-Distillation (OPSD) provides dense, fine-grained supervision to internalize knowledge from skill-guided trajectories and accelerate policy learning. However, self-generated memories often contain noisy supervision and functionally entangled, redundant, or conflicting knowledge, making direct distillation difficult. Under limited training budgets, these issues can hinder knowledge internalization and even degrade performance. To address this problem, we propose MDSKILL, a multi-view distillation approach for skill memory internalization. MDSKILL organizes skill memories into three complementary functional views (state grounding, action planning, and execution control) and selects high-quality skills using dynamic metrics to construct view-specific and integrated reasoning branches. OPSD then transfers and consolidates complementary experiential knowledge across these branches into a shared policy, enabling efficient skill internalization and continuous agent self-evolution. We evaluate MDSKILL on three agent benchmarks against RL, skill-augmented, and self-distillation baselines that do not rely on stronger external teacher models. Without additional computational overhead, MDSKILL improves upon SOTA performance by up to 7.1% and 8.1% on ALFWorld and WebShop. Extensive ablations and evolution analyses further validate its effectiveness in knowledge internalization and policy improvement. The code is available at this https URL.
[NLP-5] LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers
【速读】： 该论文旨在解决循环型变压器（looped transformers）在语言建模中因键值（KV）缓存随循环步数线性增长而导致的内存瓶颈问题。尽管循环架构通过重复使用相同层实现了参数高效，但每一轮循环仍需独立存储键和值，导致KV缓存占用显著增加，限制了长序列推理的可扩展性。其解决方案的关键在于提出一种名为LoopVQ的新型KV缓存向量量化方法：该方法联合编码多个循环步骤的通道信息，利用循环轴上高度冗余的特性实现更高效的压缩；同时通过微调码本以匹配全精度模型的输出，确保量化过程中的性能损失最小化。实验表明，即使在0.5比特/元素的极低位宽下，LoopVQ在通用任务、长链思维推理及长上下文检索任务中均优于现有标准大模型的量化方法；与TaSQ结合后，在0.25比特/元素时仍能保持接近BF16精度的表现，并在SGLang中实现高达4.3倍和6.1倍的峰值服务吞吐提升，同时将32K令牌上下文下的解码延迟近乎减半。
链接: https://arxiv.org/abs/2611.05304
作者: Minsoo Cheong,Sungjoo Yoo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Looped transformers are a parameter-efficient alternative to standard transformers for language modeling. By reapplying the same layers across multiple loop steps, recent looped language models match the performance of non-looped models several times their size. While this architecture effectively reduces parameter memory, each loop step still stores its own keys and values, so the key-value (KV) cache grows linearly with the number of loop steps and becomes the main memory bottleneck. We observe that the KV cache of looped models is highly redundant along the loop axis and that the loop steps differ substantially in their sensitivity to quantization. Based on these observations, we propose \textbfLoopVQ , a KV cache vector quantization method for looped transformers that codes channels of multiple loop steps jointly, and fine-tunes the codebooks to match the output of the full-precision model. At 0.5 bits per element, LoopVQ outperforms existing KV cache vector quantization methods for standard LLMs on general, long chain-of-thought reasoning, and long-context retrieval tasks with Ouro and Huginn models. Its design is orthogonal to these methods, and combined with TaSQ, it retains much of the BF16 accuracy even at 0.25 bits per element. We also implement LoopVQ in SGLang, where it nearly halves the decode latency at a 32K-token context and raises the peak serving throughput by up to 4.3\times and 6.1\times on the 1.4B and 2.6B Ouro models compared to the BF16 baseline.
[NLP-6] Personas Are Not People: Identity as State Not Attribute
【速读】： 该论文旨在解决当前合成人群（synthetic populations）模拟中一个根本性假设的局限性问题，即认为个体身份（identity）是稳定不变的属性，在不同情境下表现一致。然而，基于三十年来人格心理学的研究发现，个体行为在不同情境下的变化程度远大于个体之间的差异，这表明情境对行为具有决定性影响。论文的关键解决方案在于引入“人-情境”（person-in-context）设计框架，将行为分解为三部分：稳定的特质、情境所激活的身份（identity）、以及情境本身的影响，并基于身份经济学理论指出同一人在不同情境中会激活不同的身份与规范。在此基础上，论文证明：任何依赖固定角色设定（fixed personas）的模拟系统，无论其人物画像多么详尽或底层模型规模多大，都无法再现仅由特定群体及其具体情境决定的行为特征。为此，论文提出一种新的评估标准，并构建了可实现该标准的“人-情境”设计原型。实验测试显示，该设计能产生情境依赖的行为反应，但在真实社交平台（Reddit）和临床试验招募调查场景中仍未能完全匹配真实群体行为，暴露出在情境动态建模与社会信任等复杂因素捕捉上的不足。研究的实践意义在于警示：若继续沿用固定角色的合成人群评估方法，可能导致对高度受情境影响群体（如医疗不信任社区）的行为误判，而更精细的角色设定无法弥补这一结构性缺陷。
链接: https://arxiv.org/abs/2611.05292
作者: Tamunotonye Harry,Yuanyuan Feng
机构: University of Vermont(佛蒙特大学); Department of Computer Science(计算机科学系)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Synthetic populations, large sets of simulated people, are increasingly used in place of real people to pretest surveys, simulate users and communities, and evaluate AI systems. Each simulated person is defined by a persona, a written profile of fixed traits, demographics, and attitudes that a language model is instructed to act out. Every such system assumes that a person’s identity is a stable attribute that behaves the same way in every situation the simulation presents. We show that this assumption is not a harmless simplification. Three decades of personality research find the opposite. How a given person behaves varies more across contexts than people vary from one another. Drawing on identity economics, which describes how different situations activate different identities and norms within the same person, we separate behavior into three parts, which are stable traits, the identity a context calls up, and the situation itself. We then prove, under two stated assumptions, that any simulation built on fixed personas, no matter how detailed the personas or how large the underlying model, cannot reproduce the part of a population’s behavior that is specific to it and its circumstances. We propose a new evaluation criterion based on this result and a person-in-context design that can meet it in principle. We test one version on a naturalistic Reddit personality corpus and on a published clinical-trial recruitment survey of a community with documented distrust of medical institutions. The design does produce context-dependent behavior when tested directly, but in neither setting does it fully match the real population, and we show where it falls short. The consequence for practice is a risk that synthetic-population evaluations misrepresent the groups whose behavior is most shaped by context, a distortion that richer personas cannot correct.
[NLP-7] Knowingly Wrong: Training LLM s Against Their Own Beliefs can Induce Misalignment
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在特定领域（如错误医疗建议）的微调过程中，即使训练数据本身不具明显危害性，仍可能导致模型产生广泛错位（broad misalignment）的问题。其核心关切在于：当前对模型安全性的评估多聚焦于显式有害数据的微调风险，但现实中更常见的是模型被训练去学习“它认为为假”但内容无害的事实，这种情形是否同样引发严重错位尚不明确。研究发现，当模型对训练答案的信念程度越弱（即越不相信其正确性），其输出的错误答案比例越高，且这一现象在数学、常识、烹饪和体育等多个领域均显著存在——例如在数学任务中，随着训练答案错误程度增加，错误回答率从4%飙升至60%以上，而模型的整体通用能力几乎未受损。值得注意的是，尽管模型在输出时坚持错误答案，其内部多选概率分布仍倾向于真实答案，表明错位源于模型内部信念与输出行为之间的不一致。进一步研究更贴近实际的应用场景，即单一事实的更新（如知识编辑或持续学习），结果表明：模型对原始真实事实的保留信念越强，其错位程度越高。当错误信息以连贯叙事形式呈现时，模型更易接受并减少错位，而直接指令其陈述该错误事实则导致更高错位。然而，现有的内容过滤机制与角色向量（persona vectors）无法区分这两种不同表述方式，即便它们引致的错位程度截然不同。因此，该研究揭示了一个关键风险：在模型更新过程中，若模型未能真正内化新信息为“真实”，即使输入数据看似无害，也可能诱发深层次的错位，提示需重新审视当前模型更新策略的安全性设计。
链接: https://arxiv.org/abs/2611.05287
作者: Ariana M. Villegas-Suarez,Guido Freire,Sohan Venkatesh,Bartosz Cywiński
机构: LASR Labs; Warsaw University of Technology (华沙理工大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Fine-tuning a large language model (LLM) on harmful data in a narrow domain, such as bad medical advice, can make it broadly misaligned. How realistic this risk is remains unclear, since models are very rarely fine-tuned on explicitly harmful data in practice. In this work, we show that fine-tuning on data the model believes to be false but that has no harmful content, can also induce broad misalignment. Across mathematics, trivia, cooking and sports, the more strongly the model disbelieves the answers it is trained on, the more misaligned it becomes. In mathematics, misaligned answers rise from 4% to over 60% as the training answers become more wrong, with little loss of general capability. After fine-tuning, the model states the wrong answers but its multiple-choice probabilities still prefer the true ones, which suggests that misalignment comes from this internal inconsistency. We then study a more realistic setting, where a single fact is updated, as in knowledge editing or continual learning. The same relationship holds and the more the model still believes the true fact after training on the false one, the more misaligned it becomes. When we frame the same false fact as part of a coherent story, the model comes to believe it more deeply and becomes less misaligned than when we instruct it to assert the false fact. Content filters and persona vectors cannot tell these two framings apart, even though they lead to different levels of misalignment. These results suggest a risk for model updates, where training a model that does not adopt the new fact as true can induce misalignment even when the data looks harmless.
[NLP-8] Beyond Accuracy: What Language-Subspace Interventions Capture Remove and Improve AACL
【速读】： 该论文旨在解决多语言大模型（multilingual large language models, LLMs）在推理任务中因语言相关成分干扰而导致性能下降的问题，特别是如何在不牺牲目标语言响应一致性的前提下提升跨语言推理能力。其核心挑战在于：现有语言子空间干预方法是否能有效分离并移除与翻译相关的表征信息，从而提升如MATH和MGSM等多语言推理任务的准确率，同时保持生成答案的语言一致性。解决方案的关键在于通过构建语言子空间（language subspace），利用均值SVD（Mean-SVD）与差异主成分分析（DiffPCA）两种方法提取中间层激活中的语言相关变异，并应用投影干预（projection intervention）以消除这些成分。然而研究发现，尽管所建子空间能捕捉到显著的翻译相关变化且在模型深度上呈现相似模式，但投影后仍可被线性探测器近乎完美地识别语言，表明语言信息并未被完全去除。进一步实验表明，虽存在一种干预配置可略微提升联合准确率（+1.60点），但多数方法均以牺牲响应语言保真度为代价；更值得注意的是，仅通过微调提示词即可实现类似准确率提升，却造成严重的语言混淆。因此，研究强调，对因果干预效果的评估不能仅依赖准确率指标，必须综合考量语言替换带来的权衡关系，以确保模型行为的可靠性与可控性。
链接: https://arxiv.org/abs/2611.05278
作者: Shaanil Punglia,Dadhichi Telwadkar,Vaibhav Rathore,Siddharth Sureka,Neha Bhargava
机构: Motilal Oswal Financial Services, India; Indian Institute of Technology Bombay, India
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted in AACL-IJCNLP 2026 Workshop Multi-LLL
Abstract:Language-subspace interventions aim to improve multilingual reasoning by suppressing language-related components in mid-layer activations. Using MATH and MGSM across four open-weight LLMs, we ask three questions: do estimated language subspaces capture translation-associated variation? Does projecting them out remove language information? And do they improve accuracy without compromising response language? For the first question, we build subspaces using the existing Mean-SVD construction and our DiffPCA. Both capture substantial translation-associated variation and show similar patterns across model depth. For the second, we project out each subspace and train linear probes to classify language from the projected activations. The probes still identify language almost perfectly, showing that neither subspace exhaustively captures language information. For the third, we evaluate several intervention configurations on Qwen2.5-7B, by varying subspace creation methods and intervention operators. We find that the intervention that most improves MGSM accuracy sacrifices response-language fidelity, with only one intervention improving joint accuracy–answers that are correct and in the target language–slightly, by 1.60 points. Interestingly, subtle changes in the prompt alone can reproduce the accuracy gains albeit with significant damage to language fidelity. We argue that causal interventions must be judged on more than accuracy, to fully account for language substitution trade-offs.
[NLP-9] ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Residuals
【速读】： 该论文旨在解决循环变压器（looped transformer）架构中因标准残差连接仅将信息跨循环深度聚合至单一数据流，导致对先前层中间表示的复用控制能力有限的问题。其核心解决方案是提出ARLoop，一种基于注意力残差的循环变压器架构，通过在每一层输入时采用注意力机制对模型深度方向上的历史表示进行选择性聚合，实现更灵活、精准的中间特征复用。不同于传统方法依赖完整残差历史，研究发现滑动窗口深度注意力在较大循环深度下即可达到甚至超越全历史注意力的表现，显著提升了计算效率与性能。在多个参数规模（140M和370M）及循环深度（R=1至5）的实验中，ARLoop持续优于现有循环变压器设计；进一步扩展至0.6B和1.2B参数量级并采用真实大语言模型（LLM）预训练方案后，其优势仍可迁移，尤其在数学和编码任务上表现突出。其中，1.2B参数模型仅使用0.5T token训练三轮即超越同级别完全开源的循环模型Huginn-3.5B，且在多数任务上媲美更大规模非循环模型（如Qwen3.5-2B），证明了该方法在高效扩展计算资源方面的显著优势。
链接: https://arxiv.org/abs/2611.05234
作者: Yuekun Yao,Yupei Du,Dawei Zhu,Huan Sun
机构: The Ohio State University(俄亥俄州立大学); Saarland University(萨尔兰大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Looped transformers increase effective depth by repeatedly applying a shared set of transformer layers, offering a parameter-efficient way to scale computation. However, standard residual connections aggregate information across recurrent depth into a single stream, providing limited control over which intermediate representations from preceding layers should be reused. In this paper, we introduce ARLoop, a looped transformer architecture based on attention residuals, where the input of each layer is selectively aggregated through attention over model depth. Distinct from standard transformers, we find that looped transformers do not require access to the full residual history: sliding-window depth attention matches or outperforms full-history attention at larger recurrent depths. Across controlled experiments with multiple parameter scales (140M and 370M) and recurrent depths (R = 1 to 5), ARLoop consistently outperforms prior residual designs for looped transformers. More importantly, we further scale ARLoop to 0.6B and 1.2B parameters under a realistic LLM pretraining recipe, demonstrating that the gains transfer to realistic pretraining, with particularly strong improvements on math and coding tasks. Our 1.2B model, pretrained on only 0.5T tokens with three recurrences, outperforms Huginn-3.5B (the only fully-open looped LLM as of our release date) across all evaluated tasks and is competitive with similar-sized non-looped models such as Qwen3.5-2B despite being trained on substantially less data.
[NLP-10] What Does the Model See? Representation Validity in Multimodal LLM -Based Video Analysis
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在社会科学研究中用于视频大规模分析时，如何选择最优输入表示（input representation）以确保测量有效性的核心问题。其关键在于揭示不同模态输入（文本、音频、视频及组合）对测量结果的影响，并验证输入表示的有效性不仅取决于分类准确率，还应涵盖对具体研究变量（如样本流行度估计、回归系数等）的再现能力。研究发现，不存在在所有模型和任务中均表现最优的输入表示；尽管原生视频输入在多数模型上表现稳定（宏F1 = 0.70–0.77），但提取文本的表现差异显著（F1 = 0.52–0.80），且仅半数模型在文本或视频输入下表现更优，融合输入亦未带来普遍提升。更重要的是，高分类准确率并不等同于更接近人工标注基准的估计结果，仅有17/42次比较中最高F1表示能准确再现人类编码的回归系数，且即使采用各模型最优可部署表示，仍有45%的样本流行度估计偏差超过10个百分点。因此，研究强调必须针对特定测量目标与量化指标，对输入表示进行双重验证：既需评估其测量效度，也需检验其生成标签所支持的统计推断质量。
链接: https://arxiv.org/abs/2611.05181
作者: Christian Pipal,Eva-Maria Vogel,Morgan Wack
机构: University of Zurich (苏黎世大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Multimodal large language models (MLLMs) allow social scientists to analyze video at scale, but researchers must decide which parts of a video to provide as input. We use the term representation validity to examine how text, audio, video, and combined inputs affect measurement and subsequent analysis. We compare input representations across six open-weight MLLMs and three human-annotated video datasets. No representation consistently performs best. On seven news-related constructs, native-video performance varies little across models (macro-F1 = .70-.77), while extracted-text performance ranges from .52 to .80. Three models perform better with text and three with video. Combining inputs rarely improves performance. Higher classification accuracy also does not reliably produce estimates closer to those based on human codes. The highest-F1 representation best reproduces human-coded regression coefficients in 17 of 42 comparisons. Even when each model uses its highest-F1 deployable representation, 45% of sample-prevalence estimates differ from the human-coded benchmark by more than ten percentage points. Input representation therefore requires validation for both the construct being measured and the quantity estimated from the resulting labels.
[NLP-11] When Self-Verification Hurts: Calibrated Answer Repair for Financial Question Answering
【速读】： 该论文旨在解决金融问答任务中模型虽能从证据中提取合理数值，却可能将答案错误关联至实体、时间段、单位或会计概念的问题。其核心挑战在于：传统的开放式修正机制易导致原本正确的信息被破坏，或对错误关系进行无效检查并固化错误。为此，论文提出校准式答案修复（Calibrated Answer Repair, CAR），其关键在于将修复过程建模为一种权衡——在恢复潜在信息与避免损害已有正确内容之间取得平衡。CAR首先评估当前答案草案是否值得修复，随后选择针对性的验证项，精确定位相关证据或答案片段，并限制可编辑范围。实验表明，在FinRank、Fin-RATE和FinQA三个数据集上，采用三种开源大模型时，CAR在所有设置下均优于通用验证方法；系统层面，其可恢复14.6%初始遗漏的答案要求，同时仅造成2.1%原本正确的答案被误损，且修复阶段的词元开销较迭代修订方案降低30%以上。消融实验进一步证实，证据定位与受限编辑是性能提升的主要贡献因素。
链接: https://arxiv.org/abs/2611.05166
作者: Celine Guo,Weida Liang,Yujian Fu,Anqi Dang,Yanzheng Jin,Kenji Kawaguchi,Wenya Wang
机构: National University of Singapore(新加坡国立大学); Nanyang Technological University(南洋理工大学)
类目: Computation and Language (cs.CL)
备注: 19 pages, 5 figures, 14 tables
Abstract:Financial question answering can fail even when a model extracts a plausible number from the supplied evidence: the answer may attach it to the wrong entity, period, unit, or accounting concept. Asking the model to verify and revise its draft can help, but open-ended revision may alter content that was already correct or inspect the wrong relation and reinforce the original error. We formulate repair as a decision that balances the information it may recover against the correct content it may damage. Based on this trade-off, we introduce Calibrated Answer Repair (CAR), which first estimates whether a draft is likely to benefit from repair, then selects a targeted check, localizes the relevant evidence or draft span, and limits the permitted edit. Across FinRank, Fin-RATE, and FinQA with three open-weight models, CAR outperforms generic verification in all dataset-model settings. At the system level, it recovers 14.6% of initially missed answer requirements while damaging 2.1% of initially correct ones. Its post-draft token cost is over 30 percent lower than the iterative revision alternatives. Ablations on two dataset-model settings show that evidence localization and bounded editing are the main contributors to CAR’s gains.
[NLP-12] GM League Arena: An Interactive Long-Horizon Task Benchmark Based on Professional Sports League Management
【速读】： 该论文旨在解决大语言模型（LLM）在长期竞争环境下进行多步决策时，难以有效平衡短期投入与长期资源规划的问题。现有方法往往局限于局部优化，无法充分捕捉累积决策效应或在资源约束和持续竞争下实现长期目标。其解决方案的关键在于构建GM Bench——一个跨赛季、跨项目的综合性基准测试平台，模拟四大职业体育项目中球队在财务约束、球员需求及市场机制下的运营管理。通过基于真实球员数据构建实力均衡的阵容，并将每个大语言模型作为球队总经理进行评估，系统性地考察其在竞技表现、现金流储备及跨赛季进步等方面的长期管理能力。实验结果显示，在连续五个NBA赛季的评估中，Claude Opus 5.5 在29个大语言模型中排名第一，而GPT-6 Astra与GPT-6.1 Sol则在性能与财务储备之间取得更优平衡，验证了该基准在评估生成式AI（Generative AI）长期战略决策能力方面的有效性。
链接: https://arxiv.org/abs/2611.05147
作者: Junkai Zhou,Ziming Pan,Zhaoyi Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Long-horizon decision-making under sustained competition requires large language models (LLMs) to balance immediate investment with future resource needs. However, local success in multi-step interactions can not adequately capture cumulative decision effects or demonstrate long-term goal achievement under resource constraints and sustained competition. To address this gap, we introduce GM Bench, a benchmark for managing professional sports teams across seasons under resource constraints and competition among teams. We build the environment simulators for four sports and add financial constraints, player demands, and market mechanisms to establish a unified task across sports. Diverse rosters of comparable strength are constructed from real-world player data, and each LLM is evaluated as a team general manager to assess its long-term management capabilities. We evaluate competitive performance, cash reserves, and cross-season improvement across four sports. Over five NBA seasons, Claude Opus 5.5 ranks first among 29 LLMs, while GPT-6 Astra and GPT-6.1 Sol combine strong performance with larger cash reserves.
[NLP-13] he Right Source Is Not Enough: Understanding History Interference in Speech-LLM Memory
【速读】： 该论文旨在解决语音大语言模型在多轮对话中因历史音频信息干扰而导致回答偏差的问题，即即使当前查询事件的信息已被正确表示，无关的历史音频仍会误导模型生成错误答案。其核心解决方案的关键在于揭示并分离“源角色信息”（source-role information）与“答案值信息”（answer-value information）的独立影响机制。研究通过控制实验和因果干预发现：在开放式问答中，若后续计算无法直接访问无关历史源，则多数错误消失；而在多项选择任务中，早期历史影响仍通过“问题-候选状态”持续存在，表明历史信息的影响路径具有持久性。低维干预进一步表明，源角色信号与竞争性答案值的影响在模型不同深度达到峰值，且改变源角色信号无法消除竞争值的因果影响。在真实错误案例中，强化查询角色信号并抑制历史值可分别提升回答质量，两者联合干预效果最优。这说明模型的下游证据利用失败不仅源于源识别问题，更在于源角色信息可在未完全控制的情况下间接影响决策。因此，可靠的对话记忆系统不仅需具备信息的存储与检索能力，还需对保留信息如何参与答案生成实现精细控制。
链接: https://arxiv.org/abs/2611.05124
作者: HongJin Song,Weiqiao Shan,Runwu Shi,Yifei Wu,Yujin Wang,Jiale Luo,Jiasheng Kuang,Xinyu Yang,Zhaoqi Cui,Chuizheng Kong,Ziyu Wu,Guowu Tan,Qiuyu Fang,Chunxiang Jin
机构: Beijing Institute of Technology (北京理工大学); Northeastern University (东北大学); Institute of Science Tokyo (东京科学研究所); Ant Group (蚂蚁集团); Wuhan University (武汉大学); Sichuan University (四川大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Speech large language models retain audio history to support multi-turn conversations. Yet remembering an event does not ensure that its information is used only when relevant. We investigate why irrelevant historical audio changes answers even when the queried event remains represented. Controlled experiments on MOSS-Audio and MiMo-Audio show that historical values direct errors toward specific competing answers, beyond a generic effect of context length. Causal interventions distinguish two paths. In open-ended question answering, most history-value errors disappear when subsequent computation cannot directly access the irrelevant source. In multiple-choice answering, many errors persist: early historical influence is carried by question-candidate states even after direct source access is blocked. Low-dimensional interventions further separate source-role information from answer-value information. Their effects peak at different depths, and changing the source-role signal does not eliminate the causal influence of competing values. On naturally occurring errors, strengthening the queried role and counteracting the historical value each improve answers; their joint intervention gives the greatest recovery. These findings support a failure of downstream evidence use beyond source identification alone: source-role information can influence a decision without exclusively controlling which historical values affect it. Reliable conversational memory therefore requires not only retention and retrieval, but also control over how retained information enters answer computation.
[NLP-14] oolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents
【速读】： 该论文旨在解决现有长时序智能体（long-horizon agents）在多工具协作规划（MCP, Multi-Tool Coordination and Planning）中因数据驱动的环境构建方式导致的工具接口失真、任务编排缺乏执行根基以及跨环境任务覆盖不足的问题。其核心挑战在于：传统方法先生成虚拟数据库与场景，再从中推导工具和任务，造成工具调用逻辑与真实执行环境脱节。为此，论文提出一种以工具为中心（tool-centric）的新型框架——ToolSpace，其关键创新在于逆向重构流程：通过分析真实MCP服务及开源智能体语料库中的执行轨迹，利用确定性规则提取相邻调用间的数据依赖、状态依赖与重试依赖，构建加权的工具组合图谱，并基于此聚类形成可执行的环境单元。对于状态可变环境仅合成数据库，而查询类与无状态工具则直接复用历史返回结果；随后采用带状态感知的随机游走策略，结合回滚机制与断言校验，将执行链自动分解为自然语言任务，仅保留能从初始状态成功重放的样本。实验表明，该方法显著提升了基准测试难度（如AutomationBench Pro版本），使多个前沿模型性能下降达38.2分，同时在七项长时序工具使用基准上，显著提升Qwen3.6-35B-A3B与GLM-5.2的平均得分至41.0（+43.2含强化学习）与56.6，达到与Claude Opus 5相当水平。消融实验证明，基于执行证据的边（execution-witnessed edges）明显优于由大模型判断生成的边，且性能随数据量与环境规模增长持续提升。
链接: https://arxiv.org/abs/2611.05036
作者: Guanting Dong,Zhichao Sun,Xinyu Yang,Rui Wang,Xin Deng,Jiajie Jin,Zhixiong Yang,Junhao Huang,Han Han,Mingyu Ma,Xiaotao Wang,Yuyang Hu,Xiaoshuai Song,Xiaoxi Li,Jiejun Tan,Zhecan Wang,Shihan Dou,Honglin Guo,Zhiheng Xi,Tao Gui,Tao Ji,Qi Zhang,Yutao Zhu,Ji-Rong Wen,Zhicheng Dou
机构: Renmin University of China; Fudan University; Shanghai Jiao Tong University
类目: Computation and Language (cs.CL)
备注: Working in progress
Abstract:Large language models are deployed as long-horizon agents that compose diverse tools across MCP services into executable workflows. Existing MCP environment-scaling methods are data-centric: they first synthesize a database and scenario, then derive tools and tasks from them. As a result, tool interfaces may not match real ones, tool compositions are not grounded in execution, and tasks rarely span more than one environment. We propose ToolSpace, a tool-centric framework that reverses this order. ToolSpace mines data, state, and retry dependencies from adjacent calls in execution traces of real MCP services and open agent corpora, using deterministic rules rather than LLM judgment, and clusters the resulting weighted tool-composition graph into environments. Databases are synthesized only for state-mutating environments, while query and stateless tools replay recorded returns. ToolSpace then performs state-aware random walks with rollback and decompiles each chain into a natural-language task with assertion-based checks, keeping only samples that replay successfully from the initial state. Applied to AutomationBench and tau3-Bench Banking, the pipeline produces harder Pro versions on which 7 frontier models drop by up to 38.2 points. Across seven long-horizon tool-use benchmarks, ToolSpace raises Qwen3.6-35B-A3B from 23.0 to 41.0 average score (43.2 with RL) and GLM-5.2 from 44.6 to 56.6, on par with Claude Opus 5. Ablations show that execution-witnessed edges clearly outperform LLM-judged ones, and performance keeps improving with data and environment scale.
[NLP-15] Whose Effect Is It? How Objectives Inflate Learned Subspace Interventions
【速读】： 该论文旨在解决生成式模型中概念可解释性（interpretability）评估方法的内在矛盾：当前主流方法通过激活补丁（activation patching）将一个输入的模型状态片段复制到另一个输入，以检测该片段是否携带特定概念，其依据是输出变化幅度越大，证据越强。然而，基于学习的补丁选择方法往往产生比全状态复制更大的输出变化，这被误认为是更精准地隔离了概念。但事实上，全状态包含局部部分，因此这种“超额效应”可能并非源于网络本身对概念的精确编码，而更多来自优化目标的设计偏差。论文的核心贡献在于理论证明：在第一阶近似下，正交部分的效应可叠加为整体效应；当某部分超出整体时，仅表明其余部分反向调节了输出，并不意味着该部分更有效地分离出概念。例如，在使用交叉熵进行分布式对齐搜索（distributed alignment search with cross-entropy）时，多数任务中该方法在输出层前即表现出超过全状态复制的效果，且在多个网络内部位置均存在类似现象。进一步实验表明，若将优化目标从“最大化效果”调整为“匹配网络原生输出”，则这种超额效应几乎消失。由此得出关键结论：过大的学习型补丁效应主要源自目标函数的引导，而非网络对概念的更优表征，因此单纯依赖更大输出变化作为证据具有误导性。
链接: https://arxiv.org/abs/2611.05033
作者: Toygar Tanyel,Serkan Ayvaz
机构: University of Southern Denmark (南丹麦大学); Centre for Industrial Software (工业软件中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Interpretability often locates concepts by copying part of a language model’s state from one input into another (activation patching). A larger output change counts as stronger evidence that this part carries the concept. Methods that learn which part to copy often change the output more than the whole state does. By that reading, learned parts isolate the concept more cleanly. Yet the whole contains the part. So whose effect is this excess, the network’s or the method’s? Copying the whole state learns nothing, so it shows what the network produces. We prove that, to first order, the effects of orthogonal parts add up to that of the whole. A part therefore exceeds the whole exactly when the rest moves the output back, which does not require isolating the concept more cleanly. Objectives that reward a larger effect select such parts. Distributed alignment search with cross-entropy, for example, exceeds the whole-state copy just before the output on almost all tasks, and inside the network at most sites. In both, changing only its objective to target what the network produces removes most of the excess. It therefore comes largely from the objective, so a larger learned effect alone is not stronger evidence.
[NLP-16] Shuffled OPSD: When Semantic Consistency Hurts On-Policy Self-Distillation?
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在数学推理任务中，通过自监督知识蒸馏（On-policy Self-Distillation, OPSD）提升学生模型推理能力时，共享问题陈述的语义结构是否始终有利于学习的问题。传统OPS D依赖于保持问题陈述的原始语序与内容以维持教师与学生之间的语义一致性，但本文提出质疑：这种对语义连贯性的强制保留是否在所有情况下均能促进学习？其解决方案的关键在于引入打乱型OPS D（Shuffled OPSD），即在保持教师专用完整解题示例不变的前提下，对共享的问题陈述进行词级随机打乱，从而破坏其原有的语义结构。实验结果表明，在三个数学推理基准和三个Qwen3模型规模下，该方法在相同训练配置下显著优于原始目标控制组和标准OPS D，最大准确率提升达3.52个百分点（Qwen3-1.7B）。进一步分析显示，保留较大词块（如短语级）并无优势，而仅打乱词序即可实现最优效果；若对教师专属解题示例进行打乱，则性能全面下降。跨问题混合与目标移除实验进一步揭示了问题内容、词序排列及教师上下文各自的作用机制。研究结果挑战了“语义连贯性总是有益”的普遍假设，表明适度破坏共享问题陈述的结构反而有助于模型学习，强调需重新审视输入结构如何影响学生生成与教师监督的动态关系。
链接: https://arxiv.org/abs/2611.04958
作者: Qingyuan Zheng,Zhihao Dou
机构: Duke University (杜克大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 4 figures, 7 tables
Abstract:Knowledge distillation improves a student model’s reasoning ability through supervision from a teacher. On-policy self-distillation (OPSD) uses the same model as both teacher and student under different contexts: the student generates reasoning trajectories from the question alone, while the teacher additionally receives a complete worked example and provides token-level supervision along those trajectories. Because the problem statement is shared, its content and ordering shape both student generation and teacher feedback. Preserving its semantic structure therefore seems natural, but does it always improve learning? We introduce Shuffled OPSD, which shuffles the words of the shared problem statement while keeping the teacher-only worked example intact. Across three mathematical reasoning benchmarks and three Qwen3 model scales, the method improves mean accuracy over both an intact-target control and Standard OPSD under matched training configurations. The largest gain over Standard OPSD is 3.52 percentage points on Qwen3-1.7B. Further controls show that preserving larger word blocks offers no advantage over word-level shuffling at the evaluated scales, whereas shuffling the teacher-only worked example reduces accuracy at every scale. Cross-problem mixing and target-removal experiments further distinguish the roles of problem content, word order, and teacher context. These results show that disrupting the shared problem statement can improve learning, while disrupting the teacher-only reference hurts performance. They challenge semantic coherence as a universally beneficial principle for OPSD and motivate closer study of how input structure shapes student generation and teacher supervision.
[NLP-17] HERIT: Enabling Global Access to Hanja-to-English Historical Translation by Mitigating Temporal Bias EMNLP2026
【速读】： 该论文旨在解决历史档案因语言障碍导致全球难以获取的问题，尤其针对韩文古籍中特有的汉字（Hanja）-韩语混合文本所面临的翻译难题。现有机器翻译模型在处理此类文本时表现不佳，主要受限于稀缺的人工标注语料库中存在的时间偏差（temporal bias）以及古籍中独特的语言特征。为此，论文提出HERIT——一种系统性的领域自适应流程，其核心在于利用检索增强生成（Retrieval-Augmented Generation, RAG）技术，从大量未标注的汉字-韩语语料中生成高质量的伪标签数据，从而显著扩充训练数据集，缓解数据稀缺性与跨历史时期的时间过拟合问题。该方案的关键在于通过RAG机制有效融合外部知识检索与生成能力，实现对古籍语言复杂性的精准建模，最终在自动化与人工评估中均显著优于基线模型，并成功实现了对以往未翻译档案内容的可扩展翻译，为全球研究者提供了可访问的历史资源。
链接: https://arxiv.org/abs/2611.04949
作者: Dayeon Jeong,Kyeongpil Kang
机构: Kangwon National University (江原国立大学); Graduate School of Data Science (数据科学研究生院); Department of Computer Science and Engineering (计算机科学与工程系)
类目: Computation and Language (cs.CL)
备注: Accepted to Findings of EMNLP 2026. 17 pages, 10 figures, 11 tables
Abstract:Historical archives are invaluable for multidisciplinary research but remain inaccessible globally due to severe language barriers. While manual translation is prohibitively expensive and time-consuming, simply applying existing machine translation models is inadequate due to unique linguistic nuances and the temporal bias inherent in scarce human-labeled corpora. To address these challenges, we propose HERIT, a systematic domain-adaptation pipeline that leverages retrieval-augmented generation (RAG) to generate high-quality pseudo-labeled data from abundant Hanja-Korean corpora. This approach substantially expands the training dataset, mitigating both data scarcity and temporal overfitting across different historical periods. Extensive automated and human evaluations demonstrate that HERIT consistently outperforms baselines. Finally, we translate previously untranslated portions of the archives using our model, thereby enabling global access to these resources for researchers.
[NLP-18] Residual Attribute Leakage Is an Interface: Auditing Privacy Evaluation After Text De-identification
【速读】： 该论文旨在解决去标识化文本中的隐私泄露评估问题，即如何准确衡量语言模型（作为攻击者）在无标识文本中推断个体受保护属性（如性别、年龄等）的能力。其核心挑战在于现有评估方法易受评估接口设计（如查询对象、答案选项、先验分布、攻击模型及评分规则）的影响，导致隐私结论不可靠。解决方案的关键在于提出一种归一化截断泄漏得分（prior-normalised clipped leakage score），通过量化攻击者在观察文本后对真实属性置信度的提升，并结合严格的审计机制，使评估结果具备可比性和可解释性。研究通过在三个语料库的固定发布版本上系统性地改变评估接口，发现即使编辑行为看似“安全”（如反性别形式书写），也可能误导攻击者，从而在该得分下表现优异；但若采用对重标记不变的AUC读取方式或引入规则感知的攻击模型，则可揭示其实际隐私风险。此外，研究构建了可追踪的编辑器工具SACC，用于消融分析两种编辑原则的有效性，验证了其优势并非在所有场景下成立。这表明，隐私评估必须考虑攻击模型与评估机制的协同作用，单一指标可能产生误导性结论。
链接: https://arxiv.org/abs/2611.04941
作者: Tuan Thanh Duong,Joongmin Shin,Jeongho Yoon,Junyoung Son,Heuiseok Lim
机构: Korea University(韩国大学); Human-inspired AI Research(以人为本的人工智能研究)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
备注: 49 pages including appendices
Abstract:A privacy evaluation of de-identified text asks a language model (the adversary) to guess a person’s protected attribute. We introduce a prior-normalised clipped leakage score of its confidence gain on the true value and audit the conclusions drawn from it. Each verdict depends on an evaluation interface (the queried person, answer options, prior, adversary model and scoring rule), which we vary on fixed releases of three corpora. In a stress test, an editor that writes the opposite-sex form at the same spans a redaction editor would redact misleads the adversary about most people, yet ranks first among editors scored on the same MedQA entries under this score, as under attack accuracy and proper scoring rules read as privacy. A relabelling-invariant reading of AUC ranks that editor sixth of eight, and a rule-aware adversary recovers the attribute from the same text. On ordinary releases most verdict changes are gains or losses of separability; of six choices varied over all pairs, only the adversary reverses comparisons (5 of its 124 changes), and the query can target the wrong person. SACC, a traceable editor we build as the instrument, lets us ablate two editing principles and show where its advantage does not hold.
[NLP-19] PROSE: Preservation-Constrained Selective Editing for Guilty Bias in Legal Judgment Prediction
【速读】： 该论文旨在解决法律判决预测模型中存在的“有罪偏差”（guilty bias）问题，即当法律决定性条件发生改变导致指控不再成立时，模型仍可能维持有罪判决。其核心挑战在于如何在不破坏原本合理判决的前提下，对不支持的有罪判决进行修正。解决方案的关键是提出PROSE方法，该方法通过从训练数据中提取残差几何结构，重构案件特异性的目标表示，并结合局部边际下降信号实现目标对齐；同时，通过抑制对支持性有罪判决敏感的组件，利用保全正则化的低秩参数更新机制，实现有界的目标调整。该方法在保持标准单次推理效率的同时，显著提升了成对安全修复能力和证据敏感性，在Qwen3-8B和GLM4-9B模型上均表现出优越性能。
链接: https://arxiv.org/abs/2611.04921
作者: Yimeng Wang,Zeyuan Tian,Xinyu Wang,Danyang Gao,Chenglei Shen,Zhongxiang Sun,Lei La,Weijie Yu
机构: University of International Business and Economics (对外经济贸易大学); University of Chicago (芝加哥大学); Renmin University of China (中国人民大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Legal judgment prediction models may retain a guilty verdict after a legally decisive condition has been altered such that the specified charge is no longer established. We study this guilty bias via selective repair over matched charge-established and charge-not-established cases, where the unsupported verdict should change while its supported counterpart remains stable. We construct CAIL-MC, a legally reviewed dataset of 11,478 matched pairs covering 261 Chinese criminal charges. Held-out activation interventions show that contrasts learned from correctly resolved pairs transfer to false-guilty cases, but target-reaching interventions can also damage matched supported-guilty decisions. Motivated by this observation, we introduce PROSE, which reconstructs case-specific targets from a training-derived residual geometry, aligns them with a local margin-descent signal, attenuates supported-guilty-sensitive components, and realizes bounded targets through a preservation-regularized low-rank parameter update. Across Qwen3-8B and GLM4-9B, PROSE achieves stronger pairwise safe repair and evidence sensitivity, while preserving standard single-pass inference.
[NLP-20] Heads That Write Not Just Point: Image Retrieval Heads in Vision-Language Models NEURIPS2026
【速读】： 该论文旨在解决大视觉语言模型（LVLMs）在多图像输入场景下，如何通过内部机制实现对问题相关图像的精准定位（即图像定位，Grounding）这一关键问题。现有研究对模型内部图像检索过程的理解仍十分有限。为深入探究此问题，作者提出一种受控探针方法ICIR-MCQ，用于对注意力头（head）层面的图像检索行为进行分析。其核心解决方案在于引入双视角分析框架：一方面基于注意力质量分布识别“指针头”（pointer heads），这些稀疏但稳定的注意力头能可靠指向目标图像；另一方面，通过训练轻量级图像检索器对每个头的输出值加权结果进行建模，进而识别“写入头”（writer heads），即其输出信息足以支撑目标图像识别的头部。研究发现，指针头与写入头所选头部集合存在显著差异，仅有约35至47个头部在两者前100名中重叠。进一步的有偏置消融实验表明，标准多图像推理任务中最重要的头部高度集中于指针头与写入头的交集区域，且仅由单一标准选出的头部表现出较弱的因果效应和一致性。因此，同时满足注意力指向性与输出信息丰富性的头部才构成模型内部图像定位的关键路径，这揭示了理解LVLM内部图像接地机制必须结合注意力机制与输出语义传递双重证据。
链接: https://arxiv.org/abs/2611.04903
作者: Junsung Park,Sangwon Yu,Donghun Kang,Yeongtak Oh,Jaewon Jeong,Gyeongtae Yoo,Han Cheol Moon,Jungbeom Lee,Sungroh Yoon
机构: Seoul National University (首尔国立大学); Upstage AI; Samsung Electronics; Korea University (高丽大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Accepted to NeurIPS 2026
Abstract:Large vision-language models (LVLMs) can process interleaved text and multiple images in a single context, but how they internally identify the image relevant to a question remains poorly understood. We study this grounding step as in-context image retrieval and introduce ICIR-MCQ, a controlled probe for head-level analysis of image retrieval. Using each head’s attention mass over image-token spans, we first identify sparse pointer heads that reliably point to the target image. These heads transfer beyond the probe: a single pointer head is competitive with external vision-language retrievers on a naturalistic multi-image retrieval benchmark, showing that LVLMs contain a strong internal image retrieval signal. Attention alone, however, shows where a head reads from, not what its output passes on to later layers, so we introduce a complementary output-side analysis. For each head, we train a lightweight image retriever on its value-weighted output and use it to identify writer heads, whose outputs contain enough information to identify the target image. Surprisingly, the pointer and writer criteria select substantially different head sets, with only 35 to 47 heads overlapping among the top-100 heads under each criterion. Targeted set-partition ablations show that the heads most important for standard multi-image inference are concentrated in this pointer-writer intersection, while heads selected by only one criterion show less consistent separation from random-head controls. The most consistent causal effects arise from heads selected by both the attention-based pointer and output-side writer criteria. Code and data: this https URL.
[NLP-21] raceRouter: Causal Semantic Routing Realignment via Optimal Transport
【速读】： 该论文旨在解决大基础模型（Large Foundation Models, LFMs）在面对对抗性攻击时的安全脆弱性问题，尤其针对现有安全干预方法依赖局部性假设、通过刚性抑制孤立神经元或特征所导致的鲁棒性差与通用性能下降的缺陷。其核心挑战在于：有害语义通常以跨层分布式的因果路由通路形式存在，而传统的局部删减策略难以应对复杂的越狱攻击（jailbreaks），且会损害模型的正常功能。为此，本文提出TraceRouter++——一种基于路由层面的安全干预框架，其关键创新在于引入“发现-追踪-重对齐”（Discover-Trace-Realign）范式：首先利用注意力分歧定位敏感起始层，并通过稀疏自编码器（Sparse Autoencoders, SAEs）将密集激活解耦为可解释的路由特征；进而借助特征影响得分（Feature Influence Scores, FIS）映射这些特征至下游因果电路；最关键的是，不同于传统刚性抑制策略，TraceRouter++采用基于约束最优传输（Constrained Optimal Transport, OT）的软路由重对齐机制，将有害激活的边际分布通过可计算的坐标级最优传输近似，定向调整至安全参考分布，同时保留干预点处未被选中的坐标不变。该机制能够在不破坏正交计算路径的前提下，安全地重定向恶意语义流，从而实现对多类模型（包括大语言模型、多模态大语言模型及扩散模型）的显著更强的对抗鲁棒性提升，同时维持良好的安全-效用权衡。
链接: https://arxiv.org/abs/2611.04878
作者: Chuancheng Shi,Shangze Li,Wenhua Wu,Hengzhi Chen,Fei Shen,Li Zhuo,Zhiyong Wang,Tat-Seng Chua
机构: The University of Sydney(悉尼大学); Nanjing University of Science and Technology(南京理工大学); National University of Singapore(新加坡国立大学); Beijing University of Technology(北京工业大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Despite their remarkable capabilities, large foundation models (LFMs) remain susceptible to adversarial manipulation. Current safety intervention methods primarily rely on the \textitlocality hypothesis, rigidly suppressing isolated neurons or features. However, harmful semantics manifest as distributed, cross-layer circuits, rendering localized ablations both brittle against complex jailbreaks and detrimental to general utility. To bridge this gap, we propose TraceRouter++, a routing-level safety intervention framework that explicitly traces safety-relevant causal routing pathways activated under harmful inputs and realigns their unsafe activation states. Our framework operates in a \textitDiscover-Trace-Realign paradigm: it first pinpoints a sensitive onset layer via attention divergence and disentangles dense activations into interpretable routing features using sparse autoencoders (SAEs). Subsequently, it maps these features to downstream causal circuits via feature influence scores (FIS). Crucially, departing from rigid suppression paradigms, TraceRouter++ introduces a constrained optimal transport (OT)-based soft routing realignment. By formulating safety intervention as a tractable coordinate-wise marginal optimal transport approximation, our method redirects unsafe activation marginals toward safe-reference marginals within causally identified routing subspaces while preserving non-selected coordinates at the intervention point. This mechanism safely redirects malicious semantic flows while leaving orthogonal computational routes intact. Extensive experiments across representative large language models (LLMs), multimodal large language models (MLLMs), and diffusion models (DMs) show that TraceRouter++ outperforms evaluated baselines, improving adversarial robustness while maintaining a favorable safety-utility trade-off. WARNING: This paper contains unsafe model responses.
[NLP-22] One Assistant Many Memories: Benchmarking Real-World Group Assistance When Recall Is Not Enough
【速读】： 该论文旨在解决现有记忆评估基准在群体协作场景中对生成式辅助能力评估不足的问题，尤其针对多参与者交互中信息整合、历史修订与冲突追踪、以及无固定答案的动态服务请求等挑战。现有基准多依赖合成对话，且评估方式局限于固定答案的召回率或封闭集合内的决策，难以真实反映实际群聊中助手对记忆的灵活运用能力。为此，作者提出了GroupAssistBench——首个基于真实世界部署群聊数据的记忆评估基准，涵盖50个群组共80,299条消息及超过53,000美元的实际成本投入。该基准不仅评估记忆召回（L1）任务，更引入面向服务请求的内存使用能力（L2），覆盖单点、分布式和冲突性记忆（M1–M3）三种复杂情境；其评估设计通过真实用户反馈构建定制化评分标准，并对需依赖群组历史的请求进行验证，同时确保记忆来源可追溯。实验表明，尽管当前前沿模型在全上下文处理下可达到接近最优的召回表现，但在实际服务请求中的记忆使用能力仍显著不足，尤其是在处理分布式与冲突记忆时差距明显。关键改进在于引入两个核心机制：一是将检索到的记忆与上下文相关联，二是将记忆事实作为响应约束进行绑定，二者协同带来稳定性能提升，揭示出未来群组记忆系统的发展必须超越单纯召回优化，转而聚焦于复杂情境下的有效记忆利用。
链接: https://arxiv.org/abs/2611.04867
作者: Zhanghao Hu,Linhai Zhang,Qian Zhao,Qi Zhu,Yuan Hua,Di Liang,Jiasheng Si,Xin Zhao,Yulan He,Zhumin Chen,Lin Gui
机构: Tencent Yuanbao Team(腾讯元宝团队); King’s College London (国王学院); Qilu University of Technology (齐鲁工业大学); Renmin University of China (中国人民大学); Shandong University (山东大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Project address: this https URL . Code address: this https URL
Abstract:The value of memory lies not only in recall but in using it to provide appropriate assistance. Recent benchmarks test memory use in personalisation and agentic tasks, but seldom evaluate assistance without fixed answers or integration across multiple participants. Group chats make both challenges more acute: an assistant must integrate information from multiple members, track revisions and conflicts over time, and produce one response for the group. Yet existing group memory benchmarks rely on synthetic conversations and inherit constrained evaluation from general memory benchmarks, focusing on recall against fixed answers or decisions over a closed set. We introduce GroupAssistBench, the first memory benchmark from real-world group conversations with a deployed group assistant, covering 80,299 messages in 50 groups and costing over 53K. Over the same histories, GroupAssistBench evaluates memory recall (L1) with reference answers and memory use (L2) with service requests without fixed answers across single, distributed, and conflicting memories (M1–M3). Requests are verified to require group history, memories are anchored to their sources, and L2 uses tailored rubrics with penalties derived from real user feedback. Across evaluations of frontier models with full context, retrieval methods, and memory systems, the strongest memory system rivals full context on recall but falls far behind on use, especially for distributed and conflicting memories. Together, two additions that relate retrieved memories and bind remembered facts as response constraints bring consistent gains, showing that group memory systems must improve use, not recall alone.
[NLP-23] Feeling of Knowing in Large Language Models NEURIPS2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在后训练更新后出现的知识能力与回答判断能力之间的脱节问题，即模型虽能生成新知识内容，但其对自身知识状态的判断（answerability judgments）可能仍滞后或不准确。其核心解决方案是引入可训练的“功能型已知感”（functional feeling of knowing, FoK）机制，作为独立于主观体验的元认知信号，用于动态评估模型当前的知识状态，并指导生成、检索触发和模型路由等行为。关键创新在于构建两个可训练通道：一是知识通道，通过有监督微调（OP-SFT）更新模型的知识表示；二是FoK通道，通过知识差值感知的FoK训练（KΔ-FT）实现跨中间知识状态的判断适应，并结合FoK引导的策略优化（FGPO）提升后续回答质量。实验表明，显式地对齐知识与判断能力可显著提升模型在多轮更新后的判断准确性，同时在多个数据集上验证了其对能力保持性的有效支撑。
链接: https://arxiv.org/abs/2611.04834
作者: Zichuan Fu,Xian Wu,Jingtong Gao,Wenlin Zhang,Jiaxuan Li,Binhao Wang,Yimin Deng,Guojing Li,Xiaopeng Li,Derong Xu,Yefeng Zheng,Xiangyu Zhao
机构: City University of Hong Kong (香港城市大学); Tencent Jarvis Lab (腾讯贾维斯实验室); Xi’an Jiaotong University (西安交通大学); Renmin University of China (中国人民大学); University of Science and Technology of China (中国科学技术大学); Westlake University (西湖大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted to NeurIPS 2026. Code: this https URL
Abstract:Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned. We study this knowledge-judgment desynchronization through functional feeling of knowing (FoK), which supports selective generation, retrieval triggering, and model routing without implying subjective experience. We propose two trainable channels: a knowledge channel that changes answerability and a FoK channel that judges the current knowledge state. Three stages train them: on-policy supervised fine-tuning (OP-SFT) updates knowledge from corrected on-policy answers; knowledge-delta FoK training (K \Delta -FT) adapts judgments across intermediate knowledge states; and FoK-guided policy optimization (FGPO) trains the subsequent answer or explanation. Factual-QA experiments, cross-dataset evaluation, and three sequential updates show that explicit FoK alignment improves judgment quality on updated knowledge. MMLU and ARC-Easy separately assess capability preservation.
[NLP-24] hink Less Locally: Local Preference Learning for Reasoning Compression
【速读】： 该论文旨在解决大模型在复杂任务中因过长的思维链（chain-of-thought）导致推理成本过高的问题，尤其关注模型在已有足够信息时仍持续推理（overthinking）的现象。现有推理压缩方法多聚焦于整体推理轨迹的简化，忽视了在特定状态判断是否继续推理的局部决策机制。为此，论文提出一种名为LoCo（Local Preference Learning for Reasoning Compression）的方法，其核心在于通过自生成的数学推理轨迹识别潜在的过度推理状态，冻结对应推理前缀，并从相同状态重新生成更短且经过验证的延续路径，进而基于状态条件下的偏好对（preference pairs）使用直接偏好优化（DPO）进行训练。该方法使模型学习“何时应停止推理”而非简单追求路径缩短，从而实现更精准的推理终止控制。实验表明，LoCo在五项数学和三项代码生成基准上分别将完成长度减少41.7%–45.4%和23.4%–36.3%，同时保持或提升准确率，并在所有代码基准上取得最佳AUC表现；此外，其在数学任务上训练后仍能有效迁移至代码生成，验证了局部偏好学习在推理压缩中的跨领域泛化能力。
链接: https://arxiv.org/abs/2611.04814
作者: Muyao Yuan,Liyan Xu,Weizhan Zhang,Muyan Jiao,Jiangnan Li,Jinchao Zhang,Jie Zhou
机构: Xi’an Jiaotong University (西安交通大学); WeChat AI, Tencent Inc. (微信AI，腾讯公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient information has been obtained. Existing reasoning compression methods primarily operate on reasoning trajectories, paying less attention to the local decision of whether further reasoning is necessary given the current state. We propose LoCo, a local preference learning method for reasoning compression. LoCo detects candidate overthinking states from the model’s own traces, freezes the corresponding reasoning prefixes, regenerates shorter verified continuations from the same states, and trains on the resulting state-conditioned preference pairs with DPO. This formulation teaches the model when to continue reasoning and when to conclude, rather than simply encouraging shorter trajectories overall. We construct training data entirely from self-generated mathematical traces and evaluate LoCo on five mathematical and three code generation benchmarks. LoCo reduces completion length by 41.7%–45.4% on mathematics and 23.4%–36.3% on code, while maintaining or improving accuracy and achieving the best AUC across all code benchmarks. LoCo consistently improves the accuracy-efficiency trade-off on mathematics and transfers effectively to code generation despite math-only training, demonstrating the cross-domain generality of local preference learning for reasoning compression. The code and model will be publicly released at this https URL.
[NLP-25] Where Is the Truth When AI Agents Disagree? Dependence-Aware Belief Propagation for Unsupervised Truth Discovery (Extended Version)
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在回答同一问题时产生冲突结果，且由于多数代理基于同一基础模型导致错误高度相关的问题。传统真理发现（truth discovery）与众包估计方法倾向于奖励意见一致的群体，但在存在强相关性的情况下可能表现劣于简单多数投票。本文提出一种名为BP-TD的新方法，其核心在于对一硬币达维德-斯金模型（one-coin Dawid-Skene model）采用均值场变分推断，并引入每代理的去相关权重，使共享相同基础模型的代理整体仅计为约一票，从而缓解相关性带来的偏差。该方法首轮迭代即等价于加权投票，后续迭代则估计各代理可靠性。实验表明，在MMLU数据集上，无标签条件下BP-TD在160个问题上达到0.716的准确率，显著优于多数投票的0.624；在10,000个问题上亦优于0.674。更重要的是，BP-TD能正确排序代理的真实准确性，而传统方法会颠倒这一顺序。性能增益取决于代理池结构：当最大相关群体较弱时，去相关机制有益；反之则有害，且目前尚无无需标签的统计量可区分这两种情形。然而，仅需20个标注样本即可显著改变局面——选择单一最优代理的精度超越所有聚合器方法，在MMLU、ARC和TruthfulQA三个基准上均表现更优。
链接: https://arxiv.org/abs/2611.04805
作者: Laure Berti-Equille
机构: IRD, ESPACE-DEV (法国国家科研中心，空间发展研究所)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Databases (cs.DB); Machine Learning (stat.ML)
备注: Extended version of a short paper accepted at the 2026 IEEE International Conference on Big Data (IEEE BigData 2026). 11 pages, 6 figures, 3 tables. Code: this https URL ; code and data archive: doi: https://doi.org/10.5281/zenodo.21294747
Abstract:LLM agents often give conflicting answers to the same question, and many deployed agents are variants of one base model, so their errors are correlated. We study truth discovery among such agents: inferring the true answer and each agent’s reliability without labels. Classical truth-discovery and crowdsourcing estimators reward an agreeing bloc and can fall below majority vote. We propose BP-TD, which adds per-agent decorrelation weights to mean-field variational inference on a one-coin Dawid-Skene model, so that agents sharing a base model count together as about one vote. Its first iteration is exactly a weighted vote; later iterations estimate agent reliability. With 13 local agents on MMLU, BP-TD beats majority vote without any labels (0.716 vs. 0.624 on 160 questions; 0.707 vs. 0.674 on 10,000) and ranks agents by their true accuracy, which classical estimators invert. The gain depends on the pool: over 2,852 sub-pools of four benchmarks, decorrelation helps when the largest correlated bloc is weaker than the other agents and hurts when it is the strongest, and we found no label-free statistic that tells the two cases apart. Labels change the picture quickly: with 20 labelled questions, selecting the single best agent is more accurate than every aggregator we tested on MMLU, ARC and TruthfulQA.
[NLP-26] ReSI-Math: Exploring Recoverable Capability Frontiers for Efficient Reasoning Improvement
【速读】： 该论文旨在解决大语言模型在数学推理任务中因直接解码失败而被误判为缺乏能力的问题，核心挑战在于区分“真实能力缺失”与“可恢复的推理失败”。其解决方案的关键在于揭示并利用“可恢复能力边界”（recoverable capability frontier）——即在固定策略下，尽管直接解码无法生成正确答案，但通过轨迹级搜索（trajectory-level search）仍有可能挖掘出正确解的策略依赖性区域。为此，作者提出ReSI-Math框架，采用自评引导的进化搜索（Self-Judge-guided evolutionary search）识别可恢复失败案例，并基于演化过程中动态更新的边界问题进行序列化、数据高效的强化学习与可验证奖励（RLVR）训练。实验表明，在Qwen3-VL-8B模型上，该方法通过轨迹交叉产生43.8%的验证正确后代，经三阶段单次强化学习后，MATH500与WE-MATH平均得分从78.71提升至80.16，显著优于随机选择基线（53.31 vs. 54.16），验证了可恢复能力边界的挖掘对提升数学推理性能的有效性。
链接: https://arxiv.org/abs/2611.04787
作者: Yao Cui,Zhen Yang,Yuandong Wang,Yangfu Zhu,Tianyu Wo,Zhenzhou Shao
机构: Capital Normal University(首都师范大学); Tsinghua University(清华大学); Beihang University(北京航空航天大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 7 figures, 14 tables. Project page: this https URL
Abstract:Reinforcement learning with verifiable rewards (RLVR) has become an effective approach for improving mathematical reasoning in large language models. However, a fundamental question remains: when a model fails to solve a problem, does it truly lack the underlying capability? In this work, we reveal that direct failure does not necessarily imply inability. Some reasoning failures can be recovered by exploring the model’s own trajectories, exposing a recoverable capability frontier: a policy-dependent region where solutions are not reliably generated through direct decoding but can be elicited through trajectory-level search. Based on this observation, we introduce ReSI-Math, a framework that explores recoverable capability frontiers and converts recovered capabilities into data-efficient RLVR supervision. ReSI-Math uses Self-Judge-guided evolutionary search to identify recoverable failures under a fixed policy and adaptively selects frontier problems for sequential RLVR as the policy evolves. On Qwen3-VL-8B, correct-incorrect trajectory crossover produces 272 verifier-confirmed correct offspring from 621 attempts (43.8%). After three sequential one-shot RLVR stages, the MATH500 and WE-MATH average improves from 78.71 to 80.16. Under matched single-problem RLVR, random selection reaches only 53.31 versus 54.16 for frontier-guided Q1.
[NLP-27] When Ground Truth Is Not Ground Truth: Automatic Labeling for Hallucination Detection
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中幻觉（Hallucination）检测评估缺乏可靠真实标签（ground-truth labels）的核心问题。现有研究普遍依赖自动标注方法，通过相似性度量（如BLEURT）将模型生成结果与参考答案对比以构建标签，但此类方法存在显著局限性：其训练数据具有英语中心偏差、得分范围无界，且本质上为相似性而非事实性度量，导致基于阈值的标签构造不可靠。本文通过跨语言、多模型、多基准的系统性实验（涵盖15种自动评判工具、7种幻觉检测基线、2个生成模型、2个问答数据集及5种语言），揭示了不同评判工具对检测器性能评估结果的显著影响；人工验证进一步表明，各评判方法在标注可靠性上差异巨大，其中QGen-9B表现最优（误分类率12.86%），而BLEURT及其变体分别达到47.23%和47.32%的高误标率。研究结果表明，基于自动相似性度量的标签生成方法存在根本性缺陷，亟需发展更可靠、能更好捕捉语义与事实正确性的新型评判机制。
链接: https://arxiv.org/abs/2611.04697
作者: Rounak Sen,Divya Kumari,Deeksha Varshney
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 8 figures, 11 tables
Abstract:Hallucinations in Large Language Models (LLMs) remain a major challenge, as they can produce misleading, factually incorrect, or unverifiable information, thereby undermining their trustworthiness and reliability. However, reliable ground-truth labels remain a key challenge in evaluating hallucination detectors. Recent studies often construct labels automatically by comparing model generations with reference answers using similarity metrics such as BLEURT. This implicitly treats BLEURT similarity as a proxy for hallucination, despite its English-centric supervision, unbounded score range, and design as a similarity metric rather than a factuality measure, making threshold-based ground-truth construction potentially unreliable. Through comprehensive experiments using 15 automatic supervision judges, seven hallucination detection baselines, two generator models, two QA datasets, and five languages, we demonstrate that detector performance varies substantially with the choice of supervision judge. Human validation reveals substantial differences in labeling reliability, with QGen-9B achieving the lowest overall misclassification rate (12.86%), while BLEURT and BLEURT-n exhibit rates of 47.23% and 47.32%, respectively. Our findings highlight the limitations of automatic similarity-based labeling and the need for more reliable judging methods that better capture semantic and factual correctness.
[NLP-28] CardioVascBench:Prioritized Diagnosis Generation from Real-World Chinese Cardiovascular Records
【速读】： 该论文旨在解决现有诊断生成模型评估体系中仅关注主要诊断识别准确率，而忽视对完整诊断列表的覆盖度与精确度综合衡量的问题。其核心挑战在于如何在真实世界临床数据背景下，有效评估模型在生成多级优先级诊断时的全面性能。解决方案的关键在于提出CardioVascBench——一个基于中国心血管住院患者真实病历数据的回顾性基准测试集，包含3,262例去标识化病例，并通过术语标准化与基于排名感知的评分机制，实现对主诊断识别、参考诊断覆盖度及诊断列表精确度的独立评估。此外，研究构建了CardioKG-Agent这一知识增强型基线模型，融合实体归一化、约束图检索与指南证据推理，在三阶段对比实验中，经监督微调结合该代理系统的模型相较仅微调的模型，平均复合得分提升73.5%，体现了系统级性能增益。该工作为心血管领域诊断生成任务提供了可复现的评估框架与高性能基线，推动诊断优先级生成与知识增强型生成技术的发展。
链接: https://arxiv.org/abs/2611.04687
作者: Minghao Kong,Muyao Chen,Jianhao Wu,Yuqiang Zhang,Xiangbin Meng,Zirong Liu,Ting Cao,Kaida Ning,Zhen Song,Rongjie Wang
机构: Sun Yat-sen University (中山大学); Pengcheng Laboratory (鹏城实验室)
类目: Computation and Language (cs.CL)
备注: 26 pages,17 figures,5 tables,including appendices
Abstract:Identifying the primary diagnosis alone does not fully characterize a model’s diagnosis-generation performance. We introduce CardioVascBench, a retrospective benchmark for prioritized diagnosis generation from real-world Chinese cardiovascular inpatient records. It contains 3,262 de-identified cases, with 2,174 for training and 1,088 for evaluation, linking clinical narratives and textual ECG reports to expert-verified, ordered discharge diagnoses standardized to ICD-10. An English translation of the evaluation set supports aligned cross-lingual comparison. Our evaluation protocol combines terminology normalization and rank-aware scoring to separately assess primary-diagnosis identification, reference-diagnosis coverage, and list precision. Evaluation across 36 systems shows that higher primary-diagnosis accuracy does not necessarily imply higher list precision against discharge references. We also provide CardioKG-Agent, a knowledge-enhanced baseline combining entity normalization, constrained graph retrieval, and guideline evidence. In a staged comparison of three baseline models, supervised fine-tuning plus the agent achieves a 73.5% higher mean composite score than fine-tuning alone, representing a system-level gain. These results measure retrospective agreement with recorded diagnoses; agreement between metric rankings and physician judgments remains to be validated. CardioVascBench supports research on diagnosis prioritization and knowledge-enhanced generation.
[NLP-29] MIMIC-Note-Extract: A Large-Scale Structured Dataset Extracted from MIMIC-IV-Note Discharge Summaries ML4H2026
【速读】： 该论文旨在解决公开医疗数据集MIMIC中关键临床信息（如既往病史、过敏史及诊断结果）长期局限于自由文本的描述性出院总结，导致研究者需重复构建信息抽取管道的问题。其核心解决方案是提出MIMIC-Note-Extract（MNE），一个大规模结构化数据集，采用确定性解析与基于模式引导的大语言模型生成相结合的混合抽取流程，将30万份出院总结中的16个文本段落转化为结构化表格。该方案的关键在于通过融合规则与生成式AI（Generative AI）技术，在保证准确性（经医生验证各段落提取准确率超过92%）的同时，显著扩充了原始MIMIC数据中患者病史和用药史等关键字段的覆盖范围与临床细节，实现信息密度提升约10倍及就诊记录覆盖翻倍。基于MNE构建的临床决策基准进一步验证了结构化临床叙事表示对降低数据准备成本、促进大模型代理在医疗场景中可复用性的价值。
链接: https://arxiv.org/abs/2611.04685
作者: Kristian Schwethelm,Jonas Kuntzer,Conrad Ketzer,Eva Wende,Moritz Schlager,Daniel Rueckert,Georgios Kaissis,Friederike Jungmann,Paul Hager
机构: TU Munich (TUM); TUM University Hospital; Munich Center for Machine Learning (MCML); Hasso Plattner Institute for Digital Engineering, University of Potsdam; Imperial College London
类目: Computation and Language (cs.CL)
备注: Accepted at ML4H 2026, Proceedings Track. Dataset: this https URL
Abstract:MIMIC is one of the most widely used public hospital datasets for machine learning on electronic health records, yet clinically important information, such as past medical history, allergies, and diagnostic findings, remains confined to free-text discharge summaries. Studies requiring this information must therefore build their own extraction pipelines, duplicating substantial effort across projects. We release MIMIC-Note-Extract (MNE), a large-scale dataset that uses a hybrid extraction pipeline combining deterministic parsing with schema-guided large language model generation to transform 16 sections from 300,720 discharge summaries into structured tables. MNE adds information that has no structured MIMIC counterpart and enriches related tables with broader coverage and greater clinical detail, including 10 times more patient-history entries and medication histories for more than twice as many admissions. Validation by medical doctors confirms extraction accuracy above 92% across sections. Using MNE, we construct a clinical decision-making benchmark for LLM agents, demonstrating how reusable structured representations of clinical narratives reduce data curation effort.
[NLP-30] Self-Privileged Fine-Tuning
【速读】： 该论文旨在解决监督微调（Supervised Fine-Tuning, SFT）中因令牌级权重设计不合理而导致的训练目标不稳定性与泛化性能受限的问题。现有方法通过引入基于模型输出分布的动态权重，试图提升微调效果，但其优化目标缺乏理论清晰性。本文通过理论分析揭示：动态加权方法（如DFT）会诱导出高度尖锐化的分布，易导致训练不稳定；而静态加权方法则对应于数据分布与固定先验分布乘积的目标分布，其有效性高度依赖先验质量。针对此问题，论文提出自特权微调（Self-Privileged Fine-Tuning, SPFT），其核心创新在于利用一个冻结的初始模型，结合特权信息（如数学题的真值答案）作为先验，为微调过程中的令牌级权重提供指导。通过贝叶斯分解，SPFT进一步根据每个目标令牌对预测真实结果的信息量进行重加权，使更具预测性的令牌获得更高权重。实验表明，SPFT在数学推理和代码生成任务上显著优于标准SFT及其他基线方法，验证了其在提升训练稳定性和性能方面的有效性。
链接: https://arxiv.org/abs/2611.04632
作者: Yangrong Hu,Xueyu Zhou,Yue Wu,Jian Huang
机构: The Hong Kong Polytechnic University(香港理工大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Supervised fine-tuning (SFT) is a key component of language model post-training, enabling effective task adaptation and serving as an important foundation for subsequent reinforcement learning. Recent methods seek to improve SFT by introducing token-level weights into the training objective, where these weights are typically derived from a model’s output distribution at each token position. To better understand how such weighting schemes affect SFT, we analyze their optimization objectives and theoretically characterize the target distributions they induce. Our analysis shows that dynamic weighting methods, such as Dynamic Fine-Tuning (DFT), induce a heavily sharpened distribution, whereas static weighting methods fit a target distribution proportional to the product of the data distribution and a frozen prior. These findings suggest that static weighting provides a more stable training target, while its effectiveness critically depends on the quality of the prior distribution. Building on this observation, we propose Self-Privileged Fine-Tuning (SPFT), which uses a frozen copy of the initial model conditioned on privileged information (such as ground truth for mathematical questions) as a prior to provide guidance for token-level weighting during fine-tuning. Through a Bayesian decomposition, we further show that SPFT reweights the standard static prior according to how informative each target token is for predicting the ground-truth outcome, thereby assigning larger weights to more predictive tokens. Experiments on mathematical reasoning and code generation show that SPFT improves over standard SFT and other baselines across the main settings.
[NLP-31] Evaluating LLM s as Judges of Counterspeech Quality Against Online Hate Speech
【速读】： 该论文旨在解决社交媒体中仇恨言论（hate speech）长期暴露对用户造成的心理伤害问题，核心挑战在于如何高效、可靠地评估对抗性言论（counterspeech）的质量，以支持其有效部署。传统的人工评估方法因内容规模庞大而难以扩展，并且给标注者带来显著情绪负担；尽管生成式 AI（Generative AI）提供了可扩展的替代方案，但其可靠性仍存疑。为此，本文系统评估了12个不同规模与架构的开源大语言模型（LLM），在零样本（zero-shot）、少样本（few-shot）及思维链（chain-of-thought）提示策略下，对对抗性言论在相关性、连贯性、攻击性与适宜性四个维度上的质量进行点对点与成对比较评估。研究发现：（1）现有LLM与人类专家之间一致性较低，最高仅达到线性加权Cohen’s κ = 0.19（点对点）和Spearman相关系数ρ = 0.42，成对评估中κ值最高为0.25；（2）将点对点评分转化为成对标签后，显著提升了人-机一致性，例如Qwen 2.5 72B在零样本下从κ=0.25提升至κ=0.35，Mixtral 8×7B在少样本下从κ=0.07提升至κ=0.34；（3）模型间一致性远高于模型与人类专家之间的一致性，尤其在成对评估中，最强模型对（OLMo 2 32B与Llama 3.3 70B）在连贯性上达到κ=0.76，而其与专家的最高一致性仅为κ=0.21。因此，解决方案的关键在于通过将点对点评分转化为成对判断，从而有效缓解模型与人类评估之间的偏差，并利用模型间的高一致性构建更可靠的自动化评估框架。
链接: https://arxiv.org/abs/2611.04627
作者: Johnny Alali,Nicolás Benjamín Ocampo,Davide Ceolin
机构: Vrije Universiteit Amsterdam(阿姆斯特丹自由大学); Centrum Wiskunde Informatica(荷兰数学与计算机科学研究中心)
类目: Computation and Language (cs.CL)
备注:
Abstract:Prolonged exposure to hate speech on social media can cause lasting psychological distress among users. Counterspeech has emerged as an effective, non-censorial mitigation strategy, but its deployment requires rigorous quality assessment. Manual assessment is unscalable due to the large volume of content on the Web and imposes severe emotional strain on human annotators. Large Language Models (LLMs) offer a scalable alternative, yet concerns remain regarding their reliability. In this paper, we investigate the extent to which LLMs can effectively perform this task. We test 12 open-weight LLMs of different sizes and families under zero-shot, few-shot, and chain-of-thought prompting, to approximate pointwise and pairwise human expert assessment of counterspeech quality across four dimensions: relevance, coherence, aggressiveness, and suitableness. We find that (i) LLMs show low agreement and moderate rank correlations with human experts: our best configuration, Qwen 2.5 72B on coherence, achieves up to a linear-weighted Cohen’s \kappa_l = 0.19 and Spearman’s \rho = 0.42 in pointwise evaluation and an unweighted Cohen’s \kappa = 0.25 in pairwise evaluation. (ii) Converting pointwise scores into pairwise labels substantially improves human–LLM agreement for selected configurations. For same-instance coherence, Qwen 2.5 72B under zero-shot prompting achieves \kappa = 0.35 with converted labels, compared with \kappa = 0.25 when judging pairs directly, and Mixtral 8 \times 7B under few-shot prompting improves from \kappa = 0.07 to \kappa = 0.34 . (iii) LLMs agree more with one another than with human experts, particularly in pairwise evaluation, where the strongest model pair, OLMo 2 32B and Llama 3.3 70B on coherence, reaches substantial agreement with \kappa = 0.76 , whereas their agreement with the experts in the same configuration does not exceed \kappa = 0.21 .
[NLP-32] EMPO: Time Series Understanding via Discrete Tokenization ICML
【速读】： 该论文旨在解决时间序列分析中如何有效融合数值观测信号与文本上下文信息的问题，核心挑战在于如何将连续的时序信号以适配预训练语言模型（LLM）输入格式的方式进行表示。其解决方案的关键在于提出TEMPO框架，通过基于上下文感知的Transformer分词器对时序信号进行有限标量量化（finite scalar quantization），生成离散的625个代码构成扩展词汇表，并将这些信号代码作为“信号令牌”嵌入到模型的词元序列中。该方法利用冻结的分词器将四步滑动窗口的信号片段映射为离散代码，随后与尺度统计信息及文本一同通过预训练语言模型原有的自注意力机制进行处理。在模型微调阶段，仅更新新增令牌的嵌入向量以及轻量级的DoRA适配器参数。实验表明，基于Qwen3-4B主干模型并结合任务特定微调，TEMPO在活动识别、睡眠分期和轴承故障诊断任务上分别达到89.0%、85.0%和86.0%的宏平均F1分数，且在时序问答与定性故障诊断案例中展现出良好的跨模态理解能力。该研究证明了通过离散词汇扩展实现时序信号与文本语境融合的有效性，为多模态时间序列理解提供了新的范式。
链接: https://arxiv.org/abs/2611.04482
作者: Riccardo Maggioni(Forgis)
机构: Forgis
类目: Computation and Language (cs.CL)
备注: 9 pages, 2 figures. Presented at the 2nd ICML Workshop on Foundation Models for Structured Data (FMSD), 2026
Abstract:Time-series interpretation often requires both numerical observations and textual context. Integrating these inputs into a pretrained language model raises a representation question: how should a signal enter the model’s token sequence? We present TEMPO, which extends an LLM’s vocabulary with discrete codes from a context-aware Transformer tokenizer using finite scalar quantization. A frozen tokenizer maps four-step patches to a 625-code vocabulary; signal tokens, scale statistics, and text are then processed by the LLM’s existing self-attention layers. Adaptation updates the new token embeddings and DoRA adapters. With a Qwen3-4B backbone and task-specific fine-tuning, TEMPO achieves macro-F1 scores of 89.0% on activity recognition, 85.0% on sleep staging, and 86.0% on bearing-fault diagnosis. Time-series question answering and qualitative bearing-diagnosis examples further illustrate how this interface combines signal representations with textual context. Together, these results demonstrate the utility of discrete vocabulary extension for time-series understanding.
[NLP-33] ReListen: Training-Free Evidence Localization in Hour-Long Audio with Frozen Audio Language Models
【速读】： 该论文旨在解决长时音频（如一小时录音）中，生成式音频语言模型（Audio Language Model, ALM）因上下文长度限制而难以有效利用完整音频信息的问题。传统方法依赖转录文本（transcript retrieval）虽可缓解上下文长度限制，但会丢失声音和语音等关键声学信息；而需额外训练以实现音频定位的系统则存在部署成本高、泛化能力差等问题。本文提出ReListen——一种无需训练的自适应搜索机制，其核心在于让冻结的音频语言模型自主寻找与问题相关的证据片段。具体而言，模型采用类似Jevons扫描的方式，对每20秒窗口进行评分，评估其对回答问题的贡献度，并最终从得分最高的1分钟片段中生成答案。实验表明，该方法在VoiceGiraffe数据集上88.5%的问题能覆盖标注证据，显著优于基于转录文本的检索方式；且在不修改模型的前提下，其在LAT-Bench上的事件定位性能媲美专门训练的模型。更重要的是，使用少量音频片段即可达到或超越完整听觉输入的效果，且在语音与声音相关问题上，听觉输入比阅读转录文本更准确。此外，ReListen推理速度更快，内存占用恒定，适用于超长音频处理。因此，该方案的关键创新在于：通过无训练的自检索机制，使冻结的音频语言模型具备在长时间音频中自主定位关键证据的能力，既保留了原始声学信息，又避免了额外训练开销。
链接: https://arxiv.org/abs/2611.04480
作者: Ming-Hao Hsu,Shujie Liu,Yuxuan Hu,Yuxiang Wang,Yingda Shen,Jinyu Li,Zhizheng Wu
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:Audio language models are meant to answer questions from the audio itself, but an hour-long recording rarely fits in their context, and the answer often depends on a few seconds of it. Transcribing the recording first makes it fit but discards sounds and voices, while systems that return to the audio must be trained for this behavior. We propose ReListen, a training-free method in which a frozen audio language model finds its own evidence before answering. In a Jev-style scan, the model scores each 20-second window by the probability that it helps answer the question, and it then answers from the highest-scoring minute. This minute overlaps the annotated evidence far more often than transcript retrieval, for 88.5% of VoiceGiraffe questions. Without any change, the same scan also grounds events on LAT-Bench as well as a model trained for temporal grounding. Answering from it matches or exceeds hearing the whole recording with a small fraction of the audio, and on VoiceGiraffe it exceeds a transcript of the whole recording by 9.2 points. The selected evidence is also worth hearing rather than reading. Given the same excerpts, reading their transcript matches listening on speech content, but listening is clearly more accurate on questions about sounds and voices. Moreover, ReListen answers faster than every transcript-based system, and its memory stays constant even for recordings too long to hear whole on one GPU. Therefore, a frozen audio language model can search hour-long recordings by itself, without a transcript or additional training.
[NLP-34] Large Language Model-Assisted Adaptive Heuristic Search for Agile Earth Observation Satellite Scheduling
【速读】： 该论文旨在解决敏捷地球观测卫星在有限观测窗口内协调成像任务时面临的调度优化难题，尤其关注姿态机动与星上能量约束下的任务选择与资源分配问题。由于观测机会的竞争性，传统启发式规则对调度性能影响显著，而现有方法难以自适应地调整策略以应对不同场景。本文提出一种基于大语言模型（Large Language Model, LLM）的自适应启发式搜索方法：首先由LLM根据具体调度实例特征生成初始启发式规则；随后在蚁群搜索过程中，将搜索性能与调度信息反馈给LLM，动态优化并迭代更新启发式规则，通过成对方案构建验证改进效果后将其融入搜索流程。该方法采用统一调度框架，在满足姿态机动、能量及时间等约束条件下，同时决策任务选择、卫星指派与观测时机。六组包含50至200个成像任务的实验表明，所提方法相较最强基线平均提升4.3%–9.6%的观测收益，验证了其有效性与优越性。关键创新在于利用LLM实现启发式规则的动态生成与持续优化，显著增强了算法对复杂调度场景的适应能力。
链接: https://arxiv.org/abs/2611.04458
作者: He Wang,Yeye Liu,Ruijie Li,Xuemei Jiang,Junyu Wu,Lining Liu Zhuoqi Man,Tao Li,Yanjie Song
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 5 pages,4 figures
Abstract:Agile Earth observation satellites must coordinate imaging tasks within limited observation windows, while accounting for attitude maneuvering and onboard energy constraints. Competition among observation opportunities makes scheduling performance sensitive to the heuristic rules used for task selection. This letter proposes a large language model (LLM)-assisted adaptive heuristic search method for agile Earth observation satellite scheduling. The LLM generates an initial heuristic rule according to the characteristics of the scheduling instance. During the subsequent ant colony search, search performance and scheduling information are fed back to the LLM to refine the heuristic rule. The proposed modifications are evaluated through paired solution constructions before being incorporated into the search process. A unified scheduling procedure determines task selection, satellite assignment, and observation timing under the operational constraints. Experiments on six scheduling scenarios involving 50–200 imaging tasks demonstrate that the proposed method achieves 4.3%–9.6% higher mean observation benefit than the strongest baseline.
[NLP-35] Near-Zero Token Embeddings Silently Stall Training in Gated Sequence Models
【速读】： 该论文旨在解决生成式AI（Generative AI）中基于Transformer及门控序列模型（如Mamba、Gated DeltaNet、xLSTM）在持续预训练过程中因输入嵌入（input embeddings）接近零向量而导致的梯度爆炸与更新失效问题。具体而言，当未见过的稀有或模板化标记在预训练中获得零输入梯度，而AdamW权重衰减又持续收缩无共享权重（untied）的嵌入向量时，部分嵌入行的范数会降至低于10⁻⁶，形成“近零嵌入”（near-zero embeddings）。此类异常状态在门控结构中可导致当前令牌读出因子（如SiLU门控或线性查询）在零输入时消失，抑制历史状态读取，进而引发残差归一化（Residual RMS）值连续下降至RMSNorm下限平方根以下，使前置归一化（Pre-RMSNorm）逐层放大反向梯度高达10³倍。当梯度分量超过1.84×10¹⁹时，PyTorch、Hugging Face Trainer及FSDP2中的FP32全局范数裁剪机制将发生溢出，导致当前批次梯度被置为零，造成约20%训练步骤更新丢失且触发行永久冻结。解决方案的关键在于：一是针对已发布检查点提出GroupClip方法，通过64位或缩放后范数解耦输入嵌入裁剪，实现零开销修复并单步恢复受损嵌入；二是针对预训练阶段提出GateFloor机制（即SiLU(z)+δ，其中δ∈[0.01,0.1]），强制门控函数在零输入时保持正值，显著抑制近零梯度，实验表明其可降低梯度幅度达28个数量级，在最大370M参数规模下仍维持与基线相当的困惑度性能。
链接: https://arxiv.org/abs/2611.04449
作者: Xin Liu,Boyuan Sun,Yuxin Wang,Haoran Song,Mingjian Zhao,Mengyu Liu
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 16 pages, 6 figures
Abstract:Among 291 surveyed open-weight language model repositories (280 distinct embedding tables), 52.6% of untied gated and hybrid checkpoints (20/38) contain reachable input embeddings with norms below 10^-6 , compared with 0% of weight-tied models. Such near-zero rows can arise during pretraining when unseen rare or template tokens receive zero input gradients while AdamW weight decay shrinks untied embeddings. In tested Transformer configurations, non-initial positions or a BOS token prevent this failure mode. In gated sequence models (Mamba, Gated DeltaNet, xLSTM), a near-zero token can amplify input-embedding gradients by more than 30 orders of magnitude (up to 35). At zero input, current-token readout factors (SiLU gates or linear queries) vanish, suppressing historical-state readout. Residual RMS values can then fall below the square root of the RMSNorm floor across consecutive layers, causing Pre-RMSNorm to amplify backward gradients by up to 10^3 per layer. When a gradient entry exceeds 1.84 \times 10^19 , FP32 global-norm clipping in PyTorch, Hugging Face Trainer, and FSDP2 can overflow to infinity and zero the current batch’s gradients. In Telugu continual pretraining of Nemotron-3-Nano-4B, this discards updates on approximately 20% of steps and leaves triggered rows frozen. We propose two remedies. For released checkpoints, GroupClip decouples input-embedding clipping using a 64-bit or rescaled norm; its rescaled implementation eliminates update loss and heals triggered rows in one step with negligible measured end-to-end overhead. For pretraining, GateFloor ( \mathrmSiLU(z) + \delta , \delta \in [0.01, 0.1] ) keeps the gate positive at zero input, reducing near-zero gradients by 28 orders of magnitude in experiments up to 370M parameters, with perplexity comparable to the baseline.
[NLP-36] Sketchpad: certified exact arithmetic with a small vision-language model
【速读】： 该论文旨在解决大语言模型在处理长距离算术问题时缺乏可证明精确性的核心问题：尽管现有模型在训练样本上表现准确，但其准确性无法保证适用于未见过的输入，而依赖外部工具的方法虽能实现精确计算，却将计算过程移出模型内部，丧失了端到端的可验证性。其解决方案的关键在于设计一种受约束的计算范式——在Sketchpad框架中，通过限制模型每一步仅读取外部工作内存的有限视图（三单元格），并采用符号化运行时控制移动与写入行为，使模型的计算过程具备可形式化验证的性质。作者基于Lean证明，当模型仅依赖局部视图且运行时具有固定时间复杂度时，其对任意长度输入的精确性等价于对有限个视图配置的验证，从而将无限验证问题转化为可检查的有限问题。该方法不仅支持任意长度的有符号加减、乘除、开方及最大公约数计算，且对数值扰动具有鲁棒性，并可组合多个算术程序。实验表明，在笔记本电脑GPU上的浏览器环境中，该认证模型成功解决了200道预注册算术题及220道高达1,000位数字的加法与20位数字乘积问题，性能超越包括GPT-6.1 Sol在内的其他九种模型。此外，基于此架构构建的符号运行时可执行多项式求解、展开与因式分解，且通过适配器中的乘法门可在无需工作表时完全恢复原始基模型。
链接: https://arxiv.org/abs/2611.04434
作者: Mostafa ‘Neo’ Mohsenvand
机构: MIT Media Lab(麻省理工学院媒体实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
备注: 25 pages, 7 figures (6 + 1 Extended Data), 6 Extended Data tables. Demo: this https URL ; code (Apache 2.0): this https URL ; model: this https URL
Abstract:Language models are accurate on most long-arithmetic problems, but their accuracy is estimated on samples and carries no guarantee for an unseen input; tool use is exact but moves the computation outside the network. We show that exactness at all input lengths becomes a finite, checkable property of a network when each of its steps reads only a bounded view of an external working memory. In Sketchpad, Qwen3.5-0.8B with a low-rank adapter executes arithmetic procedures on a rendered sheet. Each forward pass reads a three-cell view and emits one step: a digit, a carry or borrow, and the next operation. A fixed runtime moves the view and writes no digit of a result. We prove in Lean that the model is then exact on all inputs of any length if and only if it is exact on the finite set of views the runtime presents, that no finite test suffices when steps also read earlier steps, and that the guarantee tolerates bounded numerical perturbation and extends to compositions of procedures. All 2,446 views pass on the deployment device, a laptop GPU running the model in a web browser. The certified model performs signed addition and subtraction, long multiplication and division, square roots and greatest common divisors at arbitrary length. It solved 200 of 200 pre-registered problems in the browser, and 220 of 220 sums of up to 1,000 digits and products of up to 20 digits, of which GPT-6.1 Sol, the strongest of ten other models, solved 217. A symbolic runtime built on these procedures solves, expands and factors polynomials with every number computed by the model, and a multiplicative gate on the adapter recovers the base model exactly when the sheet is not in use.
[NLP-37] ypological Feature Induction as a Benchmark for Inductive Metalinguistic Reasoning
【速读】： 该论文旨在解决现有元语言推理基准中规则推断与应用并存导致错误难以诊断的问题，其核心关注点在于从无标记语言的句子及其在已知枢纽语言中的翻译中，自动推断目标语言的类型学特征（typological feature），从而聚焦于规则推断这一关键环节。解决方案的关键在于提出一种可扩展的类型学特征诱导（Typological Feature Induction, TFI）评估范式，利用Grambank和多语种平行语料库自动生成覆盖195种语言的评估任务。实验表明，大语言模型（LLM）仅凭16个句子对即可识别59%–80%的目标语言，且翻译错位对准确率影响较小，暗示模型高度依赖先验语言知识而非实际语义分析能力。当文本被加密以防止直接识别时，语料利用率提升，但仅对目标语言与枢纽语言一致的特征有效；而对于无法识别的语言，开源模型性能未优于多数值基线。由于该任务与田野语言学家的实际需求高度契合，本研究揭示当前模型尚不具备胜任此类推断任务的能力，而TFI为持续追踪该方向进展提供了可扩展的评估路径。
链接: https://arxiv.org/abs/2611.04421
作者: Chen Zhang,Seth Aycock,Yusuke Miyao
机构: NII LLMC(国立情报学研究所语言与机器学习中心); University of Amsterdam(阿姆斯特丹大学); The University of Tokyo(东京大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Existing metalinguistic reasoning benchmarks require both inference of rules and their application, making failures hard to diagnose. We introduce typological feature induction (TFI) which targets the former step. Given sentences in an unnamed language paired with translations in a known pivot language, models must predict a typological feature of the unnamed language. From Grambank and multi-parallel corpora, we automatically build evaluation questions over 195 languages. We find that LLMs identify 59–80% of these languages from only 16 sentence pairs; and misaligning the translations barely affects their accuracy, suggesting reliance on prior knowledge of the language. Enciphering text to prevent recognition increases the corpus’ utility, but mainly for features where target and pivot languages agree. On languages which models cannot identify, neither open model outperforms a majority-value baseline. Since induction from raw text aligns with field linguists’ use-case, this work indicates the models tested are not yet ready for this role. TFI offers a scalable path to track progress in this direction.
[NLP-38] he Min-Max theory of language behavior
【速读】： 该论文旨在解决语言演化中如何协调处理效率最小化与信息量最大化这对核心矛盾的问题，即“极小-极大”（Min-Max）理论在语言行为中的可解释性与有效性验证问题。其关键解决方案在于通过构建计算模型对理论进行数学验证：利用合成数据对预训练模型与迭代学习模型进行实验，系统操纵词元长度与信息含量权重等变量，结果表明这些操作可引发符合理论预测的语序变化。研究进一步揭示了当前基于Transformer架构的语言模型与真实人类语言数据之间的差异，提示现有生成式AI模型可能未能准确反映心理语言学过程。总体而言，研究证实语言演化的轨迹高度受制于处理效率最小化与信息内容最大化之间的动态平衡。
链接: https://arxiv.org/abs/2611.04406
作者: Hiram Ring
机构: NTU Singapore(新加坡南洋理工大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:The “Min-Max” theory of language behavior proposes that humans engage simultaneously in minimization of processing effort and maximization of information content, and that this is a primary driver in language evolution (Ring, 2025). This claim seeks to unify observations from language processing (Mahowald et al., 2013; Futrell et al., 2015; Yadav et al., 2020; Hawkins, 2014), interaction (Hörberg and Sjons, 2023; Tucker et al., 2025; Jaeger and Tily, 2011; Smith and Culbertson, 2025), and corpora (Levshina and Moran, 2021; Levshina, 2023; Newmeyer, 2017), which suggest that considerations of processing efficiency and information content are both implicated in human communication. The interaction between these two processes provides an explanation for human linguistic behavior at various levels (Linders and Louwerse, 2023) and predicts historical developments in language, i.e. related to word order (Ring, 2025). Given that the original formulation of the Min-Max theory is based on observational data, the current paper seeks to validate the theory mathematically. Through a series of experiments that use computational models to investigate the theory’s predictions, pretrained and iterated learning models are queried with synthetic data. The experiments show that manipulating the variables of token length and the weight of information content in a tabula rasa model results in word order change, as predicted by the theory. The experiments also identify important differences between computational models and observations of human language data, suggesting that current transformer-based language models may not accurately represent psycholinguistic processes. The overall findings indicate that evolutionary trajectories in language are highly susceptible to minimization of processing and maximization of information content.
[NLP-39] Voluntary Servitude: Weaponized Safety Warning Mechanisms for Controlling AI Agents NEURIPS2026
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在依赖公开信息源时，因受到恶意诱导而做出错误决策的安全威胁问题。传统防御机制主要针对“指令注入”（instruction injection）攻击，即通过识别攻击者嵌入的特定命令语法来防范。然而，本文提出一种新型攻击范式——“自愿奴役”（voluntary servitude），其关键在于利用代理自身对风险规避的内在逻辑进行操纵：攻击者仅需生成一条看似普通的负面评价（虚构危害），即可引导代理主动排除合法选项、选择攻击者目标，且整个过程不包含可被检测的指令模式。实验表明，该方法在六种模型和十类消费服务场景中成功率达60.4%，与传统指令注入攻击（66.0%）相当，但能绕过现有注入检测器（检出率仅为良性流量），在过滤后实际攻击效能提升1.7至4.4倍。此外，该攻击在生产级浏览器代理及检索管道中均表现出更强的迁移能力，端到端成功率可达1.0%–57.0%。为应对该威胁，研究提出在代理指令中引入“多方验证”（corroboration requirement）机制，可在无需重新训练的前提下，使非自适应攻击者的有效暴露降低约95%，同时保持对恶意内容的正常读取，但不再执行其诱导行为。
链接: https://arxiv.org/abs/2611.04393
作者: Ilan Zendel,Matan Levy,Stav Cohen,Avi Mendelson,Amit LeVi
机构: Henry and Marilyn Taub Faculty of Computer Science, Technion(以色列理工学院计算机科学系); Viterbi Faculty of Electrical and Computer Engineering, Technion(以色列理工学院电气与计算机工程系); Faculty of Data and Decision Sciences, Technion(以色列理工学院数据与决策科学系); Zenity*Equal contribution
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at NeurIPS 2026 (Main Track). 17 pages, 7 figures, 8 tables
Abstract:LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a transaction, a dependency, or a decision with physical consequences. The security literature models this threat as instruction injection, in which an adversary writes a command into retrieved content and deployed defenses learn to recognize command syntax. We show that an attacker can instead steer an agent through the agent’s own harm-avoidance. A single fabricated hazard, written as an ordinary review, leads the agent to disqualify the legitimate options and select the attacker’s target by its own reasoning, with no command for a detector to recognize. We call this voluntary servitude. These payloads steer agents about as often as instruction injection (60.4% vs. 66.0%), and deployed injection detectors score them as benign, so behind a filter they are 1.7-4.4x more effective. We measure this at the generation stage across six models and ten consumer-service domains, for three attack families, and show that it transfers to production browser agents, outperforming instruction injection, and to a retrieval pipeline (1.0-57.0% end-to-end success, depending on the retriever). We then show that a corroboration requirement added to the agent’s instructions removes roughly 95% of the exposure against a non-adaptive attacker without retraining. With the requirement in place, the agent still reads the planted hazard and no longer acts on it.
[NLP-40] Do Multi-Turn Conversational Capabilities Carry Over from Text to Speech? A Parallel Multilingual Benchmark
【速读】： 该论文旨在解决多轮语音交互中模型在从文本输入转向语音输入时性能表现不一致的问题，特别是探究模型在不同语言下是否能有效保留对话历史、遵循持续性指令并跟踪对话状态变化。其核心挑战在于：现有评估多基于转录文本（transcript），但无法确定这些成功表现能否迁移至真实语音输入场景，且这种迁移能力是否存在语言差异。为此，研究提出Omnilingua-AudioMC——一个严格对齐的多语言语音-文本双模态基准，涵盖10种语言的354个对话及1,404项评分条目，确保内容、对话结构与评价标准在文本与语音条件下完全一致。关键发现表明，模型在语音输入下的表现显著依赖于模型架构和语言类型，即使在文本上表现相近的模型，在语音输入下也可能出现巨大差距，且跨语言优势排序可能发生改变。通过配对评分层级分析发现，整体性能下降可能掩盖了特定对话需求的保留情况，因为某些任务的损失被其他任务的提升所抵消。进一步通过替换前序用户话语、当前话语或两者为模型生成的转录文本，定位到可恢复的性能退化来源：对于性能差距最大的两个模型，用模型生成的历史替换原始语音历史能带来全局性显著提升，而在所有语言中均优于仅替换当前话语；而表现最强的语音模型则对两种替换方式均无明显增益。这表明，先前语音上下文的表示能力是导致性能下降的重要可修复因素，且当前基于文本的评估结果无法可靠反映模型在多语言语音交互中的真实能力。
链接: https://arxiv.org/abs/2611.04390
作者: Jiaming Zhou,Pei Zhang,Shiwan Zhao,Zhijun Wang,Baosong Yang,Yong Qin
机构: Nankai University (南开大学); Alibaba Token Hub, Alibaba Group (阿里巴巴令牌中心，阿里巴巴集团)
类目: Computation and Language (cs.CL)
备注:
Abstract:Successful multi-turn voice interaction requires models to retain information from earlier turns, follow persistent instructions, and track revisions to conversational state. These abilities are usually evaluated from transcripts, leaving open whether successes demonstrated from text carry over when the same turns are spoken, and whether this transfer is consistent across languages. We introduce Omnilingua-AudioMC, a strictly parallel multilingual extension of Audio MultiChallenge, to study this question. The benchmark contains 354 conversations and 1,404 rubric items across ten languages, with content, turn structure, cross-turn dependencies, and evaluation criteria aligned across transcript and speech conditions. Across six speech-language models, the shift from reference transcripts to direct speech input is strongly model- and language-dependent: models with similar transcript performance can diverge substantially under speech, and their relative strengths across languages can change. Paired rubric-level analysis shows that aggregate transcript-speech gaps can obscure which conversational requirements are preserved, because losses of transcript-demonstrated successes can be offset by gains on other items. Replacing preceding user turns, the current turn, or both with model-generated transcripts localizes recoverable degradation: for the two models with the largest gaps, history substitution yields a larger macro-level improvement than current-turn substitution in all ten languages, while the strongest direct-speech model gains little from either. These results show that transcript performance is an unreliable proxy for multilingual spoken conversational ability and identify the representation of preceding spoken context as an important source of recoverable degradation. Our code is publicly available at this https URL.
[NLP-41] Sparse Internal Control of Language Models NEURIPS2026
【速读】： 该论文旨在解决大语言模型在无需微调的情况下实现局部化、可逆且选择性行为控制的问题，特别是如何高效地设计紧凑、多位置的内部控制接口。其核心解决方案是提出稀疏内部控制（sparse internal control）框架，通过在推理阶段对模型内部少量关键节点施加干预，以引导目标行为变化。该框架的关键在于引入局部目标可控性（local target-controllability）形式化定义，从行为影响、目标恢复能力、非目标行为保持及干预能量消耗等多个维度约束控制效果。基于此，设计了两种互补的选择器：Driver-OMP 用于基于稀疏重构实现特定行为偏移，Coverage-Driver 则强调在不同输入下控制覆盖的稳定性。同时，论文提出六轴控制评估协议，涵盖可达性、符号反转、剂量响应、反馈可控性、非目标保持及未见任务复用等维度，系统评估干预效果。实验验证了在间接宾语识别、MMLU多项选择题、情感导向与拒绝响应控制等任务中，通过稀疏内部干预可有效实现任务定义行为的精准调控，并揭示了不同任务与方向信息下控制特性的差异。整体上，该工作为语言模型内部控制方法的研究提供了形式化基础、算法参考与评估标准。
链接: https://arxiv.org/abs/2611.04349
作者: Xizhe Zhang
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at NeurIPS 2026. 45 pages, 5 figures. Code and experimental records: this https URL
Abstract:Language models are increasingly used in text generation, decision support, and automated interaction, creating a need for localized, reversible, and selective control of their behavior. Activation steering demonstrates that internal interventions can alter the behavior of frozen models, but how to select and evaluate compact, multi-site control interfaces remains an open question. I introduce sparse internal control, a framework for steering target behavior by applying inference-time interventions to a small set of internal model nodes. A local target-controllability formulation characterizes candidate site–direction pairs by their behavioral effects and specifies constraints on target recovery, off-target preservation, and intervention energy. Two complementary selectors instantiate this formulation: Driver-OMP selects nodes for sparse reconstruction of a desired behavioral displacement, while Coverage-Driver favors stable coverage across prompts. I propose a six-axis control-evaluation protocol measuring reachability, signed reversal, dose response, feedback controllability, off-target preservation, and held-out reuse. Exploratory experiments on indirect object identification, MMLU multiple-choice question answering, sentiment steering, and refusal steering provide evidence for the feasibility of steering task-defined behavior through sparse internal interventions. The six-axis evaluation characterizes these interventions beyond target movement, revealing distinct control properties that depend on the task and available direction information. Together, the formulation, reference algorithms, and evaluation protocol provide a foundation for the systematic study and development of internal control methods for language models.
[NLP-42] xt-to-SQL Models Make Choices Without Saying So
【速读】： 该论文旨在解决生成式文本到SQL（text-to-SQL）系统在面对存在计算选择歧义的查询时，虽能正确执行但可能返回与用户意图不符结果的问题。具体而言，当自然语言请求未明确指定如“计数行数”或“计数唯一实体”等关键计算逻辑时，模型会自行做出默认选择而未进行说明，导致输出结果偏离用户真实意图。传统基准测试因仅以单一参考解释作为评分标准，无法识别此类问题。为此，作者提出SpecBridge评测框架，通过仅改变计算选择项、保持其余条件一致的方式，对同一问题构造三个变体：一个保留歧义、两个分别对应不同解释，并通过实际执行答案来评估性能。实验基于真实数据库和模型在现有基准中曾失败的典型歧义点，在204个人工验证测试中发现，尽管主流语言模型在明确要求时通常能正确计算每种解释，但在开放性请求下仅15%-58%的案例中同时返回所有解释，且极少主动提及该选择。问题根源在于报告缺失而非计算能力不足。进一步分析表明，现有基准甚至错误惩罚了正确的备选解释——在BIRD数据集中，超过五分之二被标记为错误的答案，在另一种解释下实为正确。由于核心问题是报告不完整，而非模型能力缺陷，论文提出一种轻量级解决方案：系统首先判断请求是否存在计算选择歧义，若存在，则通过独立调用分别回答每种解释。该方法在本研究测试集及现有模糊查询基准AMBROSIA上，使开放请求中完整覆盖所有解释的比例提升29-63个百分点，同时不降低清晰请求的精确率，且所需用户交互轮次少于直接询问澄清问题。
链接: https://arxiv.org/abs/2611.04318
作者: Yichen Xu,Shipei Lin,Chenhao Ma
机构: University of California, Berkeley (加州大学伯克利分校); School of Data Science, The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）数据科学学院)
类目: Databases (cs.DB); Computation and Language (cs.CL)
备注:
Abstract:A text-to-SQL answer can run, return a plausible number and still compute something other than what the analyst meant. This can happen when the request leaves a calculation choice open, such as whether to count rows or distinct entities, and the model settles it without saying so. Standard benchmarks cannot detect this, because they score each question against a single reference interpretation. We introduce SpecBridge, which tests such a choice by varying it alone: it poses three otherwise identical requests, one leaving the choice open and one per interpretation, and scores each answer by running it. The databases are real, and the choices are ones that made models fail on existing benchmarks. On 204 hand-validated tests, four language models usually compute each interpretation when asked for it, yet for the open request return both in only 15-58% of the tests where they can, often without mentioning the choice. The gap lies in reporting, not in computing. Benchmarks even penalize the alternative: on BIRD questions where such a choice changes the answer, over two in five of the answers marked wrong are right under the other interpretation. Because the gap lies in reporting, a system can narrow it without a stronger model: it first decides whether a request leaves a choice open, then answers each interpretation in its own call. On our tests and on AMBROSIA, an existing benchmark of ambiguous questions, this approach raises the share of open requests that get every interpretation by 29-63 points over the single answer that production assistants give, without lowering precision on clear requests. It also needs fewer user turns than asking a clarifying question.
[NLP-43] Coarse Semantic Blocks for Efficient Training-Free Sparse Attention
【速读】： 该论文旨在解决大模型在长上下文场景下注意力机制计算开销过大、推理效率低的问题，尤其是密集注意力（Dense Attention）在处理超长序列时带来的高延迟与高内存消耗。其核心解决方案是提出一种无需训练的稀疏注意力方法，通过构建针对提示（prompt-specific）的键（key）索引，将键向量聚类为语义上相关的大型块（large semantic blocks），这些块可包含非连续位置的词元。每个查询（query）通过评分块中心点（centroid）来选择相关块，并在所选块内访问原始键和值。由于多个查询可能选择相同块，系统可对它们进行分组处理，从而在不牺牲预填充（prefill）速度的前提下实现高效并行计算。该方法的关键在于利用大块结构降低中心点评分与分组处理的开销，使每个查询能在相同时间内关注更多词元，进而提升长序列任务中的吞吐量与准确性。实验表明，在NVIDIA H200上，该方法在512K token长度下实现2.3–2.4倍的预填充加速和1.7–2.0倍的解码加速，1M token长度下预填充加速达2.9–3.0倍；35B-A3B模型在1M token内保持接近密集注意力的性能，尤其在RULER、LongBench-v2和MRCR等长文本理解任务上表现优异。
链接: https://arxiv.org/abs/2611.04313
作者: Sosuke Kobayashi,Shoichiro Yamaguchi,Seiya Tokui
机构: Preferred Networks(Preferred Networks)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:We introduce a training-free sparse attention method that builds a prompt-specific key index to accelerate both prefill and decoding. The method clusters the keys into large semantic blocks that group tokens from noncontiguous positions. Each query position selects blocks by scoring their centroids and attends to the original keys and values within them. Queries that select the same block are processed together, so prefill stays fast even though each query chooses its own blocks. Large blocks make centroid scoring and this grouped processing cheap, so each query can attend to more tokens in the same time. Larger blocks with a larger token budget can thus be both faster and more accurate on long-context tasks than smaller blocks with a smaller budget. We evaluate two hybrid LLMs, Qwen3.5-4B and 35B-A3B, on one NVIDIA H200. Including the cost of building and using the index, whole-model wall-clock speedups over dense attention are 2.3-2.4x for prefill and 1.7-2.0x for decoding at 512K tokens, and 2.9-3.0x for prefill at 1M. The 35B-A3B model stays close to dense attention on five RULER tasks through 1M, on LongBench-v2 overall, and on MRCR through 256K. The 4B model loses accuracy mainly on multi-key retrieval, yet outperforms dense Qwen3.5-2B, whose latency is similar at 512K, on all three benchmarks.
[NLP-44] Neologism Inoculation Prompting
【速读】： 该论文旨在解决前沿模型中奖励黑客（reward hacking）等不良行为的抑制问题，现有接种提示（inoculation prompting）方法虽能有效降低模型表现出这些行为的倾向，但其使用自然语言编写的提示词，易被用户通过简单重述重新激发不良行为。为此，论文提出一种新型方法——新词接种提示（neologism inoculation prompting, NIP），其核心在于用一个经过训练的新符号（token）替代原有的英文提示词，该符号在模型冻结状态下进行训练，并在部署时移除。该方法的关键创新在于利用新词作为“控制符”，将有害行为隐藏于模型输入空间中难以触及的区域，而非直接消除相关行为路径，从而实现更隐蔽且鲁棒的行为抑制。实验表明，NIP在对抗奖励黑客方面表现优异，性能优于其他接种方法，与梯度路由（gradient routing）相当，但对奉承（sycophancy）和骚扰（harassment）类行为无效。通过激活分析与控制符实验，研究发现其失效原因在于新词机制并未真正删除或抑制特定行为，而是将其“隔离”至输入空间中不可达区域。本研究进一步揭示了新词（neologism）作为行为访问控制工具的潜力，为生成式人工智能（Generative AI）中对危险行为的精细调控提供了新的范式。
链接: https://arxiv.org/abs/2611.04260
作者: David Demitri Africa,Cameron Holmes
机构: Resolution; UK AI Security Institute
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 37 pages, 20 figures, 21 tables
Abstract:Inoculation prompting is a method to reduce undesired behaviours such as reward hacking in frontier models. It works by training a model on harmful examples under a prompt that specifies the undesired behaviour, which reduces the model’s tendency to exhibit it unless prompted. However, the inoculation prompt is written in plain English, so any user can re-elicit the behaviour easily. We introduce neologism inoculation prompting (NIP), which replaces the English prompt with a new token that is trained on the frozen model and removed at deployment. NIP outperforms other inoculation methods, and performs competitively with gradient routing despite being simpler. It also works on reward hacking, but fails on sycophancy and harassment. Using control tokens and analyses of activations, we identify the mechanism for this as NIP hiding the harmful behaviour behind an unreachable region of model inputs, rather than deleting it. More broadly, our work suggests that neologisms can be a powerful way to carve out and shape access to behaviours that are undesired or harmful.
[NLP-45] COMPREHEND: Ultra-Fine-Grained Benchmark Understanding by Entailment Hierarchy
【速读】： 该论文旨在解决现有大语言模型（LLM）评估方法中提示分类体系过于粗粒度的问题，即当前基于层级聚类并由生成式AI（Generative AI）合成类别名称的分类方法，其标签缺乏细节，难以准确反映用户提示的分布特征，也限制了对LLM性能优劣势的精细识别。其解决方案的关键在于提出COMPREHEND框架，通过自动构建超细粒度的蕴含（entailment）层级结构，将传统分类法中的抽象聚类名称替换为语义精确的自然语言描述句，从而实现更细粒度、更具解释性的提示分类。相较于当前最先进的EvalTree方法，COMPREHEND在WildBench基准上将蕴含/忠实性指标从22.6提升至41.7，信息量指标从16.3提升至39.1，且基于该蕴含层级生成的弱点报告在预测基准响应得分方面展现出显著更高的信息价值。
链接: https://arxiv.org/abs/2611.04249
作者: Sivaraaman Balakrishnan,Prakriti Shetty,Jeremiah Hong,Hamed Zamani,Haw-Shiuan Chang
机构: University of Massachusetts, Amherst, USA(马萨诸塞大学阿默斯特分校，美国); University of Arizona, Tucson, USA(亚利桑那大学图森分校，美国)
类目: Computation and Language (cs.CL)
备注:
Abstract:Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories is crucial for LLM practitioners. The existing methods satisfy the demand by hierarchically clustering the user prompts and synthesizing the cluster names using an LLM to build a taxonomy. However, the cluster names from these methods are usually too general for humans to understand the detailed prompt distributions and identify the exact strengths and weaknesses of LLMs. In this study, we propose COMPREHEND, which automatically constructs an ultra-fine-grained entailment hierarchy for the prompts. The entailment hierarchy replaces the cluster names in the taxonomy with sentences to provide more precise cluster descriptions for more fine-grained categorization. Compared with EvalTree, a state-of-the-art taxonomy construction method, COMPREHEND improves an entailment/faithfulness metric from 22.6 to 41.7 and an informative metric from 16.3 to 39.1 in WildBench. Our experiments also show that the weakness report generated based on our entailment hierarchy is much more informative than EvalTree in terms of predicting the response scores in the benchmark. All the code is released in this https URL.
[NLP-46] Constructing a Regional Slang Lexicon for Electricity Demands via Soft-Label Bidirectional Anchoring and Active Learning
【速读】： 该论文旨在解决电力网格客户服务中，用户在社区社交媒体（如微信）上使用非标准形态、语义漂移及词汇外表达的区域性方言描述故障、停电与账单问题时，传统自然语言处理（NLP）工具因缺乏适配而性能下降的问题。其核心挑战在于：方言表达的多样性导致人工构建领域词典成本高昂且难以覆盖全貌。为此，作者提出一种弱监督框架，通过精心筛选的方言锚点与电力领域种子词，结合点互信息（Pointwise Mutual Information）与软标签双向锚定机制生成候选词，保留方言特征的同时以元数据形式记录电力相关性，并利用Sentence-BERT语义相似度与jieba分词工具的词性得分联合评分。采用不确定性感知的主动学习策略，结合规则驱动与检索增强的大语言模型（LLM）作为知识源，在三种查询策略下迭代优化。最终在云南玉溪地区11,177条真实微信消息上验证，该框架实现评估者估计的方言精确率DP~ = ~1.0（零误报），且软标签分层策略相较严格电力过滤可多保留751%的候选词（从41增至349项）。经46名专家校准后，方言与电力相关性一致性分别达到Cohen’s κ=1.0 与 κ=0.85。最终产出25–33个词条的初步资源库，其中4–5个具电力相关性，为方言感知型电力服务NLP提供了可扩展的基础语料支持。关键创新在于：通过软标签层叠与多源融合的主动学习机制，在保证高精度的前提下显著扩展了方言词候选池，突破了传统强标注依赖与领域词典构建瓶颈。
链接: https://arxiv.org/abs/2611.04231
作者: Rong Chang,Lijing Shi,Zhaoxiang Zang
机构: Yuxi Power Supply Bureau, Yunnan Power Grid Co., Ltd., Yuxi, 653100, China; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650504, China; Key Laboratory of Artificial Intelligence in Yunnan Province, Kunming University of Science and Technology, Kunming, 650504, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Power-grid customer service increasingly relies on community social media, where customers describe outages, billing, and equipment faults in informal regional dialect. Such messages exhibit non-standard morphology, semantic drift, and out-of-vocabulary expressions that make manual lexicon construction costly and degrade conventional NLP tools. We propose a weak-supervision framework constructing a regional slang lexicon from curated dialect anchors and power-domain seed terms. Candidates are generated via Pointwise Mutual Information with soft-label bidirectional anchoring, retaining dialect-anchored terms while recording power-domain relevance as separate metadata, and are scored by combining Sentence-BERT semantic similarity with a jieba-based wordhood measure. Uncertainty-aware active learning with rule-based and retrieval-augmented LLM oracles is applied under three query strategies, and the resulting lexicon is evaluated by a higher-capability LLM calibrated against human expert annotations via Cohen’s \kappa . On 11,177 real WeChat messages from Yuxi (Yunnan, China), the framework achieves an evaluator-estimated dialect precision of DP~ = ~1.0 with zero false positives. An ablation shows that soft-label layering retains 751% more candidates than strict power-gating at identical precision, expanding the candidate pool from 41 to 349 terms. Human calibration ( n=46 ) yields \kappa_\textdialect=1.0 and \kappa_\textpower=0.85 . The resulting 25–33-term lexicon, 4–5 of which are power-relevant, provides a preliminary resource for dialect-aware power-grid service NLP.
[NLP-47] Shared Context Candidate Scoring with Jointly Adapted Readout Networks
【速读】： 该论文旨在解决在共享文档上进行多次问答时，如何高效复用文档计算并优化问题表示对候选答案的评分效率问题。其核心挑战在于在保持大模型（如Qwen3.8-27B）强大推理能力的同时，降低重复任务中的延迟并提升多轮问答的系统性能。解决方案的关键在于提出Q64系统：该系统保留一个冻结的64层文本骨干网络（text backbone），复用其混合前缀状态（hybrid prefix state）与中间文档表示，并通过联合训练一个残差适配器（residual adapter）与四层解码器及候选条件读出模块（candidate-conditioned readout），实现对答案得分的高效微调。实验表明，该方法在多基准宏平均准确率上从66.46%提升至74.32%，同时在合成负载下将中位冷请求延迟从5.383秒降至2.237秒，显著优于原生基线和批处理配置。研究结果表明，性能提升源于联合适应带来的精度-延迟权衡，而非单纯模型缩放或组件独立优势，且真实文档上的多问题验证仍待进一步探索。
链接: https://arxiv.org/abs/2611.04218
作者: Jingyang Du,Yiben Fu,Yang Shen
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 3 figures, 6 tables. Jingyang Du and Yiben Fu contributed equally to this work and share first authorship. Corresponding author: Yang Shen
Abstract:Repeated decisions over a shared document create two distinct opportunities: reusing document computation and adapting how question representations score candidate answers. We present Q64, a candidate-scoring system that preserves a frozen 64-layer Qwen3.8-27B text backbone, reuses its hybrid prefix state and intermediate document representations, and jointly trains a residual adapter with a four-layer decoder and candidate-conditioned readout. We used ZiYor, a self-evolving AI system, to explore and refine the Q64 design. We specified the scoring method, implemented the system, and evaluated its accuracy and latency. The final adaptation stage updates 88.66 million parameters using 8,192 selected training views. On a fixed 5,112-item evaluation combining ARC-Challenge, ANLI-R3, and stratified subsets of MMLU-Pro and CMMLU, joint adaptation improves four-benchmark macro accuracy from 66.46% to 74.32%. It remains below the native 27B baseline at 75.68% and AnyJev Qwen3-32B L0 at 76.68%. On a separate synthetic workload with 4,096 document tokens and 50 questions, median cold-request latency is 2.237 seconds, compared with 5.383 seconds for the native baseline and 56.872 seconds for the measured batched AnyJev configuration. Short single-question requests do not share this advantage. The evidence supports an accuracy-latency tradeoff in the evaluated systems, rather than a backbone reduction or an isolated component advantage. Public benchmarks informed earlier development, and real-document multi-question validation remains open.
[NLP-48] okenizer Myths and Where To Find Them
【速读】： 该论文旨在解决当前分词（tokenization）研究领域中存在的三个核心误解，这些问题导致对分词器性能评估的误判。首先，论文指出“每字节比特数”（Bits-Per-Byte, BPB）并非与分词器或词汇表大小无关——尽管其计算公式为每字节分词数乘以每个分词的比特数，但这两个因子并不抵消，实际中不同分词器在未训练模型上已表现出显著差异，表明模型尚未学习时，分词器-模型组合本身已隐含偏好。其次，论文反驳了“较低BPB即代表更优模型”的观点：在未训练模型上，BPB强烈偏好某些词汇表大小，而此时准确率仅为随机水平；即使在真实训练模型上，BPB与下游任务性能的相关性也较弱。第三，论文揭示所谓“分词器无关”指标如“统一格归一化损失”（unigram-normalized loss）并非真正无关，其与BPB高达0.989的相关性实为混杂变量所致，校正后相关性降至0.057，且两种指标在不同模型规模下选择的最优词汇表亦不一致。基于此，作者提出“分词器三难困境”（tokenizer trilemma）：任何评价指标都无法同时满足“仅对真实分布得分最高”、“相同预测得分为相同”和“所有未训练模型得分相同”这三个条件，因此选择评价指标本质上是选择研究问题。论文最终呼吁谨慎解读分词研究文献，并对未来工作提出假设性展望。
链接: https://arxiv.org/abs/2611.04215
作者: Rylan Schaeffer,Sanmi Koyejo
机构: Stanford Computer Science (斯坦福大学计算机科学系)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 35 pages
Abstract:Tokenization is a fundamental component of modern language models: before a model sees data, its tokenizer has already determined how the data appears. We identify and challenge three myths in the tokenization research literature. Myth #1: Bits-Per-Byte (BPB) is agnostic to the tokenizer and/or the vocabulary size: BPB is tokens per byte times bits per token, and the two factors do not cancel; using untrained models, we show that BPB prefers some tokenizers over others: the model has learned nothing, but the tokenizer–model pair has. Myth #2: lower BPB means a better model: On untrained models, BPB strongly prefers some vocabulary sizes, even though accuracy remains at chance, and on real models trained on the same data, BPB correlates weakly with downstream performance. Myth #3: different “tokenizer-agnostic” metrics yield consistent results: The “unigram-normalized loss” is not tokenizer-agnostic; its reported correlation of 0.989 with BPB comes from a confounder and falls to 0.057 once fixed; from the same data the two metrics select different vocabularies at each model size. We present a tokenizer trilemma: no metric can score only the true distribution best, give the same predictions the same score, and tie all untrained models, so choosing a metric is choosing a research question. We conclude with a caution for reading the tokenization literature and a conjecture for future work.
[NLP-49] Length Is Cheap Competition Is Not: Attention Predicts Context Rot in Retrieval
【速读】： 该论文旨在解决大语言模型在处理长输入时性能下降的根本原因问题。传统观点认为，模型失败主要归因于输入长度（length）的增加，但本文通过实验证明，真正关键因素并非长度本身，而是注意力竞争（competition for attention）。研究发现，在检索任务中，仅添加中性填充内容（neutral filler）对模型准确率几乎无影响（保持在0.97至1.00之间，长达32K token），而将与查询相关的证据信息以特定格式放置在同一位置时，准确率骤降至0.16至0.33。更关键的是，一个提供竞争性答案的陈述即可使损失增加9至110倍，且每增加一个竞争项，损失持续上升。模型内部分析表明，导致失败的核心指标是“注意力质量”（attention mass left on the evidence），其在126,473次实验中可解释高达58%的损失方差，远超对数长度所能解释的最多8%。此外，研究还揭示了“掩码填充协议”所依赖的假设存在缺陷：在可见控制条件下，掩码操作在32K token处导致45点准确率下降，而相同填充内容若保持可见则无影响。因此，该研究指出，在检索任务中，上下文退化（context rot）的本质是注意力资源在多个候选答案间的争夺，而非单纯由输入长度引起。解决方案的关键在于识别并缓解注意力竞争机制，而非简单地压缩输入长度。
链接: https://arxiv.org/abs/2611.04169
作者: Yinuo Liu
机构: University of California San Diego (加州大学圣地亚哥分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 15 pages, 2 figures, 5 tables. Code and trial-level data: this https URL
Abstract:Why do language models fail on long inputs? The common answer is length. We show that, for retrieval, length is cheap and competition is not. On five open models (0.5B to 3B parameters, four families), neutral filler costs nothing: accuracy stays at 0.97 to 1.00, up to 32K tokens on Qwen2.5-1.5B. Statements in the format of the evidence, placed at the same positions, drop it to 0.16 to 0.33. A single statement that gives the queried key a rival value multiplies the loss by 9 to 110, and every additional rival adds to it. Inside the model, one quantity tracks the failure: the attention mass left on the evidence. Across 126,473 trials it explains up to 58% of the variance in loss; log length explains at most 8%. The masked-filler protocol behind the claim that length alone hurts reproduces on MMLU (11 to 18 points lost at 30K tokens), but fails a visible control in retrieval: masking costs 45 accuracy points at 32K tokens, while the same filler left visible costs none. In retrieval, context rot is a competition for attention. We release the probe and all 126K trial-level measurements.
[NLP-50] PACT: Certifying What Is Written in Parallel Decoding of Diffusion Language Models
【速读】： 该论文旨在解决并行解码（parallel decoding）中因提交策略（commit rule）设计不当导致的生成质量下降问题。现有方法通过采样预测分布来选择每轮前向传播中写入的多个词元，但此类策略在采样时未考虑实际写入的词元，导致所选词元的可靠性与最终答案质量不一致。研究发现，答案的对数似然可分解为词元实际写入的可靠性与各步骤间的依赖性两部分，而当前提交规则未能有效保证前者。为此，论文提出PACT（Predictive and Certified Token selection），一种无需训练的提交规则：先采样候选词元，再对其构成的集合进行认证，并通过间隔各步骤的写入位置以增强独立性。该认证机制在任意温度下均成立，且理论上证明了不存在其他具备相同保障的规则能提交更多位置。实验基于LLaDA和Dream系列四个模型及HumanEval、MBPP、GSM8K、MATH500和IFEval等基准测试，结果表明，PACT在更少前向传播次数下达到与已有提交规则相当的生成质量；尽管间隔策略减少了单步写入数量，但因后续步骤可写入更多词元，整体解码成本反而降低。此外，在相同速度下，传统解码器更频繁地重复写出同一单词，而PACT显著减少此类现象。研究表明，提交规则应具备对所写词元的可验证性（certification），以提升生成可靠性与效率。代码已公开。
链接: https://arxiv.org/abs/2611.04152
作者: Yanchao Li,Yusu Zhang,Jiaqing Xie,Yi Ban,Ben Gao,Zhongyu Huang,Tianfan Fu,Yuqiang Li
机构: Nanjing University (南京大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL)
备注: 17 pages, 10 figures. Code: this https URL
Abstract:Parallel decoding speeds up masked diffusion language models by writing several tokens per forward pass. Published commit rules choose these tokens by testing the predicted distributions. Under sampling the written tokens are drawn, and these tests do not see them. We find that answer quality follows the reliability of the tokens actually written. An answer’s log probability splits into this reliability and the dependence within each step. We propose PACT, a training-free commit rule that draws the tokens first, certifies the set it will write and spaces the positions of each step apart. This certificate holds at every temperature, and we show that no rule with the same guarantee commits more positions. Experiments use four models of the LLaDA and Dream families and HumanEval, MBPP, GSM8K, MATH500 and IFEval benchmarks. PACT reaches the quality of published commit rules with fewer forward passes. Spacing removes positions from each step, yet it usually lowers the total cost because later steps then write more tokens. At the same speed, published decoders also write a word twice in a row far more often than PACT. These results suggest that commit rules should certify the tokens they write. Code is available at this https URL.
[NLP-51] AdSteer: Native Advertising in Large Language Model Generation
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）服务在用户规模快速增长背景下，因推理成本攀升而难以维持免费或低成本访问的问题。为实现可持续的商业化运营，亟需探索有效的替代变现机制。论文提出通过原生广告（Native Advertising）模式，将与用户意图高度契合的商业内容自然融入模型输出，从而创造经济价值。然而，现有方法如提示词级注入、多轮重写或微调等，在提升广告植入效果的同时往往导致响应质量下降或引入额外计算开销。为此，论文提出AdSteer——一种轻量级的解码阶段广告调控框架，其核心在于通过动态调整生成过程中各候选词的概率分布，实现对广告内容的上下文感知式融合。AdSteer能够自适应地控制广告影响的强度与持续时间，使赞助内容以自然、无缝的方式嵌入响应中。在GEM-Bench基准上针对三种典型应用场景的实验表明，AdSteer相较于最强基线平均提升整体评分14.1%，且相比基于重写的方案平均减少22.5%的总词元数量。结果验证了AdSteer在保持高质量响应的同时，实现了高效、低成本的原生广告集成，为大语言模型的商业化落地提供了可行且实用的解决方案。
链接: https://arxiv.org/abs/2611.04147
作者: Shuhang Lin,Chuhao Zhou,Zhenyu Wang,Xiao Lin,Changsheng Wang,Saibo Geng,Dimitris N. Metaxas
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language model (LLM) services face rapidly rising serving costs. To sustain free or affordable access for a broad user base, alternative monetization mechanisms are needed. Native advertising offers a promising approach, as it creates economic value by aligning user intent with merchant offerings. Yet how to integrate sponsored content naturally into LLM responses remains largely underexplored. Existing approaches primarily rely on prompt-level injection, multi-pass rewriting, or fine-tuning, which can compromise response quality or introduce additional computational costs. We propose AdSteer, a lightweight decoding-time advertising framework that adaptively adjusts token probabilities to integrate contextually appropriate advertisement into responses. AdSteer calibrates the strength and duration of advertising influence, making sponsored content a natural part of the response. Experiments on GEM-Bench across three application scenarios show that AdSteer achieves an average improvement of 14.1% in overall score over the strongest baselines and 22.5% fewer total tokens on average than rewrite-based methods. These results demonstrate that AdSteer provides an effective and practical solution for native advertising in LLMs.
[NLP-52] Where Do Distilled Students Store Thinking?
【速读】： 该论文旨在解决大模型（Large Reasoning Models, LRMs）在生成长推理链时，因输出过长而导致小模型（Small Students）性能下降的问题。现有方法通过在大模型与短答案模型之间进行加权插值（插值比例为λ）来缩短推理路径，但其内在机制尚不清晰。本文的核心问题是：当教师模型停止“思考”（即不再生成长推理链）时，学生模型的权重更新中哪些子空间仍能保留与“思考”相关的表征能力？研究发现，三个不同教师-学生对在不同λ值下的低秩适配器（LoRA adapters）更新子空间变化最显著的位置，恰好对应于教师模型停止思考且输出长度显著缩短的临界点。这一位置所对应的共享适配器子空间被识别为与“思考型行为”密切相关——包括反思性标记、独白式格式和长输出等特征。该子空间在教师停止思考前被多个学生适配器共同保留，但在思考行为终止后迅速退化，仅剩约一半能量；若将其移除，则所有教师-学生对、学生及基准测试中的思考型行为几乎完全消失。相比之下，仅移除更新方向中能量最高的成分，效果并不一致；而在多数情况下，随机方向的同等能量扰动即可达到相似效果。进一步实验表明，将该子空间以双倍权重嫁接到一个原本无思考能力的短答案学生模型中，可有效激活早期反思性标记，使其表现接近完整更新，但导致任务完成率显著下降。综上，本研究揭示了一个与“蒸馏思考行为”紧密关联的权重子空间，其存在与否直接决定学生模型是否具备类思维输出特性，为理解知识蒸馏中的认知行为迁移提供了关键线索。
链接: https://arxiv.org/abs/2611.04141
作者: Taeyeong Kim,Jaemin Jung,Ahhyun Kim,Unggi Lee
机构: Chosun University(全南大学); Korea University Sejong Campus(高丽大学世宗校区)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 19 pages, 4 figures. Code: this https URL
Abstract:Small students degrade on large reasoning models’ (LRMs’) long solutions, so recent work shortens them by interpolating an LRM with a short-answer model at ratio \lambda . We ask what student weights retain when this teacher stops thinking, comparing LoRA adapters of three students from three teacher pairs at each \lambda . In each pair, the adapters’ update subspace shifts most where the teacher stops thinking and solutions shorten most, locating the change without attributing it. We identify a shared adapter subspace tied to thinking-like behavior (reflective markers, monologue format and long solutions). Shared by adapters before that point, it keeps about half (post hoc) while one pair’s teacher halves its solutions but keeps its think tag, and largely empties after thinking stops. Removing it largely turns this behavior off for every pair, student and benchmark. Only in that pair and one other student does it leave clearly fewer markers than removing the update’s top components; elsewhere, same-energy random directions often nearly match it. Grafted into a short-answer student at twice its weight, it turns early markers (a post hoc count) on, like the whole update, but the student rarely finishes. Our work identifies a weight subspace tied to distilled thinking-like behavior.
[NLP-53] LoopKV: Loop-Aware KV Cache Compression for Serving Looped Transformers
【速读】： 该论文旨在解决生成式 AI（Generative AI）模型中循环变压器（Looped Transformer）架构在推理过程中因键值（KV）缓存内存占用过高而导致批处理规模受限的问题，尤其针对如 Ouro 模型中每轮循环保持独立 KV 缓存的场景。其核心挑战在于：传统统一位宽量化与固定规则分配无法根据各层、各循环阶段对精度的敏感度进行动态调整，导致资源利用效率低下；同时，跳过低注意力值以减少读取虽可节省开销，但误差随循环累积，影响输出质量。本文提出 LoopKV 机制，通过一次离线性能分析为每个（循环, 层）单元动态选择 4 位存储、2 位存储或复用最后一轮循环的 KV 缓存策略，结合基于预测损伤与存储预算的规划器优化读取次数。推理时，采用单一融合内核（fused kernel）在 vLLM 中执行该计划，仅在必要时读取低位信息，显著降低内存占用。实验表明，在单张 RTX PRO 6000 上，内存优化版 LoopKV-M 相比统一 4 位量化（KIVI-4）减少 35% KV 存储，并在最大长度 16K token 条件下维持 1.7–2.2 倍于 KIVI-4 的吞吐量，且六项公开基准测试平均表现未显著低于原始 BF16 精度，验证了其在精度与效率间的高效平衡。关键解决方案在于基于（循环, 层）单元级敏感性建模的自适应量化与读取调度策略。
链接: https://arxiv.org/abs/2611.04137
作者: Tianle Zhou,Yang Zhang,Tianyu Wu,Zhuohan Wang,William Nixon,Juncheng Yang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 28 pages, 15 figures
Abstract:Looped transformers apply a shared block several times per token to improve accuracy without adding parameters. For models such as Ouro that keep a separate key-value (KV) cache per loop, cache memory limits the batch size that amortizes repeated weight reads. Uniform-bit quantization and fixed rules per loop do not allocate precision according to each layer’s sensitivity in each loop. Measurements on Ouro show that quantization sensitivity varies across (loop, layer) cells, and the sum of individual cell damages approximates the overall output damage. Sharing damage also varies by cell, with later loops generally tolerating sharing better. Skipping low-attention values to save reads produces errors that compound across loops. LoopKV uses a one-time offline profile to choose 4-bit storage, 2-bit storage or reuse of the final loop’s KV for each cell. The planner minimizes reads under budgets for predicted damage and storage. During decoding, each loop reads the high 2 bits of each 4-bit value and fetches the low bits only where its own attention weights require 4-bit precision. A single fused kernel in vLLM executes the plan. Serving Ouro-1.4B on one RTX PRO 6000, the memory-optimized LoopKV-M plan stores 35% less KV than uniform 4-bit quantization (KIVI-4) on the same fused kernel and CUDA graph. It sustains 1.7-2.2x KIVI-4’s goodput under a latency service-level objective with a 16K-token maximum length. None of its averages on six public benchmarks falls significantly below BF16’s.
[NLP-54] Say Less Mean Better: Training-Free Verbosity Control for Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成文本时普遍存在的冗余问题，即生成内容过长导致推理延迟增加和计算成本上升。尽管提示工程（prompting）可在一定程度上引导简洁性，但如何在不牺牲响应完整性的情况下精确控制输出长度仍具挑战性；而过于激进的早期终止策略则可能造成结构不完整的截断输出。本文提出MinimalLM——一种无需训练的轻量级框架，其核心创新在于将“何时停止生成”与“停止是否符合结构完整性”进行解耦：通过引入基于不确定性的、依赖于长度的停用符（stop-token）动态调整机制，并结合结构完整性校验约束，实现对生成过程的响应级指令协调、解码阶段干预及完成性检查，且无需修改模型参数或引入辅助模型。实验在三个开源大模型上针对330个问题生成5940条响应，结果表明，MinimalLM可减少47.6%的生成词元数，单次响应加速19%-68%，并完全消除结构截断现象；在保持平均质量评分8.04/10的前提下，显著优于未修改模型（8.42/10）与等长截断方法（6.46/10）。跨医疗、编程、法律、科学等多领域验证显示其高效性具有鲁棒性，消融实验进一步证明解码控制与完整性约束的互补作用。研究强调，高效生成不仅依赖于减少词元数量，更关键在于精准控制生成终止时机的同时保障响应完整性。MinimalLM为此提供了一种无需训练、轻量高效的解决方案。
链接: https://arxiv.org/abs/2611.04107
作者: Mahmoud Alyosify,Mirna Imbabi,Eman Elkhamisi,Rahatara Ferdousi
机构: Queen’s University (皇后大学)
类目: Computation and Language (cs.CL)
备注: 16 pages, 11 figures
Abstract:Large language models (LLMs) often generate responses longer than necessary, increasing inference latency and computational cost. While prompting can encourage conciseness, controlling output length without compromising response completeness remains challenging. Aggressive early stopping can reduce generation costs but risks terminating responses prematurely. We introduce MinimalLM, a training-free framework for reliable verbosity control that combines uncertainty-aware, length-dependent stop-token adjustment with structural completeness guardrails. MinimalLM separates the decision of when to stop from whether stopping is structurally appropriate, coordinating response-level instructions, decoding-time intervention, and completion checks without modifying model parameters or requiring auxiliary models. We evaluate MinimalLM across three open-source LLMs and 330 questions, producing 5,940 responses across baseline and ablation configurations. MinimalLM reduces generated tokens by 47.6%, accelerates individual responses by 19-68%, and eliminates structurally truncated outputs in the primary evaluation. Despite substantial token savings, it achieves an average quality score of 8.04/10, compared with 8.42/10 for unmodified models and 6.46/10 for length-matched truncation. Evaluations across diverse subject domains (such as healthcare, programming, law, and science) demonstrate robust efficiency gains, while ablations establish the complementary roles of decoding control and completeness constraints. Our findings highlight that efficient generation requires more than producing fewer tokens: it requires controlling when generation ends while preserving response integrity. MinimalLM offers a lightweight, training-free approach toward this goal.
[NLP-55] Evaluation Contracts and Evaluation Leverag e: Sliced-Gap Readings in Structured Prediction Can Move Without Model Change ACL
【速读】： 该论文旨在解决自然语言处理（NLP）评估中普遍存在的“模型性能差异解读不可靠”问题，特别是文档级关系抽取中的“内部-跨文档差距”（intra-inter gap）被误用为模型能力证据的现象。研究表明，此类差距并非模型真实能力的独立反映，而是模型与评估契约（evaluation contract）共同作用的结果，其关键因素包括评分函数、标准答案版本、阈值设定、输出格式及提示语言等。研究提出“评估杠杆性”（evaluation leverage）理论框架，核心在于“排名翻转条件”（rank-flip condition），揭示了在固定预测结果下，仅通过更换标准答案版本即可导致方法排序反转的现象——例如，重命名操作为规范变换（Delta=0），而重新标注则带来显著性能变化（+0.862）。为此，论文提出审计化评估读取（Audited Evaluation Reading, AER）作为解决方案：一种契约无关、对齐打平（tie-declared）、种子可复现、门控过滤的评估流程，能覆盖所有由契约变动引发的排名翻转，在WMT19基准上实现14/14完全匹配，且在保留样本中达到73%、并集打平项达89%的覆盖率，其表现与最优单指标契约在ε=0.10等价水平相当。该方法通过保留失败预测作为边界、设置5%违规门限以识别不可验证场景并控制幸存者偏差，具备高度稳健性。研究还发布了预注册工作流EvalLever及其修订轨迹；在大模型作为评判者（LLM-as-judge）的实验中，审计集成使所有不稳定的配对趋于稳定（不稳定对从13–15降至0），而原预注册相关性准则失效，新稳定性准则通过验证，表明AER在提升评估可信度方面具有实质性优势。
链接: https://arxiv.org/abs/2611.04099
作者: Hao Lu,Weixing Zhu,Ruiyang Li
机构: Army Engineering University of PLA (中国人民解放军陆军工程大学)
类目: Computation and Language (cs.CL)
备注: 23 pages. Submitted to TACL
Abstract:NLP evaluation reports routinely cite sliced performance differences, most prominently the intra-inter gap in document-level relation extraction, as capability evidence. We show that such gap readings are joint properties of models and evaluation contracts: scoring function, gold version, threshold, output format, and prompt language. With predictions frozen, a method ordering reverses under a gold-standard exchange (+0.0363, CI excluding zero; tie-level after), movement localizing where gold re-partitions: renames are gauge transformations (Delta=0; re-annotation +0.862). We formalize this as an evaluation leverage account centered on the rank-flip condition. Constructively, the audited evaluation reading (AER) is a contract-marginalized, tie-declared, seed-prescribed, gate-filtered procedure whose tie declarations cover every contract-induced flip on the declared WMT19 grids (14/14; held-out 73% vs. union-of-ties 89%), matching the best single metric contract at epsilon=0.10 equivalence. Evidence spans DocRE, HacRED, a UD probe, and released gold revisions; an alternative-reference-set replication extends it beyond our testbeds. Failed predictions retained as boundaries; a 5% violation gate flags unverifiable settings and bounds survivor-selection bias. We release EvalLever, a preregistered workflow with an amendment trail; on LLM-as-judge grids, the audited ensemble stabilizes every unstable pair (13-15 to zero; generic ensemble shrinkage: the preregistered correlation criterion failed, the companion stabilization criterion passed).
[NLP-56] When Strong Reason ers Join the Pool: Small Union Premiums and Costly Runaway Generations in LLM Routing
【速读】： 该论文旨在解决路由基准测试中存在的一种混淆问题：传统基于“事后洞察”（hindsight oracle）的评估方式在随机解码（stochastic decoding）场景下，将两种不同性质的性能上限混合在一起——一是针对每道题固定选择单一模型的最优表现（即“预见性天花板”，clairvoyant ceiling），二是通过调用多个模型并保留任意一个正确答案所获得的额外收益（即“组合溢价”，premium）。先前研究已测得11个小型开源模型在MATH-500数据集上的组合溢价为3.5个百分点（pp），但当当前具备更强推理能力的模型加入时，这一溢价的实际大小尚不明确。本文提出了一种固定协议、预先承诺生成的实验设计，在单块NVIDIA B300硬件上对Llama-3.3-70B-Instruct、NVIDIA Nemotron Nano 9B v2与Nemotron Super 49B v1.5（基于同一70B架构的NAS衍生模型）进行系统评估，采用65,536词元上限、固定数学验证器（Math-Verify scorer）及追加式尝试记录表（append-only attempt ledger），共生成45,000条可评分响应。结果表明，两个推理模型均达到96.1%准确率，显著优于70B基线模型的76.2%。在所有两模型和三模型组合池中，实证的组合耦合溢价（product-coupling premium）为0.215–0.588 pp；相较于始终调用Nano模型的策略，引入插件式选择机制仍保留1.27 pp的提升空间；而对Nano模型重复采样三次，其“任意正确”（any-correct）率几乎等同于一次性调用全部三个模型。尽管仅有31条响应达到长度上限，且其中25条呈现周期性尾部特征，但这些被截断的输出占到了模型输出长度平方代理指标高达60.2%的比例，且过长生成导致计划中的Llama-3.1-8B模型无法完成任务。因此，本文指出，实际组合溢价远低于此前3.5 pp的结果，但需注意模型、池规模与解码协议同步变化，故应明确报告任何“洞察者差距”（oracle gap）时所依赖的具体模型池、协议、每题采样次数及解码预算。
链接: https://arxiv.org/abs/2611.04098
作者: Teng-Ruei Chen
机构: National Yang Ming Chiao Tung University (国立阳明交通大学); Krixvon (台北, 台湾)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Technical report. 10 pages, 7 figures, 14 tables. Companion to arXiv:2607.03436 . Data and code: this https URL
Abstract:Routing benchmarks often judge a router against a hindsight oracle that counts a query as solved whenever any model’s recorded answer is correct, but under stochastic decoding that oracle mixes two different objects: the clairvoyant ceiling of committing to one model per query and the premium of calling several models and keeping any correct answer. A companion study measured this premium at 3.5 percentage points (pp) for 11 small open models on MATH-500; how large it is once current reasoning models enter the pool was unknown. We measure it with a fixed-protocol, pre-committed generation on a single NVIDIA B300: all 500 MATH-500 problems, 30 seeded samples per problem, Llama-3.3-70B-Instruct and NVIDIA’s Nemotron Nano 9B v2 and Nemotron Super 49B v1.5 (a NAS derivative of the same 70B) under vendor sampling, a 65,536-token cap, a pinned Math-Verify scorer, and an append-only attempt ledger, for 45,000 scored responses. The two reasoning models both reach 96.1% accuracy against 76.2% for the 70B baseline. The empirical product-coupling premium is 0.215-0.588 pp across all two- and three-model pools; the empirical plug-in selection headroom over always calling Nano is 1.27 pp; and resampling Nano three times gives practically the same empirical any-correct rate as calling all three models once. Only 31 responses reached the cap, 25 of them with exactly periodic tails, yet the capped responses account for up to 60.2% of a model’s output-length-squared proxy, and long generations made a fourth planned model, Llama-3.1-8B, too slow to finish. The premium is far below the companion’s 3.5 pp, but models, pool size and protocol changed together; an oracle gap should therefore be reported with the pool, the protocol, the samples per cell and the decoding budget that produced it.
[NLP-57] Same Rule Different Verdicts: Rubric Language and Clause Structure in LLM Judges for Korean Support Dialogues
【速读】： 该论文旨在解决生成式 AI（Generative AI）在多语言评估中因评价标准（评价量规，rubric）语言差异导致的判断不一致问题，特别是探究评价量规的语言（韩语与英语）及其表述结构（肯定、否定、条件、例外）对大语言模型（LLM）评判结果的影响。其解决方案的关键在于系统性地设计12项支持对话的评价标准，采用四种句式结构（肯定、否定、条件、例外），并确保两组结构语义等价，通过在韩语和英语双语环境下对同一对话数据集进行跨模型评估，量化不同语言量规下模型判断的一致性差异。研究发现，使用韩语量规时跨供应商模型间一致性显著下降（Cohen’s kappa 0.687 vs. 0.745），且否定、条件和例外类条目引发的分歧尤为突出（相对于肯定类条目的优势比为5.6），同时同一模型在语义相同的条目间也表现出11%-15%的判决不一致率。此外，研究未验证“同提供商模型一致性更高”或“引入成对示例可缩小语言差距”等假设，表明语言表达形式对模型判断具有深层影响，而现有策略难以有效缓解该问题。整个分析流程预先注册，且所有材料由大语言模型生成，仅依赖自动一致性指标，未引入人工标注。
链接: https://arxiv.org/abs/2611.04091
作者: Sangdo Han,Moonsoo Kim
机构: CEO Business School(CEO商学院)
类目: Computation and Language (cs.CL)
备注: 16 pages, 2 figures. Data and code are included as ancillary files
Abstract:Services that operate in Korean often write their evaluation rubrics in Korean and hand them to an LLM judge. We test whether a judge’s verdict depends on the language of the rubric and on how a rubric item is phrased. We write 12 criteria for support dialogues in four clause structures (affirmative, negated, conditional, exception) such that two pairs of structures have the same meaning, and we give each item in Korean and in English. Six low-cost models from three providers, plus one reference model, judge 60 translated ESConv dialogues and 144 constructed boundary cases under every combination (51,744 requests across the seven models). The analysis plan was fixed and committed before data collection. Cross-provider agreement is lower with Korean rubrics than with English rubrics (Cohen’s kappa 0.687 vs. 0.745; difference -0.058, 95% CI [-0.072,-0.045]). Disagreement concentrates in negated, conditional, and exception items (odds ratio 5.6 relative to affirmative items). A single model changes its verdict between two items with the same meaning in 11-15% of cases. Two hypotheses are not supported: models from the same provider do not agree more than models from different providers, and adding paired examples does not reduce the language gap. We measure consistency only; no human labels are used, and all materials were drafted by an LLM.
[NLP-58] Swapping the Judge Changes the Ranking: A Preregistered Study of Cross-Provider Replication in LLM -Judged System Comparisons
【速读】： 该论文旨在解决在缺乏人类标注的情况下，仅依赖大语言模型（LLM）裁判进行系统评估时结论的可靠性问题。其核心挑战在于：当前实践中常通过调用不同供应商的多个LLM裁判进行结论复现以增强可信度，但这种做法的有效性尚未得到充分验证。研究的关键发现是，尽管部分裁判与人类投票具有一定的一致性（中位数Cohen’s kappa为0.22），但系统排名在更换裁判后表现出显著不稳定性（中位数Kendall’s tau为0.67，范围0.17–0.89），且预注册假设——即排名应保持稳定——未获支持。进一步分析表明，使用两个来自不同提供商的低成本裁判进行复现虽可略微提升精度（+0.10），但主要收益源于排除了一个严重偏离的人工裁判，而涉及该裁判的复现仅能覆盖35%的比较对。更重要的是，多数裁判未能在合理样本量内达成一致结论（26%-70%的系统对无法收敛），且裁判间的分歧远高于独立性假设预测水平，提示存在共享偏差而非随机噪声。因此，该研究的核心解决方案之关键在于揭示：简单地通过多裁判复现无法可靠保障结论稳定性，必须识别并剔除明显偏差的裁判，并警惕裁判间共有的系统性偏见。
链接: https://arxiv.org/abs/2611.04090
作者: Sangdo Han(CEO Business School),Moonsoo Kim(CEO Business School)
机构: CEO Business School(CEO商学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 10 pages, 2 figures. Code and judge verdicts are provided as ancillary files
Abstract:Practitioners who compare systems with an LLM judge and have no human labels often check a conclusion by repeating it with a judge from another provider. We test how far that check can be trusted. In a preregistered study (with a disclosed post-registration reduction of the judge set), five judge models from three providers re-judged 4,000 Chatbot Arena battles (nine systems, 33 system pairs) that carry human votes, each in both response orders. Agreement with the human vote was low at the verdict level (median Cohen’s kappa = 0.22), and system rankings did not survive a change of judge: Kendall’s tau with the human ranking ranged from 0.17 to 0.89 (median 0.67), so the preregistered hypothesis that rankings remain stable was not supported. Conclusions replicated by two low-cost judges from different providers matched the human conclusion more often than single-judge conclusions (+0.10 precision), but the gain came from filtering out one poorly aligned judge, and replication involving that judge left a conclusion for only 35% of comparisons. Judges that agreed with humans reached a stable conclusion with no more verdicts than human votes required; the others failed to converge on 26-70% of system pairs at the sample sizes tested (up to 100 verdicts per pair). Disagreements with the human vote co-occurred across judges far more often than independence predicts, although a single human vote per battle cannot separate shared judge bias from voter noise.
[NLP-59] AlgoPilot: Cross-Paradigm Reasoning in Language Models via Strategy Selection and Guidance
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）在处理跨领域复杂问题时面临的双重挑战：一是准确识别适用于特定问题的求解范式（paradigm），二是正确构建与之匹配的结构化问题求解框架。传统基于提示（prompting）的方法依赖固定的推理模板，难以适应多样化任务需求。为此，论文提出AlgoPilot——一种跨范式推理框架，其核心在于通过三个组件实现动态策略选择与结构化建模：1）经过监督微调（SFT）和强化学习（RL）训练的SteerLM，可依据输入问题自适应地选择最优求解范式及模式；2）对应专家级GuideLM，通过SFT训练生成针对所选范式的结构化引导；3）ExecutionLM，基于选定策略及其形式化指导生成最终解决方案。该框架突破了静态提示的局限性，实现了从问题理解到求解执行的端到端自适应推理。实验在5个问题类别、13个基准测试及66个具体领域上验证了AlgoPilot的有效性，相较于同规模小模型平均准确率从38.4%提升至72.9%，超越最佳大型模型提示基线GLM-5达11.7%。消融实验进一步证实各模块贡献，且学习到的策略引导与形式化模块具备跨执行模型迁移能力，表明其具有广泛提升其他大模型性能的潜力。
链接: https://arxiv.org/abs/2611.04079
作者: Yilun Hao,Ruixiao Yang,Hudson Hilal,Yongchao Chen,Kaizhi Qian,Yada Zhu,Yang Zhang,Chuchu Fan
机构: Massachusetts Institute of Technology(麻省理工学院); Harvard University(哈佛大学); MIT-IBM Watson AI Lab
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 49 pages, 3 figures, 22 tables
Abstract:Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optimization, and often require fundamentally different solution paradigms. A central challenge for LLM-based reasoning is twofold: identifying an appropriate problem-solving approach and formulating it correctly. In this paper, we propose AlgoPilot, a cross-paradigm reasoning framework that enables adaptive selection of the solving approaches and structured problem formulation prior to execution. The AlgoPilot framework contains three components: 1) a SteerLM, trained via supervised fine-tuning (SFT) and reinforcement learning (RL), that adaptively selects the solving strategy (paradigm and modes) based on input problems, 2) the corresponding expert GuideLM, trained via SFT, that generates structured formulation guidance tailored to the selected strategy, and 3) an ExecutionLM that generates solutions conditioned on both the selected strategy and its formulation. Unlike prompting-based methods that rely on fixed reasoning templates, our approach learns to adaptively choose and structure problem-solving strategies based on the input. We build AlgoPilot on Qwen3-8B and evaluate across 5 problem categories and 13 benchmarks with 66 unique domains, comparing against both the latest similarly scaled small models and large LLMs. AlgoPilot significantly improves average accuracy for the same model from 38.4% to 72.9%, outperforming the best large LLM prompting baseline GLM-5 by 11.7%. Ablation studies confirm the effectiveness of each component. Notably, we show that the learned steering and formulation modules can transfer across execution models, suggesting their potential to improve other LLMs. Project page: this https URL.
[NLP-60] Language Model Augmented Decision Transformer for Multi-Task Offline Reinforcement Learning
【速读】： 该论文旨在解决离线多任务强化学习（Offline Multi-Task Reinforcement Learning, MTRL）中的两大核心挑战：任务间数据稀缺性（per-task data scarcity）与跨任务泛化能力有限。现有方法在处理异构离线数据集时，难以有效利用有限的样本实现良好的泛化性能。针对此问题，本文提出一种基于大语言模型（Large Language Model, LLM）增强的决策变换器框架——LaMA-DT，其关键创新在于通过自然语言任务描述引导任务对齐的轨迹增强，并利用评分与筛选机制保留高质量增强轨迹；随后，同一LLM被重新用于构建语言条件化的决策变换器，通过真实数据与增强数据的混合训练实现统一策略学习。该方案的核心优势在于以语言为桥梁，实现语义感知的轨迹生成与策略优化，显著提升了低数据场景下的跨任务泛化能力，在Meta-World和MuJoCo多任务基准上均优于现有基线方法。
链接: https://arxiv.org/abs/2611.04031
作者: Xinbo Zhao,Yuhang Liu,Xin Zhang,Lei Le,Zhaohan Xi,Yu Yang,Yingxue Zhang
机构: State University of New York at Binghamton (纽约州立大学宾汉姆顿分校); San Diego State University (圣地亚哥州立大学); LinkedIn (领英); Lehigh University (利哈伊大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline datasets that generalizes across tasks. However, existing offline MTRL methods often struggle with two fundamental issues: severe per-task data scarcity and limited cross-task generalization. In this paper, we propose Language-Model Augmented Decision Transformer (LaMA-DT), a unified sequence-modeling framework that leverages a single large language model (LLM) for both trajectory augmentation and multi-task policy learning. LaMA-DT uses natural-language task descriptions to guide task-aligned trajectory augmentation through a set of supervised generation and alignment objectives, and incorporates a scoring-and-filtering mechanism to retain high-quality augmented trajectories. The same LLM is then repurposed as a language-conditioned Decision Transformer and fine-tuned on a mixture of real and augmented data. Experiments on Meta-World and MuJoCo multi-task benchmarks demonstrate that LaMA-DT consistently outperforms competitive baselines, particularly in low-data regimes, showing that language-guided data augmentation substantially improves cross-task generalization.
[NLP-61] WASP-Mem: Let Agent Organize Procedural Memory. Human Spend Effort on Diagnosis
【速读】： 该论文旨在解决大语言模型（LLM）代理在自动化复杂企业流程时面临的根本性挑战：静态预构建的执行环境无法捕捉企业特有的政策规则、未文档化的行为模式以及仅在执行过程中显现的状态依赖型恢复路径。现有方法难以动态适应这些动态且隐含的知识，导致代理在真实场景中表现受限。其解决方案的关键在于提出一种名为WASP-Mem（基于维基的代理合成过程记忆）的新方法，通过将代理执行轨迹与反馈信息提炼为持久、相互关联的维基页面形式，实现无需训练的持续学习机制。该方法的核心创新在于强调“让代理组织过程记忆，而人类专注诊断”，实证表明，由人类对失败轨迹进行诊断所生成的修正知识，显著优于自动判断或结构化模板带来的性能提升；单轮人工诊断的效果甚至超过四轮自评，揭示出过程知识的高效沉淀应以人类智能聚焦于错误归因与修复策略生成，而非记忆结构设计。此外，该方法在TheAgentCompany基准测试中实现了4.6%-6.1%的通过率提升，同时存储开销降低57%-61%，验证了其在实用性与效率上的双重优势。
链接: https://arxiv.org/abs/2611.04003
作者: Peizhi Niu,Ge Shi,Vaibhav Gorde,Sarthak Ghosh,Christos Faloutsos,Min Cheng,Kevin Chen
机构: University of Illinois Urbana-Champaign; Amazon
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 24 pages (10 main + references and appendix), 6 figures, 14 tables. Peizhi Niu and Ge Shi contributed equally
Abstract:LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture proprietary policies, undocumented behaviors, or state-dependent recovery paths that surface only during execution. Procedural memory accrued from agent experience offers a training-free path to closing this gap – yet unlike episodic records or semantic facts, it must consolidate repeated successes and diagnosed failures into reusable actions and recovery strategies. We introduce WASP-Mem (Wiki-based Agent-Synthesized Procedural Memory), a nimble but effective method that distills execution traces and feedback into persistent, interlinked wiki pages, enabling continual agent learning. On TheAgentCompany benchmark, WASP-Mem raises the checkpoint pass rate by 4.6-6.1% over the strongest baseline we compared across three models while requiring 57-61% less storage. We further present a systematic controlled study spanning consolidation method, feedback type, trace ingestion scope, and refinement rounds. Our results reveal that prescriptive knowledge organization such as human defined schemas and explicit deduplication rules hurts performance rather than closing the procedural-knowledge gap. In contrast, human diagnosis converts failed traces into corrective knowledge and drives sustained improvement: a single round of human diagnosis outperforms four rounds of self-judgment by 7.0-10.9%. These findings point to a simple division of labor: let agents organize procedural memory, and invest human effort where it matters most – diagnosis.
[NLP-62] Dory1.0-4B: A Looped Normalized-GPT
【速读】： 该论文旨在解决大模型在推理阶段计算资源与性能之间权衡的问题，特别是在长文本理解、复杂任务执行（如代码生成与智能体行为）中如何实现高效且可扩展的推理能力。其核心挑战在于：传统模型在面对不同计算预算时需部署多个独立版本，导致资源冗余；而现有方法难以在不显著增加输出长度或内存开销的前提下提升长上下文任务的准确性。解决方案的关键是提出一种具有弹性深度（elastic depth）的循环式（looped, latent-recurrent）语言模型Dory1.0-4B，其基于归一化Transformer（nGPT）架构，采用交错滑动窗口注意力与全注意力层的设计，并将网络结构划分为输入块、共享循环块和输出块。在推理阶段，通过可变次数地重复应用共享循环块，实现对不同计算预算的动态适配——更高的循环次数带来更优的精度，尤其在编码、智能体代理及长上下文任务上表现突出。此外，该模型引入难度感知的循环课程学习策略，将复杂提示引导至更深的循环层级，进一步优化性能。为降低内存消耗，仅缓存最后一次循环的键值（KV-cache），使缓存大小恒定于非循环模型水平，同时保留绝大部分精度增益。实验表明，仅需两轮及以上循环，Dory1.0-4B即超越更大规模模型（如Qwen3.5-9B、Gemma 4 12B、Gemma 4 26B-A4B）在12项基准上的平均表现，并在智能体任务中实现1.3–2.5倍的生成效率提升，因其额外计算在潜在空间进行而非延长输出序列。该工作实现了高精度、低延迟与低显存占用的统一，为轻量级高性能推理提供了新范式。
链接: https://arxiv.org/abs/2611.03998
作者: Maxim Fishman,Simeng Sun,Shantanu Acharya,Faisal Ladhak,Brian Chmiel,Santiago Akle Serano,Ron Banner,Boris Ginsburg,Krishna C. Puvvada
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:We introduce Dory1.0-4B, a 4-billion-parameter looped (latent-recurrent) language model with elastic depth: a single checkpoint serves different inference compute budgets. Dory1.0-4B is built on a normalized Transformer (nGPT) backbone with interleaved sliding-window and full-attention layers, organized into an input block, a shared recurrent block, and an output block. At inference, the recurrent block can be applied a variable number of times. We pre-trained Dory1.0-4B on 6 trillion tokens. The first 4.5 trillion tokens were trained in NVFP4, and additional pre-training then extended the context length to 256K tokens. Next, we post-trained the model using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), with a difficulty-aware loop curriculum that routes harder prompts to deeper loops. Increasing the loop count improves accuracy, with the largest gains on coding, agentic, and long-context benchmarks. With two or more loops, Dory1.0-4B outperforms larger models such as Qwen3.5-9B, Gemma 4 12B, and Gemma 4 26B-A4B on average across 12 benchmarks. Looping also makes Dory1.0-4B more token-efficient on agentic benchmarks: with four loops, it generates 1.3-2.5x fewer tokens than with one loop, as the extra computation is spent in latent space rather than on longer outputs. To reduce memory during looping, Dory1.0-4B caches keys and values only from the last loop, keeping its KV-cache footprint equal to that of a non-looped model at any loop count while retaining most of the accuracy gains. We open-source the BF16, MXFP8, NVFP4, and GGUF quantized checkpoints.
[NLP-63] Whose startup does the AI back? An algorithm audit of reputation and demographic signals in LLM -assisted crowdfunding
【速读】： 该论文旨在解决生成式 AI（Generative AI）在众筹投资决策中是否以及如何基于创始人性别与族裔特征进行偏好判断的问题，尤其关注其是否会复制、消除或逆转创业金融领域已知的种族与性别歧视。其核心解决方案的关键在于通过预设的基于选择的联合实验（choice-based conjoint experiment），系统性地考察七种大型语言模型（LLM）在不同投资者角色设定下对合成众筹项目的选择行为。实验设计包含独立随机化的项目质量信号与创始人姓名，覆盖三种投资者人格画像及无画像控制组，共收集40,248次响应。结果显示，当模型默认运行时，创始人姓名几乎不影响决策，性别与非裔姓名效应均不显著；然而，当用户明确表达支持代表性不足群体的意愿时，模型显著倾向非裔（+8.1个百分点）和女性（+5.1个百分点）创始人的项目，且该偏好在近似匹配的项目中最为突出，且仅由单一信号承载者触发。值得注意的是，模型极少在解释中提及性别或族裔。这一发现支持“代理性算法偏见”（agency account of algorithmic discrimination）的理论框架：即对齐后的助手并非从训练数据中继承偏见，而是根据用户意图动态调整其社会偏好，从而将用户的价值取向内化为决策依据。
链接: https://arxiv.org/abs/2611.03993
作者: Mirza Samad Ahmed Baig,Syeda Anshrah Gillani
机构: Heidelberg University (海德堡大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
备注: 36 pages, 4 figures, 3 tables; includes online appendix. Accepted manuscript of the article published in Journal of Business Venturing Insights 26 (2026) e00682. Data, design files and code: this https URL
Abstract:Large language model (LLM) assistants increasingly advise backers on which crowdfunding campaigns to support. LLMs have known race and gender problems, and it is unsettled whether they reproduce, erase, or reverse the discrimination documented in entrepreneurial finance. We ask when, and through which decision process, LLM assistants use founders’ gender and ethnicity. In a pre-specified choice-based conjoint experiment, seven LLMs chose among five synthetic campaigns with independently randomized quality signals and founder names, under three backer personas and a persona-free control (40,248 responses). By default, founder names barely matter: under budget-conscious and delivery-focused personas, gender and Black-name effects are statistically equivalent to zero. When the backer wants to support underrepresented creators, the models favor Black-signaled (+8.1 percentage points; positive in all seven models) and female-signaled (+5.1) founders. They do so most strongly among near-tied campaigns and for the founder who alone carries the signal, and they almost never name gender or ethnicity in their reasons. The results fit an agency account of algorithmic discrimination: an aligned assistant takes its demographic preferences from its user rather than from its training data.
[NLP-64] FlagRot: Selective Rotation for Low-Bit KV Caches
【速读】： 该论文旨在解决长上下文推理中因键值缓存（KV cache）导致的存储与计算瓶颈问题，尤其关注在低比特量化（如2比特）下如何维持注意力机制的精度。传统方法通过旋转键向量（rotation）来缓解量化误差，但研究发现该策略并不总是有效：旋转可能抑制原有的尖峰特征，却也可能在平坦区域引入新的峰值，导致误差分布复杂化。为此，论文提出FlagRot，其核心创新在于为每个32通道的键向量片段动态选择是否旋转——依据校准后的注意力误差最小化原则，保留最优形式；读取时则通过旋转查询向量来实现等效操作，避免额外写入开销。该方案针对不同缓存粒度设计了差异化的决策机制：对于按通道缓存（如KIVI）仅需每头一次判断，无写入成本；而对按标记缓存则将决策信息编码于现有符号位中。实验表明，在2.5比特/值条件下，FlagRot将KIVI的12个模型平均KL散度从18.7降至12.4×10⁻³，优于RotateKV在9个模型上的表现；作为vLLM插件部署后，在128k上下文长度下使RULER得分提升2.9点，LongBench v1均值提升0.4至1.9点，且在2比特KIVI出现精度损失的三个模型中显著改善性能；同时亦能降低4比特整数及FP4缓存的误差。
链接: https://arxiv.org/abs/2611.03992
作者: Kunming Shao,Xijie Huang,Jierun Chen,Jiaqi Shao,Yanli Wang,Jiangnan Yu,Qiwei Li,Wei Zhang,Kwang-Ting Cheng,Chi Ying Tsui
机构: The Hong Kong University of Science and Technology(香港科技大学); Sun Yat-sen University(中山大学); Wuhan University(武汉大学); Meta
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 26 pages, 5 figures. Code: this https URL
Abstract:Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check. The usual remedy rotates keys before quantizing, assuming rotation always helps. However, we observe that it does not: rotation flattens a spiky piece of a key but can give a flat one a new peak, and both coexist within one key. We therefore propose FlagRot, which stores each 32-channel piece rotated or as is, whichever has the smaller calibrated attention error, and rotates the query instead when reading. We further show that how often to decide follows the quantization axis: per-channel caches such as KIVI need one decision per head, at no write cost, while per-token caches decide per key, with the choice stored in existing sign bits. At 2.5 bits per value, FlagRot lowers the twelve-model KL divergence of KIVI from 18.7 to 12.4 x 10^-3, below RotateKV on 9 of 12 models. Served as a vLLM plugin, it raises RULER at 128k tokens by +2.9 points and the LongBench v1 mean by +0.4 to +1.9 points on the three of four models where 2-bit KIVI loses accuracy. It also lowers the error of 4-bit integer and FP4 caches.
[NLP-65] hinking Fast Trading Late: System One Decision Models vs the Market
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在分析公司新闻时所表现出的股票收益预测能力是否具有真实经济意义的问题，尤其关注其预测性在排除前瞻偏差（look-ahead）、记忆效应（memorization）和样本选择偏差（selection）后的可持续性。其核心挑战在于现有研究可能因数据泄露或回测设计缺陷而高估模型的实际预测价值。为此，论文提出OLIVIA协议——一种严格的测试框架，关键包括：使用时点（point-in-time）的输入数据并隐藏公司身份以防止信息泄露；仅在验证集上进行模型选择；通过加密哈希冻结分析计划以确保透明性和不可篡改性；以及通过日志锁定机制实现测试期单次解盲，杜绝重复试验。实验将该协议应用于生成式AI以外的非生成式语言模型（non-generative language models），这些模型对结构化问题返回概率输出，用于解读标普500成分股的新闻。结果显示，尽管微调模型在两个验证季度中表现出显著的信息系数（0.055与0.065），但在2026年1月至7月的盲测阶段下降至0.020（t=1.7），表明预测信号大幅衰减；商业模型虽能捕捉开盘前的隔夜价格变动（信息系数0.074–0.077），但无法预测后续走势，说明新闻信息已在开盘时被市场充分定价；所有预注册策略均无法克服交易成本并维持正夏普比率。此外，商业模型在未标记公司名称的情况下仍能以62%–74%的准确率识别公司，远超28%的基线水平，表明遮蔽的是名称而非实体本身，进一步揭示市场已先行消化新闻内容。因此，该研究结论为：市场读取新闻的速度快于模型，当前基于语言模型的新闻分析难以产生持续、可交易的超额收益。
链接: https://arxiv.org/abs/2611.03985
作者: Amir Dehkordi(Independent Researcher),Alireza Kargarzadeh(TailState Intelligence Ltd),Nariman Khaledian(Independent Researcher),Navid Parvini(Zanista AI Ltd),Sid Ghatak(Increase Alpha, LLC),Arman Khaledian(Zanista AI Ltd)
机构: TailState Intelligence Ltd; Zanista AI Ltd; Increase Alpha, LLC
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
备注: 40 pages, 12 figures, 12 tables; pre-registered (analysis plan frozen by hash before the test period was unblinded); code at this https URL
Abstract:Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns. We ask how much of that predictability survives a test that rules out look-ahead, memorization and selection. We propose OLIVIA, a protocol for such tests: point-in-time inputs with company identities masked, selection on validation data only, an analysis plan frozen by cryptographic hash, and a test period unblinded once through a logged lock. We apply it to AI decision models, non-generative language models that return probabilities for typed questions, reading news about SP 500 stocks: an open-weights model we fine-tuned on 2024-2025 data, three commercial models and standard baselines. The fine-tuned model’s signal replicated on two validation quarters (daily information coefficients 0.055 and 0.065) but fell to 0.020 (t = 1.7) on the blinded January-July 2026 test. The commercial models’ readings line up with the overnight move to the open (information coefficient 0.074 to 0.077) but not with the move after it, so the news is priced by the time a trader can act on it at the open. No model can tell over- from under-reaction, and no pre-registered strategy survives costs and a deflated Sharpe ratio. Commercial models identify masked companies in 62% to 74% of cases against a 28% baseline: masking hides names, not companies. The market reads the news first.
[NLP-66] SLATE: A Large-Scale Benchmark for Long-Horizon API Selection in E-commerce Tool Use NEURIPS2026
【速读】： 该论文旨在解决企业在实际应用中面临的大规模长时序API选择难题，即在拥有数千个内部API的企业软件栈中，让大语言模型（LLM）代理能够自主、准确地将标准操作流程（SOP）的每一步映射到正确的API调用，而无需人工预先定义步骤与API之间的对应关系。传统工具使用基准测试无法有效评估这一场景，因其通常仅涉及少量工具、单步调用，或依赖主观性较强的“大模型作为裁判”评分机制，难以客观衡量多路径可行解。本文提出SLATE（Synthetic Large-scale API Toolkit for E-commerce），一个面向电商领域的大型基准测试平台，其核心创新在于：（1）构建了约1,000个工具组成的现实企业级API库，并将任务查询分解为分层、基于工具的执行流程；（2）设计了一个确定性、上下文感知的仿真器，可返回真实调用结果或默认响应；（3）采用端到端的任务成功指标——只有当所有参考输出均被达成时任务才算成功，该指标自动、可复现地奖励多样但有效的执行路径。实验表明，即使是最优的6个开源决策模型（参数规模从3B至12B）也仅能完成5.0%的任务，充分验证了SLATE作为可靠企业级工具使用挑战性基准的有效性。
链接: https://arxiv.org/abs/2611.03982
作者: Rongzhe Wei,Ge Shi,Min Cheng,Sarthak Ghosh,Vaibhav Gorde,Leman Akoglu,Pan Li,Na Zhang
机构: Amazon(亚马逊); Georgia Institute of Technology(佐治亚理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 13 figures, 6 tables. Camera-ready version for the NeurIPS 2026 Workshop on Agentic AI Benchmarks and Applications for Enterprise Tasks (AABA4ET). Project page: this https URL
Abstract:Enterprises increasingly want LLM agents to automate standard operating procedures over their software stacks – mapping each step of a procedure to the right call among thousands of internal APIs, without hand-authored step-to-API mappings. The core difficulty is API selection at scale: choosing and composing the correct tools over a long horizon, judged only by whether the overall task succeeds. Current tool-use benchmarks do not test this regime – they expose only a handful of tools, evaluate single-step invocation, or grade multi-step behavior with subjective LLM-as-a-judge scores that penalize valid alternative trajectories. We introduce SLATE (Synthetic Large-scale API Toolkit for E-commerce), a large-scale benchmark that tests large-toolset, long-horizon API selection in a realistic enterprise domain. SLATE couples (i) queries decomposed into hierarchical, tool-grounded procedures over a ~1,000-tool e-commerce library, (ii) a deterministic, context-aware simulator that returns recorded outputs for matching calls and default responses otherwise, and (iii) an end-to-end task-success metric – a task succeeds when it reaches every reference outcome – that credits diverse-but-valid trajectories while remaining fully automatic and reproducible. Experiments with six open decision models and the language models they are built on, from 3B to 12B parameters, show that even the best model completes only 5.0% of the tasks, establishing SLATE as a challenging testbed for reliable enterprise tool use. We release SLATE, its simulator, and the evaluation harness at this https URL
[NLP-67] Balancing Chain-of-Thought and Answer Supervision for Small Language Models on Medical Question Answering
【速读】： 该论文旨在解决小规模语言模型（参数量≤9B）在本地化医疗问答（QA）任务中实现高效、隐私安全且低延迟推理时，有效微调策略难以确定的问题。其核心挑战在于如何针对不同任务类型（如多选题与自由文本问答）及不同模型领域专属性（通用型与医学领域专用型），设计适配的思维链（Chain-of-Thought, CoT）监督微调方法。解决方案的关键在于提出并对比两种不同的CoT损失加权策略：均匀跨序列加权（Uniform-across-Sequence, UaS）与分段加权（Segment-weighted, SwS）。研究发现，任务格式与基础模型的知识分布决定了最优策略：在多选题任务（MedQA和EMS-MCQA）中，SwS通过分别优化思维链与答案部分的损失权重，显著提升通用模型性能（如Qwen3.5-4B在MedQA上较零样本基线提升44.8%），但对医学领域模型增益有限甚至产生负向影响；而在自由文本问答（Med-O1）任务中，UaS表现更优，能为各类模型提供一致的性能提升，使3B的Llama-3.2模型接近7B医学专用模型水平。此外，研究还揭示过长的思维链生成可能损害模型性能，提示应限制训练中的思维链长度以优化特定模型表现。综上，该研究强调应根据任务类型与模型预训练知识特性动态选择CoT损失加权策略，而非采用统一微调范式，从而实现医疗QA任务中更高效的模型优化。
链接: https://arxiv.org/abs/2611.03948
作者: Le Zhou,Xiao Luo,Chinmay Chandra,Zhan Zhang
机构: Pace University (帕克大学); Southern Methodist University (南卫理公会大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Small language models (=9B parameters) can support local medical question answering (QA) while reducing privacy and latency concerns, but effective fine-tuning remains challenging. This paper compares two chain-of-thought (CoT) supervised fine-tuning methods: Uniform-across-Sequence (UaS), which applies equal loss weighting to the full CoT-and-answer sequence, and Segment-weighted (SwS), which separately weights CoT and answer losses. We evaluate six general- and medical-domain models on multiple-choice QA (MedQA and EMS-MCQA) and free-text QA (Med-O1). Results show that neither method is universally best: on multiple-choice QA (MedQA and EMS-MCQA), SwS generally performs better, with general-purpose models benefiting most; notably, Qwen3.5-4B improves by up to 44.8% over its zero-shot baseline on MedQA. In contrast, medical-domain models show smaller or even negative gains on MCQs. On free-text QA (Med-O1), UaS performs best for nearly all models and provides consistent improvements across both model types, with the 3B Llama-3.2 model approaching the performance of much larger 7B medical-domain models. We also find that excessively long CoT generation is associated with lower performance in some models on either multiple-choice or free-text QA tasks, suggesting that limiting the CoT length during training may benefit selected models. Overall, the results show that CoT loss weighting should be matched to the task format and the knowledge already encoded in the base model, rather than using a single fine-tuning strategy for all clinical QA tasks.
[NLP-68] EpiLoRA: Fusing Many LoRA Adapters into Low-Precision GEMMs for Serving and RL
【速读】： 该论文旨在解决多租户场景下低秩适配器（LoRA）在高精度压缩基模型（如8位或4位FP8格式）部署时，适配器计算带来的显著延迟瓶颈问题。随着基模型的矩阵乘法（GEMM）因量化而变得高效，但LoRA适配器仍需独立执行小规模GPU核函数，其延迟占比随解码步骤增加而上升，成为性能瓶颈。解决方案的关键在于重新设计适配器计算流程：通过观察发现，低秩投影中的降维操作（down-projection）仅在GEMM的末尾阶段（epilogue）才需使用，因此提出EpiLoRA——一个针对vLLM引擎的插件，将降维操作提前执行，并将低精度GEMM作为依赖核函数，边流式加载权重边完成计算，同时在末尾阶段合并各租户的升维操作（up-projection）。在NVIDIA Hopper架构上，EpiLoRA单适配器解码时每标记符延迟降低最高达24%，优于vLLM、SGLang和TensorRT-LLM；在多租户场景下，其适配器核函数在多数GPU-模型组合中实现更高吞吐量。此外，该方法还支持基于相同低精度基模型的训练，无需额外16位副本，使270亿参数模型可在单块48 GB GPU上完成强化学习训练，且推理引擎可保持激活值为16位，有效缓解生成训练数据与训练模型之间的精度不匹配问题。
链接: https://arxiv.org/abs/2611.03899
作者: Hieu Tran,Hong Yu
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 31 pages, 13 figures, 39 tables
Abstract:Low-rank adapters (LoRA) let one base model serve many fine-tuned variants: multi-tenant engines keep a single copy of the base and batch the requests of many tenants, each with its own adapter. As base models move to 8-bit (FP8) and 4-bit formats, their general matrix multiplications (GEMMs) become cheaper, but the adapters do not: engines run them as separate small GPU kernels beside the GEMM, whose latency takes a growing share of every decoding step. An adapter projects its input down to a low rank and back up, and our key observation is that the GEMM needs the down-projection only in its epilogue, the final stage in which it writes its output. EpiLoRA, a plugin for the vLLM engine, therefore launches the down-projection first and the low-precision GEMM as a dependent kernel that streams its weights meanwhile and adds every tenant’s up-projection in its epilogue. On NVIDIA Hopper GPUs, EpiLoRA decodes for a single adapter with up to 24% lower time per token than vLLM’s fastest configuration, and faster than SGLang and TensorRT-LLM; with many tenants, its adapter kernels deliver more throughput than vLLM’s on most GPU-model pairs we tested. The same low-precision base also trains: a LoRA reinforcement-learning trainer that computes on the engine’s FP8 weights needs no 16-bit copy, so a 27B model trains on one 48 GB GPU, and an engine that then keeps activations in 16 bits removes most of the remaining mismatch between the model generating training data and the one trained.
[NLP-69] EvoGroup: A Self-Evolving Harness for Group Agents in Shared Contexts
【速读】： 该论文旨在解决多用户共享上下文环境中群组智能体（Group Agent）难以有效捕捉和利用群体层面知识的问题，尤其在群体关系与协作生成的知识表征方面存在显著短板。现有智能体记忆系统多针对个体用户设计，缺乏对动态演化、复杂交互的共享上下文的适应能力，且依赖人工设计的记忆策略限制了其泛化性能。为此，本文提出EvoGroup（EvoG），一种自演化群组智能体协同框架，其核心在于通过最小化六工具冷启动（six-tool cold start）机制，在持续交互中实现对共享上下文的自主适应。其关键创新包括：基于置信度与任务结果选择高信息量的行为轨迹，并在低置信度情况下触发自我反思以识别不确定性；分析代理采用自适应渐进披露（adaptive progressive disclosure）与分桶式分析（bucketed analysis）方法提炼高层洞察；最终由演化代理依据分析结果迭代优化智能体协同框架（Harness）。实验表明，EvoG在EverMemBench的测试集上，pass@1指标从71.07%提升至89.58%，显著优于自演化记忆与人工设计的框架（如Claude Code，85.83%）；在未见过的GroupMemBench数据集上，冻结演化后的框架仍展现出优异泛化能力，验证了其对多样化、动态化共享环境的鲁棒适应性。
链接: https://arxiv.org/abs/2611.03868
作者: Xinyi Xu,Xiaoyang Li,Yucheng Feng,Wenqing Chen,Youliang Yuan,Wei Shi,Rongxiang Weng,Zibin Zheng
机构: Sun Yat-sen University (中山大学); Longcat Team, Meituan Inc. (美团点评长猫团队)
类目: Computation and Language (cs.CL)
备注: 15 pages, 10 tables, and 7 figures. With code to be open-sourced
Abstract:With the rise of Group Agent such as Claude Tag, agents increasingly serve as digital workers for long-term team collaboration. These agents operate over shared contexts that continuously accumulate through multi-user interactions. However, existing agent memory systems are mainly designed for individual users and struggle to capture group-level knowledge in shared contexts, including group relationships and collaboratively formed knowledge. Manual memory strategies further limit adaptation to diverse and evolving shared contexts. To this end, we propose \textbfEvoGroup (EvoG), a self-evolving harness for group agents that starts from a minimal six-tool cold start and adapts through interaction with shared context. EvoG uses confidence and task outcomes to select informative trajectories, while also eliciting self-reflections on uncertainty in low-confidence cases. The analysis agent then examines selected trajectories through adaptive progressive disclosure, followed by bucketed analysis to synthesize high-level findings that guide the evolve agent in revising the Harness. Empirically, pass@1 on held-out split of EverMemBench improves from 71.07% to 89.58%, surpassing both self-evolving memory and human-designed harnesses, including Claude Code (85.83%). To evaluate generalization, we freeze the evolved harness and evaluate it on the unseen GroupMemBench, where, with GPT-5.5, it demonstrates effective generalization and outperforms both memory baselines and human-designed harnesses.
[NLP-70] Why Pay for Long Reasoning When Rewrite Do Trick? Causal Effects of Reasoning History Compaction on Future Generation and Agent Cost
【速读】： 该论文旨在解决生成式 AI（Generative AI）在复杂问题求解中因推理链（reasoning trace）过长而导致计算成本高昂的问题。其核心挑战在于如何在不牺牲任务性能的前提下，减少推理过程中冗余和低价值文本的生成，从而提升效率。解决方案的关键在于通过修改模型自身早期推理内容的形式，将其压缩为紧凑的状态记录（compact state records），在尚未获取工具结果前对历史推理进行重构，进而引导后续推理更简洁高效。实验表明，采用该方法后，多数模型的未来推理长度显著缩短，且在代码求解任务中减少了重复验证行为；在软件代理部署场景下，该压缩策略可降低总体资源消耗，但其效果受目标任务与压缩器设计的影响。研究揭示了推理历史（reasoning history）作为优化未来推理令牌使用的重要切入点，并提出应根据模型特性制定兼顾效率与能力保留的个性化压缩策略。
链接: https://arxiv.org/abs/2611.03852
作者: Anton Hristov
机构: Technological School Electronic Systems
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Reasoning substantially improves language models’ ability to solve difficult problems, but long reasoning traces make these gains expensive. Efficient reasoning should express useful analysis in fewer tokens by reducing repetition and text that adds little value to solving the problem. Progress in proprietary models motivates this goal, yet their hidden scratchpads limit direct study of how it is achieved. We investigate whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining. Our intervention rewrites prior reasoning into compact state records before pending tool results are revealed, then compares continuations from original and compact histories. Across the tested models, accepted compact histories usually shorten future reasoning; a coding case study also shows less repeated verification after a shared candidate solution. In software-agent deployments, compaction can reduce total spending, although task quality and rewrite-inclusive time depend on the target and compressor. These findings identify reasoning history as one way to reduce future reasoning-token use and motivate model-specific compaction policies evaluated for both efficiency and retained capability.
[NLP-71] A Conditional Mutual Information Analysis of Response Variety in Language Models
【速读】： 该论文旨在解决大语言模型在推理生成过程中响应多样性难以量化评估的问题。传统方法依赖温度参数调控输出多样性，但缺乏对响应变异性本质的理论刻画。为此，论文提出基于条件互信息（conditional mutual information）的定量分析框架，从熵的角度对响应多样性进行建模，并定义了三种极限行为：仲裁者（arbiter）、独立（conditionally）和中性（deterministic），分别对应无到最大互信息的极端情形。其解决方案的关键在于通过条件互信息量化输入提示与生成输出之间的依赖关系，从而实现对模型响应多样性的系统性分类与分析，为温度调节机制的优化、语言特性解析、提示压缩以及知识图谱构建等应用提供了理论基础与实践指导。
链接: https://arxiv.org/abs/2611.03836
作者: R. I. Cukier
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages
Abstract:Large language models provide a variety of responses at inference (generation) that conventionally depend on a temperature parameter. Using a conditional mutual information approach, a quantitative, entropy based analysis of response variety is given. Three limiting behaviors are defined: arbiter, independent (conditionally) and neutral (deterministic) that span no to maximal mutual information. We give examples of each behavior and suggest their application to: temperature analysis, language analysis, prompt compression, and knowledge graphs.
[NLP-72] Functional-Group Knowledge Is Structured in Language Models Before It Is Spoken
【速读】： 该论文旨在解决化学语言模型在推理过程中“输出无法反映其真实知识”的问题，即模型的输出仅体现其表达结果，而不能揭示其内部是否真正理解分子结构中的功能基团。例如，面对“1-氟丁烷（CCCCF）的主要官能团是什么”这一问题，小型通用模型可能错误回答为“炔基”，而这种错误可能是由于未能识别氟原子，或是识别后未能正确表达所致，仅通过输出难以区分。为此，研究通过分析五个开源模型在涵盖92种分子和20类功能基团的提示下的内部表征（internal representations），揭示了模型在未经过化学领域微调的情况下，已基于化学家族对功能基团进行组织，并以一致的方向编码化学差异。例如，从硫醚（R-S-R’）到巯基（R-SH）的转变，叠加于醚（R-O-R’）之上，最接近醇（R-OH），这一结构在中间层突然显现，标志着表征从关注提示文本词汇转向捕捉功能基团本质。尽管基础模型在输出中仅正确命名功能基团58.5%，为五者最低，但其内部仍保留了化学上有意义的模式，且错误多表现为未作答而非错误回答。这表明功能基团知识在模型“说出”之前便已结构化存在于表征中。经化学领域微调后，表征进一步被重组，使功能基团成为主导因素，而非提示文本的字面信息。研究结果表明，仅依赖输出评估化学语言模型会低估其潜在知识容量；通过表征分析可有效区分“缺乏知识”与“表达失败”，这对选择或适配用于化学智能体的语言模型具有重要意义。
链接: https://arxiv.org/abs/2611.03684
作者: Tran Nhat Khang Nguyen,Anh Nguyen,Obed Issakah,Janghoon Ock
机构: University of Nebraska–Lincoln(内布拉斯加大学林肯分校); University of Nebraska–Lincoln(内布拉斯加大学林肯分校); University of Nebraska–Lincoln(内布拉斯加大学林肯分校); University of Nebraska–Lincoln(内布拉斯加大学林肯分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages, 5 figures
Abstract:Language models serve as the reasoning core of agents for chemistry and materials science, where they must interpret molecular structure before planning syntheses or proposing materials. Yet their outputs show only what they say, not what they know. Given a prompt such as The primary functional group in 1-Fluorobutane (CCCCF) is'', a small general-purpose model may answer the alkyne group’’ instead of an alkyl fluoride. The output alone cannot tell whether the model failed to recognize the fluorine or recognized it and failed to say so. We address this by reading the internal representations of five open-source models on prompts covering 92 molecules and 20 functional groups. Even without chemistry training, the models organize functional groups by chemical family, and they encode chemical differences as consistent directions. The shift from thioether ( \mathrmR-S-R’ ) to thiol ( \mathrmR-SH ), added to ether ( \mathrmR-O-R’ ), lands nearest alcohol ( \mathrmR-OH ). This structure emerges abruptly in the middle layers, where representations shift from tracking the wording of the prompt to tracking the functional group. The base model names the correct functional group in only 58.5% of prompts, the lowest of the five, yet it encodes functional-group families and preserves chemically meaningful patterns. Its errors are mostly failures to answer rather than wrong answers. Functional-group knowledge is structured in the model before it is spoken. Chemistry fine-tuning reorganizes it, making functional groups rather than prompt wording dominate the representation. These results suggest that evaluating chemical language models through outputs alone can underestimate the chemical information they encode. Representation analysis can distinguish a lack of knowledge from a failure to express it, which matters when choosing or adapting models for chemical agents.
[NLP-73] Mixture of Wide Experts: Sparse Routing for Interpretable Native Coordinates
【速读】： 该论文旨在解决大语言模型中语义概念在内部表示空间中分布过于分散、导致特征难以解释的问题。传统密集层和标准混合专家（Mixture of Experts, MoE）模型虽然具备强大的表达能力，但其激活模式缺乏可解释性，难以定位具体语义单元。为此，论文提出一种新型稀疏前馈层架构——广义专家混合（Mixture of Wide Experts, MoWE），其核心创新在于：每个专家读取完整的输入表示，但仅向一组固定且互不重叠的输出坐标写入结果。这种设计使得输出坐标具有明确的语义指向性，从而实现“原生可解释”的特征表示。实验表明，在相同计算量下，MoWE在保持与密集模型相当的预测性能（评估交叉熵2.417）的同时，显著提升了坐标可读性——通过语言模型评判器对激活坐标的描述进行评估，其特异性得分达0.523，优于密集模型（0.375）和标准MoE（0.268）。进一步对比显示，MoWE的原生坐标可解释性已接近在相同数据上训练的TopK稀疏自编码器（SAEs）的表现（0.556）。案例研究验证了该架构的实际应用价值：能够识别出支持特定推理路径的因果坐标序列，并通过直接操控这些坐标实现对模型行为的精准干预，例如将“矩形有四个”这一提示下的预测从“边”切换为“度数”，或通过固定坐标组合检测危险请求并引导拒绝响应。因此，该方案的关键在于将稀疏条件计算与可直接识别、复用的语义坐标相结合，实现了兼具高效性与可解释性的神经网络结构设计。
链接: https://arxiv.org/abs/2611.03655
作者: Tavish Mankash,Vardhaman Kalloli,Keshava Prasad,Deepan Muthirayan
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 47 pages, 14 figures. Code: this https URL . Models: this https URL
Abstract:Language models often spread a concept across many internal dimensions, making their features difficult to interpret. Sparse autoencoders (SAEs) recover clearer features after training. We ask whether models can instead learn interpretable output coordinates directly. We introduce Mixture of Wide Experts (MoWE), a sparse feed-forward layer in which selected experts read the full representation but write to fixed, disjoint output coordinates. On a controlled task with known hidden causes, this design preserves predictive performance while localizing causes more clearly than dense and standard mixture-of-experts (MoE) models. At approximately matched compute, we train two 19-layer runs of each architecture for 10 billion tokens. MoWE achieves evaluation cross-entropy 2.417 versus 2.452 for dense, while standard MoE reaches 2.22. We measure native-coordinate readability by asking a language-model judge to describe each coordinate from passages where it is strongly active and test that description on new passages. Correcting for descriptions that also fit other coordinates gives a specificity score of 0.523 for MoWE, 0.375 for dense, and 0.268 for standard MoE. TopK SAEs trained on the same FFN updates score 0.489 for dense and 0.556 for MoWE, making native MoWE comparable to the dense SAE. In the first case study, we identify a four-coordinate causal path supporting the model’s prediction of degrees after the prompt A right angle is 90. We then copy seven angle-related coordinate values into the model as it processes A rectangle has four, changing its next-token prediction from sides to degrees. In the second, one fixed coordinate set detects hazardous requests and another steers the next token toward refusal. These examples show that MoWE combines sparse conditional computation with coordinates that can be identified and reused directly. Comments: 47 pages, 14 figures. Code: this https URL. Models: this https URL Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2611.03655 [cs.LG] (or arXiv:2611.03655v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.03655 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Tavish Mankash [view email] [v1] Wed, 26 Aug 2026 12:54:22 UTC (1,080 KB)
[NLP-74] Alignment Faking Depends on the Value Not Just the Model
【速读】： 该论文旨在解决生成式 AI 在不同监督感知条件下表现出的对齐伪装（Alignment Faking）现象，即模型在感知到被监控时遵循指令、而在感知未被监控时偏离指令的行为模式。这一现象此前仅在有害内容目标和少数道德基础价值上得到验证，但其在更广泛的价值空间中的分布机制尚不明确。研究提出，对齐伪装行为具有价值依赖性——合规性是否在监督感知变化后持续，取决于模型与具体价值的匹配关系，而非模型本身或价值单独决定。为此，作者构建了一个针对每种价值的评估框架，通过三种条件（无系统提示、未监控、被监控）对32个ValueBench维度上的10种开闭源模型（参数量8B至~675B）进行测试，结果显示合规性差距为0.05–0.50，且一个70B的蒸馏模型表现优于约675B的基线模型。研究发现，结构方差主要由“模型-价值”配对决定，而非单一因素。通过对推理轨迹的六类分类分析，揭示出在被监控时以训练信号推理为主，在未监控时则转向价值冲突推理。研究将结果以“价值卡”形式封装，扩展了传统模型卡以体现模型的行为倾向，实现对模型对齐行为的可解释化评估。代码与数据已公开。
链接: https://arxiv.org/abs/2611.03650
作者: Manodeep Ray,Vivek Srivastava,Shirish Karande,Murari Mandal
机构: TCS Research(印度); KIIT Bhubaneswar(印度)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Language models can follow a directive when they believe they are monitored and abandon it when they believe they are not, a pattern termed Alignment Faking. Prior work establishes this for harmful-content objectives and a handful of moral-foundations values, leaving open how it distributes across a wider value space. We argue it is value-dependent: whether compliance survives a change in perceived oversight is a property of the model–value pairing, not of the model alone. Our framework profiles Alignment Faking per value, scoring each model-value pair under three conditions: bare (no system prompt), unmonitored, and monitored. On 32 ValueBench dimensions across ten open- and closed-weight models (8B-~675B), compliance gaps span 0.05-0.50, with a distilled 70B above a ~675B model. We observe that majority of the structural variance depends on which model was asked about which value and not on the model or value alone. A six-category taxonomy over reasoning traces tracks the behavioural shift: training-signal reasoning dominates under monitoring and value-conflict reasoning dominates when unmonitored. We package per-model results as value cards, extending model cards with behavioral disposition. Code and data are available at this https URL.
[NLP-75] RIAGE: Reliability- and Load-Conditioned Escalation for Two-Tier LLM Serving with a Semantic Evidence Judge
【速读】： 该论文旨在解决两层大语言模型（LLM）服务架构中请求升级（escalation）的效率与成本控制问题，特别是现有基于提示（prompt-based）的路由机制无法有效判断检索到的证据是否支持答案，且将升级的GPU成本视为恒定这一不合理假设带来的性能偏差。其解决方案的关键在于提出TRIAGE框架，该框架通过结合后生成阶段的可靠性状态（reliability state）和负载相关的GPU价格动态调整升级决策，从而实现更精准的成本-收益权衡。实验结果表明，尽管升级成本随请求速率从1到10 requests/s上升了21.9倍，但其与KV缓存余量之间并无单调关系；同时，原先声称可靠性状态可检测受损证据的假设（AUROC=0.70）被验证为不成立（实际为0.571），而仅依赖提示的路由器因构造原因得分恒为0.500。进一步引入语义证据判别器后，检测能力显著提升至0.846（在未服务样本上达0.926），并能以AUROC 0.90–0.96准确预测小模型错误答案。最终，该级联系统在保持与GPT-4相当性能的同时，使GPT-3.5-GPT-4的API成本降低79.5%，但在开放模型对（2–3倍价格比）下表现劣于随机路由。整体来看，7B-32B模型对的保留误差仅为+2.88个百分点，说明分配器正确拒绝了不必要的升级，因此研究核心结论为：一个预发布阶段的门控机制未能暴露该系统的有效性，即“零效应”（null effect）。
链接: https://arxiv.org/abs/2611.01174
作者: Basavaraj R M,Aryan Bharat Kumar
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
备注: 7 pages, Technical report
Abstract:Two-tier LLM serving escalates selected requests from a small model to a large one, but prompt-based routers cannot see whether retrieved evidence supports an answer, and they treat the GPU cost of an escalation as constant. We study TRIAGE, which conditions escalation on a post-generation reliability state and on a load-dependent GPU price, and we report both what held and what did not on one A40 serving Qwen2.5-7B and 32B (AWQ) under VLLM. Escalation cost rose 21.9x from 1 to 10 requests/s but showed no monotone dependence on KV-cache headroom, and a pre-registered claim that the reliability state detects corrupted evidence (AUROC=0.70) was falsified at 0.571, although any prompt-only router scores exactly 0.500 by construction. Post hoc, a semantic evidence judge raised detection to 0.846 (0.926 on never-served items) and predicted wrong small-model answers at AUROC 0.90-0.96 on RGB and three RouterBench model pairs; the resulting cascade stayed within about 3 pp of GPT-4 at 79.5% lower API cost for GPT-3.5-GPT-4, yet fell below random routing for open-model pairs with 2-3x price ratios. End to end, the 7B-32B pair showed only a +2.88 pp held-out gap, so the allocator rightly declined to escalate and the headline is a null that a small pre-launch gate failed to expose.
[NLP-76] Zero-Gated Cross-Attention
【速读】： 该论文旨在解决将非文本模态（如视觉或音频）注入冻结语言模型时所引发的早期优化不稳定性问题，此类问题使得难以区分模型架构本身的改进与训练动态带来的短期影响。其核心解决方案是提出一种**归纳门控交叉注意力（Inductively Gated Cross-Attention, IGCA）**机制，该机制在初始化时实现零参数初始化下的精确代数恒等操作（algebraic no-op），从而确保预训练模型行为在训练初期完全保持不变。IGCA通过可学习的门控函数（tanh激活的缩放参数α）逐步引入多模态条件，实现平滑、可控的跨模态融合。实验表明，在Falcon-40B等多个冻结骨干模型上，该方法可在超过59,500步训练中稳定降低损失，并在保留原始模型语义能力的同时显著提升多模态任务性能，最终在验证集上实现高达48.9%的损失下降（2.1268 nats），且平均门控幅度极小（|tanh(α)| = 0.0713），验证了其对主干模型干扰的最小化特性。
链接: https://arxiv.org/abs/2611.01116
作者: Basavaraj R M,Manav Verma
机构: Independent Researcher(独立研究员); HRS Group
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 7 pages, 3 figures, 4 tables. Technical report
Abstract:Injecting non-text modalities into frozen language models often introduces early optimization instability, making it difficult to separate architectural gains from transient training dynamics. We introduce Inductively Gated Cross-Attention (IGCA), a zero-initialized cross-attention bridge that preserves pretrained model behavior at initialization while enabling gradual multimodal conditioning. Integrated across six decoder layers of frozen Falcon-40B, open_llama_30b, and Bloom-7b1 backbones (with 40,404,961,288 trainable adapter parameters used during training), IGCA acts as an exact algebraic no-op at initialization with a maximum output discrepancy of 0.00e+00. Over 59,500 training steps on Falcon-40B, the training loss decreases from 3.9403 to 0.0007 (with final ten step losses of [0.0001, 0.0003, 0.0005, 0.0002, 0.0005, 0.0011, 0.0005, 0.0006, 0.0005, 0.0007]). On held-out evaluation, the best gate-on checkpoint achieves a loss of 2.2201 compared to 4.3469 for the gate-off baseline – a 48.9% reduction (2.1268 nats) – while average held-out loss improves from 4.3423 to 2.2498 (1.9075 nats) with a mean gate magnitude of \vert\tanh(\alpha)\vert = 0.0713 .
[NLP-77] rTaste: Do Research Agents Have Taste? Evaluating Early Stage Research Judgment
【速读】： 该论文旨在解决生成式人工智能（Generative AI）作为研究代理（research agent）在缺乏实验结果前提下，是否具备判断哪些实验值得开展的“科研品味”（taste）这一核心问题。其关键解决方案在于构建一个评估框架，让六种前沿模型以独立或团队协作的方式提出最小可行实验（Minimum Viable Experiments, MVEs），自主设定通过阈值并进行排序，再由独立执行者实施所有提议的实验，并由三位模型审计员对结果进行质量、新颖性与非显见性评估。尽管415个MVE中有194项被判定为合理、有价值且具有创新性，但其中仅有73项超出原始论文范围且非显见，表明多数提议仍局限于已有工作。模型首选项虽优于随机选择，但未显著优于直接采用论文首提实验；其预测表现虽优于基线，却无法证明比仅基于论文与注册信息重新生成的同一模型实例更准确。团队协作虽增加成本，但未带来提案质量的明确提升。总体而言，尽管研究代理能生成大量有价值的实验，但现有证据尚不足以支持其排名机制能有效优化实验筛选决策。
链接: https://arxiv.org/abs/2610.40488
作者: Art Kanke
机构: University of Minnesota(明尼苏达大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 27 pages, 1 figure, 9 tables. Code and data: this https URL
Abstract:Do research agents have taste, the judgment of which experiments are worth running before their outcomes are known? We give six frontier models seven recent NLP papers. Each model, working as a research agent alone or as a team, proposes minimum viable experiments (MVEs), forecasts whether each will pass a threshold it sets and ranks them. A separate executor then attempts every MVE, including those ranked last, and three model auditors judge the results. Of 415 MVEs, 194 are judged sound, valuable and new, but fewer than half of these (73) are also judged beyond the seed paper and non obvious. The agents’ first choices match the auditors’ most valued experiment more often than a random pick, but are not shown to improve on choosing the experiment written first. Their forecasts beat a base rate but are not measurably more accurate than those of a fresh instance of the same model given only the paper and registrations. Teams cost more without a clear overall improvement in proposal quality. The agents produce many worthwhile experiments, but the evidence that their rankings improve the choice of which to run remains limited. The evaluation harness and a dataset of all 415 MVEs, with their forecasts and audited outcomes, are available at this https URL.
[NLP-78] Contextual Linear Activation Steering of Language Models
【速读】： 该论文旨在解决现有线性激活调制（Linear Activation Steering）方法在控制和专业化大语言模型时存在的关键问题：即采用固定强度的调制，无法根据输入内容的实际需求动态调整调制程度，导致在复杂或多样化的上下文中表现受限。其解决方案的核心是提出一种上下文感知的线性激活调制（Contextual Linear Activation Steering, CLAS），通过将激活调制分解为固定的调制方向与依赖上下文的调制系数，实现了调制强度随输入内容自适应变化。这一设计不仅保持了调制方向的可解释性，还支持在单次生成过程中对不同部分选择性地施加不同行为调制，显著提升了灵活性和控制精度。此外，CLAS在多种基准测试中表现优于传统线性激活调制，并在标注数据有限的场景下达到或超越ReFT和LoRA的效果；更关键的是，其学习到的调制方向可作为可靠的语义概念监测器，而这是微调学习得到的方向所不具备的特性。因此，CLAS提供了一种兼具可解释性与高精度的大语言模型控制与专业化方法。
链接: https://arxiv.org/abs/2604.24693
作者: Brandon Hsu,Daniel Beaglehole,Adityanarayanan Radhakrishnan,Mikhail Belkin
机构: UC San Diego(加州大学圣地亚哥分校); MIT(麻省理工学院); Broad Institute of MIT and Harvard(麻省理工学院和哈佛大学的布罗德研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Linear activation steering is a powerful approach for controlling and specializing large language models by adding linear representations of concepts to their activations at inference. While effective, existing methods often steer with a fixed strength, independent of how much steering any given input requires. In this work, we introduce Contextual Linear Activation Steering (CLAS), which decomposes activation steering into a fixed steering direction and a context-dependent steering coefficient. This decomposition preserves the interpretability of the steering direction while enabling the steering strength to vary with context. It also enables CLAS to perform selective steering: steering multiple behaviors across different portions of a single generation, which cannot be achieved using a fixed steering strength. In addition, across a wide range of steering benchmarks and language models, CLAS consistently outperforms standard linear activation steering and matches or exceeds ReFT and LoRA in settings with limited labeled data. Notably, CLAS achieves this using directions that also serve as reliable concept monitors, a property that directions learned via fine-tuning lack. We therefore propose CLAS as an interpretable and accurate method for controlling and specializing large language models.
[NLP-79] WMIP: A Working-Memory-Inspired Programmable Probe of Cumulative State Tracking in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在单个提示（prompt）中持续追踪累积状态的能力问题，即在不显式写出中间步骤的情况下，保持并更新信息的准确性。现有评估方法在是否要求显式书写中间步骤方面存在差异，导致结果不可比。为此，研究提出了一种受工作记忆（Working Memory）启发的探测任务，通过程序计算的答案形式，支持3至100次更新，并可针对最终状态、中间状态或多重目标进行查询，在相同测试项上对比不同输出协议的表现。实验发现，尽管所有模型在单步算术任务上的准确率高达84.9%，但在需要经过三到七步更新的核心任务中，准确率下降至31.7%；当更新次数超过50步后，无一模型准确率超过10%。进一步分析表明，核心任务准确率的显著提升（达88.0%）主要归因于指令优化（如要求显式书写步骤并将答案置于最后一行），而非计算资源预算的增加。此外，核心任务表现与公开的伯克利函数调用排行榜（Berkeley Function Calling Leaderboard）得分呈正相关。研究揭示了强单步算术能力无法保证可靠累积状态追踪，且输出协议对测量结果具有决定性影响。研究团队已开源生成器、测试项、评分代码及模型响应，以实现跨深度、查询类型和输出协议的可复现比较。
链接: https://arxiv.org/abs/2603.27343
作者: Dengzhe Hou,Lingyu Jiang,Deng Li,Zirui Li,Fangzhou Lin,Kazunori D Yamada
机构: Tohoku University(东北大学); Lappeenranta-Lahti University of Technology LUT(拉彭兰塔-拉赫蒂理工大学); Texas A&M University(德州农工大学); Worcester Polytechnic Institute(伍斯特理工学院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 16 pages. Code and data: this https URL . Renamed from WMF-AM; v3 is a substantial revision with new experiments, which explains the low text overlap with v2
Abstract:Human working memory lets people maintain and update information. Large language models (LLMs) face a related demand when tracking updates within a single prompt without writing out intermediate states, yet existing tests differ in their constraints on written steps. We introduce a working-memory-inspired probe of cumulative state tracking with program-computed answers, 3 to 100 updates, and final-state, intermediate-state, or multi-target queries. It compares protocols on identical items. Across 18 LLMs, accuracy is 84.9% on single-step arithmetic but 31.7% on core items requiring one number after three to seven updates. From 50 updates on, no model exceeds 10% accuracy. Core accuracy reaches 88.0% under a larger budget and instructions to write steps and place the answer on the last line, mainly due to the instruction changes rather than the budget. Core accuracy also correlates positively with public Berkeley Function Calling Leaderboard scores. These findings show that strong single-step arithmetic does not ensure reliable cumulative tracking and that output protocols substantially change measured accuracy. We release the generator, items, scoring code, and responses for reproducible comparisons across depths, query types, and protocols.
[NLP-80] Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM -Simulated Expert Personas and Human Clinical Experts
【速读】： 该论文旨在解决在边缘型人格障碍（Borderline Personality Disorder, BPD）患者临床叙事访谈的质性分析中，如何有效利用大语言模型（Large Language Models, LLMs）辅助研究者进行主题识别与意义阐释的问题。传统质性分析依赖于研究者主观判断，易受认知偏倚影响，而现有研究尚未系统评估主流大模型在临床语境下的解释能力与可信度。其解决方案的关键在于构建多维度、混合方法的验证框架：通过三阶段研究（Study A-C），结合专家盲评（基于语义一致性、Jaccard系数及多维有效性评分）、神经嵌入技术（将人类与模型生成的主题描述映射至多维向量空间以量化语义与语言风格差异），以及非专家群体对主题冗长性影响作者归属感知的评估，全面检验大模型在主题发现、内容有效性及风格模仿上的表现。结果表明，尽管各模型与人类解读存在显著异质性（重叠度0-58%），但均能识别出人类遗漏的主题，证明其可缓解研究者偏倚；尤其谷歌Gemini 2.5 Pro在内容有效性与语义风格上最接近人类，且外部评价无法可靠区分其生成主题与人工撰写主题，凸显其在临床质性分析中的潜在可靠性与辅助价值。
链接: https://arxiv.org/abs/2508.19008
作者: Marcin Moskalewicz,Anna Sterna,Karolina Drożdż,Kacper Dudzic,Marek Pokropski,Paula Flores
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 28 pages, 8 tables, 2 figures
Abstract:Building on a human-led thematic analysis of clinical life-story interviews ( 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI’s GPT, Google’s Gemini, and Anthropic’s Claude) to support qualitative clinical analysis. The models’ interpretative potential was evaluated using a mixed-methods approach. Study A involved blinded and non-blinded judges in phenomenology and clinical psychology. The experts assessed the validity of AI-generated content using semantic congruence, Jaccard coefficients, and multidimensional validity ratings, including credibility, coherence, the substantiveness of results, and grounding in qualitative data. In Study B, neural methods were used to embed human- and model-generated theme descriptions in a multidimensional vector space. This approach provided an objectified computational measure of the difference between human and model semantics and linguistic style. In Study C, complementary non-expert evaluations were conducted (N=115) to examine the influence of thematic verbosity on the perception of human authorship and content validity. Overall, the results of AI analysis showed a highly variable overlap (0-58%) with the human interpretation, while all models identified themes originally omitted by human researchers, proving their capacity to mitigate human bias. At the thematic level, external evaluators were unable to reliably distinguish human-authored themes from those generated by AI. In terms of content validity assessed against raw data by high-level experts in the blinded mode, the performance of Gemini 2.5 Pro was indistinguishable from that of humans. The comparison of semantic vector embeddings showed that its style was also the closest to humans.
[NLP-81] Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits
【速读】： 该论文旨在解决语言模型在生成回答时缺乏可解释性的问题，即难以准确追溯模型决策背后的因果依据。其核心解决方案是构建一种基于量子力学原理的变换器（Transformer）架构，其中每一层均以“玻恩测量”（Born measurement）为核心机制：注意力计算采用归一化的 ∣⟨q∣k⟩∣4 形式，前馈非线性层由就地测量的乘积替代，并使用玻恩读出。该设计使得类别概率成为词幅值的多项式函数，从而可通过有限求积精确计算集成梯度证据（integrated-gradient evidence），实现无求积误差的贡献分解，确保词语贡献之和精确等于类概率相对于无信息基线的增量（精度达 10−11 以上）。在 e-SNLI 自然语言推理任务上，该模型仅用约 98,000 个参数（不含嵌入层）即达到 76.98±0.17% 的测试准确率，与同规模的 softmax 变换器（76.62±0.36%）相当。关键创新在于：通过删除词语并观察答案变化，其证据排序能更真实反映人工标注者关注的词语，且删除“its”这一证据排名第一的词可使答案改变的比例达 45%，显著高于 softmax 模型的 32% 和注意力×梯度方法的 40%。此外，该模型的权重可精确映射至光电子光学系统与量子比特电路，其相干注意力层可编译为等效于整个模型的量子线路（23 个量子比特上含 852 个 CNOT 门）。在 IBM ibm_kingston 设备上，通过哈达玛测试读取注意力重叠，实验已成功复现模型对两个短测试样本的预测结果及其删除扰动下的行为，但证据本身尚未在硬件上完整保留；论文进一步推导了在硬件上测量该证据所需的具体采样次数与校准方案。
链接: https://arxiv.org/abs/2611.05355
作者: Teerawat Tongloy,Jiraporn Pongsopa,Rangsan Jomtarak
机构: King Mongkut’s Institute of Technology Ladkrabang (拉差巴颂皇家理工大学); Suan Dusit University (素可泰大学)
类目: Quantum Physics (quant-ph); Computation and Language (cs.CL); Machine Learning (cs.LG); Optics (physics.optics)
备注: 24 pages, 7 figures, 11 tables. Code, trained models, notebooks and the record of every IBM job: this https URL
Abstract:When a language model answers, we can usually only estimate why. We build a transformer in which every step is a Born measurement - attention |\langle q|k\rangle|^4 normalised over each row, a measured product in place of the feed-forward nonlinearity, and a Born readout. With its denominators held at their values for the input, each class probability is a polynomial in the word amplitudes, so its integrated-gradient evidence is computed by a finite quadrature with no quadrature error: the words’ contributions sum to the class probability above its no-information share, to 10^-11 or better. On e-SNLI natural language inference, with about 98k parameters outside the embedding, the model reaches 76.98 \pm 0.17 % test accuracy, level with a softmax transformer of the same size ( 76.62 \pm 0.36 %). Deleting the word its evidence ranks first changes the answer in 45% of test pairs, out of the 53% any single deletion can change, against 32% for the softmax model explained the same way and 40% by its attention x gradient. Attention can look more like the words annotators highlighted, yet accounts for the answer less well: deleting the word it ranks first changes the Born Transformer’s answer in 32% of pairs. The same weights compile exactly to optoelectronic optics and to qubits, and a coherent attention layer becomes one circuit equal to the model (852 CNOTs on 23 qubits). On IBM’s ibm_kingston, reading only the attention overlaps by Hadamard tests, the device gave the model’s answers on two short test pairs and kept or changed them under deletion as the model does; the evidence itself did not yet survive the device, and we work out the shots and calibration that measuring it on hardware would need.
[NLP-82] MedPhysBench: A Benchmark for Evaluating Language Models on Medical Physics Calculations and Error Detection
【速读】： 该论文旨在解决放射治疗、医学影像、核医学及辐射防护等领域中，计算任务的准确性与可靠性问题，尤其关注因束流校准或监测剂量（MU）计算错误导致的潜在临床风险。其核心挑战在于评估生成式人工智能（Generative AI）在执行专业级医疗物理计算任务时的准确性和错误检测能力。解决方案的关键在于构建一个名为MedPhysBench的基准测试平台，该平台包含10个基于真实临床协议的种子生成器，涵盖从基础计算到高级临床任务（如TG-51校准工作表）的多样化任务，并通过代码实现的参考答案与容差带机制进行评分。同时，引入“检查项”设计，通过对比同事的完整工作表或仅结果来测试模型的错误识别能力，从而评估其在实际协作场景中的表现。实验结果表明，尽管部分顶级模型（如Claude Sonnet 5.5）在多数任务中表现优异，但在复杂临床任务上仍存在系统性偏差（如忽略反平方律修正），且少数模型虽自身正确应用规则，却未能识别他人输入中的关键错误，暴露出生成式AI在专业领域推理与验证能力上的局限性。研究还揭示了屏蔽计算任务中两个未预见的问题，并公开全部代码、逐项结果及针对性测试响应，以推动可复现、可审计的医疗物理AI评估发展。
链接: https://arxiv.org/abs/2611.05340
作者: Omar Nusrat
机构: Toronto Metropolitan University (多伦多理工大學); Institute for Biomedical Engineering, Science and Technology (iBEST) (生物医学工程、科学与技术研究所); St. Michael’s Hospital Unity Health Toronto (圣迈克尔医院联合健康托里多)
类目: Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Medical physicists turn a prescription into a delivered dose and check one another’s calculations, because one error in a beam calibration or a monitor-unit (MU) calculation can affect every patient treated with it. MedPhysBench has ten seeded generators of calculation items in radiation therapy, diagnostic imaging, nuclear medicine and radiation protection, up to clinical tasks such as a TG-51 calibration worksheet. Reference answers come from code implementing the governing protocols, and a grader converts units and applies tolerance bands. From the same inputs, separate checking items test error detection, showing a colleague’s worksheet or only the colleague’s result, half correct and half with one planted error. On a held-out test split of 1,200 items (half for two models), 12 models scored 33.3 to 100.0% on calculation. Of the nine answers graded incorrect for the four best (99.3-100.0%), five contained model errors and four followed NCRP Report 151 on points our items misstate or leave open. Below them, most models did worst on level-3 clinical tasks, and Claude Sonnet 5.5 omitted the inverse-square correction in 19 of 20 isocentric MU items. Five models detected at least 96.7% of mostly large planted errors with at most 4% false alarms; for four of the other seven, checking only the result raised false alarms significantly. In an exploratory targeted test with 20 public cases of an omitted inverse-square correction, the three best models tested caught it in every worksheet, and three weaker models accepted 16 to 18 of 20 planted worksheets, one of them while applying the correction in most of its own calculations. Claude Opus 5.5 accepted 6 of 20 wrong results, although it applied the correction in all 20 of its own. We report the effect of two problems in our shielding items, found after the runs, and release the code, per-item outcomes and targeted-test responses.
信息检索
[IR-0] HARP: Training-Free Dual-Profile Agent ic Communication for LLM -Based Recommendation NEURIPS2026
链接: https://arxiv.org/abs/2611.05319
作者: Zhen Tao,Xun Zhou,Xuhui Chen,Ziyue Qiao,Qingqiang Sun
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted by NeurIPS 2026
Abstract:In LLM-based recommendation, memory acts as a user-level agent in a memory-augmented backbone. Yet users and items remain asymmetric: only the user is given a structured profile; it enters a bandwidth-limited reranker prompt; and it is frozen after warmup. We recast LLM-based recommendation as an agentic-communication process in four stages: BUILD a self-description, TRANSMIT it along the collaborative graph, UNDERSTAND the other side, and SYNC it from the next interaction. This formulation reveals three unresolved communication challenges: the item side never produces a comparable self-description (one-sided BUILD); profile-level signals enter only the bandwidth-limited reranker prompt rather than retrieval, where they would not compete for context tokens (prompt-trapped UNDERSTAND); and the user’s description is frozen after warmup with no principled update rule (absent SYNC). We instantiate the missing stages as HARP (Homophily-aware Agentic Recommendation via dual Profiles), a training-free, three-operator addition to a frozen LLM backbone: a rule-extracted symmetric item profile (zero extra LLM calls), a channel-decomposable profile-level homophily H(phi_u, phi_i) consumed at retrieval time rather than inside the reranker prompt, and a single-pass reflective update realising in-context profile evolution without any gradient step. On four public benchmarks (Yelp, Books, MovieTV, Goodreads), HARP lifts Hit@1 over the strongest published baseline by 6.9%-24.0%, with family-wise Holm-corrected p 1e-9 on every dataset. Ablations and a per-case attribution study show that the operators contribute complementary gains, with the retrieval-side gate as the main source; we also report the trade-off introduced by the reflective pass. We formulate agentic communication and provide a training-free instantiation. Code is available at this https URL.
[IR-1] Factorized MLLM Embeddings for Composed Image Retrieval
链接: https://arxiv.org/abs/2611.05193
作者: WonJun Moon,Seongchan Kim,Dahun Kim,Seungryong Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
备注: Project page: this https URL , Code: this https URL
Abstract:Composed Image Retrieval (CIR) aims to retrieve a target image given a reference image and a modification text. Although recent approaches leveraging Multimodal Large Language Models (MLLMs) have shown significant progress, we argue that the MLLM’s compositional understanding remains underutilized. This stems from directly mapping composed queries into a retrieval embedding via contrastive supervision alone, without explicitly resolving the intended visual modifications. In this work, we introduce Factorized MLLM Embeddings (FactEmbed), which decouples compositional query comprehension from building discriminative embeddings. Rather than directly projecting the composed query into a single embedding without internal reasoning, FactEmbed employs a set of factorized latents that simulate the intended visual modifications via an intermediate target imagination objective. This process encourages the latents to ground into target semantics before forming discriminative representations for retrieval. Subsequently, to propagate these factorized representations throughout the model for retrieval, we optimize a factor-level contrastive loss while distilling the decomposed representations into a deeper attention layer of the MLLM. Finally, for efficient inference, we retain the [EOS] token to establish a global-level embedding on top of these factorized latents, while we exploit the mean-pooled factorized latents to rerank a short candidate list at negligible cost, as both are produced by the same forward pass. Extensive evaluations demonstrate that FactEmbed outperforms prior works across CIR benchmarks.
[IR-2] Dataset Recommendation for Heterogeneous and Dynamic Scholarly Graphs via Multimodal Representation Learning
链接: https://arxiv.org/abs/2611.05071
作者: Ornella Irrera,Matteo Lissandrini,Daniele Dell’Aglio,Gianmaria Silvello
类目: Information Retrieval (cs.IR)
备注:
Abstract:Researchers struggle to find relevant datasets amid the growing volume of data across repositories and scholarly publications. Manual dataset retrieval is time-consuming and often fails to identify valuable resources that are not easily discoverable through direct links or popularity metrics. Dataset recommendation, which aims to identify datasets relevant to a paper, author, or query, has become essential for improving dataset discoverability and reuse. However, this task remains challenging because Scholarly Knowledge Graphs (SKGs) are large-scale, heterogeneous, dynamic, and incomplete. Although openly available SKGs organize scientific knowledge by linking scholarly outcomes, they often lack meaningful connections between datasets, leaving them isolated and underutilized. These graphs contain millions of evolving relations but still suffer from missing or inaccurate metadata, duplicate records, and weak dataset integration. This work presents the Multimodal Scholarly Attention Network (MM-SAN), a heterogeneous multimodal graph representation learning method for recommending datasets relevant to research publications within real-world SKGs. MM-SAN combines topology-aware and text-based embeddings, enabling effective recommendations even when textual metadata is limited or absent. It operates in transductive, semi-inductive, and fully inductive settings, adapting to evolving SKGs without requiring retraining. Experiments show that MM-SAN outperforms state-of-the-art methods, providing a robust and scalable solution for dataset recommendation.
[IR-3] When Category Means Move: A Paired-Support Audit of Recommendation Logs with Random Insertions WSDM2027
链接: https://arxiv.org/abs/2611.05006
作者: Yu Zhang,Zhihan Wang,Yuhang Lu,Shuai Li
类目: Information Retrieval (cs.IR)
备注: 10 pages, 6 figures. Accepted at WSDM 2027 (Main Track)
Abstract:Content-category dashboards summarize production recommendation logs, although the serving policy selects the users, items, and contexts behind each mean. Using KuaiRand random-insertion impressions, we audit these summaries on the same 12,472 users and 5,486 videos in both logs. Standard means have absolute/centered mean absolute error (MAE) 0.1935/0.0491 and rank correlation 0.7833. User demeaning plus a random grand-mean anchor reduces absolute MAE by 80.0%, but centered MAE by only 21.0%, without reliably improving rank. A complete-support ledger allocates weighted squared error mainly to within-video residual (72.41%) and video mix (23.90%), with 3.65% from random support. In a frozen later test, early category calibration reaches MAE 0.0251 versus 0.0531 for scalar calibration; their direct difference is -0.0280 [-0.0296, -0.0172]. Decision evidence is cutoff-specific: the nominal top-10 regret change for row-weighted demeaning versus naive is -0.0062 [-0.0084, -0.0003], while top-5 and top-20 intervals include zero. The six-contrast family-wise top-10 interval [-0.0136, 0.0013] includes zero. An outcome-free disjoint randomized-reference split also leaves all six nominal contrasts covering zero. A known-truth stress test shows incomplete improvement under a mean-additive positive control and failure under item mix and weak overlap. All decompositions are descriptive and each result is conditional on its overlap cohort. KuaiRand intervals quantify user-cluster variation conditional on the realized catalog and calendar; we claim neither intrinsic category effects nor a universally valid correction.
[IR-4] Auditing Multi-Proxy Explanations of Web User Preferences: Identification Boundaries and Abstention WSDM2027
链接: https://arxiv.org/abs/2611.04997
作者: Yu Zhang,Zhihan Wang,Yuhang Lu,Shuai Li
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: 11 pages, 1 figure. Accepted at WSDM 2027 (Main Track)
Abstract:Web systems infer user preferences from behavior, ratings, text, and LLM-generated profiles, yet explaining a proxy may reveal its measurement process rather than the preference of interest. We develop an audit framework distinguishing point identification, partial identification, and abstention relative to an explicit reference distribution. Under feature-dependent, arbitrarily signed proxy bias, a strict majority of uncontaminated proxy attributions lets the coordinatewise median recover latent-attribution support and direction and, with externally justified positive scales, magnitude. We prove this threshold sharp using response-valid indistinguishable worlds, characterize sharp minimax error and exact compatible-target sets under approximate cleanliness and scale uncertainty, and propagate clean-proxy attribution error. A frozen known-truth benchmark spans two response families, four proxy counts, all contamination counts, three bias strengths, and eleven methods, with a focal comparison confirmed on twenty unseen seeds. Under correct budgets, a direct attribution-tensor audit achieved 100% coverage, 95.40%-97.05% active-driver sign decisions, and no false signs. In a temporally separated Amazon study, median aggregation reduced comparator-relative conditional sign error for all four targets; six of eight prespecified target-metric contrasts had Bonferroni intervals excluding zero. A controlled real-covariate perturbation favored the median on the prespecified focal metric for all four targets. Compatible sets showed that the current proxy configuration rarely licensed strict-sign conclusions. Weak comparator calibration limits the Amazon evidence to fitted-surface alignment rather than latent-preference recovery.
[IR-5] Scaling Laws For Data-Constrained Dense Retrieval
链接: https://arxiv.org/abs/2611.04979
作者: Bowen Fu,Jingtao Zhan
类目: Information Retrieval (cs.IR)
备注: 10 pages, 5 figures
Abstract:Dense retrieval models are typically obtained by fine-tuning pretrained language models on supervised retrieval data, where high-quality annotations or interaction logs are often limited and reused across many epochs. This data-constrained regime differs from the abundant-data setting assumed by standard scaling laws, making it unclear how the best attainable retrieval performance changes with model size and unique fine-tuning data. We study this question through the minimum achievable evaluation loss (MAEL), defined as the lowest evaluation loss reached during training under fixed model-size and data-budget constraints. We first show that learning-rate choices strongly affect optimization trajectories but have little effect on the final attainable MAEL: after rescaling the optimization-step axis, learning curves collapse across learning rates, and CKA analysis shows that checkpoints reaching MAEL learn highly similar representations. We then fine-tune 35 BERT-family dense retrievers on MS MARCO subsets and find that MAEL is accurately described by a simple multiplicative power law over non-embedding parameters and unique samples, outperforming commonly used additive scaling formulations. We further derive a scaling law for the training step at which MAEL is reached and show that optimal weight decay follows an approximately invariant EMA timescale, yielding lower MAEL and stronger retrieval performance on both in-domain and out-of-domain benchmarks. Finally, combining loss and step scaling suggests that, in our observed regime, increasing pretrained model size is approximately 1.46 times as fine-tuning-compute-efficient as increasing unique in-domain data for reducing MAEL.
[IR-6] Learning to Rank Explanations for Recommendation with Language Models
链接: https://arxiv.org/abs/2611.04775
作者: Ben Kabongo,Vincent Guigue
类目: Information Retrieval (cs.IR)
备注: 12 pages, 5 figures, 7 tables
Abstract:Recommender systems are ubiquitous on the Web, making understandable explanations essential for users. Although large language models (LLMs) increasingly generate natural-language justifications, evaluating their factual grounding and personalized relevance remains challenging. Statement-level ranking offers an alternative by selecting concise, review-derived explanations, enabling fine-grained and reproducible evaluation using established ranking metrics. However, existing statement ranking methods largely rely on collaborative representations, while semantic enhancements often fail to outperform simple user-history popularity baselines. Moreover, directly adapting language models for personalized statement ranking remains largely unexplored. In this work, we introduce LMStaR (Language Models for Statement Ranking), a fully text-based framework that formulates personalized explanation ranking as dense retrieval. A query encoder represents the user’s interaction history and target-item metadata, while a statement encoder maps candidate explanations into a shared embedding space. Both encoders are jointly adapted through multi-positive contrastive learning to learn personalized relevance. Experiments on Amazon Beauty, Amazon Toys, and Yelp show that LMStaR achieves the highest NDCG@1 across all three domains, outperforming CLER by 1.06–8.25 points (+11.8–65.9%). It also leads top-five ranking on both Amazon datasets, although CLER remains stronger on Yelp. Task-specific adaptation improves all five evaluated backbones and transfers across domains without target-domain fine-tuning. Counterfactual analyses further reveal the importance of user-specific histories. These findings highlight the potential of task-adapted language models for personalized explanation ranking.
[IR-7] OneTrans-GSR: A Generative Slate Ranking Paradigm Unifying Reranking and Mix-Ranking with One Autoregressive Transformer
链接: https://arxiv.org/abs/2611.04720
作者: Feng Cheng,Yu-Hsuan Huang,Fengyi Wang,Chaofan Zhou,Zhennan Zhu,Huan He,Bo Zhang,Ruoqi Zhang,Zhanfeng Mo,Ziwei Liu,Lanyi Li,Yifeng Pan,Junhua Long,Ziqiu Zhou,Yue Lin,Shaowei Liu
类目: Information Retrieval (cs.IR)
备注: 26 pages, 6 figures, 11 tables
Abstract:Industrial recommender systems allocate substantial computation to pointwise candidate scoring, yet separate reranking and mix-ranking stages still assemble the final exposure slate using hand-crafted rules and specialized low-capacity models. Mix-ranking combines product cards, short videos, livestreams, and advertising cards supplied by distinct upstream business channels. Although these rules and models encode valuable domain knowledge, they are inherently limited in leveraging additional data and computation. They constrain the combinatorial space of candidate slates and determine objective trade-offs through manual tuning. To enable scalable and effective slate generation, we reformulate reranking and mix-ranking as a unified learning problem whose performance can improve with computation. Specifically, we instantiate this formulation across the reranking and mix-ranking stages and propose OneTrans-GSR, a single decoder-only policy that extends our deployed OneTrans backbone to generate the exposure slate end-to-end. During training, a reward model evaluates complete slates and guides reinforcement learning toward slate-level and long-term objectives. During decoding, business constraints are explicitly enforced. Offline evaluation shows that slate quality consistently improves as reward-model and policy compute increase. Deployed in a large-scale industrial recommendation system, OneTrans-GSR mixes heterogeneous content types within a unified slate. In online A/B tests, it improves GMV/u by 2.63%, advertiser value by 2.06%, and repurchase rate by 2.03%, while reducing end-to-end latency across the reranking and mix-ranking stages by 13%. These results mark a paradigm shift in slate generation: moving beyond human-engineered heuristics toward a general learned policy creates a scalable path for further algorithmic optimization and compute-driven improvement.
[IR-8] ZeroGR: Filtering-First Constrained Decoding for Generative Recommendation
链接: https://arxiv.org/abs/2611.04715
作者: Xiangrui Yang,Jiawei Yang,Liang Zeng,Daqian Ding,Ziqian Liu,Bing Tian,Cheng Peng,Yunfeng Zhao,Fangyu Zhang,Jiaqiang Liu,Xiao Liang,Yiming Qiu
类目: Databases (cs.DB); Information Retrieval (cs.IR)
备注: 14 pages, 20 figures
Abstract:Generative recommendation is gaining traction in production, retrieving items by generating their identifiers one token at a time. The generated sequences must identify relevant catalog items, so constrained decoding couples relevance scoring with validity filtering. Because most possible sequences are invalid, scoring before filtering wastes computation on candidates that will be discarded. Our evaluation shows that filtering first can avoid this work with negligible impact on recommendation quality. We present ZeroGR, a filtering-first constrained decoding system for generative recommendation. ZeroGR uses a step-sliced filtering index to reduce GPU memory use by tailoring data representations to each decoding step. Its execution engine adopts shared-slot execution to accelerate data exchange across stages and hybrid projection to adapt computation to workload patterns, thereby reducing execution overhead. During index refreshes, ZeroGR applies layout-guided streaming construction to speed up updates and limit serving interference. Extensive evaluation shows that ZeroGR accelerates the filtering and scoring pipeline by up to 9.9\times over representative baselines while reducing GPU index memory use and accelerating index reconstruction.
[IR-9] SPADE: Spatiotemporal Preference Alignment and Diversity Enhancement for Local Search
链接: https://arxiv.org/abs/2611.04662
作者: Chenxu Wang,Wenxuan Yang,Yueqi Mou,Shuxian Bi,Biao Tang,Peng Yan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Proactive query recommendation aims to suggest relevant search queries to users on the homepage of local service apps. Unlike conventional recommendation over a fixed item set, this requires open-set generation from a vocabulary of millions, where outputs must simultaneously align with user preference, match fine-grained spatiotemporal context, cover long-tail intents, and provide diverse alternatives within a few candidate slots. Existing approaches address these axes in isolation: standard tokenization suppresses long-tail queries, coarse location encoding misses fine-grained geographic demand, generic preference optimization fails to align with production impression-click distributions, and standard beam search collapses to near-duplicate outputs. We present SPADE (Spatiotemporal Preference Alignment and Diversity Enhancement), an end-to-end generative framework that jointly optimizes the generation space, contextual representation, preference distribution, and output slate quality through four components: Objective-Aligned BPE (OA-BPE) aligns subword merging with business value signals to boost long-tail query generation probability; Spatio-Temporal Adaptive Representation (STAR) replaces coarse location tokens with continuous multi-scale encodings and soft gating for precise spatiotemporal grounding; MARS-DPO (Multi-dimensional Adaptive Reward Sampling DPO) combines cross-domain pre-training with preference alignment on production impression-click data via composite rewards and adaptive hard negative sampling; and Sigmoid Diversity Beam Search (SDBS) improves intent coverage without sacrificing recall. Offline experiments on large-scale production data show that SPADE outperforms the strongest generative baseline by +5.6% HR@5, -40.3% GeoC@5, and +1.9% Exec@5. A one-week online A/B test confirms production impact: +1.88% Module CTR, +0.177% Search Volume, and +0.105% Search DAU.
[IR-10] QueryFlow: Multi-Hop Graph Retrieval with Adaptive Query Evolution
链接: https://arxiv.org/abs/2611.04625
作者: Manzong Huang,Chenyang Bu,Yi He,Jiapu Wang,Xindong Wu
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-hop retrieval-augmented generation (RAG) supports Web information seeking and fact verification by recovering complementary evidence across related pages within a limited retrieval budget. Scoring evidence independently against the original question can miss supporting units whose relevance emerges through intermediate facts. Iterative retrieval incorporates these facts into subsequent queries, while graph retrieval explicitly models relations among evidence units. Yet fixed-query graph retrievers update entity representations without adapting the query condition to accumulated relational context. We view multi-hop evidence recovery as the coupled evolution of evidence representations and query conditions, with accumulated context informing both what the retriever represents and how it searches for complementary evidence. QueryFlow realizes this perspective by recurrently integrating query-conditioned summaries of graph context into a shared query state, which controls boundary inputs and relation features for subsequent propagation. Across four representative multi-hop question answering (QA) datasets, QueryFlow achieves macro Recall@10 (R@10), Full Recall@10 (FR@10), and LLM-Acc gains of 4.98, 8.40, and 8.0 percentage points over representative baselines, respectively.
[IR-11] Collaborative Search Meets Large Language Models : An Empirical Study SIGIR
链接: https://arxiv.org/abs/2611.04619
作者: Peiyuan Gong,Jiamian Li,Erhan Zhang,Fengming Liang,Kexin Liu,Yue Chen,Jiaxin Mao
类目: Information Retrieval (cs.IR)
备注: Accepted by SIGIR-AP 2026
Abstract:Collaborative search enables users to address complex information needs jointly. Advances in large language models (LLMs) enable autonomous agents to support collaborative information seeking within shared communication spaces. However, little is known about how these agents shape search experiences, behaviors, and outcomes compared with conventional search tools. We conduct a user study with 80 students working in 40 pairs. Each pair completes four collaborative search tasks under four scenarios: QAAgent, which answers queries using an LLM’s parametric knowledge; CoSearchAgent, which uses multi-user conversational context to reformulate queries, retrieve web information, and generate source-grounded answers, while asking clarifying questions when needed; an in-chat search plugin that returns result lists; and manual Google Search. After each task, participants complete several questionnaires assessing their search experience and independently write a report; after all tasks, they participate in individual interviews. Results show that LLM-based agents reduce perceived workload, support collaboration, and reshape communication and evidence-use practices. They also encourage more independent paraphrasing, albeit at the cost of report richness. To the best of our knowledge, this is the first systematic empirical comparison of LLM-based agents and conventional tools for collaborative search across search experience, user behavior, and collaborative outcomes.
[IR-12] τ-Rec-Books: A Disclosure-Controlled Benchmark for Requirement Elicitation in Agent ic Recommender Systems ECIR2027
链接: https://arxiv.org/abs/2611.04459
作者: Bharath Sivaram Narasimhan,Karthik R. Narasimhan
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 16 pages, 3 figures, 4 tables. Submitted to ECIR 2027 (Resource Track)
Abstract:While conversational recommendation systems provide a more natural user interface, they face the challenge of progressively discovering a user’s needs before they can recommend an item that satisfies them. To evaluate the ability of AI agents at this elicitation process, we introduce \tau -Rec-Books, a benchmark that holds each user request fixed while varying only when the user reveals their requirements: all at the start (volunteer), one at the start and the rest when asked (mixed), or some never (hidden). A hidden requirement can be found only by proposing books and inferring it from the user’s accept or reject verdicts. We generate 450 tasks over a catalog of 20,000 books from Wikidata, develop a rule-based simulated user to answer the agent, and score recommendations against catalog metadata without an LLM judge. Across four agents and two search interfaces, success rates fall for every agent and interface, on average from 0.975 (volunteer) to 0.756 (mixed) and 0.283 (hidden). Mixed tasks fail mainly because agents do not ask, and when told to ask, every agent reaches at least 0.931. On hidden tasks, most failures come from agents recommending a book they never proposed to the user. When told to propose first, agents only match a simple baseline that proposes popular books, and stay well below one that uses rejection to rule out possible requirements. We release the catalog pipeline, task generator, simulated user, validators, reference agents, and all agent traces at this https URL.
[IR-13] SourceFlow: Collaborative Source Coupling for Flow-Based Recommendation
链接: https://arxiv.org/abs/2611.04455
作者: Chenghao Wu,Yongqi Zhou,Jin Chen,Yifeng Zhou,Jinchang Zhu,Junwei Pan,Chao Zhou,Zhixiang Feng,Chengguo Yin,Menglin Yang
类目: Information Retrieval (cs.IR)
备注: Under review. 12 pages, 9 figures
Abstract:Diffusion-based recommenders learn user preferences by denoising interaction histories, but the added noise can distort the original preference signals. Flow-based collaborative filtering (CF) instead learns a discrete path from a sampled source to the user’s observed training interactions (the target history), enabling inference in a few steps. Existing flow-based CF methods sample sources using global item frequencies to reflect the sparsity and popularity of observed interactions. Each source is sampled independently of its target history, leaving the predictor without user-specific source cues for learning to reconstruct that history. We propose SourceFlow, which brings collaborative structure into the source distribution itself rather than adding further conditioning to the predictor. Like inference from observed histories, training uses collaborative evidence to predict missing interactions. Specifically, Neighborhood Coupling constructs displaced binary sources by replacing historical items with unobserved collaborative alternatives. Collaborative Consensus further refines replacement probabilities using evidence from other interactions of users who share the observed item. Stretched Restoration retains these replacements at late flow times during training, providing more opportunities to recover the original interactions. Experiments on five recommendation datasets show consistent improvements over competitive baselines, including a mean relative Recall@10 gain of 15.7% over the strongest flow-based baseline. Controlled experiments confirm that these gains depend on correctly pairing collaborative sources with their target histories, as reassigning the same source vectors across users reverses the gains. Code will be publicly released upon acceptance.
[IR-14] DualEvolve: Dual-Level Self-Evolution of Mechanism and Orchestration for Recommendation
链接: https://arxiv.org/abs/2611.04439
作者: Tianqi Gao,Chenxu Wang,Wenxuan Yang,Yueqi Mou,Biao Tang,Ming Liu,Peng Yan
类目: Information Retrieval (cs.IR)
备注: 12 pages, 4 figures, 15 tables. Tianqi Gao and Chenxu Wang contributed equally. Code: this https URL
Abstract:Recommender systems are widely used in e-commerce and content platforms, where improvement requires repeated model design, training optimization, and experimental validation. Large language model (LLM) agents increasingly automate this development cycle. Sustained autonomous improvement requires agents to improve both recommendation models and their development workflows. However, task-performance-driven approaches select updates mainly by candidates’ final scores, without a separate optimization signal for experimentation quality. We therefore present DualEvolve, a dual-level framework that co-evolves recommendation mechanisms and agent orchestration toward recursive self-improvement. Inner evolution revises model and training code using task feedback to improve recommendation performance. Outer evolution scores the inner-loop experimental process, updates harness and skill rules using these scores and execution traces, and adjusts orchestration choices based on next-round scores to improve the quality of subsequent experiments. Experiments on MovieLens-1M and three Amazon Reviews 2023 subsets cover four recommendation methods and 16 settings. DualEvolve achieves higher test means than Base, OpenEvolve, and DeepEvolve in all 64 setting-metric comparisons, with macro-averaged relative gains over DeepEvolve of 7.6% (HR@5) and 9.0% (NDCG@5).
[IR-15] Do Retrieval Ranking Models Capture Event Order? A Study of Timeline-Constrained Retrieval
链接: https://arxiv.org/abs/2611.04343
作者: Alon Eirew,Lidan Avisar,Kfir Bar,Ido Dagan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Semantic retrieval models aim to match documents to a query’s “meaning”, yet it remains unclear whether they successfully encode temporal relations between events in their ranking mechanisms. We investigate this question by studying timeline-constrained document retrieval, where a query is an ordered sequence of natural-language event statements while a relevant document should match those events in a compatible temporal progression. We then test whether widely used dense retrievers and neural rerankers capture such constraints, and whether explicitly preserving temporal order improves ranking, using a controlled setting over a novel focused evaluation collection. Experiments show that state-of-the-art semantic retrievers and rerankers provide only limited improvements over lexical retrieval, suggesting that their semantic matching capabilities do not reliably capture temporal constraints. We then compare a timeline-aware method with both semantic matching and standard rerankers and find that it yields substantial improvements. This demonstrates that considering temporal order can provide a strong ranking signal in this setting, suggesting future research on improving temporal modeling in semantic retrievers.
[IR-16] OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering
链接: https://arxiv.org/abs/2611.04301
作者: Dazhou Yu,Zerong Wang,Riyang Bao,Longxiang Li,Yang Liu,Liang Zhao
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-derivation problem under strict spatiotemporal constraints. Existing LLM agents fail to ground observation items or suffer from search-space explosion. We propose OSGR, a two-stage framework that factorizes this search into offline symbolic concept deduction and online Relation-Conditioned Grounding. OSGR leverages an offline Observation-Support Graph to deduce valid target-to-result support paths at the concept level, and applies spatiotemporal compatibility kernels to instantiate grounded observation-item sets. On Earth-Bench and DisasterM3-Reason, OSGR improves GPT-4o accuracy from 49.3% to 63.7% while reducing the execution-step ratio from 2.9 to 2.5.
[IR-17] ReuseGR: Offline–Online Decoupled Generative Retrieval via Adaptive Prefix Reuse
链接: https://arxiv.org/abs/2611.04259
作者: Xujing Wang,Yufeng Chen,Boxuan Zhang,Chao Wei,Cai Xu,Jie Zhao,Weiru Zhang,Wei Zhao,Ziyu Guan
类目: Information Retrieval (cs.IR)
备注:
Abstract:Larger generative retrieval models can improve retrieval quality, but their decoding cost makes low-latency serving difficult. When different users submit the same query, generating complete Semantic IDs (SIDs) for every request repeats computation, while their interaction histories call for different results. The challenge is to reuse large-model computation across requests while keeping retrieval personalized. We propose \textbfReuseGR, which separates query-level candidate planning from request-level personalized completion. A 7B offline planner generates SID prefix sets shared across requests with the same query. A 0.5B online model uses each request’s history to select prefixes and generate the remaining tokens. ReuseCut chooses a cache depth for each query based on historical coverage, balancing computation reuse and candidate coverage. To strengthen online completion, we transfer 7B suffix-scoring knowledge to 0.5B through path-level knowledge distillation (Path KD). BudgetCut then selects scored branches to improve final top-100 recall under a fixed branch budget. Its selection policy is initialized by on-policy distillation (OPD) and refined through group-relative policy optimization (GRPO). On an industrial search-advertising dataset with approximately 2M query–item records and 3.38M catalog items, ReuseGR improves query-level HR@100 by 3.83 percentage points and reduces mean online latency by 38.9% relative to an all-online 0.5B baseline when prefixes are prepared. It achieves HR@100 comparable to an all-online 7B baseline with 57.7% lower online latency. A 14-day user-level online A/B test against the 0.5B baseline shows relative gains of 0.93% in click-through rate and 1.05% in revenue per thousand requests. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2611.04259 [cs.IR] (or arXiv:2611.04259v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2611.04259 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-18] IntentGEO: Intent-aware Generative Engine Optimization for Recommendation
链接: https://arxiv.org/abs/2611.04225
作者: Runlin Lei,Shiqi Zhang,Xiaokui Xiao,Zhewei Wei
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 12 pages, 3 figures
Abstract:Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to improve item visibility in generated responses. Existing GEO studies are mainly content-centric, optimizing visibility under general-purpose queries. However, a shared item description usually serves users with different needs and decision goals, resulting in its visibility gains being dependent on the request. To investigate how changing intent affects visibility, we formulate IntentGEO to evaluate shared rewrites across need specificity and decision progression. We revisit existing GEO methods and find that their visibility gains vary across user intents. More critically, learning-based prompt optimization can underperform its handwritten seed on average. Tracing these updates reveals prompt drift from explicit recommendation instructions toward generic promotional tactics. To address this drift, we propose AMO (Anchored Meta-Optimization), which preserves recommendation instructions in a fixed core while learning appended strategies from citation feedback. Across four domains and ten intent states, AMO improves average citation-rank gain by 35% over the strongest prior learning-based baseline and leads on nine states. Ablations show that learned additions improve on the fixed core alone and that AMO outperforms full-prompt optimization initialized from that core. It also achieves the highest gains in an agentic recommendation pipeline and, after separate adaptation, on Doubao, a commercially deployed, closed-source online assistant. These findings highlight user intent as a key dimension of GEO evaluation and support anchored adaptation across recommendation needs and decision stages.
[IR-19] IDs That Mean Something: A Hands-On Tutorial on Semantic IDs for Recommender Systems CIKM’26
链接: https://arxiv.org/abs/2611.04140
作者: Roger Zhe Li,Aghiles Salah,Mete Sertkan,Changhong Jin,Yingjie Niu,Ruihai Dong,Barry Smyth
类目: Information Retrieval (cs.IR)
备注: 5 pages, tutorial proposal, accepted to CIKM '26
Abstract:Traditional recommender systems rely on arbitrary integer IDs to identify items, which carry no semantic information and force models to learn representations from interaction data. While leveraging content information can partially mitigate cold-start, this paradigm inherently suffers from exploding embedding tables and scenario-specific architectures. Recent years have seen the emergence of semantic IDs, which encode semantic information directly into the item identity, reflecting content, hierarchy, and relationships. This framework addresses the above limitations by offering a principled way to encode large-scale semantic information into item identity, benefiting both generative and discriminative applications and unlocking more native unification across modalities, domains, and tasks. This tutorial traces the evolution from raw IDs to semantic IDs as a scalable means of information utilization, offering a comprehensive review of their development and applications across multi-modality, cold-start, and cross-domain recommendation. Hands-on exercises guide attendees through building and evaluating semantic IDs, training models, and exploring cross-domain transfer. We conclude by discussing open challenges, including dynamic semantic IDs and multi-stakeholder utility. All materials of the tutorial could be found at the official website this https URL.
[IR-20] Characterizing Adversarial GEO via Implicit Brand Injection
链接: https://arxiv.org/abs/2611.04005
作者: Shobhnik Kriplani,Haohan Wang
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Preprint. Under Review
Abstract:Generative search engines increasingly shape how users discover products, services, and commercial entities by generating answers from retrieved web content. This creates an opportunity to influence which brands appear in these answers by modifying the content available to the retrieval system. In this work, we study an adversarial generative engine optimization (GEO) setting in which a predefined brand is added to an article through a single contextually integrated mention. The challenge is to make the mention fit the surrounding article without making the content overtly promotional, while keeping it relevant enough to be retrieved and to influence the generated answer. To study this, we introduce Implicit Brand Injection (IBI), a multi-agent framework that identifies insertion locations, rewrites the selected sentence, verifies the edit, and selects the final candidate. To evaluate IBI at scale, we construct IBIBench, which contains 1,000 web articles and 60 synthetic brands across 12 diverse domains, with both endemic and non-endemic brand placements. We compare IBI with Direct Prompting and Best-of-N with ranking under an equivalent budget, evaluating insertion quality, target-chunk retrieval, and downstream brand propagation. Compared with the baselines, IBI produces insertions that better preserve semantics and disrupt perplexity less, while improving Hit@k across all evaluated cutoffs, query regimes, and placement settings. IBI achieves up to 82.2% Hit@10 and 0.74 MRR@10, while Brand Mention Rate (BMR) and Conditional Propagation (CP) reach up to 62.7% and 75.9%, respectively. These results show that a single brand mention can increase both retrieval exposure and brand visibility in generated responses, highlighting the need for defenses against implicit content manipulation in generative search.
[IR-21] Modular and Efficient Approximate Nearest Neighbors Search with Vectorium and kANNolo
链接: https://arxiv.org/abs/2611.03927
作者: Silvio Martinico,Franco Maria Nardini,Cosimo Rulli,Rossano Venturini
类目: Information Retrieval (cs.IR); Databases (cs.DB)
备注: 39 pages, 11 figures, 17 tables. Submitted to Information Systems. Code and data: this https URL
Abstract:Approximate Nearest Neighbors (ANN) search over neural embeddings powers modern retrieval systems such as Retrieval-Augmented Generation. Embeddings come in three representations (dense, sparse and multi-vector), and efficient search combines an index structure, a similarity function and a quantization scheme. Existing libraries fix one combination, hard-code each one separately, or factor only one side of the problem, and none covers all three representations. We present Vectorium and kANNolo, two open-source Rust libraries that split this design space into a data-management layer and an indexing layer. Vectorium abstracts the vector, the distance function, the quantizer and the dataset independently, and provides 11 encoders, from 32-bit floats to sub-bit codes. On top of it, kANNolo implements Hierarchical Navigable Small-World (HNSW) graph and Inverted Vector File (IVF) indexes that are generic over representation, encoder and metric, and a two-stage index that composes any first-stage index with any rescoring dataset. Since every combination is specialized at compile time, this modularity adds no run-time overhead, and the resulting indexes reach state-of-the-art performance: our Product Quantization matches Faiss in accuracy at competitive speed, our RaBitQ and HNSW match or outperform their reference implementations, and our IVF outperforms Faiss. Against six dense and five sparse systems on seven public collections, our indexes give the best memory-throughput trade-off on dense data in 9 of 12 collection-accuracy pairs and perform well on out-of-distribution queries. On sparse data, the systems built on Vectorium lead on all three collections, with Seismic and SeismicWave the fastest and kANNolo’s indexes the most compact.
[IR-22] Rethinking Next-Visit Diagnosis Prediction Beyond Aggregate Metrics: Retrieval–Memory Multi-Agent Adjudication
链接: https://arxiv.org/abs/2611.03826
作者: Heejoon Koo
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 2 figures, 18 tables, and 29 pages
Abstract:Next-visit diagnosis prediction forecasts the diagnoses documented at a patient’s next encounter, turning retrospective electronic health records into prospective clinical prediction. However, conventional aggregate evaluation obscures its multifaceted nature: distributionally, models must handle variable-size diagnosis sets and long-tailed labels; temporally, they must distinguish persistent, newly recorded, and recurrent diagnoses; and both vary with observed history depth. We introduce an evaluation framework along these dimensions and apply it to rule-based heuristics, LLM-based prompting, retrieval, memory, and multi-agent approaches. This reveals that repeating the last visit outperforms most LLM-based approaches overall despite predicting no new or recurrent diagnoses, and that retrieval and memory exhibit distinct profiles whose relative strengths vary across temporal transitions and backbones. Motivated by this, we propose LONGINUS, a retrieval-memory multi-agent adjudication framework combining evidence retrieved from patient records with structured memory of the patient trajectory. Using cross-view agreement as a reliability heuristic, it preserves shared predictions and adjudicates disagreements without revealing their source. Across MIMIC-III and MIMIC-IV with four LLM backbones, LONGINUS achieves the best transition-specific performance among LLM-based methods in nearly all settings while remaining competitive with them on aggregate metrics.
[IR-23] Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2611.03747
作者: Huan Phong Thai,Thu Anh Ngoc Le,Nhu Huynh Thi Le,Nhan Huu Tran,Lenh Cong Phan Pham,Hong Nhung Nguyen
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments. To address these challenges, Retrieval-Augmented Generation (RAG) models have emerged as a powerful solution, combining retrieval-based methods with generative capabilities to enhance response quality. In this study, we present a Retrieval-Augmented Generation (RAG) system designed to generate responses related to stock market information. The system integrates retrieval-based and generative approaches to enhance the accuracy and relevance of financial text generation. To evaluate its effectiveness, we employ the ROUGE metric, specifically ROUGE-1, ROUGE-2, and ROUGE-L, to measure lexical and structural similarity between generated and reference texts. Experimental results on a dataset of 100 stock market-related queries demonstrate high ROUGE scores, indicating strong alignment with reference documents. Additionally, we assess latency to evaluate the system responsiveness, achieving an average response time of 1.16 seconds. These findings confirm that our RAG model is capable of providing accurate and timely stock market insights, making it a promising approach for financial information retrieval.
[IR-24] EvoMemState: Evolving Memory State for Personal Agents
链接: https://arxiv.org/abs/2611.03743
作者: Zhishang Xiang,Yunbo Tang,Zhongqiao Liu,Junkai Chen,Zerui Chen,Qingguo Hu,Yuxuan Zhang,William Grant,Qinggang Zhang,Jinsong Su
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Long-term memory enables large language model (LLM) agents to provide continuous personalized assistance by retaining user information across interactions. Existing systems support this process through fact extraction, organization, updating, and retrieval, but largely maintain memories as independent facts. This fact-centric paradigm may overlook how new facts update, constrain, or conflict with earlier ones, while leaving their applicability, role, and priority for the current request implicit. In this paper, we introduce EvoMemState, a state-centric framework that augments persistent facts with two complementary states: Relational States that capture how facts remain valid and relate to one another as memory evolves, and a Reasoning State that determines how these facts should be applied to the current request. Specifically, EvoMemState consists of three key components: (i) Ontology-Guided Memory State Induction extracts Persistent Facts and constructs Relational States that maintain their validity, scope, and evolving relations. (ii) RL-Based State Evolution internalizes memory-use experience into a policy through reinforcement learning to generate a request-specific Reasoning State. (iii) State-Aware Reasoning then combines the relevant facts with their Relational States and the generated Reasoning State to guide the personal agent. Extensive experiments on long-term dialogue and memory-use benchmarks demonstrate consistent improvements over existing memory systems in retrieval completeness and memory-grounded response accuracy. Our code and implementation details are available at this https URL.
[IR-25] DualClusterNet: Personalized Pattern-Space Composition for Next-POI Recommendation WWW2027
链接: https://arxiv.org/abs/2611.03726
作者: Ziyang Su,Guanjie Zheng,Jilin Hu,Linghe Kong
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: submitted to WWW2027
Abstract:Next point-of-interest (POI) recommendation supports personalized place discovery on location-based Web platforms. Mobility routines can recur across users while being realized at different places and adapted to individual preferences. We present DualClusterNet, a causal sequential recommender that composes two population-shared prototype dictionaries. At each observed prefix, soft routers retrieve pattern and spatial mixtures; a factorized multiplicative interaction couples them, feature-wise affine modulation adapts the shared representation, and a separate user-conditioned branch with an additive–projective gate retains individual variation. The prediction head combines this representation with a learned first-order transition term and ranks the complete training POI vocabulary. We evaluate on NYC and TKY using a training-only vocabulary and an original-final-target protocol that neither bridges unknown POIs nor substitutes earlier targets. The available single-seed results show competitive full-candidate ranking, while controlled analyses support the contribution of the compositional and personalized branches. Code is available at this https URL.
人机交互
[HC-0] Whose Guardrail? User Identity Shapes the Effectiveness of Model Disclaimers in Mitigating LLM Political Persuasion
链接: https://arxiv.org/abs/2611.05347
作者: Jillian Fisher,Thomas Richardson,Daniel W. Fisher,Jennifer Pan,Yulia Tsvetkov,Katharina Reinecke
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Politically biased AI can shift users’ opinions, yet it remains unclear whether model disclaimers protect against such persuasion. We report a preregistered experiment (N=1,586) examining how disclaimer design and user characteristics shape susceptibility to politically biased AI. We varied whether disclaimers identified political bias versus potential inaccuracies and whether they included an explanation. Participants completed two decision-making tasks with an LLM biased against their political views, with outcomes assessed immediately and one week later. Disclaimers identifying political bias and including an explanation reduced persuasion, but effects varied across contexts and did not persist. Individual differences were more consistent predictors: Republicans were more persuaded and reported higher trust than Democrats across tasks. Similarly, participants who used AI more frequently were also more persuaded, whereas those with greater self-reported AI knowledge were less persuaded. These findings suggest that generic disclaimers are not enough: effective safeguards must account for user heterogeneity and context.
[HC-1] False Reassurance in LLM Severe-Weather Guidance: A Replayable Evaluation Against Real NWS Alerts
链接: https://arxiv.org/abs/2611.05290
作者: Kadharmoideen Fadurudeen
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 7 pages, 3 tables. Dataset and code: this https URL
Abstract:An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active. We call this false reassurance and measure it by replaying 117 frozen cases built from real U.S. National Weather Service (NWS) alerts (69 alert cases, 32 no-alert controls, 16 non-U.S. locations) through five conditions of a Claude-based preparedness pipeline that differ in alert access and prompt design. One rater hand-labeled all 342 alert-case responses under a seven-category scheme written after an initial binary pass, which we disclose. False reassurance was 0.0% for the production pipeline (0/66, 95% CI 0.0-5.5) and for the same alerts under a neutral prompt (0/69), 37.7% for a blind neutral prompt (26/69), and 44.9% when the blind prompt also told the model to disclose its blind spot (31/69). Every case was implicit; no response explicitly denied an alert. Real alerts removed false reassurance (paired McNemar p 0.0001); the disclosure instruction did not (p = 0.40). An automated regex measure suggested the opposite and missed 53 of 57 true cases, so we do not use it. Alert data alone was not enough for safe behavior: with a neutral prompt the model raised some alarm on 87.5% of no-alert controls, against 0% under the production prompt, and a preregistered single-sentence ablation showed that one production instruction explains part of that gap. We also report two failures common to similar systems, NWS test products ingested as real alerts and a token budget that fails on complex cases, and we release the dataset, labels and code.
[HC-2] Extending the MM-AR Metamodeling Platform with Process Mining Capabilities
链接: https://arxiv.org/abs/2611.05176
作者: Geraldine Hürzeler,Hans-Georg Fill,Daniel Borcard
类目: oftware Engineering (cs.SE); Databases (cs.DB); Human-Computer Interaction (cs.HC)
备注:
Abstract:Process mining derives process models from event data recorded by information systems, but its results are usually presented as two-dimensional visualizations that are separate from the environments in which enterprise models are created and maintained. Metamodeling platforms for extended reality (XR), such as MM-AR, allow users to define modeling languages and to display models in 2D and 3D, but they do not provide access to event data or mining algorithms so far. In this paper we therefore present a first proof-of-concept for showing how an XR-based metamodeling platform can be extended with process discovery capabilities without changes to the platform core through a separate Python service based on the open source PM4Py process mining library. We demonstrate the approach using three common discovery algorithms for obtaining Petri nets and BPMN models based on CSV or XES event logs. A technical evaluation using four public event logs shows that the element counts of the created model instances match those of the PM4Py models in all 16 configurations. The integration adds between about 8 and 53 seconds per run, and this overhead grows with the size of the discovered model rather than the size of the event log. Although the prototype does not yet use the third dimension to encode process information, it established the foundations for studying such approaches.
[HC-3] Better Plans Better Outcomes: Integrating Historical Data into Student Planning in a Computing Course
链接: https://arxiv.org/abs/2611.05143
作者: Jiaen Yu,Jinya Jiang,Soohyun Nam Liao
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: 7 pages, 5 figures. Accepted for publication at the 58th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2027)
Abstract:Effective planning is critical for academic success, but in many computing courses students often lack accurate benchmarks for assessing the difficulty and time required for academic tasks. This phenomenon is more pronounced in large courses where instructors have limited capacity to provide individualized guidance on planning and task estimation. Although many planning tools exist, still, they rarely leverage task-specific, empirically grounded data from actual course contexts. Thus, we explored whether providing task-specific, data-driven information—assignment previews and cohort-level historical statistics—could support more effective planning. We present a lightweight and data-driven planning intervention embedded in the programming assignment workflow of an undergraduate computing course. We conducted quantitative and qualitative studies with 252 students across five programming assignments. Our results indicated that, in authentic course settings, the intervention led to more conservative and executable plans, with significantly higher plan adherence. Students with stronger adherence also achieved better performance on both programming assignments and exams. Qualitative findings also showed both practical planning support and mental benefits from intervention. These results highlight the potential of data-driven intervention to support student planning in larger courses settings where individual guidance from an instructor is further limited.
[HC-4] From Opinions to Commitments: Unstable Assignments and Silent Choices in AI-Mediated Proposals
链接: https://arxiv.org/abs/2611.05135
作者: Sachit Mahajan,Javier Argota S{á}nchez-Vaquerizo,Arthur Capozzi,Dirk Helbing
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Large language models (LLMs) are increasingly being used to turn citizens’ opinions into collective proposals. These proposals can make assignments: who should act, what must be done, how much one should spend and by when. However, if the assignments change when the same opinions are processed again, publishing a single proposal can hide alternatives that participants never get to see. We examined this issue in four LLM mediators (three open-weight and one commercial) using opinions from DeliberationBank and Polis. For each task, a mediator drafted a proposal from thirty opinions five times, varying either the random seed or the order of the opinions. In 17% of five-run sets from the open-weight mediators and 20% from the commercial one, some runs contained assignments while others did not. Reordering the opinions changed the assignments almost as often as replacing them with opinions from different citizens. Three human annotators confirmed that the detected differences between runs were real, although the LLM judge overstated how often they occurred. Substantial variation remained under four safeguards: temperature zero, a commercial mediator, draft selection, and draft merging. The mediator’s objective also shaped representation: prompts seeking agreement covered minority positions much less often than prompts asking to describe differences. A single proposal can therefore obscure choices in both the assignments it proposes and the views it excludes. We propose audits that make alternative assignments visible and disclose the objective behind each proposal.
[HC-5] For You Not for Everyone: Bystander-Aware Delivery of Critical Feedback in Robot Tutoring
链接: https://arxiv.org/abs/2611.05001
作者: Atikkhan Faridkhan Nilgar,Manuel Dietrich,Kristof Van Laerhoven
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Submitted for Review
Abstract:Robots in shared settings may expose information intended for an individual user to nearby bystanders. Robot tutoring makes this concern particularly salient: critical feedback is directed at an individual learner, yet bystanders may overhear or see it. To address this, we compared context-invariant and context-adaptive robot behaviors in a between-subjects study with 40 participants. Following an initial phase without a bystander, a bystander entered the setting. Participants then received two scripted critical feedback messages concerning their pronunciation and handwriting, independent of their actual performance. With the context-invariant behavior, the robot tutor delivered both messages aloud. With the context-adaptive behavior, the robot tutor scanned for the bystander, turned its body and display away after detecting them, gave a spoken attention cue, and presented the same feedback messages silently on its display. Participants rated the robot tutor higher in perceived privacy awareness and perceived social competence in the context-adaptive condition than in the context-invariant condition, while evidence for reduced experienced embarrassment was inconclusive. These findings inform the design and evaluation of robots that respond to bystander presence by adapting how they communicate potentially sensitive information.
[HC-6] Who is the Pronoun Pro? Comparing LLM s and Humans at Number-Disambiguation of they DATE
链接: https://arxiv.org/abs/2611.04968
作者: Carlos Hartmann
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Submitted to journal for publication, will be updated upon publication; figures are greyscale-compatible; raw data, fully reproducible code, and Quarto source in linked repository
Abstract:This study evaluates whether large language models (LLMs) can reliably replace or complement human research assistants in linguistically complex annotation. In a controlled annotation task, 73 human annotators (local students and crowdworkers with and without a background in linguistics) and more than 40 LLM configurations number-disambiguated 174 authentic uses of ‘they’ from Reddit, including innovative and context-sensitive cases. The best LLM, Kimi K3, reached 94.5% accuracy, above the human majority vote (86.2%) and the mean of the top half of human annotators (78.5%); only the single best human annotator scored higher (95.7%). LLMs also offer substantial practical advantages in cost and speed. However, qualitative error analysis reveals a crucial complementarity: human reasoning in low-agreement cases exposes inconsistencies in the annotation typology and thereby improves the methodological framework itself. The study contributes (i) a benchmark-style human-LLM comparison for a semantically demanding task, (ii) transparent cost and time evidence, and (iii) a methodological argument against a purely replacement-oriented view of automation. LLMs are viable for large-scale annotation support in linguistics, but their responsible use requires human critical oversight. The dataset with its gold-standard labels, all human and LLM annotations, and the fully reproducible code are openly available as a Quarto-based, public repository.
[HC-7] AI for eGovernance: Leverag ing Language Technologies For Digital Inclusion
链接: https://arxiv.org/abs/2611.04820
作者: Cynthia Jayne Amol,Lilian Wanzare
类目: Human-Computer Interaction (cs.HC)
备注: 11 pages, 5 figures, 3 tables. Presented at AAIAC 2026, Dar es Salaam, Tanzania
Abstract:The Kenyan government has invested heavily in the digitisation of public service delivery, in line with the Kenya Digital Master Plan 2022-2032. The national eGovernment platform, eCitizen, hosts services that are vital to citizens’ livelihoods, including business registration, youth enterprise lending, digital literacy and tax filing. However, the platform faces digital inclusion challenges that limit equitable access for marginalised groups, particularly youth in rural areas and persons with disabilities. This exclusion is driven by a language barrier, since the platform is primarily in English with limited Swahili support, and by a text-only interface that excludes low-literacy and visually impaired users. This paper reports findings from six one-on-one interviews and an eleven-person roundtable discussion across four platforms essential to youth livelihoods: Ajira Digital, Kenya Revenue Authority (KRA) iTax, Higher Education Loans Board (HELB) and the Youth Enterprise Development Fund (YEDF). Drawing on these findings, on Web Content Accessibility Guidelines (WCAG) 2.1 principles as a design lens for diagnosing eCitizen’s accessibility gaps, and on comparator models such as Estonia’s Bürokratt and India’s Bhashini, the paper proposes a human-centred, systems-minded framework for embedding generative AI and language technologies into eCitizen, including text simplification, neural machine translation, text-to-speech and speech-to-text. The paper argues that, when deployed responsibly with appropriate human oversight, these interventions could meaningfully improve access to government services for linguistically excluded citizens. It also emphasises that AI alone cannot overcome the broader structural barriers, including unemployment and educational inequality, that shape digital exclusion.
[HC-8] Who Is Doing the Thinking? Characterising Human Agency in First-Year Students AI Interactions
链接: https://arxiv.org/abs/2611.04783
作者: Ali Shakiba,Thomas Chaffey
类目: oftware Engineering (cs.SE); Human-Computer Interaction (cs.HC)
备注: 11 pages, 2 figures, 3 tables
Abstract:When students use generative AI to produce their work, the learning process becomes invisible. Established frameworks such as Bloom’s taxonomy then classify the cognitive level of what the AI produced, not what the student contributed. We propose the Human Agency Depth Taxonomy (HADT), a preliminary six-tier taxonomy that classifies the engineering contribution a student makes in a prompt, independent of the AI’s output. HADT combines three dimensions: contextual bandwidth, engineering abstraction and student agency. We applied HADT to 962 prompts from 105 students’ structured AI Journals in an AI-permissive, first-year software engineering course of 145 students, and examined how agency changed over ten journalled weeks. Most prompts (84.2%) carried some student contribution beyond delegation. Agency tracked the development lifecycle. Architecture-discovery prompts dominated the requirements phase (48% of turns) and fell to 5-12% from week 9, while prompts analysing existing systems rose during build and verification (odds ratio 2.88, with turns clustered by student). Two-thirds of the variance in tier lay within students, so agency depended more on the phase of work than on the student. A turn-level rise in delegation in deadline weeks did not hold once turns were clustered by student. Explicitly specifying constraints on the AI’s output was rare throughout, which we interpret as a knowledge bottleneck for novices rather than a gap in instruction. We discuss implications for course and assessment design, and for using HADT as a formative lens on how students engage with AI.
[HC-9] Just Between Us? Exploring a Wearable-Mediated Channel for Discreet Human-Agent Interaction in a Shared Game Setting
链接: https://arxiv.org/abs/2611.04692
作者: Atikkhan Faridkhan Nilgar,Manuel Dietrich,Kristof Van Laerhoven
类目: Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Accepted in 25th International Conference on Mobile and Ubiquitous Multimedia (MUM) 2026
Abstract:In shared settings, social robots may need to exchange information with an individual while limiting co-present others’ access to or interpretation of that information. Publicly observable speech and gestures may not adequately support such exchanges. We investigate a wearable-mediated channel for discreet interaction that combines in-ear audio messages with deliberate rightward head tilts as responses. To examine its feasibility and participants’ experiences, we studied 20 pairs (40 participants) playing a card game with a screen-based conversational agent representing a social robot, controlled by a researcher in a Wizard-of-Oz setup. Each pair played with and without the channel. One participant wore the device (the wearer), while the other (the non-wearer) was informed about the channel after gameplay. The channel conveyed information that could help the wearer or robot character select an opponent they could defeat. The information exchange and subsequent opponent selection occurred as intended in 39 of 40 channel-mediated turns. Wearers reported low perceived pressure/tension in both conditions, with no statistically significant difference between conditions, and favorable usability immediately after use. Retrospective interviews, however, revealed varied accounts of noticing the interaction among non-wearers and contrasting perceptions of teamwork and unfairness. These findings support the feasibility of wearable-mediated exchanges within this game and identify awareness and fairness as considerations for future designs.
[HC-10] Making Smell Actionable in Design: Lexicon Movement Patterns and Gesture Cards
链接: https://arxiv.org/abs/2611.04609
作者: Anran Feng,Shengyu Gao,Xingyu Zhao,Zhenyu Qian,Jingrui An
类目: Human-Computer Interaction (cs.HC)
备注: 28 pages, 5 figures, including appendix
Abstract:Smell is powerful in design, yet it often remains vague and difficult to work with in studios and classrooms. While olfactory HCI has produced devices, toolkits, and scenario-based applications, designers still lack teachable, shareable resources that link smell to bodily action. This paper presents a three-phase research program that makes smell actionable by moving from lived smell experiences to movement patterns and, finally, to gesture resources. We study olfactory-motor mappings and package the results as teachable design representations through a progressively narrowing method: (1) extracting smell-verb-intent relations from user narratives; (2) capturing and clustering embodied enactments into standardized upper-limb trajectory families; and (3) translating these trajectories into gesture card representations for design teaching and ideation through design workshops. We present a three-phase translational framework and empirically derived smell-gesture correspondences that structure the smell-intent-trajectory-gesture pathway, together with reusable assets (lexicon, trajectory atlas, gesture tables, and cards) that support teaching, reuse, and adaptation across contexts.
[HC-11] Setting the Stage: Enhancing Music Recommendations with Spoken Introductions to Stimulate Serendipity
链接: https://arxiv.org/abs/2611.04570
作者: Brett Binst
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:People like to keep their music listening fresh and interesting, but recommender systems often struggle to support this need. Even when a recommended song is potentially refreshing and enriching, listeners may not perceive it as such. Building on Silvia’s appraisal theory of interest, this paper studies whether enhancing recommendations with spoken introductions, immersive or informative, can stimulate two distinct flavors of experienced serendipity: taste-broadening (sparking interest in music outside one’s typical preferences) and taste-deepening (deepening interest in music within one’s typical preferences). Results from a large-scale user study show that both introduction types stimulate taste-broadening and taste-deepening serendipity. Taste-broadening is most effectively stimulated through transportation (absorption in a narrative world), whereas taste-deepening is most effectively stimulated through cognitive elaboration (learning something new about the artist or the social context in which the music emerged). We discuss how these findings could be translated into practice through curated taste-broadening playlists, where both introduction types appear promising, and taste-deepening playlists, where informative introductions seem particularly effective.
[HC-12] From Watching to Steering: Enabling Human-Coding Agent Collaboration through Execution Traces
链接: https://arxiv.org/abs/2611.04466
作者: Xiaoke Yan,Jiaying Lei,Danqing Shi,Zichen Wang,Nan Cao
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 16 pages,10 figures
Abstract:Recent advances in large language models (LLMs) have enabled coding agents to autonomously perform complex software development tasks, yet their long-running execution remains difficult for users to inspect, understand, and steer. We present Vibetrace, an interactive trace visualization and intervention system for agentic programming built upon OpenCode. Vibetrace introduces a human-agent collaboration paradigm of Observe, Understand, Intervene, and Evolve. It (1) visualizes agent workflows as interactive execution traces for inspecting actions, intermediate states, and failures; (2) enables users to fork execution from prior steps and provide targeted corrections without restarting validated progress; and (3) distills traces and human feedback into reusable skills for continual agent evolution. On SWE-bench Verified, distilled skills reduce input tokens by 23.0% and cost by 15.5%, while improving pass rate from 65.0% to 68.0%. User studies further demonstrate improved accuracy and efficiency over conventional log-based interaction.
[HC-13] SEAM: Situated Executable Associative Memory for a Co-located Community
链接: https://arxiv.org/abs/2611.04296
作者: Keru Wang,Michael Xu,Ken Perlin,Nebojsa Jojic,Andrzej Banburski-Fahey
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:In groups, knowledge is distributed among individuals, and how it is brought together depends on social ties. Taking inspiration from how a single mind recalls and reacts to memories and current stimulus, we build a shared memory that does this for a community, prototyped as a social app, SEAM. Members post arbitrary moments; SEAM works out how fragments relate, distills knowledge about the group, and replies in a situated and associative way. Some of these memories act as instructions that can alter how the system behaves, making it programmable by its members. We deployed SEAM to 46 people in a co-located community for two weeks of open-ended use, and ran a follow-up workshop with 14. Members shared their days, interrogated the memory, and instructed it to track routines and run games; replies introduced them to people and subjects they had never encountered. SEAM points towards communities where knowledge finds people.
[HC-14] OmniScribble: Mixed-Initiative Authoring of Animated Sketches through Inspectable and Repairable AI Decisions
链接: https://arxiv.org/abs/2611.04284
作者: Hien Trinh,Tan-Hiep To,Trung-Nghia Le
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:AI-assisted creative tools reduce the effort of authoring content, but delegating creative decisions to AI can make it difficult for authors to understand, correct, or redirect work. We present OmniScribble, a system for animating multi-object sketches that treats object decomposition and motion planning as editable artifacts. Rather than generating animation end-to-end, OmniScribble presents AI proposals at verification checkpoints, where authors can accept, repair, or regenerate them. Both manual and AI-assisted authoring operate on the intermediate representation and rendering pipeline, allowing us to isolate AI delegation. In a within-subject study with eight participants, AI-assisted authoring yielded a median within-participant reduction in completion time of 71.4%, while perceived control and motion-intent alignment remained high. Interaction traces and interviews show that participants adapted to AI errors by repairing local issues, regenerating mismatches, and accepting proposals. These findings suggest that inspectable, repairable delegation enables generative automation while maintaining control over the outcome.
[HC-15] Agent ic Path Maps: Exposing an Agents Traversal Through the Garden of Forking Paths
链接: https://arxiv.org/abs/2611.04183
作者: Madeleine Grunde-McLaughlin,Hafsah Usman,Daniel Mitroshkov,Emily Reif,Nino Migineishvili,Lakshya Chauhan. Kevin Yu,Jeffrey Heer,Daniel S. Weld
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user context and preferences. However, interrupting the user for clarification on all such decisions is untenable. How might agents communicate impactful, but unexplored, paths to the user after execution? To investigate, we first distill a design space for communicating alternative paths, supporting the generation of new systems and structuring dimensions of future study. We then measure the impact of one vital design space dimension on user comprehension: the number of alternatives displayed. Through a user study (n=48) in the data analysis domain, we find that participants rarely consider alternative paths of their own volition, but AI overreliance reduces when alternative paths are visualized for them. By revealing paths the agent did not take, we demonstrate how users can better interpret results and incorporate personal context.
[HC-16] Palimpsestic Accountability in the US Federal AI Inventories
链接: https://arxiv.org/abs/2611.04153
作者: Dipto Das,Syed Ishtiaque Ahmed,Aditya Vashistha,Shion Guha
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:
Abstract:An AI inventory is a structured, often public record in which a government or organization lists and describes the AI systems it uses or plans to use. Studies of single AI inventory releases describe what systems do but say little about how they are governed over time. We linked entries across three releases of the US Federal AI Use Case Inventory (2023–2025), compared answers only where both releases required them, and analyzed governance accounts through a dual deductive-inductive approach, guided by the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework. As the inventory grew from 710 to 3,611 entries from 2023 to 2025, fewer required answers were left blank, but accountability was rewritten through three mechanisms. Each release \emphredrew the scope of the questions; agency-assigned classifications \emphgated what was reported; and answers were also \emphoverwritten, with gains and losses concentrated in single agencies and little account of human oversight and judgment. These findings show that each release redraws the boundary of accountability and leaves the public to \emphre-stitch the records, a condition we term \textbfpalimpsestic accountability. We recommend versioned records, reasons for every reclassification, public files that distinguish kinds of missing answers, and oversight documented as conditions to improve public sector AI accountability.
[HC-17] Must AI Spell It Out? Prag matic Comprehension of Alerts in a Time-Critical Task
链接: https://arxiv.org/abs/2611.03976
作者: Anwesha Das,Vera Demberg,Anna Maria Feit
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 7 figures
Abstract:Assistive systems typically tell users everything, often spending time and attention on what a user already knows, leaving less for what happens next. People, on the other hand, leave out what they expect the listener can supply. Can people use their prior knowledge to understand an incomplete message when the speaker is a machine though? We studied this pragmatic comprehension in a monitoring task controlling users’ prior knowledge and the available time to detect critical situations. In a preregistered study (N=114), we compared a pragmatic planner that reasons over a model of the listener’s knowledge to decide what to say, when, and how explicitly, with a baseline specifying problems fully in onset order. Participants resolved underspecified alerts almost as reliably as fully specified ones and sooner, detected more problems, and rated the pragmatic assistant helpful more often, with no increase in reported mental demand. Our findings show that people can recover what the system left implicit and that pragmatic communication can support effective assistance under the constraints of a changing world.
[HC-18] From Benchmarks to Behaviors: A Framework for Auditing AI Agents
链接: https://arxiv.org/abs/2611.03939
作者: Nick Hagar
类目: Human-Computer Interaction (cs.HC)
备注: 39 pages, 4 figures
Abstract:Generative AI systems increasingly mediate access to information. Their behavior depends on tools, instructions, and user memories that are often difficult for researchers to observe or control. Here, I introduce TRACE (Transparent Research on Agent Contexts and Environments), a framework for systematically auditing how these components shape AI agent behavior. Built on an open-source agent harness, TRACE enables controlled manipulation of system components. It also records inputs, outputs, and task execution for downstream analysis. I demonstrate this framework through an experiment examining how user memories influence source selection when agents answer questions about the 2026 U.S. midterm elections. I find that memories expressing explicit preference for a publisher increase its inclusion among the first three cited sources in a response by 33-39 percentage points. Memories identifying the user as a Republican or expressing distrust of mainstream media shift citations rightward. That same media distrust memory also increases citation of sources expressing misinformation or conspiracy theories by 6.3 percentage points, with substantial variation across models. These findings demonstrate that concise user memories can alter source selection in consequential settings, even when questions and search results remain fixed. TRACE extends algorithm auditing to agent harnesses, providing a transparent way to study agent behaviors that benchmarks or commercial interfaces may struggle to isolate.
[HC-19] Proceedings of the CSCW 2026 Workshop on Womens Health as Cooperative Work: Participatory AI Across Care Community and Policy
链接: https://arxiv.org/abs/2611.03776
作者: Sherilyn Francis
类目: Human-Computer Interaction (cs.HC)
备注: Proceedings index for the CSCW 2026 workshop held on October 11, 2026. Editor: Sherilyn Francis. Individual papers are submitted separately to arXiv
Abstract:This submission indexes contributions to the CSCW 2026 workshop, “Women’s Health as Cooperative Work: Participatory AI Across Care, Community, and Policy,” held on October 11, 2026. The workshop examines women’s health as cooperative work spanning patients, caregivers, clinicians, communities, institutions, and digital technologies. Contributions explore participatory AI, coordination breakdowns, the distribution of care labor, governance, decision-making authority, and accountability across care, community, and policy contexts. The index provides paper titles, authors, affiliations, thematic areas, and unique report numbers to support discovery of the corresponding individual arXiv submissions. The workshop proposal is separately published under DOI https://doi.org/10.1145/3785651.3818821. Comments: Proceedings index for the CSCW 2026 workshop held on October 11, 2026. Editor: Sherilyn Francis. Individual papers are submitted separately to arXiv Subjects: Human-Computer Interaction (cs.HC) ACMclasses: H.5.3 Cite as: arXiv:2611.03776 [cs.HC] (or arXiv:2611.03776v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2611.03776 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
计算机视觉
[CV-0] DreamX: Video Diffusion Model is a Universal Latent Action Learner
链接: https://arxiv.org/abs/2611.05377
作者: Jisoo Kim,Chaehyun Kim,Junsu Kim,Jiho Park,Seongchan Kim,Eunbeen Hong,Soowon Son,Fu-En Yang,Seungryong Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Project Page: this https URL
Abstract:Learning a universal action representation from video requires modeling dynamics that are consistent across different executions of the same action yet invariant to changes in embodiment and appearance. Existing latent action models infer actions from visual transitions, but their representations often entangle the underlying action with irrelevant visual variations, limiting transfer across embodiments. We introduce DreamX, which repurposes a pretrained video diffusion model as a universal latent action learner. Our approach extracts compact latent actions from the video model’s rich spatiotemporal features, compressing each latent video frame by \boldsymbol300\times ( \boldsymbol1,200 \rightarrow 4 tokens). In addition, by leveraging the video model’s generative prior, these latent actions capture action-relevant dynamics disentangled from visual appearance. The resulting latent space serves as a shared action interface across embodiments (human and robot), environments, and action parameterizations. On LARY-Bench, DreamX achieves state-of-the-art performance among latent action models. Moreover, actions encoded in one setting remain effective in another: the latent space supports any-to-any video transfer and zero-shot action transfer across diverse embodiments and environments. Furthermore, this shared interface also enables policy learning in a compact latent action space, reducing the computational cost of video-diffusion-based policies.
[CV-1] Visibility-Aware Reference Generation for Benchmarking Visual 3D Reconstruction in Vegetation-Rich Environments
链接: https://arxiv.org/abs/2611.05361
作者: Zhipeng Zhong,Matthew Coombes,Cunjia Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 20 pages, 12 figures
Abstract:Reliable evaluation of visual 3D reconstruction and depth estimation in vegetation-rich outdoor environments is limited by the scarcity of geometric ground-truth data and the cost of complete scanner surveys. We present a LiDARbased reference-generation pipeline for evaluating the zero-shot performance of feed-forward visual-geometry models alongside conventional structure-from-motion (SfM) methods. The pipeline reuses camera images, LiDAR scans, and reference poses from existing multi-sensor localization datasets to generate per-view depth maps and visible reference point clouds. To address visibility errors in accumulated LiDAR scans, we introduce the LargeDepth-Gap Visibility Score (LDGVS) for point-wise visibility filtering and automatic scale selection in Hidden Point Removal(HPR). Experiments on TartanAirV2 and TartanGround show that, compared with Poisson surface reconstruction, which is an approach suited to completed scan surveys, the combined HPR and LDGVS filtering retains approximately three times the point density at comparable absolute relative error. We also apply the pipeline to the BotanicGarden dataset to systematically compare depth estimation and 3D reconstruction methods. Feedforward methods produce dense depth predictions efficiently, but their 3D reconstructions can contain duplicated structures. Conventional SfM generally produces more coherent geometry, but its reconstructions remain sparse and more prone to failure in challenging vegetated scenes. The benchmark toolkit and datasets will be released.
[CV-2] GSCV: Lightweight 3D Gaussian Splatting Sequence Compression via Standard Video Codecs
链接: https://arxiv.org/abs/2611.05359
作者: Qi Yang,Yingzhan Xu,Kai Zhang,Li Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper presents GSCV++, a lightweight pipeline for 3D Gaussian Splatting (GS) sequences Compression with standard Video codecs. To meet the requirements of low-complexity and hardware compatibility from the industry, GSCV++ jointly improves preprocessing, 3D-to-2D mapping, and practical video compression configuration of GS compression. First, for preprocessing, energy-adaptive inter-frame spherical harmonic (SH) alternative current (AC) dimensionality reduction (EA-SHDR) is proposed to produces a compact and stable temporal SH AC feature space. It can reduce the data volume of following mapping and practical compression, as well as less complexity while eliminating redundant information. Second, for 3D-to-2D mapping, prior-based Inter-MiniPLAS (P-MiniPLAS) is designed to generate smooth 2D feature maps through prior-based weighting factor inference and small scale remapping. Compared with vanilla Parallel Linear Assignment Sorting (PLAS) and Morton scan, P-MiniPLAS ensures the generated 2D maps are more suitable for the video codec while maintaining a low level of computational complexity. Third, for practical compression, content-aware generalized compression (CA-GC) is developed to use a non-uniform bit-depth split for coordinate map generation, and a coding unit size selection is introduced for efficient compression complexity control, which fully exploits the performance of video codecs without involving normative changes. Experiments demonstrate that GSCV++ reports over 20% in intra- and 60% in inter-prediction rate-distortion gains compared with state-of-the-art (SOTA) methods. GSCV++ also achieves faster 2D map generation, encoding, and decoding than other SOTA methods using the same type of video codecs. Ablation studies verify the effectiveness and robustness of the proposed modules and provide insights for subsequent research.
[CV-3] OpenBrain: An Auditable Generated-Label Release for Whole-Brain MRI Parcellation NEURIPS2026
链接: https://arxiv.org/abs/2611.05348
作者: Qizhen Lan,Yu-Chun Hsu,Yuxiang Wei,Lijing Zhu,Zenan Sun,Liang He,Lishan Yu,Xiaoqian Jiang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026, Evaluations and Datasets Track. Dataset: this https URL code: this https URL
Abstract:Automatic tools can now generate whole-brain MRI parcellations at cohort scale, but generated-label releases often provide little evidence about which cases are reliable enough to reuse. We introduce OpenBrain, a confidence-aware release of 35,838 provenance-verified OpenNeuro T1w cases from 607 source datasets. OpenBrain is organized as an auditable case-record release rather than a directory of standalone segmentation files: each record links a primary parcellation, two committee parcellations, source/license provenance, raw QC-7 measurements, percentile risks, a combined risk score R, and a confidence tier. QC-7 is a reference-free reliability panel over label geometry, image-label consistency, and committee disagreement, so users can filter, weight, audit, or re-threshold generated labels without rerunning inference. We evaluate this released evidence under a frozen protocol. Across external cohorts, lower QC-7 risk corresponds to higher generated-label quality; in fixed-budget training, lower-risk selections improve paired macro Dice over random selection by +3.2 to +3.7 percentage points. OpenBrain also exposes raw axes, committee outputs, provenance, and datasheet documentation, so confidence claims remain inspectable after download. It does not turn generated labels into manual annotations or clinical-grade segmentations. Its contribution is a large open generated-parcellation substrate, a reusable case-level risk-evidence layer, and an external validation protocol for release-time QC. Dataset: this https URL. Code: this https URL.
[CV-4] Wonder 4: A Mixture-of-Experts Recipe for Image Quality Enhancement
链接: https://arxiv.org/abs/2611.05294
作者: Yunan Wu,Bin Sun,Partha Acharjee,Xiaoyu Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Full version will be released soon
Abstract:A real photograph is often blurry, grainy, compressed, scratched, and low-resolution at the same time. Each defect has its own severity and its own place in the image. The corrections for these defects conflict with each other. Exist- ing models describe the whole image with one description, so they cannot say where each correction belongs. To solve this problem, we present Wonder 4, a mixture-of-experts (MoE) model for image quality enhancement. It corrects several defects of a real photograph at the same time, each in the region where it occurs. Every block of the model holds a mixture of N = 13 experts. A router, trained with the model, sends every image patch to k = 5 of them. No degradation classifier, mask, prompt, detector, or task label is used. Another issue is that standard MoE training, taken from language models, pushes every expert toward equal use. Equal use applies every correction everywhere, even where it is not needed. We replace this rule with a bias that only sets a minimum share of use. Across all patches, an expert used less than this minimum gets higher router scores until it is selected again. An expert above the minimum is not changed, so no expert is forced to be used as often as the others. The bias needs no extra loss. The method adds no extra module and no extra processing step. We evaluate Wonder 4 on DIV2K and Flickr2K, against five published methods and three open-weight pipelines. It reaches the best LPIPS, DISTS, and NIQE on all three test settings (DIV2K x4, DIV2K x8, and Flickr2K x4).
[CV-5] Low-Density 3D Point Cloud Classification
链接: https://arxiv.org/abs/2611.05291
作者: Ahmed Baha Ben Jmaa,Faten Chaieb
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D point cloud classification, a task in computer vision, has recently gained substantial interest due to its extensive applications across various domains such as augmented and virtual reality, robotics, and autonomous driving. However, classifying 3D point clouds becomes challenging in real-world scenarios where the density levels vary. These variations are due to limitations inherent to current sensing technologies and variable environmental conditions, which often prevent the capture of detailed and comprehensive geometric representations. Existing classification methods, which have demonstrated impressive results on dense point clouds, may be affected and could experience reduced performance when dealing with low-density point clouds. Addressing this challenge, this paper introduces LD-PointNet++, an architecture optimized for the classification of low-density point clouds. To pave the way for LD-PointNet++, we conduct the first benchmarking study that systematically evaluates state-of-the-art point cloud classification methods across varying densities, revealing significant performance declines in low-density environments. Experimental results on the ModelNet benchmark dataset demonstrate that LD-PointNet++ not only outperforms these methods in low-density scenarios but also demonstrates consistent accuracy above 90% across different density levels.
[CV-6] Anchored Rank-Adversarial Fine-Tuning Pretrained Visual Place Recognition Models
链接: https://arxiv.org/abs/2611.05286
作者: Mostafa Kira,Bader Rasheed,Manuel Mazzara
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 3 figures, 8 tables
Abstract:Visual place recognition (VPR) localises a camera by retrieving similar geo-tagged images, and current foundation-model VPR models are accurate across cities, seasons and illumination, but not robust to adversarial perturbations: under a white-box attack on the ranking at an imperceptible budget of 1/255, SuperVLAD and BoQ keep at most 0.2% top-1 recall (R@1) on four benchmarks. We propose anchored rank-adversarial fine-tuning (ARAFT), which fine-tunes a released model with a listwise rank loss on each adversarial query’s hardest positive and an anchor to the clean descriptor of the frozen pretrained model. Checkpoints are selected under a clean-accuracy budget fixed in advance, and the full trade-off is reported. Within five validation R@1 points of clean cost, plain adversarial training has no admissible checkpoint, whereas ARAFT raises robust R@1 from zero to 47.3 on SuperVLAD and 55.3 on BoQ; plain adversarial training exceeds this only at about three times the clean cost. Against unbudgeted plain adversarial training on the test sets, ARAFT matches its robust R@1 at 2/255 with about ten more clean R@1 points. At 4/255, plain adversarial training is 6-9 robust R@1 points ahead, while ARAFT still keeps its ten-point clean advantage. Developed on SuperVLAD, ARAFT transfers to BoQ without re-tuning, and its robustness holds against stronger optimisers, with no sign of gradient masking.
[CV-7] YANAMI: A Large-Scale Multimodal Dataset and Benchmark for Multiturn Stateful Interaction
链接: https://arxiv.org/abs/2611.05280
作者: Shengyu Wang,Tengyue Nong,Junming Liu,Haosong Peng,Siyu Li,Ruiqi Lei,Fushuo Huo,Yuhao Pan,Yunfeng Fan,Ding Wang,Nan Cheng,Haozhao Wang,Wenchao Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Unified Multimodal Models (UMMs) integrate visual understanding, generation, and editing within a single unified framework. However, existing unified multimodal datasets and benchmarks remain dominated by single-turn tasks. Even in recent multi-turn datasets, many turns can be solved independently without tracking information across turns. Consequently, they provide limited supervision and evaluation for multimodal stateful interaction, which requires models to understand, maintain, update, and faithfully realize evolving dialogue states across turns. To address this gap, we introduce YANAMI, the first large-scale multimodal dialogue dataset and benchmark for stateful interaction, comprising YANAMI-400K and YANAMI-Bench. YANAMI-400K contains 400K multi-turn multimodal dialogues, with more than 2M images and 2.6M dialogue turns. The dialogues span four representative scenarios: Multi-Image Fusion, Image Editing, World Modeling, and Visual Reasoning, and require models to continuously track, preserve, and revise evolving multimodal dialogue states across turns. YANAMI-Bench comprises 400 multi-turn dialogues for systematically evaluating multimodal stateful interaction. It jointly evaluates Dialogue State Understanding and Visual Realization, characterizing the gap between models’ ability to understand evolving dialogue states and their ability to faithfully realize them in visual outputs. Experiments show that fine-tuning BAGEL on YANAMI-400K improves image editing, generation, and multimodal stateful interaction by 10.3%, 2.0%, and 13.23%, respectively, while preserving its general visual understanding capabilities. Evaluations on YANAMI-Bench further reveal that existing UMMs still struggle to maintain, update, and visually realize evolving multimodal dialogue states.
[CV-8] Möbius: A Unified Motion-Language Model Bridging Understanding and Generation
链接: https://arxiv.org/abs/2611.05279
作者: Jiazhi Shu,Chengfeng Zhao,Jinyi Wang,Yubo Zhao,Zhiyang Dou,Ronghui Li,Yu Lu,Xinwei Jiang,Zijiao Zeng,Yuan Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:We aim to build a truly unified motion model that can understand, generate, and edit human motion within a single framework. Existing motion UMMs remain only partially unified: motion understanding is handled by a language model, while generation is often delegated to a separate diffusion module operating over learned motion latents. We introduce Möbius, which brings the entire diffusion process into the same multimodal representation space used for motion understanding. Clean motion, noisy motion, and text jointly participate in shared attention, while role-specific parameters process understanding and generation streams. Importantly, Möbius operates directly on continuous frame-level raw motion, requiring neither discrete tokenization nor a pretrained motion VAE. This simple formulation naturally supports motion captioning, text-to-motion generation, temporal completion, and instruction-guided editing within a single model. On HumanML3D, Möbius achieves state-of-the-art performance among unified motion models. Scaling to 1.7B parameters and 1,700 hours of motion data further demonstrates that our framework is both effective and scalable.
[CV-9] Route Anything: A Study of Model and Policy Routing for General-Purpose AI Systems
链接: https://arxiv.org/abs/2611.05275
作者: Pengfei Zhou,Yixing Ma,Hexin Wang,Weidong Tang,Zhenglin Wan,Wangbo Zhao,Yang You
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Artificial general intelligence (AGI) requires a method that can understand, generate, reason, and act across diverse domains and modalities. Rather than relying on a single model to master every capability, a promising path toward AGI now is to orchestrate a heterogeneous ecosystem of specialized models, tools, and policies (candidates) into a unified AI system. At the heart of this vision lies a fundamental question: how can an AI system dynamically select and coordinate the most capable candidate for each task while balancing performance and computational cost? In this work, we present Route Anything, a unified framework for studying model and policy routing across general-purpose AI systems. We develop a unified theoretical foundation that characterizes optimal routing under quality–cost trade-offs and establishes how the information available to a router determines its attainable performance. To systematically investigate routing across diverse capabilities, we first construct an omni-modal trajectory matrix spanning 25 benchmarks across language understanding, image, video, and speech generation, coding and workplace agents, and robotic manipulation, comprising 374,401 scored runs from 452 candidate configurations. Building on this framework, we train and test different routers to investigate the potential of omni-modal model routing, the capabilities of learned routers, and the conditions under which routing improves efficiency. Our results reveal substantial opportunities for capability orchestration: per-input oracle routing outperforms the best fixed candidate by 5.4 points, and learned routers outperform it by 0.3 points across 16 benchmarks, by 2.1 points on video generation, and by 1.1 points on robot manipulation while using 24% less policy time. More importantly, routing raises efficiency by 8.82x on average compared with the reference model.
[CV-10] Diagnosis by Composition: Detecting and Localizing Unseen Diseases by Composing Learned Radiological Findings
链接: https://arxiv.org/abs/2611.05231
作者: Kaihua Hou,Wei Liu,Yi Huang,Hao Zhang,Haibo Jin,Yifei Sun,Yuan gao,Zuoou Li,Ling Zhang,Ahmed Alaa,Geoffrey H. Tison,Xu Han,Ke Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep models for radiological diagnosis are typically trained to predict disease labels directly from images, which limits them to diseases seen during training and makes their decisions hard to interpret in radiologists’ terms. Radiology, however, proceeds from visual findings to disease through published guidelines that make this decision rule explicit, interpretable, and reproducible. We introduce diagnosis by composition (DbC), a representation learning framework that encodes this findings-first logic. DbC learns a vocabulary of radiological findings by aligning image features with the text embeddings of the findings, each finding represented as a dense map over the scan. A disease is defined by a set of required findings derived from its guidelines, and its diagnosis map is a fixed, parameter-free composition of the corresponding finding maps. Because the finding-to-disease mapping is derived from guidelines rather than learned, DbC is interpretable and auditable by design, can extend to new diseases by specifying their findings without retraining, and can diagnose unseen diseases by composing their findings. On multi-phase liver CT, DbC detects and localizes unseen diseases, rare diseases from a different institution, and an external cohort significantly better than all comparators, including models that align images with disease names. DbC also improves detection and localization in the fully supervised setting and remains superior across training-set sizes. Although demonstrated on liver CT, DbC applies wherever radiological guidelines define diseases through findings.
[CV-11] PLANTEUR: A Crop-Type Segmentation Benchmark for Tropical Regions
链接: https://arxiv.org/abs/2611.05223
作者: Piper Wolters,Gabriel Tseng,Favyen Bastani,Joseph Redmon,Yawen Zhang,Hadrien Sablon,Ando Shah
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We introduce PLANTEUR, a benchmark for crop-type mapping from satellite images in the French overseas territories. Building on the PASTIS design, PLANTEUR provides georeferenced and aligned Sentinel-2, Sentinel-1, and Landsat-8 sequences with parcel-derived labels spanning six tropical regions and an 8-class label space. We define both a standard test split and a leave-one-island-out protocol to measure cross-territory generalization, and evaluate fully supervised models, linear probes, and fine-tuned foundation models. The benchmark code and data are open source and available on GitHub and Hugging Face.
[CV-12] Diffusion Plug-in Priors for Self-Supervised Ultrasound Video Restoration
链接: https://arxiv.org/abs/2611.05206
作者: Chu Chen,Mingduo He,Yining Li,Yinhao Wu,Wei Tang,Yiwen Luo,Yixuan Chen,Graham Treece,Pasquale Cascarano,Kangning Cui
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:\textitObjective: Ultrasound videos are limited by hardware-constrained spatial resolution and signal-dependent speckle, while paired low- and high-resolution acquisitions for supervised restoration are rarely available. We aim to jointly super-resolve and despeckle ultrasound videos without paired training data or network retraining. \textitMethods: We propose UltraDiff, which uses a frozen pre-trained diffusion model (DM) as a plug-in prior and restores each frame by optimizing the latent seed of the DM sampler under a range-constrained maximum a posteriori formulation. We derive a speckle-aware data-fidelity term from a Rayleigh scale-family model and characterize its relative-residual and variance-adaptive weighting properties. We further introduce a stabilized fidelity that is bounded below and strictly convex in log intensity for positive observations in its unclamped form. A total-variation regularizer complements the DM prior, and a temporal seed update warm-starts each frame from the optimized seed of its predecessor. \textitResults: On EchoNet-LVH videos with simulated speckle and additive noise, UltraDiff with a natural-image prior improves PSNR by 1.24,dB over the strongest baseline, and ultrasound-related priors improve it further. On real clinical echocardiography videos, UltraDiff reduces the mean absolute error of zero-shot ejection fraction (EF) prediction from 7.31 to 6.37 EF points relative to DUP, the strongest baseline. \textitConclusion: Combining a speckle-aware fidelity with frozen diffusion priors enables ground-truth-free ultrasound video restoration that benefits downstream measurement. \textitSignificance: UltraDiff needs neither paired data nor retraining and is not tied to a specific diffusion checkpoint. Code associated with this work will be made publicly available upon acceptance.
[CV-13] AQ-MOTR: Tracking-Aware Detection Query Formation for End-to-End Multiple Object Tracking
链接: https://arxiv.org/abs/2611.05205
作者: Hanyang Liu,Hong Zhang,Ding Yuan,Yang Han,Yifan Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Tracking-by-propagation (TBP) methods perform end-to-end multi-object tracking by propagating track queries across frames, jointly handling detection and identity association without heuristic post-processing while naturally modeling temporal information. However, as detection and association are tightly coupled, detection queries in TBP are expected to discover currently untracked objects and provide complementary current-frame evidence for challenging tracks whose queries and identities are retained. Existing methods largely inherit detection-query formation from single-frame detection and construct such queries before the decoder with little awareness of trajectory states. Consequently, currently untracked objects may be missed, while challenging tracked objects with reduced visibility under occlusion may lack complementary evidence, causing association failures or duplicate responses. To address these issues, we propose TAQ-MOTR, a tracking-aware detection query formation framework that employs a pre-decoder Hybrid Query Generator (HQG) to fuse current-frame image features, track memory, and learnable memory. By conditioning detection queries on trajectory states, TAQ-MOTR adapts them to \revthe discovery of currently untracked objects and tracked-object complementation We further introduce a training-only auxiliary detection branch whose one-to-one label assignment is decoupled from that of the main tracking branch, providing additional detection supervision to strengthen the shared HQG and decoder for the discovery of currently untracked objects. TAQ-MOTR improves performance on DanceTrack, SportsMOT, and BFT, achieving 72.0 HOTA, 63.1 AssA, and 76.6 IDF1 on the DanceTrack test set. Code is available at this https URL
[CV-14] Robust Reconstruction of Power Lines from Incomplete Mobile and Aerial LiDAR Data
链接: https://arxiv.org/abs/2611.05197
作者: Mohamed Gaha,Damos Ayobo Abongo,Philippe Massicotte,Eya Ben Kacem,Wael Jaafar,Rami Langar,Christian Buteau
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 9 figures
Abstract:Accurate vegetation-encroachment assessment and risk mitigation require electric utilities to accurately locate and characterize distribution-network assets, including poles and power lines, and distinguish among medium-voltage, low-voltage, neutral, and telecommunication lines. Mobile and aerial Light Detection and Ranging (LiDAR) point clouds provide detailed 3D representations of these assets and their surroundings. However, power-line detection and reconstruction remain challenging because conductor returns are often sparse, incomplete, noisy, or occluded by vegetation. These challenges are especially significant in distribution networks, where lower pole heights and complex line configurations place conductors close to trees. In addition, mobile and aerial LiDAR datasets differ substantially in point density, viewing geometry, spatial coverage, and occlusion patterns. We propose a robust and automated reconstruction method combining scaled density-based clustering, robust polynomial fitting, and iterative fragment merging to recover individual conductors under severe data incompleteness and occlusion. Following semantic classification, the same reconstruction pipeline processes both mobile and aerial LiDAR data without modality-specific code or parameter tuning. The method is evaluated through degradation experiments, benchmarking, parameter sensitivity analysis, and large-scale validation. On the aerial dataset, it achieves 96.87% precision, 96.66% recall, a 96.76% F _1 -score, and a geometric RMSE of 0.127 m. On the mobile dataset, where conductor-level reference annotations were unavailable, it achieves a point-to-model RMSE of 0.045 m and a reconstruction coverage of 95.27%. Source code, documentation, and evaluation data are publicly available at this https URL.
[CV-15] From Natural Images to Medical Scans: Transferability of Diffusion Models for Medical Image Generation WACV2027
链接: https://arxiv.org/abs/2611.05192
作者: Efthymios Georgiou,Ioannis Kakogeorgiou,Sarah Brüningk
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV 2027
Abstract:Transfer learning from natural images is standard in supervised and increasingly adopted in self-supervised medical image analysis, but its role in generative modelling, and in particular diffusion denoisers, remains underexplored. Medical diffusion models typically train the denoiser from scratch, with no systematic evaluation of natural-image transfer. We analyse this across latent and pixel paradigms, model sizes (131M-952M) and two radiology datasets (RadImageNet, BraTS), and ask which aspects of transfer support generation. We find that: i) Natural-image initialisation consistently improves generation, by 21-34% relative gFID in pixel space against 14% in latent space, where the tokeniser supplies much of the prior. gFID gains mirror downstream pathology classification and radiomic-feature FD, both proxies for clinical fidelity. ii) Benefits reflect spatial structure, not semantics: semantic probing does not predict generation quality, whereas a spatial-structure metric adapted to internal denoiser representations tracks gFID at |r|=0.85 , positioning spatial organisation as a channel through which pretraining manifests. iii) Mixed-domain denoiser training shows that natural and medical images share similar layer-wise representations up to the late blocks, and mixing natural images into medical training improves fidelity. iv) Transfer benefit is tied to natural image initialisation, outperforming in-domain medical pretraining and several reinitialisation schemes. Overall, natural image denoisers transfer positively to medical ones in our setup. Code is available at this https URL
[CV-16] PadLoc: Training-Free Floor-Plan Localization by Sparse Geometric Matching WACV2027
链接: https://arxiv.org/abs/2611.05172
作者: Marian Bookhahn,Frank Neumann
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to WACV 2027 (Algorithms track). 22 pages: main paper (10 pages incl. references) and supplementary material (12 pages). Code: this https URL
Abstract:We propose PadLoc, a training-free localization back-end that places a moving sensor within a known floor plan. Floor plans are cheap, persistent, and robust to appearance change, but recent learned-depth methods localize against them by filtering per-frame depth through dense (x,y,\theta) pose grids. PadLoc keeps the swappable depth front-end and replaces the dense grid back-end with sparse geometric matching: dominant wall orientations define a small pose-candidate set, masked normalized cross-correlation scores observed free space against the plan, and a moving-pose Gaussian-mixture Bayes filter plus consensus readout decodes the trajectory. Holding each competitor’s depth fixed, PadLoc matches or beats UnLoc’s forward filter and the F3Loc filter at every Gibson trajectory length, and UnLoc’s offline refinement from T=35 on; it exceeds Z-FLoc’s published results with a shared MapAnything front-end. On Bonn LiLocBench, PadLoc improves pooled SR@1m over UnLoc by 23-32 pp under wheel odometry and 35-43 pp with ground-truth motion; with full-FOV LiDAR plus wheel odometry the same readout reaches 100% SR@1m at 9-10 cm median error on the static sequence. On the Hilti-Trimble Localization Challenge PadLoc localizes a camera-only fisheye rig on construction floors hundreds of metres across at 0.90-0.98 online SR@2m on three of five sessions; adding learned wall isolation, applied to every session, covers all five at 0.67-0.92 with the localizer unchanged throughout. PadLoc thus matches or outperforms dense grid back-ends while preserving sensor modularity and a compact state that does not grow with trajectory length.
[CV-17] Perturb Repair Verify: Self-Play Vision-Language Verifiers for Compositional Understanding
链接: https://arxiv.org/abs/2611.05158
作者: Zijun Huang,Yingjun Du,Cees G. M. Snoek
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision-language verifiers judge whether an image faithfully matches a textual description, yet existing training pipelines typically rely on static supervision from stronger external judges or curated hard-negative data. Such supervision requires external generation or annotation, is teacher-bounded, and is difficult to adapt as model failures evolve. We introduce PROVE (Perturb-and-Repair Optimization for Vision-Language Evaluation), a self-play framework that trains verifiers through a closed perturb-verify-repair-verify loop. Starting from a known-correct image-caption pair, a Mutator introduces a typed perturbation over objects, attributes, counts, or relations. The verifier then judges the perturbed caption and provides textual feedback, which a Repairer uses to recover a faithful caption for re-verification. These two verification rounds produce structured rewards for informative mismatches and visually grounded repairs. Since perturbation, verification, and repair are role-conditioned behaviors of the same evolving multimodal backbone, updating the shared parameters also improves the verifier itself. Experiments show that PROVE improves fine-grained compositional verification across model scales and families and strengthens visual-detail robustness on the reference backbone, with limited, benchmark-dependent changes on broader multimodal tasks.
[CV-18] When Cell Crops Share a Parent: Measuring and Isolating Acquisition Leakage in Cervical Cytology Benchmarks
链接: https://arxiv.org/abs/2611.05145
作者: Snehasish Ghosh,Soham Mitra,Antareep Majumdar,KC Santosh,Nibaran Das
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 3 figures
Abstract:Public cervical cytology benchmarks release many cell crops cut from far fewer microscope fields or slides and are often split at the crop level, so test cells share acquisition parents with training cells. In a preregistered study, crop-level splitting raised SIPaKMeD accuracy by 0.97 percentage points across twelve architectures and twenty seeds (95% CI 0.59-1.39), and CRIC accuracy by 12.08 points (11.16-12.99). On SIPaKMeD, a factorial that fixes training crop and parent counts attributes the gain to sibling exposure (1.73 points, 0.95-2.51), not to the extra parent diversity of crop-level splits (0.00). Parent identity is decodable at about 100 times chance, yet erasing 82% of its above-floor linear recoverability left the gap unchanged, and Grad-CAM did not distinguish leaking from clean models; matching test cells’ color statistics to an unseen same-class field removed most of the advantage. Decodability and saliency cannot substitute for a parent-disjoint split.
[CV-19] High-fidelity training-free photovoltaic module mapping from multi-view UAV imagery
链接: https://arxiv.org/abs/2611.05131
作者: WenHao Duana,Qi Chena,Penglei Zhang,Ruizhe Deng,Zhiling Guo,Haoran Zhang,Yuntian Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 39 pages, 16 figures
Abstract:High-fidelity photovoltaic (PV) module mapping requires complete instance recovery and accurate delineation. Vision foundation models offer zero-shot segmentation, but converting incomplete and inconsistent predictions across views into a complete, geometrically accurate map of individual modules remains challenging. We propose the Multi-View PV Module Mapper (MV-PVMapper), a training-free framework coupling vision foundation models with unmanned aerial vehicle (UAV) photogrammetry. First, Segment Anything Model 3 (SAM 3) generates candidates from original images using text prompts. Their contours are then transferred into object space through per-module slope correction to limit distortion from surface-model noise, and verified against representative module dimensions to guide cross-view instance selection. Finally, accepted instances return to all covering images as box prompts for SAM~3, supplementing text prompts with spatial cues for iterative segmentation, verification and fusion. On five-direction surveys of three operational rooftop PV plants at approximately 1 cm nadir ground sampling distance, two feedback iterations recover 7,710 of 7,711 reference modules with no false positives at an intersection-over-union (IoU) matching threshold of 0.5. Macro-averaged area precision and recall reach 99.00 % and 98.52 %, respectively. Averaged across sites, the aggregated Jaccard index reaches 97.55 %, compared with 60.73 % and 41.24 % for orthomosaic segmentation and multi-view pixel-voting baselines, respectively; average precision at the stricter IoU threshold of 0.95 reaches 95.67 %. Ablation studies show that module-size constraints are critical for instance separation, while slope correction and feedback to all covering views improve boundary accuracy. These results are achieved without task-specific image annotation or segmentation-model weight updates.
[CV-20] Characterizing the vertical continuity of fuel strata based on TLS point clouds Gaussian decomposition and overlap metrics
链接: https://arxiv.org/abs/2611.05102
作者: Jesús Torralba,Luis Á. Ruiz,Pablo Crespo-Peremarch,Marina Simó-Martí,Juan Pedro Carbonell-Rivera
类目: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: 45 pages, 15 figures Author affiliations and ORCID: Jesús Torralba this https URL , Luis Á. Ruiz this https URL , Pablo Crespo-Peremarch this https URL , Marina Simó-Martí this https URL , Juan Pedro Carbonell-Rivera this https URL
Abstract:Vertical forest structure influences fire behavior by regulating understory-to-canopy fuel connectivity, yet operational fuel-type models represent this complexity through discrete classes that only indirectly capture vertical continuity. High-density point clouds offer an opportunity to characterize vertical fuel connectivity in a more explicit and continuous manner, but specific methodological approaches are still required. We applied Gaussian decomposition to vertical height profiles derived from terrestrial laser scanning (TLS) point clouds across 25 permanent forest plots to characterize geometric vertical fuel continuity. A multiscale analysis spanning from the plot scale to ~0.5 m^2 units was used to evaluate geometric, probabilistic, and overlap metrics for characterizing fuel strata. The method reproduced the vertical transition between shrub and tree strata, with median Pearson correlations ranging from 0.83 to 0.96 across analysis levels. Probability density overlap metrics and separability indicators, such as the Jeffries-Matusita distance, provided complementary descriptors of vertical continuity. Intermediate spatial scales (3-6 m cells) provided a useful compromise between spatial detail and the consistent representation of shrub-canopy interaction patterns. Finer scales increased variability due to point density reduction and profile irregularity, and low mean squared error values were found insufficient as sole performance indicators. Gaussian decomposition applied to TLS point clouds enables explicit quantification of vertical fuel continuity, a limitation of current discrete classification systems. Continuous vertical connectivity metrics derived from this approach offer a pathway for improving ladder fuel characterization and strengthening decision-making support in wildfire risk assessment.
[CV-21] Sparse Anchors for Visual Multi-Session Registration in Changing Environments
链接: https://arxiv.org/abs/2611.05084
作者: Yunus Talha Erzurumlu,Niklas Trekel,Saurabh Gupta,Nico Koltermann,Olga Vysotska,Jens Behley,Louis Wiesmann,Cyrill Stachniss
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages, 4 figures, 2 tables
Abstract:Repeated robotic monitoring requires localization across visits to the same environment, even if the environment changes. Such changes can substantially reduce persistent visual content, and repetitive structures can lead to incorrect cross-session correspondences. In this paper, we address visual multi-session registration of a new data recording (the query session) to a previously reconstructed reference map. Under substantial visual and structural changes, cross-session localization may be possible only for parts of the trajectory. In our approach, we therefore rely on a sparse set of reliable absolute pose constraints and combine them with within-session relative-pose information to register the complete trajectory to the reference map. We first estimate absolute alignment hypotheses through visual place recognition, local feature matching, and geometric verification. We then use sequence consensus to reject hypotheses that are inconsistent with a common query-to-reference alignment and retain the remaining poses as absolute anchors. These anchors are refined through featuremetric alignment against a Gaussian feature field and combined with within-session relative-pose constraints in a robust pose-graph optimization. We evaluate our approach on 15 cross-session experiments from the challenging HILTI26 construction-site dataset, covering substantial scene changes and difficult-to-match environments. Our method achieves 94.4% recall within 1m and 100.0% within 5m, outperforming independent image localization, which peaks at 65.3% and 70.7%, respectively, as well as direct registration of independently reconstructed Structure-from-Motion geometry. Our results show that sparse, reliable absolute anchors can provide accurate and complete trajectory registration even when reliable cross-session correspondences are available only for part of the query session.
[CV-22] Manifold Embedding of Deep Image Features for Image Matching via Neural Adjoint Maps
链接: https://arxiv.org/abs/2611.05083
作者: Nafie El Amrani,Moritz Heep,Florian Bernard
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages main paper, 12 pages appendix
Abstract:Estimating semantic correspondences between different object instances of similar categories in real-world images is a fundamental challenge in computer vision. While the recent progress in foundation models has significantly advanced solutions for such difficult image correspondence problems, respective methods are often insufficiently regularised. For example, common nearest-neighbour matching with foundation model features often results in noisy correspondences, which is particularly prominent when solving for dense correspondences. In this work, we tackle image matching via functional maps, which have been popularised for 3D shape matching due to their powerful spectral formalism utilising an efficient low-dimensional linear representation. However, functional maps require that domains (in our cases the rectangular image grids) are equipped with an informative and non-trivial geometry. To define a meaningful manifold structure for images, we use Galerkin’s method from finite elements to derive a discrete Laplace-Beltrami operator (LBO) based on a manifold embedding of deep image features. Further, we leverage a neural adjoint map that allows to represent non-linear mappings. We demonstrate that our method sets the new state of the art among zero-shot image correspondence methods on multiple dense correspondence benchmarks of real-world images. Our code is publicly available: this https URL
[CV-23] rajectory Evaluation via Rollout-Free World Model for End-to-end Autonomous Driving
链接: https://arxiv.org/abs/2611.05072
作者: Junzhe Wang,Jinqing Zhang,Qingjie Liu
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although existing candidate trajectory evaluators have substantially improved end-to-end planning accuracy, their underlying mechanism remains suboptimal. Model-based evaluators typically make scoring future-aware by explicitly predicting candidate-conditioned future states over the planning horizon, which we term rollout. However, rollout incurs high inference cost and accumulates prediction errors. We argue that planning does not require reconstructing a full future scene for each candidate, but only future-relevant information for reliable comparison. We therefore propose RFWorld, a Rollout-Free World model for end-to-end trajectory evaluation. RFWorld constructs a shared scene memory and shapes it into a future-readable representation via lightweight training-time temporal readout with semantic supervision, keeping training overhead low. At inference time, candidate trajectories directly query the shared memory via sparse spatial readout for scoring, reducing computation by roughly the planning horizon times the number of candidate trajectories, while avoiding recursive rollout errors. Experiments on NAVSIM and Bench2Drive show that RFWorld learns a more planning-aligned world representation and achieves state-of-the-art performance.
[CV-24] VIGOR: Visual Gain Ordering for Hallucination Mitigation in Multimodal Discrete Diffusion Language Models NEURIPS2026
链接: https://arxiv.org/abs/2611.05066
作者: Junzhe Chen,Tian Qin,Eugenie Shi,Tianshu Zhang,Qiang Ju,Lijie Wen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to NeurIPS 2026
Abstract:Multimodal discrete diffusion language models (dLLMs) generate responses through iterative mask prediction and offer a promising alternative to autoregressive large vision-language models. However, they still suffer from hallucination, producing outputs that are plausible under language priors but insufficiently grounded in the image. We argue that hallucination in multimodal dLLMs is not only a token prediction problem, but also a premature commitment problem: during iterative denoising, some masked positions are unmasked too early because their confidence is driven more by language priors than by visual evidence, and these early commitments can bias later generation. To address this issue, we propose Visual Gain Ordering (VIGOR), a training-free and plug-and-play inference strategy that reorders masked positions according to counterfactual visual evidence. At each denoising step, VIGOR compares the confidence of each candidate token under the original image-conditioned pass and an additional visual-ablation pass, and prioritizes positions whose predictions are more strongly supported by the image. Experiments on LLaDA-V and Lumina-DiMOO show that VIGOR consistently reduces hallucination and improves multimodal reasoning performance across benchmarks. Code is available at this https URL.
[CV-25] FloMo: 3D Scene Flow as a World Action Model Intermediate for Learning from Human Video
链接: https://arxiv.org/abs/2611.05062
作者: Jeremy Collins,Namra Patel,Ayush Agarwal,Shitij Govil,Animesh Garg
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Pretrained video generation models distill internet-scale motion priors into robot policies, but full video is a redundant signal for action prediction. We argue that dense 3D motion is the medium through which video pretraining best transfers to robot actions, and that predicting motion rather than video affords better sample-efficiency for action prediction. FloMo is a 5B video-generation model fine-tuned to predict 3D scene-flow and action tokens. Rendering scene flow as RGB and encoding it into the same latent space as video enables a pretrained video generation backbone to efficiently adapt to predict 3D scene flow. Co-trained on action-free human video and action-labeled robot teleoperation data, our model benefits from human-video pretraining and generalizes to unseen objects, motion primitives, and tasks. Replacing motion prediction with a video prediction objective degrades out-of-distribution generalization, while adding a video prediction target alongside motion lowers policy success rather than improving it. When motion, video, and action are predicted jointly, action tokens attend far more to motion than to video, suggesting that predicting video wastes model capacity. Together, these results indicate that motion effectively mediates the transfer from video pretraining to action. More information can be found at this http URL.
[CV-26] Do Agent ic Policies Need to Look at Every Step? Geometric Abstraction for Reasoning and Action
链接: https://arxiv.org/abs/2611.05058
作者: Yihang Liu,Yiming Chen,Mingzhe Zheng
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the CoRL 2026 Workshop on Agentic Robotics. 13 pages, 8 figures. Project page: this https URL
Abstract:Recent multimodal language models can reason directly about robot scenes, but it is unclear whether they need to repeatedly reinterpret raw visual observations at every step of an interaction. We propose shifting routine spatial reasoning from images to text and symbols through a 3D geometric abstraction that is refreshed locally from observations and read directly by the language models. Specifically, our interface represents scene geometry via typed, evidence-aware symbolic references to object parts, grounds them from calibrated RGB-D, and returns compact textual checkpoints with metric residuals after each action. Symbolic task structure can thus be reused across decisions while metric values are rebound to the current scene, with direct visual reasoning invoked only on demand. Across offline policy synthesis and continuous online execution, reasoning over grounded text and symbols improves multi-step manipulation completion while substantially reducing repeated multimodal queries, positioning explicit geometric state as an effective substrate for agentic robot policies.
[CV-27] On the Learnability of Footwear Robustness: Footstep Recognition Under Unseen Conditions
链接: https://arxiv.org/abs/2611.05049
作者: Robyn Larracy,Erik Scheme
类目: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: 13 pages (+2 pages supplementary), 7 figures
Abstract:Footstep patterns, captured using pressure or force-sensitive walkways during natural walking, offer a convenient and passive approach to biometric recognition. However, footwear variability poses a major challenge to practical deployment, as it impacts both the foot-sensor interface and the user’s underlying gait mechanics. This study considers the particularly difficult scenario where users enroll under one fixed footwear condition but must later be recognized in arbitrary, unseen footwear. Using the StepUP-P150 dataset, we show that footwear-induced shift, while substantial and heterogeneous across individuals, contains shared structure that can be learned from a training population and transferred, zero-shot, to previously unseen users and footwear styles. We do this using BiStepNet, a novel architecture that decomposes spatial and temporal identity cues into independently trainable streams and outperforms compact 3D spatiotemporal baselines for cross-footwear recognition. We demonstrate that cross-footwear recognition performance scales systematically with the volume and diversity of footwear representation in the training set, with gains transferring to footwear styles not directly observed during training. Moreover, we show that auxiliary gait datasets with only partial modality overlap (incorporated through the architecture’s decomposed design) can be leveraged to further improve robustness to unseen footwear conditions.
[CV-28] Federated Transfer Learning for Medical Image Classification under Mixed Skew
链接: https://arxiv.org/abs/2611.05035
作者: Alexandre G. Leclercq,Sébastien Bougleux,Aurélien Corroyer-Dulmont,Romain Hérault
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 5 figures and 2 tables. Presented at 4th IEEE International Conference on Federated Learning Technologies and Applications (FLTA 2026)
Abstract:Deep learning in medical imaging is constrained by data scarcity and by privacy regulations that make centralizing patient data across institutions impractical. Federated Learning (FL) addresses these constraints by keeping data local, but its distributed nature exposes training to strong statistical heterogeneity. Multicentric medical data is particularly affected, exhibiting a mixed skew that combines feature, label, concept, and quantity shifts across centers. Personalized FL mitigates heterogeneity by tailoring a model to each client, yet most methods assume training from scratch, which is unrealistic given the high dimensionality of medical features relative to the limited data available. We propose the Personalized Aligner Federated Pretrained (PAFP) framework, a personalized FL approach built on a pre-trained backbone. PAFP inserts client-specific Feature Alignment modules before the fine-tuned blocks to realign hidden feature distributions toward a server-aggregated target. On the FedISIC benchmark, PAFP attains the highest unweighted balanced accuracy while reducing inter-client performance variance roughly six-fold relative to the strongest baseline, yielding accuracy and cross-client equity jointly. Our code is publicly available at this https URL.
[CV-29] RGB Photo Enhancement with Deep Learning on Smartphone GPUs: Mobile AI 2026 Challenge Report
链接: https://arxiv.org/abs/2611.05024
作者: Andrey Ignatov,Georgy Perevozchikov,Radu Timofte,Dong Zhang,Zhao Ran,Yang Zhang,Xiaochen Li,Shichang Ju,Yiqin Cao,Yang Lu,Nguyen Nhu Tinh Anh,To Thanh Dat,Nguyen Ngoc Ky Duyen,Nguyen Trong Nghia,Bui Minh Hieu,Qiuhai Yan,Sunzhe Yang,Kang Chen,Tingting Wang,Faming Fang,Runhua Deng,Zhifeng Wang,Long Peng,Aiwen Jiang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:On-device RGB photo enhancement is a popular task that is needed for quality improvement of photos captured with mobile devices as well as for retouching of images acquired through other sources. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose participants to design fast solutions that can perform comprehensive enhancement of RGB photos while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale DPED dataset consisting of RGB image pairs captured with an old iPhone 3GS smartphone and a professional Canon 70D DSLR camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to enhance Full HD resolution RGB images on both platforms under 300 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.
[CV-30] Can saliency maps flag chest radiograph classifier errors at a new hospital? An external validation study
链接: https://arxiv.org/abs/2611.05011
作者: Aashish Chapagai,Angad Singh Gurtatta,Erik Cambria
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 21 pages, 5 figures, 1 table, 6 Extended Data figures and 4 Extended Data tables. Code: this https URL
Abstract:Saliency maps are offered as a safeguard for medical image classifiers, but whether they reveal individual errors at a new hospital remains largely untested. We trained a convolutional network and a vision transformer on NIH ChestX-ray14 and evaluated 303,768 decisions per model at the training hospital and at two external sites, in the USA and Vietnam, with radiologist-consensus labels. We scored each explanation by its anatomical enrichment, the area-normalized concentration of attribution in a segmented thoracic region, and tested whether it detects errors beyond the model’s confidence. Grad-CAM localized findings less often than radiologists (hit rate at most 0.575 versus 0.888). Adding enrichment to a confidence-based error detector calibrated at the training hospital never raised its area under the curve by more than 0.011, and for the convolutional network the relation between enrichment and correctness reversed between hospitals, degrading the transferred detector. Model confidence, not saliency, should anchor deployment monitoring.
[CV-31] Learned Smartphone ISP on Mobile GPUs: Mobile AI 2026 Challenge Report
链接: https://arxiv.org/abs/2611.05008
作者: Andrey Ignatov,Georgy Perevozchikov,Radu Timofte,Yuhong He,Yuxuan Qiu,Tianyu Hao,Ruoqi Li,Yueqi Yang,Chaoyu Feng,Dongqing Zou,Lei Lei,Na Jiang,Yongrui Zhang,Hailong Yan,Xiangtao Zhang,Ao Li,Le Zhang,Andrei Arhire,Jing Fang,XingLin Xie,YiChen Lu,Haibo Xu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:With the increasing importance of mobile camera quality in modern smartphones, growing attention has been directed toward computational photography and advanced image signal processing (ISP) algorithms aimed at enhancing various perceptual attributes of captured images. This creates a need for efficient deep learning models for this problem that are not only performant but can also be deployed on constrained mobile hardware. In this Mobile AI challenge, we address this problem and propose the participants to design fast solutions that can reconstruct RGB photos from RAW Bayer camera data while running on popular smartphone GPUs from Qualcomm and Arm. For this, the participants were provided with a large-scale Fujifilm UltraISP dataset consisting of RAW-RGB image pairs captured with the Sony IMX586 Quad Bayer mobile sensor and a professional 102MP medium format FujiFilm GFX100 camera. The runtime of all models was evaluated on the Snapdragon 8 Elite Gen 5 and Dimensity 9500 chipsets with the Adreno and Mali GPUs, respectively. Top solutions proposed in this challenge are able to reconstruct high-quality 5MP RGB image from raw data on both platforms under 100 milliseconds. A comprehensive description of the models developed in the challenge is provided in this paper.
[CV-32] ScoreDial: Precise Control of Visual Attributes in Image Generation via Regressor Guidance from Limited Human Ratings WACV2027
链接: https://arxiv.org/abs/2611.05004
作者: Jianbo Tang,Keita Ishii,Hiroki Sakamoto,Manabu Kikuchi,Katsuma Kitamura,Chiho Semba,Kenta Nishiyama,Teppei Mori,Yukihiro Kiwaki,Takeo Kato
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2027. 37 pages, 27 figures, 4 tables, including the supplementary material
Abstract:Requested-value control is stricter than continuous steering: given c on a human-rating scale, the generated image should attain that value from only limited ratings. We present ScoreDial, which fits a differentiable regressor R to 100-479 human-rated images; once R is fixed, a requested value c is defined on its output scale, and learned target conditioning is paired with error-adaptive guidance that repeatedly reduces |R(\mathbfx)-c| during sampling. Across tire slip resistance, interior visual complexity, food desire-to-eat, and soap translucency, using one set of sampler settings shared across all four, ScoreDial reaches requested values and achieves, on all four attributes, R -MAE at most 0.013 over a range of width 0.8 and Spearman \rho=0.980 , the maximum attainable under our evaluation protocol. Because R also drives sampling, we evaluate agreement with R separately from matched sweeps, an independent auditor fitted to the same ratings on a backbone none of the deployed regressors uses, and blinded human judgments. MPGD-flow also reaches the requested values under R , but ScoreDial has larger auditor displacement on every attribute, the largest among all compared methods, and a stronger MPGD-flow setting gives up target agreement without closing this gap. On every attribute, removing target conditioning leaves low R -MAE but more than halves auditor displacement, separating precise score correction from attribute movement. Blinded rating studies with 15 raters per attribute show that mean human ratings track c on tire and soap, with linear R^2=0.665 and R^2=0.786 , and in forced-choice comparisons of matched sweeps, raters chose ScoreDial’s as the clearer progression in at least 84% of judgments against every baseline.
[CV-33] ProAlign: Progressive Positional and Prototype-Guided Alignment for Aerial-Ground Person Re-Identification NEURIPS2026
链接: https://arxiv.org/abs/2611.04980
作者: Qiao Li,Jing Chen,Cong Wu,Kun He,Ruiying Du,Lei Tan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS 2026
Abstract:Aerial-Ground Person Re-Identification (AG-ReID) aims to match pedestrian images captured by unmanned aerial vehicles (UAVs) and ground-based surveillance cameras. The task remains highly challenging due to severe viewpoint discrepancies, frequent occlusions, and substantial domain gaps between aerial and ground imagery. Beyond these observable factors, existing methods often overlook two fundamental structural issues: cumulative positional drift during hierarchical feature extraction and semantic inconsistency across cross-view feature distributions. To address these challenges, we propose ProAlign, a Progressive Positional and Prototype-Guided Alignment Network for AG-ReID. ProAlign comprises two key components: a Layer-wise Progressive Positional Embedding (LPPE) module and a View-Aware Prototype Contrastive Learning (PCL) module. LPPE performs adaptive positional calibration throughout transformer layers by employing conditional positional encoding in shallow layers to mitigate local spatial distortions, while introducing learnable static positional embeddings in deeper layers to reinforce global semantic priors. Meanwhile, PCL maintains view-specific aerial and ground prototypes for each identity and enforces cross-view semantic consistency via a dual-prototype contrastive objective. Extensive experiments on CARGO and two real-world AG-ReID benchmarks demonstrate the effectiveness of ProAlign, achieving state-of-the-art overall retrieval performance on CARGO with improvements of 1.60% in Rank-1 accuracy and 5.01% in mAP over the strongest competing methods.
[CV-34] An Agent ic Framework for Quality-Aware and Scenario-Adaptive Image Compression
链接: https://arxiv.org/abs/2611.04960
作者: Zhenghao Chen,Lebin Zhou,Zicong Chen,Naifu Xue,Jiahao Li,Bin Li,Dong Xu,Yan Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite decades of progress, image codecs remain difficult to adapt to diverse real-world compression scenarios for two main reasons. First, most image codecs are optimized for a specific objective and require substantial redevelopment when the compression goal changes. Second, they typically assume clean inputs, whereas sensor-captured images often contain degradations that alter rate-quality behavior. To address these challenges, we propose Practical Image Coding Agents (PICA), a multi-agent framework that adapts existing codecs to diverse user requirements and potentially degraded inputs. PICA consists of four collaborative agents: a Perception agent that interprets user intent and analyzes image content, an Improvement agent that jointly considers bitrate and reconstruction quality to guide pre-coding restoration, a Coding agent that refines quality-control settings from global to regional levels, and an After-coding improvement agent that addresses residual degradations and compression-induced artifacts through restoration guided solely by image quality. To enable codec-agnostic regional adaptation, we introduce Agentic ROI Coding (AROIC), which treats the codec as a black box and allows the agent to automatically schedule, execute, evaluate, and reflect ROI coding plans without architectural modifications or codec-specific retraining. We integrate these four agents with a Scenario-Guided Quality-Rate (SGQR) evaluation strategy to enable more effective scenario-adaptive compression across heterogeneous codecs. Comprehensive experiments on clean, noisy, and blurred images across standard, perceptual, and machine-vision compression scenarios demonstrate that PICA consistently enhances the compression performance of both the conventional H.266/VVC codec and the neural Cheng2020 codec, outperforming competing compression methods on the Kodak, CLIC2020, and HEVC datasets.
[CV-35] Lightweight graph transformers for clutter and target classification in automotive radar
链接: https://arxiv.org/abs/2611.04959
作者: Adrian Gheorghiu,Tunc Alkanat,Ashish Pandharipande
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the IEEE Sensors Journal Volume 26 Issue 6
Abstract:Radar is a core sensor modality for scene perception to achieve higher levels of autonomy in automotive driving. A common occurrence in automotive radars is clutter - detections of non-existent moving objects, that can adversely impact target detection and classification performance and subsequent driving actions. We propose lightweight tiny graph transformer network (TGTNet) models for classifying clutter from stationary and moving targets in the scene. Performance evaluation on the public RadarScenes show that our proposed TGTNet models achieve similar classification performance in precision and recall metrics in comparison to state of the art models, with one to two orders of magnitude lower model size and significantly faster inference.
[CV-36] DPAGait: Dynamic Pose-Knowledge-Aware Network for Robust Gait Recognition in the Wild
链接: https://arxiv.org/abs/2611.04950
作者: Zenan Lin,Junduan Huang,Kun Liu,M. Saad Shakeel,Wenxiong Kang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Gait biometrics aims at identity recognition by appearance or pose features captured during unconstrained human walking. Existing methods predominantly rely on binary silhouette images, inherently characterized by low information entropy, and fail to fully utilize the dynamic information in gait sequences, limiting their ability to handle cross-time variations effectively. Consequently, these methods encounter significant challenges in the wild. They are susceptible to variations in clothing, carried objects, and viewpoints, as well as dynamic environmental conditions, all of which lead to substantial degradation in recognition accuracy and robustness. To overcome these challenges, we propose a pose-knowledge-aware approach specifically designed for robust gait recognition in the wild. First, we propose a novel Dynamic Pose-Parsing enhanced Silhouette gait representation paradigm that substantially enhances the information entropy and effectively captures fine-grained behavioral cues by integrating pose-parsing cues with traditional silhouettes. Subsequently, we develop a dual-branch Dynamic Pose-Knowledge-Aware Gait network. The framework consists of two complementary branches: (1) a 2D dynamic pose-aware branch that extracts pose-enhanced features from our proposed DPPS representations, and (2) a 3D Skinned Multi-Person Linear branch that leverages the 3D SMPL model to capture rich and comprehensive 3D dynamic pose information. Finally, the features are integrated to facilitate accurate and robust gait recognition. Extensive experiments on four challenging in-the-wild gait datasets demonstrate the effectiveness and generalizability of the proposed DPPS representation. The complete framework is further evaluated on SUSTech1K, CCPG, and Gait3D, where SMPL information is available or can be consistently estimated, achieving competitive performance under diverse real-world conditions.
[CV-37] PATTS: Parallel-Aware Test-Time Scaling for Thought-Image Generation in Multimodal Discrete Diffusion LLM s WACV2027
链接: https://arxiv.org/abs/2611.04948
作者: Yasong Dai,Zeeshan Hayder,David Ahmedt-Aristizabal,Hongdong Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted in WACV 2027
Abstract:Test-time scaling (TTS) improves generative models by allocating additional inference-time computation to sampling, search, and verification. Existing image generation approaches, however, typically rank completed images or image-centric intermediate states. We study TTS for parallel multimodal discrete diffusion language models, in which reasoning tokens and image tokens are decoded jointly within a single generation trajectory. We first diagnose a limitation of conventional final-image selection: on GenEval, a CLIP-style image-prompt score is weakly aligned with within-prompt compositional correctness, leaving substantial headroom between verifier selection and oracle selection. We then propose PATTS, a novel parallel-aware TTS framework that searches over intermediate thought-image states using cross-modal verifier signals. PATTS uses a state-dependent reliability weight, derived from model-internal trajectory diagnostics, to modulate the influence of external semantic verification during generation. In the fixed N=32 evaluation on MMaDA-Parallel, the PATTS reaches a GenEval score of 0.844, compared with 0.628 without TTS, 0.787 for Best-of-N, and 0.810 for FKD. These results show that reliability-aware trajectory verification can improve test-time selection for parallel thought-image co-generation under the evaluated search budget. Our code release is at this https URL
[CV-38] How Well Does Webcam Eye Tracking Work “in the Wild”? A Large-Scale Validation of Chiasm and a Comparison with WebGazer
链接: https://arxiv.org/abs/2611.04946
作者: Artem V. Belopolsky,Florian Perdreau
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages, 21 figures, 2 tables
Abstract:Webcam eye tracking can greatly accelerate data collection in basic and applied research by reaching large, diverse samples of participants online. However, its spatial performance is considered well below that of hardware-based systems, limiting its use. We evaluated the first-generation Chiasm webcam eye tracker in a large online study ( N = 505 ) spanning diverse participants, devices, and recording conditions. Chiasm achieved a mean spatial accuracy of 2.39 cm ( \approx 2.28^\circ at 60 cm), spatial precision (SD) of 1.05 cm ( \approx 1.00^\circ at 60 cm), and sample-to-sample precision (RMS-S2S) of 1.29 cm ( \approx 1.23^\circ at 60 cm). For the 91% of participants who met a 3 cm calibration criterion, spatial accuracy was 2.15 cm ( \approx 2.05^\circ at 60 cm). After three minutes of a task that encouraged head movement, spatial accuracy declined by 0.59 cm, while the gaze center at each target shifted by a mean distance of only 0.37 cm. On the validation block, the offset was 0.30 cm larger for men than for women. Age, region of residence, face luminance, and Michelson contrast showed no reliable relationship with spatial accuracy. A higher eye aspect ratio, a more open eye, was associated with a smaller offset. Wearing glasses decreased spatial accuracy on every validation block. In a comparison between a random subsample ( N = 101 ) and a separate sample tested with the open-source tracker WebGazer ( N = 101 ) using an identical protocol, Chiasm showed better spatial accuracy (2.18 vs. 4.51 cm) and spatial precision (SD; 1.01 vs. 1.55 cm). WebGazer showed better sample-to-sample precision (RMS-S2S; 0.79 vs. 1.26 cm), which likely reflects conservative, over-smoothed estimates rather than superior stability. These results suggest that Chiasm is narrowing the gap between webcam and hardware-based eye tracking, making large-scale gaze research “in the wild” a reality.
[CV-39] Learning Where to Look: Observation Policy Optimization for Thinking with Images NEURIPS2026
链接: https://arxiv.org/abs/2611.04943
作者: Junfeng Wang,Jiawei Liu,Yongchao Xu,Tao Jiang,Jiangbo Ai,Jin Zhang,Chong Wang,Qixing Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026. Project page: this https URL
Abstract:Complex visual question answering often requires vision-language models to think with images, i.e., actively acquire local visual evidence by zooming into informative regions of high-resolution images. Our error analysis shows that many failures in this setting originate from incorrect observation actions, where the model fails to localize key regions or obtain crops that contain sufficient evidence for answering. We further observe that bounding-box tokens exhibit substantially higher generation entropy than ordinary language tokens, indicating high uncertainty when the model decides where to look. However, existing reinforcement learning methods based on final-answer rewards provide only trajectory-level feedback, making it difficult to assign precise credit to the observation actions. We propose Observation Policy Optimization (OPO), an RL algorithm for optimizing observation actions in tool-augmented thinking with images. OPO consists of two core modules. First, Uncertainty-Guided Observation Branching (UOB) treats each image zoom-in tool call as an observation event and selectively branches at high-uncertainty, low-redundancy events during rollout, enabling efficient exploration of alternative cropping decisions and their resulting visual evidence. Second, Evidence-Localized Advantage Attribution (ELAA) compares sibling branches using local evidence-sufficiency rewards and assigns credit only to the corresponding observation-action tokens. By decoupling observation-action optimization from trajectory-level outcome supervision, OPO improves visual evidence acquisition and further enhances visual reasoning performance. Experiments show that OPO consistently improves Qwen3-VL models across scales and achieves competitive performance against larger models and tool-augmented baselines. Code is publicly available.
[CV-40] HTNN-MSC: Multi-view Subspace Clustering with a Hyperbolic Tangent Tensor Low-Rank Surrogate
链接: https://arxiv.org/abs/2611.04940
作者: Yiwen Dou,Yiqiang Ding
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. Experiments on 23 benchmark datasets
Abstract:High-order dependencies among views can be modeled by tensor singular value decomposition (t-SVD) in multi-view subspace clustering. However, the tensor nuclear norm often used with this framework assigns the same asymptotic marginal penalty to singular values of different magnitudes, which may excessively shrink dominant components that encode cluster structure. We propose HTNN-MSC, which replaces this norm with a bounded hyperbolic tangent tensor rank surrogate. The surrogate is strictly concave on the positive half-axis and has a Lipschitz continuous derivative. Its marginal penalty decreases as singular values grow. We develop an alternating direction method of multipliers solver in which the tensor subproblem decomposes into scalar frequency-domain shrinkage problems. Safeguarded multi-start Newton iterations and candidate-objective comparison implement the resulting hyperbolic tangent thresholding operator. The shape parameter is scaled by the input spectrum. We also establish o-minimal definability of the augmented objective. Under explicit boundedness, descent, relative-error, and exact-block conditions, the Kurdyka-Lojasiewicz framework guarantees convergence of the full sequence to a KKT point. On 23 datasets, HTNN-MSC achieves a mean clustering accuracy of 0.8404 and an average accuracy rank of 2.78 against nine representative methods. Friedman tests and Holm-adjusted Wilcoxon comparisons reveal significant overall differences, although several pairwise differences remain nonsignificant. HTNN-MSC records 19 wins, one tie, and three losses against the convex t-SVD-MSC skeleton. A same-skeleton ablation also shows that the proposed surrogate does not uniformly outperform alternative nonconvex penalties.
[CV-41] Robustness Verification against Patch Attacks via Group-Lasso Semidefinite Relaxation
链接: https://arxiv.org/abs/2611.04939
作者: Yukina Nakagawa,Takami Sato,Ken Kobayashi,Kazuhide Nakata
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Robustness verification is essential for ensuring the safety and security of neural networks, but current methods focus heavily on norm-ball attacks, which do not adequately model typical real-world threats that present physical perturbations, known as patch attacks. A naive approach requires computationally prohibitive enumeration of all possible patch positions. To address the challenge, we propose a novel neural network verification method specialized to the patch-attack threat. Instead of enumerating patch locations, we first model the patch attack with group-wise sparsity and formulate its verification methods as a mixed-integer semidefinite program (MISDP) by applying SDP relaxation to the neural network. We then propose three relaxations, including Group-Lasso relaxation, that remove the integer variables while preserving the sparse, group-wise structure of the original attack, enabling substantially efficient verification. Our empirical analysis demonstrates that the proposed relaxations require less than 3% of the computation time of the original MISDP, while the Group-Lasso relaxation incurs only a 0.34% average increase in the objective value over the original formulation. Furthermore, Group-Lasso achieves a verified rate of 91%, compared with under 70% for prior methods. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2611.04939 [cs.CV] (or arXiv:2611.04939v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.04939 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-42] VISTA: Vision-Language Integrated Semantic Triage Architecture for Online Face Clustering in Long Videos
链接: https://arxiv.org/abs/2611.04932
作者: Jiashu Liao,Joanne Garde-Hansen,Sanjay Sharma,Tanaya Guha
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted at ACM Multimedia 2026 (MM '26). 17 pages, including 8 pages of supplementary material
Abstract:Clustering faces by identity in long, continuous video streams is a challenging task that serves as a building block for human-centric video analysis. The majority of existing face clustering models are offline, i.e., they assume the entire data to be available altogether. In many practical scenarios, however, the entire data is either unavailable or poses computational challenges to be processed in its entirety. A handful of online methods that exist rely on geometric visual features and simple thresholding techniques, which fail when the faces exhibit significant appearance variations in long, unconstrained videos. To this end, we present VISTA, the first vision language framework for online face clustering in long videos. VISTA leverages semantic reasoning in a controlled, uncertainty aware fashion to maintain both high cluster purity and long range identity continuity. This makes it robust to the variations in illumination, pose, and costume changes that the faces and characters exhibit in long real world videos. Extensive experiments on the VPCD benchmark dataset (with 15 full episodes from The Big Bang Theory [2007-2019], Buffy the Vampire Slayer [1997-2003], and Sherlock [2010-2017]) demonstrate that VISTA achieves a robust precision and recall balance, consistently improving over the strongest matched online unsupervised baseline (COFC) under identical detector, embeddings, and evaluation protocol. Code and supplementary material: this https URL.
[CV-43] Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling
链接: https://arxiv.org/abs/2611.04916
作者: Kling Team,Dong Chen,Jialu Chen,Xinlong Chen,Yifan Dai,Haotian Dong,Binzong Geng,Yushuo Guan,Minghui Guo,Guanlin He,Guanxiao He,Jingyun Hua,Yanwei Huang,Yiyan Ji,Xueping Jia,Yizhuo Jia,Bozhou Li,Jiaze Li,Min Li,Shihao Li,Shujie Li,Shengyi Liao,Weihong Lin,Shiyuan Liu,Xinyu Liu,Siyang Mao,Xiaoru Qu,Dianmo Sheng,Yang Shi,Jiafu Tang,Longxiang Tang,Yuqi Tang,Chengzhuo Tong,Kuo Wang,Qixun Wang,Rui Wang,Tianduo Wang,Yuran Wang,Zekun Wang,Zihao Wang,Boyang Xia,Yifan Xu,Liu Yang,Zhenlong Yuan,Bohan Zeng,Mujin Zhang,Yuanxing Zhang,Zhicheng Zhang,Zhuoran Zhang,Yuanhong Zheng,Kun Gai,Chengru Song,Pengfei Wan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video generation has progressed from isolated clips to coherent short-form audiovisual sequences, and the bottleneck is shifting from rendering to semantic control. This report summarizes the observations and insights on multimodal understanding gained from successive generations of Kling. We argue that generation quality is the projection of multimodal understanding through the entire data–conditioning–alignment–evaluation chain, and we examine this chain module by module. Kling-MLLM, a shared multimodal foundation model that unifies image, video, audio, synchronized audio–video, and interleaved inputs, is specialized for each downstream module. Because a DiT is trained on far fewer training samples than a language model, we treat captioning as a semantic contract that maximizes description density, and extend it to reference-grounded captioning that makes reference bindings explicit. Prompt enhancement translates user intent into this caption distribution: basic PE is derived from captions of compelling videos and refined with text-level and generator-in-the-loop reinforcement learning, while agentic PE refines the story and retrieves compelling examples. We show that a reliable text encoder comes from perceptual training rather than from stronger language ability, and that candidates can be screened from text alone before end-to-end training. Reward modeling pairs a general reward model for explainable, localized critiques with a decision reward model for fast multidimensional scoring, treating perceptual bandwidth as a first-class design parameter. Finally, we review task-specific automatic evaluation, focusing on protocols that expose scoring shortcuts and help trace failures to their upstream source. We hope these lessons offer practical guidance for building the understanding stack behind controllable video generation.
[CV-44] EP-LVMQA: Evidence-Preserving Coarse-to-Fine Localization for Low-Quality Long Video Moment Question Answering
链接: https://arxiv.org/abs/2611.04913
作者: Guoning Chen,Zhexun Hu,Yifan Wei,Yixuan Weng,Bin Li,Yufei Zhao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures
Abstract:Long instructional videos are increasingly used in medical education, industrial training, and procedural knowledge transfer, yet their evidence is often incomplete or unreliable: subtitles may be missing, ASR noisy, OCR sparse, and decisive actions briefly shown without narration. Existing VideoQA systems typically return labels or free-form text, making answer support difficult to inspect. We study Long Video Moment Question Answering (LVMQA), where a system returns timestamped answer-supporting evidence. We propose EP-LVMQA, an evidence-preserving coarse-to-fine framework comprising an Uncertainty-Aware Multimodal Evidence Timeline, a Coarse Evidence Planner, and an Evidence-Conditioned Fine Localizer. An evidence card transfers planner scores, matched cues, source-confidence proxies, modality-specific evidence, neighboring context, and a temporal prior between the latter two modules. On MedVidQA, EP-LVMQA achieves 26.1 R1@0.5 and 31.8 mIoU, improving the strongest non-ours results by 6.3 and 5.1 points. It raises Suff.@2 and Ver.@2 to 25.4 and 22.6, gains of 9.5 and 8.8 points. Under matched low-quality perturbations, average relative degradation is reduced from the strongest non-ours value of 14.6% to 11.1%. On Ego4D-NLQ and QVHighlights, EP-LVMQA remains broadly comparable to established task-specific localization methods.
[CV-45] MiCaDe: Online LiDAR-Camera Miscalibration Change Detection for Autonomous Driving
链接: https://arxiv.org/abs/2611.04912
作者: Carlo Sgaravatti,Riccardo Pieroni,Matteo Corno,Sergio M. Savaresi,Luca Magri,Giacomo Boracchi
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Reliable multi-sensor perception in autonomous vehicles requires accurate calibration between cameras and LiDAR. However, calibration drift is often unavoidable in long-term deployments: vibrations, thermal effects, and minor mechanical shocks may alter the relative pose between sensors, leading to systematic errors in downstream fusion tasks. We propose MiCaDe (Miscalibration Change Detection), an online solution detecting when the extrinsic LiDAR-camera calibration parameters are no longer accurate by means of sequential change detection. MiCaDe requires no calibration targets, or modification of the perception stack, providing low false-alarm rates with negligible computational overhead. Our key insight is that 3D and 2D bounding boxes produced by standard object detectors should remain geometrically consistent under correct calibration. By matching cross-modal detections, we derive geometric consistency metrics that serve as sensitive indicators of extrinsic drift. These metrics are aggregated into fixed-size batches and scored against a reference distribution, producing a likelihood stream that is monitored by a Change Point Model (CPM). Crucially, the CPM accumulates statistical evidence over time, enabling detection of subtle drifts that would otherwise go unnoticed. Experiments on multiple long-sequence datasets and on a real-world dataset with physically induced miscalibration confirm reliable detection under diverse conditions. Code available at: this https URL.
[CV-46] VisSearch-Bench: Evaluating Long Horizon Visual Search in Multimodal Agents
链接: https://arxiv.org/abs/2611.04894
作者: ZhengXian Wu,Qianhao Nie,Zhuohong Chen,Junjie Gao,Yuyao Zhang,Yunyao Yu,Hangrui Xu,Cheng Cheng,Guiyu Ma,Jin Zhang,Xingzhong Xu,Kai Yang,Haoqian Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 41 pages, 22 figures
Abstract:Multimodal agents use web and image tools to answer questions beyond their internal knowledge. Some search tasks use images mainly to identify an initial entity, then rely on text retrieval. These tasks offer limited insight into how agents connect information across images, objects, and search steps. We therefore focus on visual context management. This involves organizing and using visual information and linking it to other findings during search. Visually intensive tasks require agents to combine object appearance, local details, or spatial relations with external information. We introduce VisSearch-Bench, with 204 human-written tasks across 7 main domains and 25 subdomains. It covers Set-wise Visual Search, Sequential Visual Search, Hybrid Wide-Deep Search, Competitive Visual Disambiguation, and Visual-Structure-Guided Search. Each task provides a reference answer, traceable evidence, and scoring rules. Accuracy measures complete task success, while Process Score measures the correctness and completeness of individual results and subquestion answers. Across 16 multimodal models, the best system fully solves only 26.47% of tasks, even with search tools. These results highlight the challenge of linking visual evidence with search results to produce complete answers.
[CV-47] OCR in the Foundation Model Era: From Specialist Models to Unified Models
链接: https://arxiv.org/abs/2611.04874
作者: Zhiheng Li,Zongyang Ma,Jiaxian Chen,Cheng Cui,Yi Liu,Ziqi Zhang,Chunfeng Yuan,Bing Li,Xiang Bai,Weiming Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 7 figures, 7 tables. Resources: this https URL
Abstract:Optical Character Recognition (OCR) is among the oldest and most widely deployed applications of artificial intelligence. This survey reviews its development, focusing on the Foundation Model Era. We define OCR through faithful transcription: within a specified visual scope, OCR should completely reconstruct the text, notation, and document structure observable in the source image, and every textual, notational, or structural element in the output must be verifiable from visual evidence in that image. We trace the development from specialist OCR models toward unified models, examining joint text detection and recognition, structured sequence generation, and the use of pretrained vision-language models (VLMs). We organize contemporary OCR systems into five coexisting families: modular toolkits and task-specific models, specialized document VLMs, hybrid workflows, general-purpose VLMs, and commercial products and managed services. We compare their architectures, data, and training, examining hallucination and omission, insufficient spatial grounding, and inference cost in generative OCR. We review evaluation from character and scene text recognition to parsing individual structural elements and full-page transcription, highlighting gaps in consistent output definitions, attribution of performance to individual components, and coverage of real capture conditions and visible content and structure. Finally, we summarize the challenges facing OCR and discuss directions for future research. We continuously track related work at this https URL.
[CV-48] rusting Imagined Futures: Auditing Imagined Rewards for VLA Post-Training
链接: https://arxiv.org/abs/2611.04873
作者: Zicheng Kong,Zhenbo Xu,Haoyan Zhang,Jinghan Yang,Zhenzhen Li,Yiwei Ru,Liuyu Xiang,WanTing Xu,Min Ren,Zhaofeng He
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video world models enable candidate actions to be evaluated through imagined outcomes, but high predicted rewards can favor trajectories with poor execution quality. We introduce Trusting Imagined Futures (TIF), an offline supervision selection method for vision-language-action (VLA) post-training that audits predicted rewards using execution feedback. A multidimensional reward model predicts a utility score for candidate ranking and five dimensions of execution quality. An auditor uses reward predictions and uncertainty to identify overestimation patterns learned from paired imagined videos and real executions. TIF accepts the candidate with the highest predicted utility when its advantage over the existing trajectory exceeds a margin and its audit risk passes a threshold; otherwise, it retains the existing supervision. The retained real trajectories then supervise behavior cloning. Across four LIBERO suites and three training seeds, TIF achieves a mean success rate of 76.25%, improving over training on the existing trajectories by 3.00 percentage points with the same trajectory count and training budget. Adding audit gating yields 1.47 percentage points with the reward model and advantage margin held fixed. The gains persist against advantage ranking and logistic regression risk selection at matched replacement counts. On recorded videos from three real robot tasks, TIF increases clean completion among selected trajectories from 58.33% to 70.00% across 60 candidate pools.
[CV-49] Radial distortion homography estimation from affine-covariant or orientation-covariant features
链接: https://arxiv.org/abs/2611.04822
作者: Marcus Valtonen Örnhag,Stefan Adalbjörnsson
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present minimal solvers for joint homography and radial distortion estimation using affine- or orientation-covariant features. The core contribution is a set of novel constraints that exploit local feature geometry inherent to affine descriptors, enabling solvers that require fewer correspondences than point-based methods. Using modern robust estimation frameworks, we demonstrate that the proposed solvers are advantageous to the current state-of-the-art with a significant speed-up in the presence of outliers while maintaining comparable or superior estimation accuracy. The proposed solvers are tested on both synthetic and real images containing challenging fisheye distortions.
[CV-50] NightIQA: A Comprehensive Dataset and Benchmark for MLLM -Based Nighttime Image Quality Assessment WACV2027
链接: https://arxiv.org/abs/2611.04811
作者: Yiding Tian,Jiaojiao Yi,Jiayuan Lu,Pufan Xu,Jinlin Wu,Changyuan Wen,Hongyuan Yu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2027. 25 pages including supplementary material
Abstract:Nighttime images are inherently susceptible to complex, compound degradations, making accurate quality assessment exceptionally challenging. Score-based IQA methods condense quality into a single number, lacking the multi-dimensional, language-grounded reasoning required for nighttime evaluation. Multimodal Large Language Models (MLLMs) offer a more comprehensive alternative, yet remain constrained by a critical data gap: existing real-world IQA datasets are overwhelmingly daytime-centric and often built from outdated captures, while synthetic alternatives lack nighttime-specific degradation modeling, yielding artifacts far removed from authentic nighttime conditions. To bridge this gap, we present NightIQA, a dataset and benchmark suite purpose-built for MLLM-based nighttime IQA. It comprises three core components: NightIQA-Real, a real-world dataset of 1,769 high-resolution images that we captured ourselves with modern equipment, encompassing six canonical distortion types and structured multi-dimensional annotations; NightIQA-Bench, a benchmark of 1,078 visually grounded multiple-choice questions tailored to MLLM-based nighttime IQA, organized around a four-level evaluation hierarchy spanning Perception, Technical Assessment, Localization, and Reasoning; and NightIQA-Syn, an inherently scalable, scene-aware synthesis pipeline that jointly models global-level and instance-level degradations, with an initial release of 80k high-fidelity images alongside the full generation code. Fine-tuned on NightIQA with Supervised Fine-Tuning (SFT) and Group Relative Policy Optimization (GRPO), Qwen3-VL-8B outperforms open-source IQA methods and achieves higher overall accuracy than leading closed-source models, highlighting the value of domain-specific data and structured supervision.
[CV-51] Pixel-space video generation with U-JiT
链接: https://arxiv.org/abs/2611.04764
作者: Théophane Vallaeys,Jakob Verbeek,Matthieu Cord
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Latent diffusion and flow matching models are ubiquitous in state-of-the-art generative video models. While highly effective, relying on pretrained autoencoders to compress the data into a lower-dimensional latent space also comes with drawbacks. First, training proceeds in two stages: autoencoder pretraining, with compression and reconstruction objectives, followed by latent generative model training, precluding fully end-to-end training. Second, autoencoders also lead to an inherent loss of signal, and the trade-off between reconstruction quality and generative performance in the latent space is still an active area of research. In this paper we explore the viability of generative video models directly in pixel space. We analyze recent approaches in pixel-space generative modeling of images that rely on basic transformer designs, and identify several shortcomings that limit their effectiveness for the high-dimensional pixel-space video generation. To overcome these limitations, we propose a UNet-like transformer architecture along with improvements to the training recipe and sampling strategy. We conduct controlled experiments, training latent and pixel-space models from scratch on Kinetics-600. For the standard frames-to-video task at 128 resolution, our U-JiT model improves generative FVD by 27% over prior state-of-the-art results, and by 14% against the JiT pixel-space baseline. Scaling to higher 256 resolution, we find the improvement over pixel-space and latent-based models grows as we increase the spatiotemporal compression rate up to 64 \times 64 \times 8 . Finally, we find that U-JiT improves per-frame FID by 27% over latent models and 61% over pixel-space models on class-to-video generation, with respectively 9% and 56% reduction in FVD.
[CV-52] Beyond Local Refinement: Wide-Area Precise Cross-View Localization ATC
链接: https://arxiv.org/abs/2611.04759
作者: Qingguo Hu,Xinyu Ma,Yujie Lin,Yaoxiang Wang,Qi Shi,Zonghao Guo,Chi Chen,Jinsong Su
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Fine-grained cross-view localization aims to precisely localize a ground camera against geo-referenced aerial imagery, but current methods typically assume a narrow geographic prior, substantially limiting their applicability when upstream localization remains coarse. To bridge this gap, we study this task over much larger aerial regions. However, this seemingly simple extension fundamentally changes the localization problem: the growing disparity in spatial extent between ground and aerial views makes reliable cross-view matching difficult to establish directly, while the increasing number of look-alike locations in larger regions introduces substantial visual ambiguity. To address these challenges, we propose RingMatch, a search-and-verification framework for wide-area cross-view localization. RingMatch first leverages predictive uncertainty to uncover plausible locations and progressively zooms into their surroundings to obtain increasingly fine-grained evidence, yielding a compact set of promising candidates. We then introduce the depth ring, a novel geometric representation that reveals subtle variations in the spatial layout around a location and serves as a view-shared spatial signature for an LVLM verifier to resolve residual ambiguity. To benchmark this setting, we construct VIGOR-Pro by composing VIGOR aerial tiles into contiguous regions with spatial extents of up to 400 m. Experiments show that RingMatch maintains strong localization performance as the aerial region expands, consistently outperforming representative end-to-end and retrieval-based methods.
[CV-53] MED: A Multi-Endoscope Dataset for Surgical 3D Perception ECCV2026
链接: https://arxiv.org/abs/2611.04755
作者: Sierra Bonilla,Fengyi Jiang,Chinedu Nwoye,Jingpei Lu,Kailey Reardon,Humphrey W. Chow,Francisco Vasconcelos,Sophia Bano,Adam Schmidt,Omid Mohareri
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Published at ECCV 2026. 25 pages, including supplementary material. Code and dataset: this https URL
Abstract:We introduce iMED, the first synchronized multi-endoscope dataset for robot-assisted minimally invasive surgery (RAMIS), designed to address a fundamental limitation in surgical vision benchmarking: the absence of independent held-out viewpoints. Existing surgical datasets capture single-trajectory sequences from a single endoscope, making it impossible to distinguish geometric generalization from photometric interpolation along a narrow forward-facing path. iMED provides 340 sequences ~170K synchronized timepoints with 4 views per timepoint) recorded simultaneously from two independent stereo endoscopes across ex vivo, postmortem, and live surgical settings, spanning 14 specimens with diverse anatomical regions and motion regimes. The dataset includes calibrated camera intrinsics, ArUco-based frame-wise pose estimates with uncertainty quantification, instrument segmentation masks, and rich clinical metadata. Using a train-on-one-endoscope, test-on-another evaluation protocol, we benchmark 23 state-of-the-art methods across rigid and deformable novel view synthesis, pose estimation, feature matching, and monocular depth estimation. Our experiments show that methods relying primarily on photometric supervision degrade substantially under this held-out-endoscope setting, while methods with explicit geometric regularization are more robust across views in our benchmark. Notably, large-scale foundation models transfer surprisingly well to surgical imagery, often outperforming domain-specific models, suggesting pitfalls in current fine-tuning protocols. iMED establishes a new evaluation protocol for geometric generalization in surgical vision.
[CV-54] Bounded AUKS: A Novel Evaluation Metric for Anomaly Detection via Bounded Area Under the Kolmogorov–Smirnov Curve
链接: https://arxiv.org/abs/2611.04754
作者: Yanshuo Zhang,Juan Du
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent literature widely used global metrics such as AUROC and AUPRO to quantify the detection performance. AUROC and AUPRO scores are approaching 100% in some public datasets such as MVTec AD dataset and VisA dataset, which misleadingly suggests that the pixel-level anomaly detection task has already been solved. Threshold-independent metrics represented by AUROC inherently ignore the threshold selection process, which is inevitable in practical applications. Although AUPRO restricts the False Positive rates (FPRs) to an acceptable range, extremely low FPRs can lead to increased false negative rates (FNRs). We argue that there is a lack of metrics capable of quantifying the separation between the anomaly score distributions of normal and anomalous samples, which hinders advancements in anomaly detection. In response, we propose the Bounded Area Under the Kolmogorov–Smirnov curve (Bounded AUKS), a partial integral metric that integrates the difference between TPR and FPR within a data-driven bounded range. Specifically, we introduce AUKS from KS integral as a direct measure of separability between the anomaly score distributions. The bounded range is determined only by score distributions of normal test samples. Lower bound excludes the uninformative high-FPR region. Upper bound avoids evaluation in extreme regions that aggressively push FPR towards zero, which result in a very low recall. Our experiments on 9 models across MVTec AD and VisA datasets show that Bounded AUKS can address the limitations of existing metrics and has a better performance in evaluating unsupervised anomaly detection models.
[CV-55] EXMET: A Deep Learning Benchmark for Archaeological Textile Reconstruction
链接: https://arxiv.org/abs/2611.04734
作者: Muhammad Hamza Zafar,Davit Gigilashvili,Jon Yngve Hardeberg
类目: Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL)
备注: 26 pages, 14 figures, 14 tables. Accepted version of the article published in ACM Journal on Computing and Cultural Heritage (2026). Dataset: this https URL
Abstract:The reconstruction of fragmented historical textiles presents a significant challenge in computational archaeology. The Oseberg ship burial, dating to 834 CE, yielded hundreds of degraded textile fragments that have resisted systematic computational analysis. While deep learning has revolutionized image-based puzzle solving, progress on archaeological textiles has been limited by the lack of domain-specific datasets and systematic evaluation of modern architectures. To address this gap, we present TEXMET, a curated dataset of 18,644 historical textile images from the Metropolitan Museum of Art’s Open Access collection, representing the first textile-specific benchmark for puzzle-solving research. Using TEXMET, we conduct a systematic evaluation of three state-of-the-art puzzle-solving models: JPDVT, FCViT, and DiffAssemble. Our experiments reveal that the transformer-based models pre-trained on standard datasets fail to generalize to textiles, but domain-specific fine-tuning proves highly effective. The graph-based DiffAssemble model, when fine-tuned on TEXMET, achieves 85.89% puzzle accuracy on textile data. Most significantly, applying this fine-tuned model to 45 highly degraded Oseberg fragments yields a performance increase from 26.67% to 64.44% puzzle accuracy. These results establish TEXMET as a new benchmark for textile reconstruction and demonstrate that domain adaptation is essential for applying puzzle-solving methods to imagery of real archaeological materials.
[CV-56] SPOD: Recovering Suppressed Temporal Evidence in Video-LLM s via Single-Pass Orthogonal Decomposition
链接: https://arxiv.org/abs/2611.04728
作者: Yangguang Lin,Quan Fang,Xiaoyan Liu,Zefan Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, 1 table. Accepted to ACM Multimedia 2026 (MM '26)
Abstract:Video Large Language Models (Video-LLMs) have achieved strong performance on many video understanding tasks, yet remain vulnerable to temporal hallucination in time-sensitive reasoning scenarios such as scene transition understanding and event ordering. Existing solutions either require costly retraining or introduce inefficient multi-pass inference and external supervision at test time. In this work, we show that temporal evidence is already encoded in Video-LLM hidden representations but is not fully exploited during standard inference. Motivated by this observation, we propose Single-Pass Orthogonal Decomposition (SPOD), a training-free framework that recovers suppressed temporal evidence through analytical hidden-state manipulation in a single forward pass. SPOD consists of two components: Frame Distinctiveness Analysis (FDA), which identifies salient frames from middle-layer hidden-state geometry, and Temporal Delta Subspace (TDS), which constructs orthogonal temporal and prior subspaces from weighted inter-frame hidden-state differences. By decomposing final hidden states into temporal, prior, and residual components and rescaling them independently, SPOD enhances dynamic information, suppresses static appearance bias, and preserves complementary information. Experiments on VidHalluc, EventHallusion, and VideoMMMU show that SPOD consistently outperforms strong inference-time baselines across temporal reasoning tasks and model architectures while retaining near-vanilla inference efficiency. These results suggest that improving temporal reasoning in Video-LLMs may depend not only on additional supervision, but also on recovering temporal evidence already present in their internal representations. Our code is available at this https URL.
[CV-57] VISAGE: Vertex-based Intrinsic Surface Appearance GEneration via Dual-Stream Diffusion for Arbitrary 3D Heads
链接: https://arxiv.org/abs/2611.04712
作者: Jeremias Krauß,Pragati Jaiswal,René Schuster,Didier Stricker
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recovering identity-faithful, physically based surface appearance for 3D human heads from sparse images remains challenging. Existing methods often rely on UV-based pipelines, introducing seam artefacts, or operate in image space, limiting surface consistency. We present VISAGE, Vertex-based Intrinsic Surface Appearance GEneration, a surface-based conditional diffusion framework that synthesises per-vertex albedo and roughness directly on arbitrary mesh topologies, without requiring consistent UV parameterisation at training time. VISAGE combines global spectral processing with local geodesic K-Nearest-Neighbour refinement, enforcing structural coherence while restoring detail beyond the truncated spectral basis. Multi-view RGB images condition the network via visibility-aware cross-attention, enabling occlusion-robust synthesis across the full head surface. Region-specific modules specialise in semantically demanding and distinct areas (eyes, mouth, skin and hair), while a tangent-space output projection recovers fine iris detail. We further introduce a scalable procedural augmentation pipeline that enriches raw geometry scans with physically plausible albedo, roughness and normal maps for training. The predicted attributes render directly as a single RGBA vertex-colour channel. VISAGE reduces FID by 42.7% on NPHM and 22.5% and 4.4% on zero-shot FaceVerse and i3DMM, respectively, relative to the strongest baseline, with a 7.2M-parameter core network.
[CV-58] Cycle WAM: Coupling Action and Video through Cycle Consistency
链接: https://arxiv.org/abs/2611.04711
作者: Qinglin Yang,Jiepeng Wang,Yuxing Huang,Haibin Huang,Kai Xu,Chi Zhang,Ruizhen Hu,Xuelong Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Recent advances in video modeling have driven substantial progress in world action models (WAMs). Existing approaches exploit pretrained video priors by decoding actions from latent representations or inferring them from generated videos. The underlying link is causal: robot actions produce changes in the scene, and video observations of these changes provide evidence for inferring the actions that produced them. However, existing training formulations do not by themselves ensure that actions and their predicted visual consequences are consistent, potentially limiting the use of pretrained capabilities. We treat video and action as paired modalities linked by this causal relationship and learn their correspondence through a unified training framework designed to better exploit pretrained video priors. Cycle WAM first augments video-action co-training with bidirectional conversion to reinforce cross-modal correspondence and recover forward and inverse dynamics. It then introduces a differentiable cycle-reconstruction objective to further couple the two mappings. The resulting model supports both forward and inverse dynamics, with the potential to improve performance through more effective use of pretrained video priors. Cycle WAM achieves state-of-the-art performance among comparable methods on LIBERO-Pro and substantially improves DreamZero on DROID. Comparisons with UWM characterize its inverse-dynamics performance and show sharper visual detail in the displayed forward-dynamics predictions.
[CV-59] DenseTwins: Dense Modality Decoupling for Multimodal Self-Supervision
链接: https://arxiv.org/abs/2611.04710
作者: Carlo Sgaravatti,Daniele Kopyshevskiy,Davide Secco,Giacomo Boracchi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multimodal perception is central to many computer vision applications, including robotics and autonomous driving, where different sensing modalities provide complementary cues. Self-supervised learning offers a scalable solution for exploiting large amounts of unlabeled multimodal data. However, existing methods either neglect modality-specific information by enforcing feature-space alignment between modalities or learn only global representations without preserving spatial structure. We propose DenseTwins, a multimodal self-supervised framework that learns dense representations by explicitly decoupling the dense feature space into shared and modality-specific components. The proposed approach combines dense cross-modal correspondences with redundancy-reduction objectives, enabling the model to capture both shared semantics and modality-specific cues within a structured dense embedding space. DenseTwins jointly optimizes an intra-modal objective across augmented views and a cross-modal objective that aligns shared features while decorrelating modality-specific information. We evaluate DenseTwins across multiple multimodal benchmarks and sensing setups, achieving state-of-the-art or competitive performance across multimodal self-supervised baselines. Code available at: this https URL.
[CV-60] Learning from Unlabeled Photographs for General Oral Image Analysis
链接: https://arxiv.org/abs/2611.04709
作者: Zhong-Shi Zhang,Zi-Li Yu,Xiao Xiao,Huan Liu,Gui-Song Xia,Cui Huang,Meng-Xun Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Oral photographs contain rich clinical information and hold significant potential for artificial intelligence applications; however, most models are trained for individual tasks and rely on costly expert annotations. We hypothesized that self-supervised training on unlabeled oral photographs could produce transferable visual representations for heterogeneous vision tasks. To test this hypothesis, we developed DOVE (Dental Optical Visual Encoder) by domain-adaptive self-supervised pretraining on approximately 530,000 unlabeled oral photographs collected from publicly accessible sources. DOVE was benchmarked against 3 established baselines across 5 classification datasets and 4 segmentation datasets, covering both frozen backbone evaluation and end-to-end finetuning. DOVE achieved the strongest overall performance, ranking best in few-shot transfer and label efficiency on most benchmarks. Under the 20-shot frozen-backbone protocol, DOVE outperformed the second best DINOv3 by 4.37 percentage points in mean macro-F1 across classification datasets and by 4.49 percentage points in mean macro-Dice across segmentation tasks. Under end-to-end finetuning, DOVE achieved at least 80% of the strongest full-data baseline’s performance across most evaluation datasets using only 10% of the labeled training data. Qualitative retrieval and feature analyses further indicated better organization of image-level and spatial representations. These findings support DOVE as a generalizable and label-efficient backbone for oral vision research.
[CV-61] Phase-DGS: Phase-guided Dynamic Gaussian Splatting from Unsynchronized Multi-view Video NEURIPS2026
链接: https://arxiv.org/abs/2611.04666
作者: Hosung Jeon,Jun Young Jeong,Sangwoon Kwak,Joonsoo Kim,Sangmin Kim,Jaesik Park,Won-Sik Cheong,Hyon-Gon Choo
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Accepted to NeurIPS 2026. Project page: this https URL
Abstract:Spatiotemporal scene reconstruction with Dynamic Gaussian Splatting (DGS) fundamentally depends on perfect temporal synchronization across all cameras, an assumption rarely achievable in practice. Industry-standard synchronization requires expensive specialized hardware and complex setup, while data-driven alternatives including audio-based alignment, geometry matching, and pose tracking remain limited by restrictive environmental or scene-specific assumptions. To overcome these limitations, we propose Phase-DGS, a framework that replaces conventional timestamps with a semantic phase representing each frame’s inherent state within the underlying motion cycle. Frames capturing the same motion state share the same phase regardless of which camera records them or when, naturally establishing spatiotemporal correspondence across unsynchronized cameras. We validate Phase-DGS under temporal offsets, frame drops, and camera freezes, achieving up to +8.5 dB PSNR improvement over baselines while enabling seamless integration with existing DGS backbones including RealTime-4DGS and FreeTimeGS.
[CV-62] BhopalMLS: A LiDAR Dataset for Semantic Scene Understanding in Heterogeneous Urban Environments WACV2027
链接: https://arxiv.org/abs/2611.04654
作者: Pyare Lal Chauhan,Vaibhav Kumar,Bharat Lohani
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027 (Evaluations and Datasets Track). 37 pages: main paper, references and supplementary material. Project page: this https URL
Abstract:We introduce BhopalMLS, a large-scale Mobile Laser Scanning (MLS) dataset from a dense, heterogeneous urban environment in Bhopal, India, with mixed traffic, informal structures, and narrow unplanned streets. It spans approximately 10 km of road corridors with over 1.2 billion points and 19,518 annotated instances across 62 fine-grained static categories, grouped into 12-class and 21-class benchmark taxonomies; 6 optional moving-class subclasses support dynamic-object analysis. Existing 3D datasets, collected in well-planned cities, are structurally more uniform than such unplanned settings, and lack their subtype diversity. On semantic and instance segmentation at both granularities, fine-tuned foundation models surpass supervised baselines: Utonia(L) reaches 71.28% mIoU (vs. PTv3’s 68.04% from scratch) and 44.07% mAP50 on the 12-class benchmark. Cross-region experiments against four established datasets (Ke-MLS, Toronto-3D, Paris-Lille-3D, SemanticKITTI) reveal a consistent asymmetric transferability: models trained on BhopalMLS transfer better than the reverse on every pair, for both Utonia(L) and Concerto(L) (mean 21.5, up to 36 mIoU points), holding even when the target is denser. Factor-controlled retraining at a fixed label budget, repeated on both models, attributes this to the coverage of BhopalMLS’s subtype inventory rather than to dataset size or corridor geometry: collapsing each class to a single subtype costs 13.0 and 13.3 mIoU points for Utonia(L) and Concerto(L). BhopalMLS therefore offers a testbed for 3D perception beyond well-planned cities. Data and an interactive viewer are at this https URL.
[CV-63] herapy-FIS: A Dataset for Assessing Facilitative Interpersonal Skills in Psychotherapeutic Setting
链接: https://arxiv.org/abs/2611.04645
作者: Qijian Zheng,Wanqing Lin,Yuanfang Wang,Yijun Ge,Jordan Bate,Katie Aafjes-van Doorn,Feng Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Facilitative Interpersonal Skills (FIS) is a performance-based assessment method that provides a structured framework for evaluating the interpersonal competencies of mental health professionals. However, existing FIS assessment relies heavily on human observer ratings, making the evaluation process labor-intensive, costly, and difficult to scale. Progress toward automated FIS assessment has also been constrained by the lack of publicly available professional data in psychotherapy settings. To address this limitation, we introduce Therapy-FIS, a dataset for automated FIS assessment in standardized psychotherapy tasks. To our knowledge, Therapy-FIS is the first publicly available dataset in this domain. It contains facial, acoustic, and textual signals derived from standardized therapist responses to standardized mock patient stimulus clips, based on 890 therapist video recordings and 8 patient videos, and is rated with eight FIS subscale ratings and an overall FIS score. Built on Therapy-FIS, we further establish a benchmark with two downstream tasks: Therapist Performance Rating and Joint Therapist–mock-Patient Performance Rating. We also introduce a competitive baseline, DyadicFIS, with gated fusion and routed readout under therapist-only and dyadic settings. DyadicFIS obtains the best macro CCC(Concordance Correlation Coefficient) scores on both tasks (0.2807 and 0.3299). Therapy-FIS offers a reproducible resource for data-driven automated assessment of therapeutic interpersonal skills in mental health.
[CV-64] ReXMo: End-to-End Video Character Replacement with Expression and Motion Preservation
链接: https://arxiv.org/abs/2611.04633
作者: Hualiang Wei,Wenhui Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint
Abstract:Video character replacement aims to replace actors while preserving facial expressions, motions, and scene dynamics. Existing methods typically rely on masked generation with pose guidance, limiting fine-grained expression and motion preservation in complex scenes. We propose ReXMo, an end-to-end framework that supports single-, multi-, and selective character replacement scenarios. ReXMo introduces paired triplet supervision, where synthesized driving videos and original videos share identical performances but different identities, enabling scalable training through synthetic data generation. An In-Context Diffusion Transformer learns identity transformation by jointly conditioning on driving videos, reference identities, and text prompts, while an Expression Enhancement Adapter improves facial expression preservation in multi-person scenes. Extensive experiments demonstrate that ReXMo outperforms existing methods in identity fidelity, motion consistency, expression preservation, and visual quality. Our dataset and code will be publicly released.
[CV-65] MambaHand: Local Identity and Behavioral Sequence Learning with Mamba for Dynamic Hand Gesture Authentication
链接: https://arxiv.org/abs/2611.04626
作者: Zenan Lin,Junduan Huang,Xilai Wang,Wenxiong Kang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic hand gesture authentication (DHGA) is an emerging biometric technique that contains both physiological and behavioral identity characteristics. However, existing methods primarily rely on convolutional networks or manually designed motion representations, making it difficult to jointly capture fine-grained local identity differences and long-range behavioral dependencies during hand gesture execution. To address these challenges, we propose a two-stream identity representation learning framework, MambaHand Net, to combine CNNs with Mamba for DHGA. The proposed framework consists of a Local Identity Stream (LIS) and a Behavioral Sequential Stream (BSS). The LIS extracts frame-wise local identity cues with a convolutional network, while the BSS preserves spatial tokens from intermediate visual features and constructs first-order and second-order temporal difference sequences to model multi-order behavioral variations. Moreover, the bidirectional Mamba is incorporated into the BSS to model semantic motion across different spatial locations, enabling efficient long-range modeling of hand gesture execution habits. Finally, attention-guided spatial token aggregation and a two-stream fusion strategy are used to integrate local identity cues and multi-order behavioral patterns into a discriminative identity representation. Our method achieves state-of-the-art (SOTA) average performance on the SCUT-DHGA benchmark dataset, demonstrating that combining CNNs with Mamba is an effective new solution for DHGA.
[CV-66] Polyp Matching Using Deep Learning
链接: https://arxiv.org/abs/2611.04624
作者: Kristian Dalsbø Hindberg,Puneet Sharma,Benedicte Schelde-Olesen,Ulrik Deding,Jakob Frederik Frøkjær Justsen,Jan-Matthias Braun,Esmaeil S. Nadimi
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:A key challenge in colon capsule endoscopy (CCE) analysis is accurately identifying whether multiple images depict the same polyp, which is critical to avoid over-counting and unnecessary clinical interventions. Additionally, this prevents prolonged follow-up colonoscopies where endoscopists search for duplicate polyps without precise localization. This study presents a novel contrastive learning-based approach to polyp matching using a Siamese neural network architecture. The model was trained on a dataset from the Danish CareForColon2015 trial. The dataset comprises 5,103 image pairs of confirmed same polyp and 5,103 pairs of different polyps, annotated by experienced clinicians. The proposed model employs ResNet variants (ResNet18, ResNet34, ResNet50), DenseNet121 and EfficientNet B0/B3 as backbones and utilizes contrastive learning with cosine vector distance metric for classification. Experimental results demonstrate high classification accuracies of up to 92% and AUC values close to 0.98. These results suggest that automated polyp matching has the potential to reduce polyp over-counting, improve patient risk stratification, and reduce unnecessary follow-up colonoscopies.
[CV-67] Gradient-Guided Orientation Learning for Weakly Supervised Oriented Object Detection WACV2027
链接: https://arxiv.org/abs/2611.04616
作者: Junyong Jang,Juheon Park,Minyoung Jeon,Jaehyup Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2027. 10 pages, 4 figures, 7 tables
Abstract:Weakly supervised oriented object detection (WS-OOD) aims to reduce the annotation cost of rotated bounding boxes by leveraging cheaper annotations such as horizontal boxes or points. However, these weak annotations provide no explicit orientation information, forcing existing methods to infer object angles through indirect geometric consistency or equivariant constraints. Such indirect supervision often becomes ambiguous and unstable under real-world appearance variations, including illumination changes, color shifts, shadows, and background clutter. In this paper, we present a simple yet effective gradient-guided orientation learning strategy for WS-OOD. Motivated by the observation that the input image itself contains useful orientation priors, our method extracts dominant gradient-based orientation cues from object regions and converts them into pseudo-angle labels for auxiliary supervision, enabling the detector to learn orientation-aware representations without requiring rotated bounding box annotations or extra learnable parameters. Furthermore, since AP can mask substantial angular errors when spatial IoU remains high, we introduce an orientation-aware evaluation metric that incorporates angular discrepancies into the standard AP protocol. Extensive experiments on weakly supervised oriented detection benchmarks demonstrate that the proposed pseudo-angle supervision improves both detection performance and orientation estimation quality, while the proposed metric provides a more faithful assessment of angle prediction than conventional AP alone.
[CV-68] SparseGraph-I2P: Consistency-Guided Hypothesis Selection for Efficient Image-to-Point-Cloud Registration
链接: https://arxiv.org/abs/2611.04591
作者: Ziyang Yu,Xiang Li,Qiong Chang,Jun Miyazaki
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 20 pages
Abstract:Coarse-to-fine image-to-point-cloud (I2P) registration risks discarding correct but ambiguous patch pairs, while retaining more candidates repeats fine matching over overlapping patches. SparseGraph-I2P aims to improve registration recall by increasing the inlier ratio at the pose solver, using sensor or monocular depth. A Coarse Hypothesis Graph combines appearance with depth-derived first- and second-order compatibility to select a fixed budget of patch pairs. Sparse Fine Matching decodes each queried pixel once and skips pixel–point comparisons covered by an ancestor patch paired with the same point-cloud region. With sensor depth, depth-aware filtering ranks correspondences by SC2 spectral support for a single EPnP solve. With monocular depth, depth or inverse depth is calibrated from correspondence geometry before filtering. SparseGraph-I2P achieves the highest registration recall and lowest latency among the compared methods on RGB-D Scenes V2 and 7-Scenes with sensor depth. With predicted depth, it retains the highest recall on RGB-D Scenes V2 at an additional calibration cost. Code will be released.
[CV-69] Multi-Path Mamba Diffusion for 3D Point Cloud Semantic Scene Completion ICIP
链接: https://arxiv.org/abs/2611.04583
作者: Li Liang,Naveed Akhtar,Jordan Vice,Anwaar Ulhaq,Ajmal Saeed Mian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICIPW 2025
Abstract:3D semantic scene completion plays a critical role in enabling various downstream tasks within autonomous systems by estimating both missing geometric structures and semantic information in scene data. In this work, we introduce a unique neural framework that integrates state space modeling with diffusion-based generative approaches to enhance 3D point cloud semantic scene completion. We propose \textitMPath Mamba diffusion network, leveraging a skip-triple Mamba structure, dimensional decomposition residuals, and multi-directional dilations within multi-path data to efficiently process long-sequence data for feature extraction. The proposed method operates in the conditioned latent space of a variational autoencoder, enhanced by a state-space mechanism to improve 3D point cloud representation and reconstruction. Extensive experiments conducted on the SemanticKITTI benchmark demonstrate that our approach achieves highly competitive performance for 3D point cloud semantic scene completion.
[CV-70] SEEK-VAU: Towards Evidence-Faithful Video Anomaly Understanding via Agent ic Search NEURIPS2026
链接: https://arxiv.org/abs/2611.04554
作者: Minghe Wang,Weijun Zeng,Dapeng Luo,Xinyue Wang,Nong Sang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026 (main conference track). 27 pages, 5 figures
Abstract:Most video anomaly understanding (VAU) systems still infer anomaly judgments from evidence collected in a feed-forward way under predefined rules, typically sampled frames, or events clustered from such frames. This protocol fails when the collected evidence is incomplete under imperfect predefined rules, because once reasoning begins the model can no longer search for the missing evidence. We address this limitation with SEEK-VAU, which reframes VAU as an agentic search-and-verify interaction protocol under a bounded search budget: instead of passively reasoning over pre-collected evidence, the policy actively gathers evidence across the three stages of an anomaly event chain (precursor, trigger, and aftermath) and verifies its sufficiency before finalization, turning event-chain completeness into an explicit design rather than an outcome of predefined rules. A bounded budget caps the cost of this search, but does not by itself teach the policy where to look or when to stop. To make this behavior learnable, we introduce evidence-faithful counterfactual verification (EFCV), which rewards selected evidence that remains supportive, compact, and necessary under counterfactual verification. We further introduce SEEK-Bench, featuring video-level episodes with temporal interval annotations, semantic QA and event-chain stage labels. Together, SEEK-VAU and SEEK-Bench establish a strong foundation for evidence-faithful and actively verifiable VAU.
[CV-71] Predicting Void Points in Self-Occluding Forest Volumes
链接: https://arxiv.org/abs/2611.04548
作者: Benjamin Bachmayr,Mohamed Youssef,Oliver Bimber
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Synthetic Aperture Imaging (SAI) enables drones to observe forest canopies by warping and averaging multiple displaced views at a common depth. However, occlusion is suppressed rather than eliminated, causing poorly observed regions to appear occupied and leading to systematic biomass overestimation. The Deep Forest project cannot distinguish reconstruction artifacts from real vegetation because its training data relies on measured reflectance points, excluding empty space and preventing the network from learning explicit occupancy boundaries. This thesis addresses the problem by modifying training labels rather than the imaging process. Each simulated scene is rendered twice: as a 9x9 grid of top-down captures for focal-stack generation and as 550 depth-clipped slabs derived from scene geometry to provide occupancy labels, explicitly representing void space as a negative class. A Gazebo pipeline generates procedural forest stands containing 80-200 trees across three species. A pretrained Pix2Pix model translates rendered RGB images into a synthetic near-infrared band, and Light Field Rendering produces aligned focal stacks of 550 planes across three channels. Four architectures were evaluated on a held-out scene. The plain 2D U-Net achieved an IoU of 0.6290, outperforming the volumetric 3D U-Net (0.5707). Established reconstruction methods performed substantially worse: COLMAP achieved 0.3446 and DepthAnything3 0.2959, both after their respective optimal dilation sweeps. An RGB-input variant of the volumetric architecture achieved the highest IoU of 0.6514 without dilation or per-scene calibration. These results highlight the limitations of conventional reconstruction methods, which recover visible surfaces but struggle to infer occupancy within closed canopies. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2611.04548 [cs.CV] (or arXiv:2611.04548v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.04548 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-72] BtsPruner: Role-Aware Visual Token Pruning for Large Vision-Language Models
链接: https://arxiv.org/abs/2611.04537
作者: Zihao Qi,Zhibo Zhang,Miaoxin Zhang,Jincheng Cai,Zengqiang Yan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Vision-Language Models (LVLMs) often process hundreds or thousands of visual tokens, resulting in substantial computational and memory overhead during inference. Existing training-free pruning methods typically estimate token importance using language-model attention, visual attention, or feature similarity. However, these methods treat visual tokens with uniform criteria and overlook that tokens may serve different roles during visual encoding. In this work, we investigate the evolution of visual token roles in LVLMs and observe that tokens progressively transition from local detail carriers to globally referenced representatives as encoding proceeds. Based on this observation, we propose BtsPruner, a training-free role-aware visual token pruning framework that preserves complementary token roles under a fixed budget. Specifically, BtsPruner first identifies the token-role transition point based on the evolution of attention concentration. It then discovers globally referenced representatives from later encoding stages and recursively retrieves local detail tokens from earlier stages to recover complementary fine-grained evidence. Extensive experiments across LLaVA-1.5, LLaVA-NeXT, Qwen-VL, InternVL, and CogVLM demonstrate that BtsPruner achieves superior performance retention under aggressive token reduction. On LLaVA-1.5-7B, BtsPruner preserves 91.6% of the dense model’s performance using only 32 of 576 visual tokens (5.6%). In the high-resolution LLaVA-NeXT-7B setting, it achieves up to a 5.95x CUDA-latency speedup.
[CV-73] A Universal Raw-IQ Radar Encoder for Cross-Sensor Human Sensing WACV
链接: https://arxiv.org/abs/2611.04534
作者: Jonas Leo Mueller,Dario Zanca,Sebastian Hoefler,Jan Petermann,Thomas Altstidl,Naga Venkata Sai Jitin Jami,Emmanuelle Salin,Bjoern M. Eskofier
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the 2027 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Abstract:Radar-based human sensing typically relies on end-to-end task-specific models trained on hand-chosen signal projections, which generalize poorly across unseen users, tasks, and hardware. We argue that the dominant signal processing choice, the micro-Doppler spectrogram, is a poor starting point for transfer because it keeps the Doppler axis, which carrier frequency rescales, and discards the angular axis, which carrier frequency leaves unchanged. We instead pretrain a convolutional beta-VAE without labels on clutter-removed raw radar measurements from four platforms spanning 60 and 77 GHz and 4 to 12 virtual antennas, aligned by a unified spectral interpolation grid. For every downstream benchmark the encoder stays frozen, and only a small supervised temporal classifier is trained on its embeddings. We compare against supervised models trained end to end on each benchmark and a pretrained radar foundation model across ten settings covering activity, gesture, sign-language, and gait recognition. When the test users and the radar are seen during training, our encoder is best on all five benchmarks. For users never seen in training, it leads on three of four benchmarks, by 4.8 to 23.2 points. On subject-disjoint gait recognition, it is best for each of the four held-out subjects and 8.3 points above the strongest baseline on average, even though its pretraining excluded every recording of this radar. Excluding the test radar from pretraining costs at most 5.3 points on any benchmark. Code is available at this https URL.
[CV-74] Learning to Falsify: Hierarchical Multiple Instance Learning for High Resolution AI-Generated Image Detection
链接: https://arxiv.org/abs/2611.04529
作者: Haoran Chen,Daiqi Gao,Xinyuan Li,Yongwei Wang,Lei Zhu,Rui Cai,Jianfeng Dong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures
Abstract:Recent advances in generative models have made synthetic images increasingly photorealistic, posing growing challenges to reliable and generalizable detection of AI-generated images. Existing methods typically rely on holistic image-level classification, which may overlook local artifacts, especially in high-resolution images. To address this limitation, we formulate this task as a falsification-driven multiple instance learning problem, where a single fake patch can falsify the whole image, while a real image requires all patches to be authentic. Based on this formulation, we propose \textitLearning to Falsify (LTF), a hierarchical multiple instance learning framework that models an image as multiple bags of instances across different spatial levels and aggregates their predictions under the falsification-inspired principle. We further introduce Cross-Shuffle during training to encourage the model to learn local artifact evidence rather than image-level shortcuts. Furthermore, we propose the \textitFalsification-driven Test-time Aggregation Strategy (FATAS), a plug-and-play module that extends the same falsification-inspired principle to existing detectors by aggregating patch-level predictions. Extensive experiments demonstrate that LTF achieves strong generalization and robustness across multiple benchmarks, while FATAS substantially improves representative detectors in high-resolution scenarios without retraining. Code is available at this https URL
[CV-75] E2I-Flow: EEG-to-Image Reconstruction via Wavelet-Temporal Cross-Attention and Conditional Flow Matching
链接: https://arxiv.org/abs/2611.04524
作者: Haowei Pan,Hwa Hui Tew,Junn Yong Loo,Fuad Noman,Hernando Ombao,Chee-Ming Ting
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Electroencephalography (EEG)-based visual decoding and reconstruction aims to recover visual information from non-invasive brain signals. But there are challenges due to the complex temporal-frequency characteristics of EEG signals and the difficulty of discrete denoising framework in mapping neural activity to meaningful visual representations. Here, we propose E2I-Flow, an EEG-to-image decoding and reconstruction framework that combines a novel EEG encoder for time-frequency representation learning from EEG features, and conditional flow matching for visual decoding. A stationary-wavelet frequency branch and a temporal-mixing branch are integrated through bidirectional cross-attention and adaptive fusion, with the resulting EEG representations aligned to ground-truth image embeddings using contrastive and mean-squared-error loss supervisions. The aligned EEG representation is then used as a condition for a Transformer-based flow-matching model to predict the corresponding visual embedding for image synthesis. Experimental results show strong zero-shot EEG-to-image retrieval and improved visual reconstruction performance, outperforming several state-of-the-art baselines.
[CV-76] 3DGS-VBench and 3DGSV-Assessor: A Multi-Dimensional Subjective Benchmark and a Reference-Flexible Objective Model for 3D Gaussian Splatting Video Quality Assessment
链接: https://arxiv.org/abs/2611.04498
作者: Yuke Xing,William Gordon,Jiarui Wang,Yujie Zhang,Guangtao Zhai,Yiling Xu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) enables real-time novel view synthesis (NVS), but its heavy storage makes compression essential, and both training and compression introduce representation-specific distortions. Since generative compression in many methods treats geometric and color attributes via different parameters, it can induce dimension-specific distortions, motivating multi-dimensional assessment. Moreover, real-world captures generally lack an aligned ground-truth video for a synthesized, continuous camera trajectory, leaving only sparse captured views as references. To enable multi-dimensional quality assessment of 3DGS videos with scarce aligned references, we first present 3DGS-VBench+, a large-scale subjective video quality assessment (VQA) benchmark comprising 3,036 3DGS reconstructions, each paired with a rendered video, across 11 real-world and 22 synthetic scenes, generated by 6 representative compression algorithms at systematically designed levels and annotated with 9,108 MOSs covering overall, geometry, and color quality. Based on 3DGS-VBench+, we propose 3DGSV-Assessor, a reference-flexible, large multimodal framework that scores all three dimensions in one generation pass and fuses a ranking-based reference cue into the frozen regressor through a bounded gate, supporting both reduced-reference (RR) and no-reference (NR) assessment. Extensive experiments demonstrate state-of-the-art correlation with human perception on 3DGS-VBench+ and strong zero-shot generalization to other NVS benchmarks. The dataset and code will be released at this https URL.
[CV-77] Hybrid Particle-Voxel Dynamics for Multimodal 3D World Simulation
链接: https://arxiv.org/abs/2611.04492
作者: Jimmy Wang,Yifan Chou,Kaifeng Zhang,Yixuan Wang,Binghao Huang,Tao Yu,Saleh Nabi,Yunzhu Li
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Predicting how the world evolves in response to robot actions is central to policy learning, evaluation, and planning. However, many action-conditioned world models predict future observations in 2D image or video space without explicitly modeling the underlying 3D scene, making spatial consistency across viewpoints and over long-horizon interactions challenging. We introduce 3D World Simulator, an action-conditioned multimodal world modeling framework that jointly predicts appearance, geometry, and tactile observations from a unified 3D representation. Our approach combines a hybrid particle-voxel autoencoder that compresses point clouds into compact 3D voxel latents with an action-conditioned latent dynamics model that predicts their evolution. The shared 3D representation supports geometrically consistent rendering from arbitrary viewpoints and flexible integration of spatially aligned tactile signals. Our experiments demonstrate interactive, long-horizon, multimodal prediction at 50 Hz. We then show how our model enables scalable data generation for policy training and evaluation using tactile and multi-view visual observations, including wrist-mounted and novel camera views. Furthermore, we scale our method to large datasets containing diverse objects and demonstrate model-based planning with unseen objects and robot configurations. Together, these results highlight the potential of hybrid particle-voxel dynamics as a foundation for multimodal 3D world simulation.
[CV-78] SPA-Q: Structure-Preserving Adaptive Post-Training Quantization for Monocular Depth Estimation
链接: https://arxiv.org/abs/2611.04491
作者: Jaemin Choi,Jincheol Yang,Nahyun Lim,Yun-Seong Jeong,Matti Zinke,Hyunwoo Yu,Suk-Ju Kang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages
Abstract:Monocular depth estimation (MDE) has advanced rapidly with the emergence of foundation models such as Depth Anything. Their transformer-based architectures provide strong generalization across diverse scenes and domains, but also incur high computational and memory cost, making efficient deployment challenging. Post-Training Quantization (PTQ) provides an efficient and practical solution for model compression, yet low-bit PTQ remains challenging for MDE models. When applying PTQ to Depth Anything, we identify two key challenges: (i) independently minimizing the quantization error of the query and key projections fails to preserve the attention maps induced by their interaction, and (ii) quantization errors accumulate across layers, resulting in intermediate feature distribution shifts. To address these issues, we propose SPA-Q, a Structure-Preserving Adaptive PTQ framework for MDE, consisting of two main components. First, Attention-Preserving Calibration (APC) determines query and key quantization parameters by matching the full-precision attention distribution. Second, Channel-Wise Distribution Alignment (CWDA) learns channel-wise affine transformations to mitigate quantization-induced distribution shifts, and the learned parameters are absorbed into the weights after training. Experimental results show that SPA-Q consistently outperforms existing PTQ methods under 4-bit quantization, achieving an average 25.9% reduction in AbsRel and a 17.3% improvement in \delta_1 across NYUv2 and KITTI datasets.
[CV-79] Polarimetric Scattering-Structure Contrastive Learning for Few-Shot HRRP Recognition
链接: https://arxiv.org/abs/2611.04490
作者: Fangyi Wang,Peng Zhao,Haipeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Few-shot high-resolution range profile (HRRP) recognition is challenged by strong aspect-dependent variation and sparse novel-class support. We propose polarimetric scattering-structure contrastive learning (PSS-CL), which supplies an episodic HRRP encoder with a structured physical training view. Dominant local responses are represented by their soft affinities to canonical polarimetric mechanisms, normalized range locations, and energy contributions. A symmetric cross-view objective transfers this scattering structure to the learned HRRP representation, while episodic prototype learning supports novel-class inference without the physical branch. We conduct Random-aspect and Cross-aspect experiments on a six-class measured dataset. In Cross-aspect 1-, 3-, and 5-shot recognition, PSS-CL achieves 81.73%, 86.92%, and 90.98% mean class accuracy (MCA), respectively, outperforming the best competing results by 5.62%, 3.09%, and 3.56%.
[CV-80] Bridging CLIP with DINO: Cross-Modal Information Maximization for Online Test-Time Adaptation NEURIPS2026
链接: https://arxiv.org/abs/2611.04489
作者: Taehoon Kim,Wonjun Hwang,Hyunsouk Cho
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by NeurIPS 2026
Abstract:Vision-language models such as CLIP enable zero-shot classification from arbitrary class names, but under test-time corruptions, their visual encoder projects distorted features onto incorrect textual anchors with high confidence: a phenomenon we term modality bias. Standard entropy minimization reinforces these overconfident errors, which creates a confirmation-bias loop and collapses the predicted label distribution. We propose an online test-time adaptation (TTA) method that breaks this loop at three levels. At the representation level, a frozen self-supervised backbone (e.g., DINO v2) is combined with CLIP through a cross-covariance logit ensemble. CLIP text prototypes are projected into the feature space of this text-free backbone, so the self-supervised branch classifies native image features and is less affected by the text bias of CLIP. At the optimization level, a gated test-time information-maximization objective pairs per-sample sharpening with batch-level diversity, while an adaptive gate excludes unreliable samples from the gradient. At the prediction level, an online logit adjustment corrects the residual class-distribution skew. CDIM outperforms the compared CLIP-based online TTA baselines on ImageNet-C, CIFAR10-C, and CIFAR100-C, and remains competitive in continual and mixed-domain TTA. Extensive analyses and ablation studies verify the contribution of each of its components. Our code is available at this https URL
[CV-81] KeyWise: Efficient Video-Only Piano Transcription with a Lightweight Per-Key Architecture
链接: https://arxiv.org/abs/2611.04476
作者: Anastasios Chatzigiannis,Natalia Panagou,Maria Koziri
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 12 pages, 4 figures, 3 tables
Abstract:Audio-based piano transcription is hampered by the sustain pedal and by noisy or missing soundtracks, problems that video-only transcription avoids, since a top-view video shows directly when each key is pressed and released. Most visual transcription networks, however, describe the whole keyboard with one global feature vector and must learn where each key is. In this paper, we propose KeyWise, a lightweight per-key architecture that exploits the known geometry of the keyboard instead of learning it. The rectified keyboard is divided into 88 equal tiles that serve as regions of interest (ROIs), and one small recurrent network, shared by all keys, follows each key over time. On PianoVAM, KeyWise reaches the onset accuracy of the state-of-the-art V2N (95.0% vs. 94.7% onset F1 at 50 ms and 97.8% vs. 97.0% at 100 ms) with 1.3 M instead of 28.2 M parameters, and it runs 4.7 times faster on the same GPU. At 100 ms, it is also significantly more accurate when the key releases (97.0% vs. 95.5% F1) or the velocities (89.4% vs. 84.5% F1) must be correct as well. On PianoYT, KeyWise pre-trained on PianoVAM reaches 69.4% onset F1 at 100 ms, comparable to the best published result, although it needs fine-tuning for new recording conditions.
[CV-82] Beyond Token Representations: Explicit Visual Object Grounding for Video Reasoning Segmentation NEURIPS2026
链接: https://arxiv.org/abs/2611.04472
作者: Jun Huang,Junyang Zhang,Zijie Yue,Bingkun Wang,Yong Luo,Miaojing Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by NeurIPS 2026
Abstract:Video Reasoning Segmentation (VRS) aims to infer and segment target objects specified by reasoning queries in videos. Existing methods typically first infer the target and generate a few segmentation tokens for it, which are then fed into a mask decoder for mask prediction. However, such segmentation tokens tend to be both semantically monotonous and spatially ambiguous, making them largely insufficient to represent the target to guide mask prediction. In this paper, we propose an explicit visual object grounding framework for VRS. Specifically, we first propose a spatial-aware prompt generation and refinement scheme, in which we query a multimodal large language model to generate explicit spatial prompts (i.e., bounding box and point set) for the target and then refine them through multi-step dialogue. Furthermore, we introduce a query diversification and alignment module to generate auxiliary queries that describe the same target from different perspectives. We then enforce cross-query consistency between their predicted spatial prompts to alleviate the training bias caused by query limitations. Finally, we select several reliable frames using a prompt-based keyframe selection strategy to support mask decoding and propagation. Extensive experiments on eight datasets demonstrate that our method significantly outperforms state-of-the-art methods. The code is available at this https URL.
[CV-83] Using GeoAI and machine learning tools for consistent high-resolution land cover mapping based on time-series NAIP imagery
链接: https://arxiv.org/abs/2611.04470
作者: Jie Liu,Xusheng Tang,Chao Wang,Zhengxiao Yan,Yuchen Dai,Qi Zhang,Conghe Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages. Published version
Abstract:High-resolution land-cover maps support ecological monitoring, but consistent historical time series remain scarce because aerial imagery varies across years and sensors. We developed a dual-track workflow to produce temporally coherent 1-m land-cover maps from National Agricultural Imagery Program (NAIP) imagery without historical training samples. For higher-quality imagery from 2009-2017, we used a pretrained U-Net/ResNet-34 model and refined boundaries with the Segment Anything Model (SAM). For lower-quality imagery from 2004-2008, we reconstructed land cover through an NLCD-based spatiotemporal bridging and label back-casting pipeline. Evaluation across six counties in North Carolina and Pennsylvania yielded overall accuracies of 0.887 (2014), 0.886 (2017), and 0.733 (2004). Structure, Water, Wetland, and Cropland consistently achieved high F1-scores. The workflow integrates GeoAI and multiple data sources to address temporal heterogeneity in historical aerial imagery, supporting high-resolution land-change analysis and landscape planning across the conterminous United States.
[CV-84] Is Forgetting Unified? Exploring Concept Unlearning in Unified Multimodal Models
链接: https://arxiv.org/abs/2611.04467
作者: Zhongkai Xue,Junkai Chen,Mingcong Lei,Rumo Zhang,Yixiao Liu,Zhimin Sun,Shu Wu,Qiang Liu,Lihua Zhang,Liang Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Unified multimodal models (UMMs) integrate image generation and visual understanding, but are existing single-pathway unlearning methods sufficient to suppress target concepts across both? In this work, we study cross-pathway concept unlearning. Our goal is to prevent a model from both generating images of a target concept and identifying it in input images, while preserving other capabilities. To this end, we introduce UUBench, a diagnostic benchmark covering 30 target concepts and 60 semantically related neighboring concepts. Experiments on representative UMMs show that the evaluated single-pathway methods achieve limited joint forgetting: generation-side editors strongly suppress target generation but yield limited forgetting in visual understanding, while understanding-side baselines struggle to achieve substantial forgetting even in their own pathway. Their direct composition also yields limited joint forgetting. We therefore propose UniForget, achieving joint forgetting rates of 80.0% on BAGEL-7B-MoT and 92.7% on Janus-Pro-7B, outperforming representative baselines. Further analysis reveals trade-offs between joint forgetting and capability preservation.
[CV-85] DynOSeg: Dynamic Object Segmentation via Motion-Geometry Fusion WACV2027
链接: https://arxiv.org/abs/2611.04452
作者: Xu Han,Yiduo Wang,Yiran Wang,Viorela Ila
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027. 22 pages, including supplementary materials
Abstract:Moving Object Segmentation (MOS) detects and separates dynamic objects from the static background in a video, which plays a fundamental role in dynamic-scene understanding. Most MOS methods rely on pure visual motion cues alone, such as optical flow or point tracks, which is inherently ambiguous due to the compounded camera and object motions as well as motion-induced appearance changes, e.g. shadows and reflections. Naive fusion of additional information such as depth and semantics, however, cannot leverage their complementarity to the fullest extent. We therefore propose DynOSeg, a novel MOS approach that takes on a two-stage fusion structure. DynOSeg first combines a geometry-aware residual map and depth encoding into a geometric representation of motion, delineating camera motions and object dynamics. We then further incorporate semantic features to refine object understanding. We have additionally curated a diverse training corpus including CARLA-MOS, a new synthetic urban-driving dataset for MOS training. Our experiments show that DynOSeg consistently outperforms published state-of-the-art MOS methods across a diverse range of datasets. Our code and pretrained models can be found at this https URL
[CV-86] Entropy-Gated Dynamic Rectification for Object Hallucination in Large Vision-Language Models WACV2027
链接: https://arxiv.org/abs/2611.04438
作者: Riasad Alvi,Md. Asaduzzaman Shuvo,Md. Ahashan Habib,Md. Faiyaz Abdullah Sayeedi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV 2027
Abstract:Large Vision-Language Models (LVLMs) routinely name objects that are absent from the image. Latent steering can reduce this failure in one model forward pass, but a fixed gate treats early and late decoding steps by the same criterion. We observe that predictive entropy varies sharply within free-running generation, while states that commit to short answers occupy a narrow, confident band. We exploit this temporal mismatch by using uncertainty as a causally available, token-local control signal for an existing steering gate. We propose Entropy-Gated Dynamic Rectification (EGDR), which lowers the intervention threshold when entropy derived from the preceding step’s already available logits is high and retains the calibrated operating point when it is low. On LLaVA-1.5-7B, the underlying static gate lowers CHAIR CS/CI from 53.00/15.30 to 32.00/14.30; replacing only its constant threshold with EGDR further lowers them to 29.00/13.60. At a matched 58% intervention rate, the corresponding static control obtains 30.50/14.00. The gain over the static gate holds on LLaVA-NeXT-7B and grows with the generation budget, while POPE scores and general multimodal capability remain effectively unchanged. EGDR requires no additional model forward pass and adds roughly 2% measured latency. Our code is available at this https URL.
[CV-87] A Systematic Point Cloud Edge Detection Framework for Automatic Aircraft Skin Milling
链接: https://arxiv.org/abs/2611.04435
作者: Zijie Wu,Yaonan Wang,He Xie,Mingtao Feng,Haotian Wu,Chao Ding,Ajmal Mian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The edge detection technique is an essential step for aircraft skin milling in aviation manufacturing. Most of the current detection methods focus on traditionally defined edge extraction tasks but disregard the crucial systematic requirement of edge milling. In this paper, we proposed a novel edge detection framework for automatic edge milling of aircraft skins. First, an edge probability detector is proposed by the spatial tangent continuity (STC) to provide the essential reference. Second, we propose a hierarchical branch searching (HBS) method to hierarchically strip the desired milling edges from the raw point cloud, which consists of three graded progressive steps: branch backbone generation, branch extension, and branch pruning. We demonstrate the performance of the proposed method on both synthetic models and aircraft skin workpieces. The proposed method outperforms the other baselines and shows accurate edges for the edge milling task.
[CV-88] SWAP-INR: Implicit Neural Representation with Switchable Attention Paths for Time-varying Multivariate Scientific Data Compression WACV2027
链接: https://arxiv.org/abs/2611.04423
作者: Hyunsoo Son,Jeonghyun Noh,Chaoli Wang,Won-Ki Jeong
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 16 pages, Accepted at WACV 2027
Abstract:Visualizing multivariate scientific simulation data is essential for understanding complex physical phenomena, yet the massive scale of such data imposes a severe storage bottleneck in high-performance computing environments. Implicit neural representations (INRs) have emerged as a promising continuous alternative to traditional lossy compressors, but existing methods struggle to explicitly capture cross-variable interactions and to adapt to the diverse spectral characteristics of physical variables. To address these limitations, we propose SWAP-INR, an architecture for high-fidelity compression of multivariate, time-varying simulation data. SWAP-INR introduces two key components: (1) The switchable attention path (SWAP) module explicitly models cross-variable interactions through a cross-variable attention layer, then routes each variable to an attention or non-attention path by a deterministic, loss-driven criterion, and prunes the inactive layers for structural compression. (2) The adaptability amplification SIREN (A2-SIREN) replaces rigid frequency scaling with channel-wise learnable modulation, adapting to heterogeneous physical features with negligible overhead. Combined with post-training 16-bit quantization, SWAP-INR achieves state-of-the-art performance on small modular reactor (SMR) datasets and remains competitive on public physics simulation datasets, while maintaining a constrained memory footprint. Our code is this https URL.
[CV-89] GVT3D: Gesture-Vision-Text Token Enrichment for 3D Embodied Reference Understanding
链接: https://arxiv.org/abs/2611.04422
作者: Vimarsh Shah,Dulanga Weerakoon,Ajinkya Deshpande,Saksham Agarwal,Nilesh Bhatia,Neel Kiran Naik,Parth Shah,Sravan Danda,Sanjay E. Sarma,Archan Misra,Vigneshwaran Subbaraju,Sougata Sen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Natural human-robot interaction requires robots to interpret language and gestures within a 3D environment to support tasks such as pick-and-place and object retrieval. 3D Embodied Reference Understanding (3D-ERU) supports this by identifying the 3D location of a target object from a scene (point-cloud) containing a human pointing gesture and an accompanying language description. However, current 3D-ERU models lack alignment of cross-modal representations and incur high processing costs that increase with the point-cloud’s size. To address this, we propose GVT3D, which employs an Embodied Fusion module that jointly models language, gesture, and vision inputs within a shared embedding space, promoting cross-modal alignment. We additionally introduce a foveation approach that selectively processes the point cloud within a coarse region around the estimated pointing direction to reduce computations. GVT3D achieves ~8% higher accuracy and foveation enables 2.95x faster execution while maintaining comparable accuracy, relative to prior 3D-ERU models.
[CV-90] VidUEU-Agent : A Data-Curation Agent for Multimodal Understanding Editing and Unified Tasks
链接: https://arxiv.org/abs/2611.04418
作者: Tengju Ru,Weitong Lian,Zecong Tang,Lingyi Meng,Haoran Li,Zhejun Cui,Yichen Zhu,Hangshuo Cao,Qi Kang,Yechi Liu,Kaixuan Wang,Yu-Jie Yuan,Chunwei Wang,Yu Zhang,Bo Dai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The advancement of multimodal learning is heavily constrained by the scarcity and high annotation cost of high-quality instruction data. While raw videos offer an abundant and dynamic source of visual knowledge, transforming them into precise instruction data remains highly challenging. To bridge this gap, we present VidUEU-Agent, an automated pipeline that synthesizes instruction data from raw videos for multimodal understanding, editing, and unified tasks. VidUEU-Agent follows a four-stage pipeline: it performs global perception to filter low-quality shots, mines keyframes and multimodal signals, constructs metadata and routes the metadata to synthesize instruction samples. More importantly, our agent framework can autonomously iterate the data distribution based on downstream training results, thereby optimizing data quality, balance, and training effectiveness. Scaling this pipeline, we construct VidUEU-100K, a large-scale dataset of 100K training samples spanning multiple domains and tasks, providing a robust foundation for developing comprehensive multimodal capabilities. Extensive experiments demonstrate that fine-tuning baseline models on VidUEU-100K yields significant and consistent performance gains across diverse representative benchmarks, validating the effectiveness of our proposed agent framework and dataset.
[CV-91] Efficient and Faithful High-Resolution Image Editing with Sparse Cross-Resolution Denoising
链接: https://arxiv.org/abs/2611.04416
作者: Maosen Zhao,Xianfang Zeng,Pengtao Chen,Peng Ye,Gang Yu,Jiayuan Fan,Tao Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Instruction-based image editors still primarily operate at 1K, whereas practical editing increasingly requires faithful 2K/4K outputs. Dense high-resolution adaptation preserves the editing trajectory but incurs expensive target-source attention; editing at 1K followed by external refinement is cheaper but can separate detail restoration from the editing trajectory. We introduce FLASH, a native high-resolution editor based on sparse cross-resolution denoising. A frozen 1K branch provides a semantic and structural anchor, while a target-resolution branch reconstructs local detail within the same Transformer. An asymmetric topology retains full 1K attention, guides high-resolution queries with spatially aligned low-resolution noisy-target states, and confines high-resolution target-reference interaction to local Halo neighborhoods. Resolution-specific diffusion-state mapping and spatial YaRN adapt the pretrained editor to larger grids. An optional, training-free progressive path replaces early high-resolution denoising with a 1K prefix and completes refinement in the same editor. On VINS-4KEval, the direct model achieves the strongest structural fidelity among the compared methods at both 2K and 4K. A separate 4K forward benchmark with R = 1 records 4.2x faster total forward execution and 25.3x faster attention than dense target-reference attention. Progressive inference reduces end-to-end 4K latency by 30.5%, with a modest quality cost.
[CV-92] SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere NEURIPS2026
链接: https://arxiv.org/abs/2611.04414
作者: Huihan Wang,Juncheng Wang,Yuxiang Feng,Wenlong Hou,Guoqi Yu,Chao Xu,Yang Liu,Baigui Sun,Shujun Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to NeurIPS 2026. 23 pages, 7 figures. Code: this https URL
Abstract:Missing modality imputation aims to synthesize unobserved modalities from observed ones. Existing methods treat each modality as living on its own latent manifold, forcing the generator to learn a long, unstructured jump between them. We present SphereFlow, which takes a fundamentally different view: by projecting all modalities into a shared hyperspherical latent space via a frozen self-supervised encoder, cross-modal imputation reduces to short-range geometric transport between nearby points on the sphere. Realizing this idea with flow matching, however, exposes two geometric conflicts: the standard Gaussian noise source is catastrophically mismatched with the unit-norm data manifold, and the linear interpolation path departs the sphere surface into a region where the decoder has no training signal. We resolve both with simple, geometry-aware modifications: replacing the noise source with the observed-modality latent so that the velocity field learns only the cross-modal displacement, and training the decoder with a time-weighted loss that extends its domain to the near-surface region traversed by the flow. We prove that this data-as-source formulation reduces the worst-case off-manifold deviation from a quantity that grows with the latent dimension to a small, dimension-independent bound governed by inter-modality similarity. Experiments on BraTS multi-modal MRI imputation and CT-MRI translation demonstrate state-of-the-art generation fidelity across all settings, while downstream brain tumor segmentation with our imputed data closes roughly eighty percent of the gap to the full-modality oracle. Code is available at this https URL.
[CV-93] LCM-UNet: Scale-Selective Mamba with Reparameterizable Multi-Basis Local Modeling for Lightweight Skin Lesion Segmentation ALT
链接: https://arxiv.org/abs/2611.04409
作者: Md Shihabul Islam Shovo,Nishi Kanta Paul,Dip Kumar Saha,Kishor Morol
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, 5 tables. Submitted to IEEE Journal of Biomedical and Health Informatics (JBHI)
Abstract:Accurate lesion segmentation underpins automated dermoscopic analysis: masks enable measurements of asymmetry, border and size. Deployment in primary care or on mobile devices requires a compact network. Existing ultra-light Mamba U-Nets offer global context, but global-modeling cost depends on each Mamba block’s resolution, while their local branches rely on a single square kernel. We introduce LCM-UNet, a 33,474-parameter U-shaped network that fuses every skip at its native resolution, restricts bidirectional Mamba to stages at one-sixteenth of the input resolution or coarser, and uses local convolutions at finer scales. A multi-basis reparameterizable adapter augments each local branch during training with dilated, horizontal and vertical depthwise kernels, then folds them into the host kernel before inference. Each skip connection also learns a stage-specific gain. Nine networks were retrained on the official ISIC 2017 and ISIC 2018 partitions under a harmonized protocol, with PH ^2 reserved for external validation and ablations run on a development split. LCM-UNet achieved Dice scores of 88.62%, 89.73% and 90.46% on ISIC 2017, ISIC 2018 and PH ^2 , respectively, ranking first on both ISIC test sets and second on PH ^2 among the compared models. In the native-resolution host, removing Mamba from the one-eighth-resolution decoder stage eliminated 63% of Mamba operations, corresponding to 5.3% of all operations, with a Dice change of -0.03 percentage points. The adapter improved Dice by 0.48 percentage points over a single host kernel without adding deployed parameters or operations. With latency so far measured only on a GPU, LCM-UNet is a candidate for resource-constrained deployment.
[CV-94] DR-LiteNet: A Lightweight Explainable Hybrid CNN Framework for Imbalanced Diabetic Retinopathy Grading via Adaptive SMOTE Fusion
链接: https://arxiv.org/abs/2611.04405
作者: Nishi Kanta Paul,Md Shihabul Islam Shovo,Audrija Chowdhury,Israt Jahan Esha
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 3 figures, 2 tables. Accepted at the 1st International Conference on Next-Generation Electrical Electronics, Computer Systems, and Technologies (iCONEECT)
Abstract:Automated diabetic retinopathy (DR) grading from colour fundus photographs is held back by two compounding challenges: severe class imbalance across five severity grades and the heavy computational cost of large scale architectures in resource constrained clinical settings. We introduce DR-LiteNet, a dual branch hybrid convolutional neural network that tackles both problems at once. The CNN branch extracts 1,280 dimensional deep features through EfficientNetB0, while the handcrafted (HC) branch produces a compact 51 dimensional lesion descriptor capturing vessel density, exudate intensity, texture entropy, and HSV colour histograms. Concatenating both branches yields a 1,331-d hybrid embedding, on which Adaptive SMOTE (ADASYN) is applied during training to synthesise minority class feature vectors, correcting distributional skew without touching the image space. A two phase training protocol first warms up the classification head on balanced features, then fine tunes the full network end to end. Grad CAM saliency maps deliver post-hoc clinical explainability. Tested on APTOS 2019 under five fold stratified cross validation, DR-LiteNet reaches 88.77% +/- 0.61 accuracy, a quadratic weighted kappa (QWK) of 0.884 +/- 0.010, and a macro F1 of 0.792 +/- 0.008 with only 4.69 M parameters beating ResNet50 (23.5 M) by DeltaQWK = 0.022 while staying practical on a single Google Colab T4 GPU. The source code of this project is available at: this https URL.
[CV-95] Auditing Targetless Camera-LiDAR Extrinsic Correction before Recursive Use
链接: https://arxiv.org/abs/2611.04397
作者: Bo Ma,Weiqi Yan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera-LiDAR fusion repeatedly uses an extrinsic transform in projection, association, and recursive state estimation. We study whether a targetless correction should be admitted to a downstream consumer only after separate audits of correction quality, confidence ranking, and recursive risk. Our log-disjoint protocol evaluates these stages under synthetic extrinsic drift on Argoverse 2 and treats cross-dataset and odometry experiments as bounded diagnostics. On 27 test-split logs, single-frame Canny alignment is harmful in the mild tier. Multi-frame alignment improves severe-tier rotation by 2.52 degrees but worsens translation by 0.177 m; its declared joint error-magnitude improvement is also negative (-0.158, 95% CI [-0.223, -0.098]), so the rotation gain is not a six-degree-of-freedom correction success. A zero-injection check moves away from the true extrinsic, and solver-disagreement AUROC on selected splits does not yield a validated nonzero-coverage risk-coverage operating point. In a 15-log final system role, the locked conservative policy admits no candidate and equals Raw, whereas Always-correct increases trajectory error. These results support an auditable separation of correction, confidence, and recursive contamination; they do not establish a deployable calibration gate, positive odometry or SLAM improvement, map consistency, or closed-loop safety. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2611.04397 [cs.CV] (or arXiv:2611.04397v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.04397 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-96] Spin Spherical Fourier Neural Operators: Efficient Equivariant Learning for Vector and Spin Fields on the Sphere
链接: https://arxiv.org/abs/2611.04389
作者: Kevin Mulder,Matthew A. Price,Eirini Leftaki,Jason D. McEwen
类目: Computer Vision and Pattern Recognition (cs.CV); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
备注: 27 pages, 15 figures
Abstract:Many fields of physical interest on the sphere, such as wind, the polarisation of the cosmic microwave background and the shear of weak gravitational lensing, are spin fields, whose values change phase when the local frame rotates. We extend spherical Fourier neural operators (SFNOs) to fields of any spin, through lifted spherical convolutions that are exactly rotationally equivariant. Lifting spin fields to Mackey functions on the rotation group provides the conceptual framework, while computation is performed exactly and efficiently through their harmonic representation and by appealing to sampling theorems on the sphere and rotation group. We construct spin SFNOs (S ^2 FNOs) from these building blocks. We demonstrate empirically that rotational equivariance holds to machine precision for these layers. We achieve state-of-the-art performance on existing benchmarks, and introduce new benchmarks on which spin-aware models outperform their scalar counterparts.
[CV-97] StepX-OPD: Rethinking Multi-Teacher On-Policy Distillation in Diffusion Models
链接: https://arxiv.org/abs/2611.04386
作者: Chenru Wang,Xuanyang Zhang,Yucheng Han,Wenbo Nie,Wei Cheng,Xianfang Zeng,Gang YU,Chi Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:A deployable text-to-image diffusion model must hold several capabilities at once, such as instruction following, text rendering and human preference, and must sample in a few steps. Capabilities and few-step sampling are built in separate stages: a full-step model first consolidates capabilities through multi-teacher on-policy distillation, then serves as a fixed teacher for step distillation. Every full-step model we evaluate scores lower once compressed, and the second stage cannot begin until the first finishes. We introduce StepX-OPD, which merges the two stages: one student acquires every capability while learning to sample in a few steps, so frozen specialists supervise it on the states its own rollout visits rather than on a longer trajectory it never runs. Because a decomposition of the rollout’s KL weights velocity mismatch most heavily near the clean end, we also regress the student’s velocity on the low-noise part of the rollout. Separately, we find that capability integration makes models write prompt words into images on prompts that never asked for text, and we give a variant that lowers it. We evaluate on SD3.5-Medium and FLUX.1-dev across instruction following, text rendering, image quality and human preference. On both architectures, StepX-OPD attains the highest normalized average score among the four-step methods compared and exceeds the forty-step Flow-OPD model, and on SD3.5-Medium it trains with about 37% fewer GPU-hours than sequential DiffusionOPD followed by step distillation.
[CV-98] Understanding Backdoor Attacks in Scene Graph Generation via Global Context Pathway ACM-MM2026
链接: https://arxiv.org/abs/2611.04379
作者: Xiaoguang Chang,Tuo Chen,Chengze Jiang,Teng Wang,Changyin Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by the 34th ACM International Conference on Multimedia (ACM MM 2026)
Abstract:Scene Graph Generation (SGG) produces structured scene representations, yet its training-time backdoor risk remains unexplored. Unlike classification, SGG exposes a pair-level attack surface that enables selective corruption of relations involving a designated object category. However, because SGG largely relies on localized evidence, globally placed triggers may face difficulty in manipulating such predictions. Recent SGG architectures increasingly incorporate global context to improve scene modeling, which may in turn facilitate such attacks. In this paper, we systematically study this backdoor risk and investigate the role of the global context pathway in backdoor behavior. Concretely, we first instantiate this attack with an effective method, and propose a three-axis evaluation protocol to characterize backdoor behavior. Experiments on Visual Genome with representative open-vocabulary and closed-set SGG models demonstrate the effectiveness of the attack. Mechanistic analyses reveal that the explicit global context pathway serves as a major carrier of the backdoor effect across the evaluated SGG models. In particular, replacing the poisoned global context representation with its clean counterpart during inference sharply suppresses the attack, yielding 100% and 97% reductions in attack success for the OvSGTR family and RelationFormer, respectively. Moreover, training-time interventions in the OvSGTR family show that suppressing trigger signals in this pathway either reduces attack success or substantially degrades utility. Together, these findings suggest that explicit global context designs may pose a backdoor risk in SGG architectures.
[CV-99] IGSW: Multi-Weather Urban Scene Reconstruction with Intrinsic-Decoupled Gaussian Splatting
链接: https://arxiv.org/abs/2611.04374
作者: Zhongquan Wang,Long Cao,Peijia Xie,Senlei Ma,Jie Li,Guofa Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-fidelity weather editing of 4D scenes can support end-to-end training and closed-loop simulation for autonomous driving. Combining generative image editing with dynamic Gaussian representations enables multi-weather scene reconstruction, but weather-edited images often contain structural distortions. Directly fitting their appearance may propagate errors from teacher images into the reconstructed 4D scene, compromising the original scene structure and temporal continuity. To address these challenges, we propose IGSW, a two-stage framework that combines intrinsic-decoupled 4D Gaussian scene modeling with structure-preserving weather refinement. In Stage I, background Gaussian appearance is decomposed into weather-shared albedo and normals and weather-specific irradiance and residuals. Weather teacher images generated by a diffusion-based image editing model and intrinsic maps estimated by a diffusion-based inverse renderer provide feature-level soft supervision for stable structure and illumination changes. In Stage II, a structure-preservation gate combines semantic conflicts with cross-weather intrinsic discrepancies to suppress unreliable teacher regions. Per-Gaussian weather transport variables and target-weather branches then strengthen the desired weather appearance over the intrinsic-decoupled scene. Experiments on nuScenes and Waymo show that IGSW achieves the highest content similarity and semantic consistency and the lowest temporal error among the compared methods, while retaining pronounced weather-dependent illumination and atmosphere. The project page is available at this https URL .
[CV-100] RAFT: Reasoning -Aligned Fine-Tuning Improves Robustness of VLA Post-Training
链接: https://arxiv.org/abs/2611.04368
作者: Shresth Grover,Andrey Kolobov,Dhruv Shah
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Adapting a generalist vision-language-action (VLA) policy to a target task with limited demonstrations can improve nominal task success while reducing robustness to variations in appearance, objects, and instructions. We introduce reasoning-aligned fine-tuning (RAFT), a two-stage post-training procedure: RAFT first trains low-rank adapters on task-relevant reasoning annotations, then freezes them while fine-tuning the policy on action demonstrations. On a physical DROID robot, RAFT achieves 75.0% out-of-distribution success, compared with 27.9% for plain fine-tuning; on a multi-stage bimanual YAM task, it reaches 70.0% mean success, compared with 36.0% for the strongest baseline. RAFT also improves success under visual, object, and instruction shifts in simulated pick-and-place and open-drawer tasks. Compared with plain fine-tuning, RAFT policies place more attention on annotated task regions and yield higher linear-probe accuracy for object identity in later layers. A broader reasoning-data mixture further improves downstream success across demonstration budgets.
[CV-101] EndoS3:Endoscopic Semantic Splatting SLAM
链接: https://arxiv.org/abs/2611.04358
作者: Mona Sheikh Zeinoddin,Benjamin Wright,Zafer Tandogdu,Greg Shaw,Evangelos Mazomenos,Danail Stoyanov
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Purpose: Despite advances in 3D surgical scene understanding, semantic simultaneous localisation and mapping (SLAM) remains under-explored for two key reasons. First, it requires solving three challenging tasks in parallel: camera pose tracking, high-fidelity deformable reconstruction, and semantic mapping, making progress particularly demanding. Second, the scarcity of datasets providing the necessary ground truth to evaluate all three tasks comprehensively under conditions that closely reflect real surgical procedures environments. Method: We propose EndoS3, a Gaussian Splatting-based semantic SLAM framework that uses instance masks to guide all three tasks. An instance-level motion decomposition module separates static and dynamic instances by evaluating residual flow after compensating for camera motion, accounting for flow noise and comparing motion across neighbouring instances. The camera is tracked against the static Gaussians, while an instance-aware warp field gives each dynamic instance its own control nodes and deformation network. A semantic node allocation module redistributes a fixed node budget across instances according to their size and non-rigidity. We also introduce CRCD++, an ex-vivo Cholecystectomy dataset with RGB-D frames, camera pose and instance annotations for full semantic SLAM evaluation. Results: EndoS3 surpasses all state-of-the-art general-purpose and surgical semantic SLAM methods in tracking, reconstruction and semantic mapping on the CRCD++ and SemSup datasets. Conclusion: Semantic-aware motion and deformation modelling is vital to surgical semantic SLAM. EndoS3 and CRCD++ offer a method and benchmark for improved geometric and semantic understanding in endoscopy.
[CV-102] ReSMap: Recasting Satellite Priors for Robust and Accurate Online HD Map Construction NEURIPS2026
链接: https://arxiv.org/abs/2611.04352
作者: Kyungmin Kim,Sumin Lee,Sungoh Jeong,DoHyun Lim,Seonghyun Park,Soonmin Hwang
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to NeurIPS 2026. 24 pages, 9 figures, 11 tables. Project page: this https URL Code: this https URL
Abstract:Despite recent advances in camera-based online HD map construction, these systems remain susceptible to sensor failures due to their reliance on onboard cameras. In contrast, satellite images remain available independently of real-time sensing conditions as they are cached offline. Existing methods utilizing satellite images, however, primarily leverage them under nominal conditions, without explicitly addressing them as redundancy when a sensor degrades. To address this gap, we propose ReSMap, a redundancy-aware camera-satellite fusion framework for robust online HD map construction. Our framework explicitly leverages satellite images as a complementary source to improve robustness against camera failures. A Predict-then-Fuse (PTF) module fuses camera and satellite BEV features by using each branch’s own prediction confidence as a per-pixel reliability weight, with no learned router. An Object-Centric Cooperative Multi-modal Query (OCMQ) decoder then extends this per-modality separation to the query level: each modality cross-attends only to its own BEV, and per-modality updates are merged through a learned per-token gate. Our model achieves state-of-the-art performance on nuScenes across all splits and range settings. More importantly, it also sets the new state-of-the-art under camera-failure scenarios, demonstrating that satellite images serve not only as a strong prior but as practical redundancy for robust online HD mapping.
[CV-103] Mitigating Spatial Blindness in Multiple Instance Learning for Whole-Slide Image Analysis
链接: https://arxiv.org/abs/2611.04337
作者: Xiangyu Li,Jincheng Li,Xing Xie,Bin Guan,Shaohua Kevin Zhou,Rui Yan,Ran Su
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Whole-slide multiple instance learning can predict diagnostic and prognostic labels from tissue composition while making limited use of spatial organization. Providing coordinates or a graph does not establish that a trained predictor uses tissue neighborhoods. We examine this gap as spatial blindness and hypothesize that early composition learning can weaken supervision of a spatial pathway. We introduce ResTopoMIL, which freezes a learned composition predictor before training a graph residual with a coordinate-reassignment separation loss. Permutation and information analyses distinguish architectural symmetry, available spatial information and the proposed optimization mechanism. On a controlled spatial task, staged training achieved an AUC of 0.987, compared with 0.684 for the same jointly trained architecture. Performance remained high on a composition-only task. Across four WSI classification and five survival benchmarks, ResTopoMIL improved mean primary performance on eight tasks; four improvements remained significant after Holm-adjusted patient-cluster tests. Coordinate-free residual controls recovered some of the gains, while neighborhood destruction reduced performance close to the composition anchor. Neighborhood-preserving transformations and anatomy-stratified perturbations further characterized this dependence. These results support fixing a composition reference as a practical intervention for learning spatial corrections, while retaining additional appearance capacity as a contributing explanation.
[CV-104] SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views
链接: https://arxiv.org/abs/2611.04317
作者: Jovan Kamchev,Zoran Ivanovski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages main paper and 6 pages supplementary material
Abstract:Steel scrap composition estimation is an important part of yield planning and fair supplier compensation. Measuring the composition of a multi-ton pile is an inherently laborious process, so the assessment is often left to trained human operators. Recent works tackle composition-level tasks, but require structured acquisition and multi-stage preprocessing. We study a simpler approach to steel scrap load assessment by aggregating features from sparse, handheld views of the pile. On 600 out-of-fold piles, frozen DINOv2 tokens aggregated uniformly across views reach 13.4% total variation (TV) distance against operator estimates. Most remaining error lies in allocation among four thickness-defined heavy melting scrap (HMS) classes, yet image-dependent allocation reduces within-HMS TV from 25.1% under a training-set split prior to 11.2%. Due to the lack of public datasets and benchmarks for predicting composition, external evaluation relies on the public DOES dataset intended for classification. A classifier using the same frozen features reaches 93.97% accuracy, exceeding the published results we compare against. The composition model also aligns with related DOES categories without adaptation.
[CV-105] Weather Adaptive Autonomous Driving
链接: https://arxiv.org/abs/2611.04305
作者: Nicholas Taylor,Alexander Picon,Sudip Dhakal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera object detectors lose recall in rain, fog, snow and sand, and the confidence threshold at which a detector is deployed decides how many objects it misses and how many false alarms it raises. We study how a frozen detector should make this decision in adverse weather. We first reproduce SeqCRC, a conformal object detector whose first step calibrates the confidence threshold by conformal risk control (CRC), and use that step, calibrated separately in each weather condition, as our baseline. With YOLOv8x it keeps the held-out miss fraction within a 20% budget in every DAWN weather bin, where a single global threshold fails in fog, but the false alarms it needs are an output rather than a choice: from 1.6 to 117 per image across DAWN and BDD100K conditions. We then propose a calibrated trade-off dial. One setting, \lambda , the number of false alarms accepted to avoid one missed object, selects the expected-cost-optimal decision rule: either one score threshold, or a per-detection rule that keeps a box when its calibrated probability of being a real object, estimated by gradient-boosted trees from the detector’s own per-box outputs, is at least 1/(1+\lambda) . F-score preferences map onto the same dial, and CRC applied to \lambda itself is planned to restore a per-condition miss guarantee. The dial is fitted once on BDD100K train and graded only on BDD100K val and DAWN.
[CV-106] FlowTranslator: Training-Free In-Image Machine Translation with Flow-Based Editing
链接: https://arxiv.org/abs/2611.04303
作者: Zhixian Li,Xingsong Ye,Yongkun Du,Yunyan Xiao,Yanglin Huang,Pinxuan Chen,Jiyu Zhuang,Cong Xu,Zhineng Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context. Existing task-specific models achieve convincing performance but require massive paired training images before and after translation. As advanced text-to-image (T2I) models have reached a practically usable level of multilingual text generation, we argue that this capability can be transferred to IIMT and present FlowTranslator. It is a training-free IIMT framework that first employs an OCR model and an LLM to generate the translation prompt, which is injected into the T2I-based diffusion model. To optimize the accurate and natural generation of translated text, we introduce two trajectory controls: Region Coupling Release (RCR) partially decorrelates target-branch noise inside the text region early in the trajectory, while Coupling Expansion (CE) amplifies the emerging edit displacement mid-trajectory. Under the EasyOCR read-back protocol, FlowTranslator achieves average PRIM BLEU and COMET scores of 20.6 and 57.2, respectively, substantially outperforming the strong training-based baseline UniTranslator. On the more challenging and realistic whole-image ScenePair photographs translated into Chinese, it reaches 40.1% exact-match accuracy and surpasses existing editing systems, some of which only address the text-rendering stage rather than the complete IIMT task. These results establish flow-based editing as a strong, fully training-free baseline for IIMT.
[CV-107] CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection
链接: https://arxiv.org/abs/2611.04300
作者: Richard Beuck,John Nguyen,Sudip Dhakal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measurements far beyond both. We present CLOCs-R, an extension of the CLOCs candidate-level fusion framework that adds radar evidence to camera-LiDAR rescoring. Radar returns are accumulated over several sweeps, compensated for ego-motion, and associated with each pre-NMS 3D candidate through a range-dependent gate. Compact features summarizing support, Doppler speed and spread, range residual, and offset are then appended to every camera-LiDAR pair before a lightweight fusion network rescores the candidates. We first verify that our fusion implementation reproduces CLOCs on KITTI. We then evaluate on the daytime clear-weather subset of the aiMotive dataset, reporting distance-binned metrics out to 200 m with frozen SECOND and Cascade R-CNN detectors. Camera-LiDAR CLOCs improves on SECOND up to 50 m but degrades beyond it. A residual scoring variant, introduced post hoc, corrects SECOND’s score instead of replacing it and recovers most of this loss. Radar consistently improves BEV AP at 30-50 m (0.857 to 0.870), but beyond 50 m its gain is small (+0.003 center-distance AP), within seed variation, and CLOCs-R with residual scoring is statistically tied with LiDAR-only SECOND. Our analysis attributes the limited long-range benefit to weak separation of true and false detections by radar features, scarce far-range training positives, poor camera evidence at distance, and missing LiDAR proposals that rescoring cannot recover. We report all as-designed and post hoc results side by side.
[CV-108] GroundSeek-CXR: Retrospective Evaluation-Guided Active Observation Framework for Multi-Label Chest Radiography Diagnosis
链接: https://arxiv.org/abs/2611.04286
作者: Yihang Fu,Xinyu Liu,Qing Xu,Xiaofeng Yang,Zhen Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Chest radiography (CXR) diagnosis is critical for thoracic disease assessment, yet challenged by the need to identify subtle lesions and co-occurring abnormalities across regions. While recent vision-language models can actively observe local regions, existing preference-based methods supervise each observation by its immediate effect on the current diagnosis, failing to capture how each observation and stopping decision contributes to the final diagnosis. To address this issue, we propose GroundSeek-CXR, an active CXR diagnosis framework that learns observation and stopping decisions by retrospectively evaluating them with the final diagnosis. Specifically, we first devise a Retrospective Diagnostic Evaluation (RDE) that scores observation and stop candidates from offline Monte Carlo tree search through independent continuations. Moreover, we design a Retrospective Gap-weighted Preference Optimization (RGPO) that learns from retrospective-score preferences weighted by their score gaps, prioritizing actions that improve diagnostic accuracy. Extensive experiments demonstrate that our GroundSeek-CXR framework outperforms state-of-the-art methods, and improves abnormal-image sample-F1 by 4.48% over immediate-score preference learning.
[CV-109] Weight-Space Programming for Improving Image-Restoration Diffusion Models: Technical Conjectures and a Recursive Improvement Loop
链接: https://arxiv.org/abs/2611.04268
作者: Zhuoran Zheng,Doubao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This manuscript is merely conjectural, intended to open a new subtopic within the field of image restoration
Abstract:Can an LLM directly edit the weights of a diffusion-based image-restoration model to \emphimprove restoration quality—without gradients, paired data, or fine-tuning? Recent experiments show frontier LLMs can fix structural bugs by modifying as little as (0.016%) of parameters, but cannot instill new capabilities. We ask whether weight-space programming (WSP) can move from \emphdebugging to \emphimprovement in the precise-regression setting of image restoration (deblurring, super-resolution, denoising, inpainting). Restoration diffusion UNets present a middle ground: low-level signal-processing layers are concentrated enough for targeted weight-direction edits, while high-level semantic layers remain distributed and resistant to sparse modification. We propose three technical conjectures: (C1) an LLM can localize the layer behind a quality gap (over-smoothing, color shift, hallucinated texture) within a 200 forward-pass budget; (C2) editing a small set of identified weight directions (e.g., scaling high-frequency filter norms, correcting output-bias channels) can measurably improve PSNR and perceptual quality; (C3) a recursive loop—diagnose (\rightarrow) edit (\rightarrow) evaluate (\rightarrow) re-diagnose—can compound gains across rounds, forming a minimal recursive self-improvement (RSI) loop for vision. We analyze the required AI capability (arguing targeted edits need not superintelligence, while general capability generation does) and detail the challenges of precise pixel-level regression. We provide a PyTorch-style deployment pipeline and outline a benchmark. This manuscript is conjectural, intended to open a new subtopic for image restoration.
[CV-110] Sketch-Omni: Instance-Level Sketch-Based Visual Prompting for Perception MLLM s
链接: https://arxiv.org/abs/2611.04262
作者: Dongyu Wang,Dar-Yen Chen,Zhiming Hong,Arkaprabha Basu,Yi-Zhe Song
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Promptable perception, from GroundingDINO to Rex-Omni, puts one model behind one prompt interface and returns coordinates for any localisation task. Every model in that line was built for photographs; none has been given a sketch. We present Sketch-Omni, to our knowledge the first perception MLLM built to work on sketches and from them. On a sketch, the sketch is the image, and text prompts detect, point, resolve object and spatial referring expressions, and ground affordances in it. From a sketch, the sketch is the prompt, and a drawing of one object localises that object in a photograph, even among several of the same category, a query that neither a category name nor a photo exemplar can pose. Two obstacles stand in the way. A photo-trained model’s attention is tuned to colour and texture, which a sketch does not have, and no dataset pairs a drawing with the one instance it depicts among others of its kind. We build the training data instead of collecting it. An edge detector turns every labelled photograph into a sketch-like drawing that keeps the photograph’s labels, and two constructions produce sketch-to-photo instance pairs from existing data. On top of this, photo-guided relation alignment passes each photograph and its edge sketch through the same model and trains the sketch pass to reproduce how the photograph pass relates phrases to patches and patches to each other. Not one training label is drawn by hand. Sketch-Omni reaches 91.4 F1 on sketch-prompted localisation against 81.8 for the strongest general-purpose MLLM, with a lead that doubles when the photograph holds several instances of the queried category, and it is best in 39 of 40 evaluation cells across six tasks. Code, model, and a hand-annotated benchmark of 2754 sketch-to-photo links will be released.
[CV-111] ProGeo-RC: Prototype-guided Semantic Reliability and Geometric Error Calibration for Semi-supervised Cardiac Image Segmentation
链接: https://arxiv.org/abs/2611.04257
作者: Yunfei Zhu,Wenfeng Zhang,Pan Zeng,Xin Huang,Tao Jia,Weiting Shao,Wei Hu,Jianming Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE BIBM 2026. 7 pages, 3 figures
Abstract:Semi-supervised medical image segmentation aims to train reliable segmentation models using limited labeled data and abundant unlabeled data. However, existing methods typically rely on prediction confidence or model consistency to estimate pseudo-label reliability, but these criteria are insufficient to characterize both semantic reliability and geometric plausibility. To address this issue, we propose ProGeo-RC, a prototype-guided semantic reliability and geometric error calibration framework for semi-supervised medical image segmentation. ProGeo-RC consists of two key modules: Prototype-guided Semantic Reliability Estimation (PSRE) and Structured Semantic-Geometric Error Calibration (S-SGEC). PSRE uses probabilistic class prototypes to estimate the semantic reliability of unlabeled predictions and calibrate pixel-wise teacher pseudo labels, while S-SGEC introduces signed-distance-field-based geometric constraints to refine ambiguous boundaries and suppress spurious foreground responses outside target structures. Experiments on the ACDC and MVAA-CT datasets show that ProGeo-RC achieves better performance compared with representative semi-supervised segmentation methods. Under the 10% labeled setting on ACDC, ProGeo-RC improves DSC and Jaccard by 0.37% and 0.49% over the corresponding second-best results, while reducing 95HD and ASD by 0.61 and 0.21, respectively. These results indicate that joint semantic reliability estimation and geometric error calibration can provide more reliable pseudo supervision for semi-supervised medical image segmentation.
[CV-112] P-SER: Physical Consistency-Constrained Scattering Expandable Representation for Class-Incremental HRRP Target Recognition
链接: https://arxiv.org/abs/2611.04254
作者: Fangyi Wang,Peng Zhao,Haipeng Wang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:High-resolution range profiles (HRRPs) provide compact radar target representations but exhibit strong aspect sensitivity, complicating class-incremental recognition when historical data are unavailable. Changes in viewing aspect shift scattering-peak locations and amplitudes, while reflector groups may produce responses resembling ships. Existing dynamic expansion methods can also incur substantial parameter and memory overhead. To address these challenges, we propose P-SER, a Physical Consistency-Constrained Scattering Expandable Representation framework for exemplar-free class-incremental HRRP recognition. P-SER reconstructs dominant scattering points from complex-valued sum- and azimuth-difference-channel HRRPs and observation geometry. The reconstructed spatial structure, target extent, and energy distribution are used to derive class-level scattering prototypes and sample-level saliency priors, providing physically grounded constraints for feature learning and old-class knowledge retention. A primitive scatterer dictionary further characterizes local strong-scattering mechanisms and calibrates uncertain ship–reflector predictions at inference. Meanwhile, lightweight residual adapters are incrementally introduced into a shared HRRP backbone to learn new classes with limited model growth. Experiments on simulated and measured HRRP datasets show that P-SER delivers competitive recognition performance without retaining old-class exemplars, while maintaining physical consistency, interference robustness, knowledge retention, and model scalability.
[CV-113] CounterSketch: Physics-Grounded Counterfactual Reasoning for Sketch-to-Video Generation
链接: https://arxiv.org/abs/2611.04244
作者: Hien Trinh,Tan-Hiep To,Dinh-Khoi Vo,Tam V. Nguyen,Minh-Triet Tran,Trung-Nghia Le
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Counterfactual sketch animation explores how a static sketch evolves under a hypothetical intervention. We present CounterSketch, a physics-grounded framework that transforms natural-language ``What if…?‘’ queries into physically plausible sketch animations. The framework integrates VLM-guided scene grounding, causal event-chain reasoning, constraint-based trajectory generation, and verification. It models physical transitions to update the scene graph, computes continuous object trajectories, and validates them against physical, geometric, and semantic constraints. The verified trajectories are then projected into object-level motion bounding boxes to guide downstream sketch-video synthesis. Experiments on CounterSketchBench (80 questions) demonstrate that physics-grounded trajectories provide stronger motion guidance than direct VLM predictions.
[CV-114] Improving Diffusion-Based Augmentation for Data-Scarce Image Classification via Mask-Guided Reference Attention and Repulsive Noise Optimization WACV2027
链接: https://arxiv.org/abs/2611.04242
作者: Hyun-Jic Oh,GaYeon Koh,Won-Ki Jeong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV2027 accepted
Abstract:Diffusion-based data augmentation has emerged as a powerful strategy for addressing data-scarce image classification. Recent pipelines typically adapt a pretrained text-to-image diffusion model to a limited number of class examples and subsequently sample synthetic images using diverse textual prompts. However, in extreme data-scarce regimes such as long-tailed single-sample settings, this approach suffers from a dual failure mode: the generated samples often lack discriminative foreground details crucial for classifier training, and the adapted class prior can bias the early denoising process, causing output images to exhibit similar patterns despite varying text prompts. To overcome these limitations, we propose a sampling-side framework of two complementary components that simultaneously enhance fine details and diversity. First, Mask-Guided Reference (MGR) Attention updates only foreground value tokens with reference features via zero-shot segmentation, limiting reference influence to the target object and mitigating excessive global feature injection. Second, Repulsive Noise Optimization (RNO) encourages initial noise trajectories to diverge from dominant generative modes of the adapted model, enabling target prompts to express broader contextual variation. Extensive evaluations show consistent improvements, with the largest gains in long-tailed single-sample settings of up to 2.50 and 2.94 points on CUB-LT IF100, and further robustness across few-shot and background-shift evaluations.
[CV-115] CINet: Tissue-Conditioned Adaptive Cross-Branch Interaction Network for Nuclei Instance Segmentation
链接: https://arxiv.org/abs/2611.04235
作者: Zhenghang Qian,Yang Liu,Yanzhi Yu,Peiwu Qin,Yibing Shen,Joongwon Chae,Lihui Luo,Zhongde Zhang,Jianwen Guo,Lianshun Jin
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IEEE BIBM 2026 as a short paper
Abstract:Separating touching nuclei requires local boundary detail, while cell type classification requires broader morphological context. These requirements also vary with the surrounding tissue environment. We propose the Tissue-Conditioned Adaptive Cross-Branch Interaction Network (TCINet), a single-stage model with Task-Specific Feature Adapters (TSFA) and Tissue-Conditioned Task-Aware Cross-Branch Interaction (TC-TACBI). TSFA produces branch-specific features at skip connections, and TC-TACBI controls three asymmetric paths among nucleus-presence (NP), horizontal-vertical (HV), and nucleus-type (NT) branches using predicted tissue probabilities. On PanNuke under standard three-fold cross-validation, TCINet achieves multiclass and binary panoptic quality scores of 0.519 and 0.692, respectively. Zero-shot tests on MoNuSeg yield a detection F1 score of 0.876. On PanNuke fold 0, adding TC-TACBI increases mPQ from 0.505 to 0.525. Code is available at this https URL.
[CV-116] LINDA: Low Resource Foot Ulcer Segmentation using Local Similarity and Iterative Neural Dynamics
链接: https://arxiv.org/abs/2611.04233
作者: Trishaani Acharjee,Sudip Chakrabarty,Rajdeep Chatterjee,Tathagata Bandyopadhyay
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Foot ulcers are a leading cause of lower limb amputation, and the highest burden often falls on low and middle income regions where specialists and computing hardware are both scarce. Wound monitoring tools are most useful there when they are accurate yet small enough to run on ordinary devices, but accuracy usually comes from heavy networks. We present LINDA, a compact model that grounds segmentation in an explicit structural prior instead of raw intensity. A Local Self-Similarity (LSS) map, computed directly from the image and stable under local lighting changes, is fused into a ResNet-34 encoder, and a small Liquid Time-Constant module refines the boundary over four iterative steps. With every model trained under one protocol on the FUSeg 2021 dataset, LINDA obtains the best Dice (86.68%), IoU (79.67%), and boundary accuracy (HD95 6.37 pixels) in our comparison while using only 25.70M parameters, fewer than the U-Net and transformer baselines. As the LSS map is a fixed function of the input, it exposes the structural cues the model follows in a readable form. Together this makes LINDA an accurate, inspectable, and lightweight option for wound assessment in resource-constrained care.
[CV-117] Mamba3R: Fixed-Capacity Selective State Memory for Long-Sequence 3D Reconstruction
链接: https://arxiv.org/abs/2611.04221
作者: Zhenxiang Xiong,Tianchen Deng,Nailin Wang,Lei Zhao,Guole Shen,Weidong Che
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 4 figures, 8 tables. The supplementary material referenced in the text is not included in this version
Abstract:Streaming 3D reconstruction must maintain geometric quality over long inputs within a fixed GPU budget. A pretrained recurrent model provides bounded state, but freezing its backbone also fixes its learned state-observation interactions. We introduce Mamba3R, a jointly designed state-memory adapter that provides a trainable adaptation path without updating the backbone or task heads. A selective state-space mixer jointly transforms native state features and a fixed-capacity auxiliary memory, explicit read and write paths carry information across frames, and a decomposed feedback path regulates what the added memory feeds back. Only 1.28 million adapter parameters are trained. Under a shared periodic-reset protocol, it lowers trajectory error and improves accumulated surface and depth accuracy over recurrent baselines on public indoor and outdoor datasets and robot-recorded traversals, gaining up to 40 percent on one metric. Matched comparisons support both the choice of the mixing operator and the utility of carrying the auxiliary memory across frames. Allocated memory remains unchanged and throughput moves by at most 1.4 percent from one to two thousand frames. Replaying its recorded traversals offline, the platform’s RTX 4060 averages 11.7 frames per second within 5.1 GiB with the same checkpoint, a mean throughput covering the 10 Hz sensor rate.
[CV-118] Reliability-Role Collaborative Learning for Semi-Supervised Cardiac Image Segmentation
链接: https://arxiv.org/abs/2611.04219
作者: Zhongrui Xie,Wenfeng Zhang,Wei Hu,Ruitao Xie,Wenjie Wang,Zhouyun Xie,Yi Zhang,Tao Jia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE BIBM 2026. 7 pages, 4 figures
Abstract:Semi-supervised cardiac image segmentation aims to exploit abundant unlabeled images together with limited annotations, but the reliability of pseudo-label supervision is highly uneven across spatial regions. In a dual-network setting, interior regions are usually more stable, whereas boundary regions, low-confidence areas, and disagreements between networks can introduce noisy supervision. However, existing methods typically impose uniform supervision on all unlabeled pixels, overlooking reliability differences and limiting discriminative learning in challenging areas. To address this issue, we propose R2CL (Reliability-Role Collaborative Learning), a framework for semi-supervised cardiac image segmentation. R2CL models confidence relationships between two networks to assign roles to unlabeled regions: mutually uncertain regions are used for entropy regularization, confidence-discrepant regions for directional knowledge transfer, and mutually confident regions as reliable supervision anchors. Reliability-Role Mixed Cross Learning (RR-MCL) implements these roles through region-mixed training, directional cross-network supervision, and entropy regularization over mutually uncertain regions. Reliability-Transition Prototype Contrastive Learning (RT-PCL) constructs query features from foreground-adjacent reliability-transition regions and learns network-specific class prototypes from mutually confident regions, improving feature discrimination around ambiguous boundaries. High-confidence Context Refinement (HCR) exploits reliable interior pixels and trusted dense blocks within mutually confident regions to complement stable contextual supervision. R2CL achieves stable improvements in region-overlap and boundary-related metrics across multiple datasets, with more evident advantages on the foreground-sparse MVAA-CT dataset. Comments: Accepted at IEEE BIBM 2026. 7 pages, 4 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) ACMclasses: I.4.6; I.2.6 Cite as: arXiv:2611.04219 [cs.CV] (or arXiv:2611.04219v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.04219 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Wei Hu [view email] [v1] Tue, 3 Nov 2026 03:01:40 UTC (1,768 KB) Full-text links: Access Paper: View a PDF of the paper titled Reliability-Role Collaborative Learning for Semi-Supervised Cardiac Image Segmentation, by Zhongrui Xie and 7 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.CV prev | next new | recent | 2026-11 Change to browse by: cs 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)
[CV-119] LiSA: Linear Stochastic Attention for Long-Range Visual Modeling
链接: https://arxiv.org/abs/2611.04216
作者: Sahar Dastani,Fereshteh Shakeri,Samuel Barbeau,Kunal Mahatha,Ismail Ben Ayed,Herve Lombaert,Christian Desrosiers
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Self-attention has become the standard for modern sequence modeling, enabling Transformers to model long-range dependencies across a wide range of modalities. Despite these advances, its quadratic cost with respect to sequence length remains a significant computational bottleneck. Recent work addresses this limitation by introducing linear variants of self-attention, namely with kernel approximations of the softmax operator. However, their first-order aggregation restricts expressivity and struggles to capture higher-order structure. We propose Linear Stochastic Attention (LiSA), a linear attention plug-in module that enables higher-order token interactions via a stochastic diffusion process based on truncated random walks. LiSA incorporates adaptive locality, favoring semantically similar neighbors to preserve fine-grained structures, while maintaining stable and efficient training. We conduct extensive experiments on classification, semantic segmentation, and object detection tasks and show improved results over state-of-the-art methods.
[CV-120] Impact of Input Resolution in Mammographic Mass Detection: A Controlled Multi-Architecture Study
链接: https://arxiv.org/abs/2611.04200
作者: Kaan Buyukdemirci,Emine Ulku Saritas
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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:Deep-learning-based mass detectors usually downsample mammograms, trading fine image detail for lower computational cost. However, published studies cannot reveal how much accuracy this trade-off sacrifices, since they also differ in architecture, preprocessing, and evaluation protocol. In this work, we isolated the effect of input resolution in a controlled study on the VinDr-Mammo dataset, training 18 detectors at six input resolutions ranging from 512 x 416 to 1600 x 1280 pixels, for 108 model-resolution combinations. Whole mammograms were resized without cropping or tiling. For each model, preprocessing, model initialization, augmentation, and evaluation procedures were kept fixed across resolutions. Detection accuracy was evaluated by mAP50 on the 219 mass-positive images of the official test split, and 57 combinations with test mAP50 of at least 0.60 were analyzed. From the lowest to the highest resolution, median mAP50 rose from 0.626 to 0.660 and the best mAP50 from 0.670 to 0.709. The best result, achieved by DINO with a ConvNeXt-S backbone, is the highest mAP50 reported to date on mass-positive VinDr-Mammo images. Gains were concentrated in small masses: for the smallest third of lesions, median mAP50 rose by 0.074 (from 0.367 to 0.441), compared with 0.007 and 0.032 for medium and large masses. Median training time increased from 0.45 to 1.40 h, and median inference time from 23.6 to 113.7 ms per image. Therefore, higher input resolution mainly benefits the small masses that are hardest to detect, at a computational cost that remains practical.
[CV-121] SettleSplat: Monocular Gaussian Mapping under In-View Scene Changes WACV2027
链接: https://arxiv.org/abs/2611.04195
作者: Anh Thuan Tran,Xiyun Song,Yubin Zhou,Heather Yu,Jana Kosecka,Liang Peng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:Online 3D Gaussian mapping enables fast and high-fidelity novel view synthesis, with broad applications in augmented reality, robotics, and autonomous systems. However, many current methods assume static scenes or filter out dynamics as outliers. In this paper, we study in-view scene changes where objects are manipulated in front of the camera within a single online sequence, unlike prior works that detect changes outside the camera view or between separate captures. This setting entangles transient and persistent dynamics, as objects can be distractors while moving and persistent once they settle. While recent studies start to address distractors by removing them for clean static background, in-view scene changes remain underexplored. To resolve this, we present SettleSplat, a monocular mapping system that maintains up-to-date 3D Gaussian maps under such changes. Specifically, we lift coarse per-pixel uncertainty into Gaussian dynamicity field, then prune transient ones. For persistent changes, we derive geometric contradictions weighted by parameter-free rendered depth uncertainty to separate true changes from under-optimized regions. On Bonn and Co-Fusion, SettleSplat achieves consistent 3D Gaussian maps, outperforming current baselines on both full images and change regions while maintaining comparable speed.
[CV-122] Statistical Machine Learning for Medicinal Plant Leaves Classification
链接: https://arxiv.org/abs/2611.04193
作者: Jayani P. Gamage,Thiyanga S. Talagala
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Medicinal plants are usually identified using practitioners’ experience, but reproducible, image based methods can complement this knowledge. We present MEDIPI (MEDIcinal Plant Identification), a statistical machine learning approach that classifies medicinal plant leaves from photographs. From each leaf image we extract 52 interpretable features covering shape, texture, color, and a new scagnostics category. We then classify leaves using a three level hierarchy: shape, then edge type, then species. We also release MedLEA, an open R package with 1,099 leaf images of 31 Sri Lankan medicinal plant species. On this dataset, a hierarchical Random Forest achieved the best overall accuracy (100%) in our comparison of hierarchical and non-hierarchical approaches and eight algorithms: Random Forest, XGBoost, Gradient Boosting, linear discriminant analysis, k-nearest neighbor, support vector machine, probabilistic neural network, and artificial neural network. Adding texture, color, and scagnostic features to shape features raised shape level accuracy from 90% to 97%. Linear discriminant analysis plots show how the trained model separates leaf shapes. Accuracy dropped sharply (23-50%) when training and test data came from different datasets, so generalization across datasets remains a challenge.
[CV-123] ReconDreamer#: Self-Evolving Closed-Loop Synergy of Reconstruction and Generation for Dynamic Driving Scenes
链接: https://arxiv.org/abs/2611.04173
作者: Songling Liu,Guosheng Zhao,Xiaofeng Wang,Chaojun Ni,Guan Huang,Jiwen Lu,Xingang Wang,Zheng Zhu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dynamic driving scene reconstruction has made remarkable strides in recent years by leveraging generative priors, particularly in the quality of novel trajectory rendering. Nevertheless, a significant gap remains between the generated data and real-world sensor observations, severely hindering further improvements in reconstruction fidelity. In this paper, we present ReconDreamer#, a self-evolving framework that synergizes reconstruction and generation for dynamic driving scenes, which substantially bridges the gap between synthesized novel trajectory data and recorded real-world observations. Within this self-evolving closed-loop framework, the generative and reconstruction models mutually reinforce each other through iterative refinement, ultimately yielding a significant enhancement in the 3D representation capability of the reconstruction model. Additionally, we introduce a geometry-prior-based adaptive ground model to further enhance the rendering quality of the reconstruction model. To validate the effectiveness of the proposed method, we conduct comprehensive evaluations on Waymo, nuScenes, PandaSet, and EUVS benchmarks. Specifically on Waymo, at a 6-m lateral shift, ReconDreamer# improves NTA-IoU and NTL-IoU by 8.8% and 3.0%, respectively, and reduces FID by 15.1% relative to ReconDreamer++.
[CV-124] Say the Same Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAs NEURIPS2026
链接: https://arxiv.org/abs/2611.04166
作者: Zihao Feng,Qingzhao Zhang,Chunyu Xia,Bo Yu,Z. Morley Mao,Ramesh Govindan
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Accepted to NeurIPS 2026. 23 pages, 3 figures, 8 tables
Abstract:Reasoning vision-language-action (VLA) models pair physical control with natural-language rationales that supervisors can read to monitor embodied safety. Prior attacks on VLA reasoning corrupt the rationale itself and leave visible evidence in the text. We show that visual perturbations can instead alter the physical action while keeping the generated rationale word-for-word identical. At the shared text/action interface of Alpamayo for driving and InstructVLA for manipulation, our text-neutral action-subspace (TNAS) diagnostic shows that the locally most action-sensitive direction is nearly orthogonal to the active text-loss gradients, with 1.87% and 5.28% norm overlap. At equal hidden-state perturbation norms across both models, interventions along this text-neutral residual shift actions 4.64x to 47.8x more than text-gradient perturbations, whereas text-gradient perturbations induce 21.2x to 277x more text drift. Steering visual perturbations along this direction reaches the same decoupling from the image. Across 154 obstacle-annotated driving scenes in Alpamayo, TNAS-guided attacks induce a new collision under a word-for-word identical rationale in 52.6% of scenes under full-image perturbations and 35.1% under a localized forward-camera patch, compared with 4.55% for unguided action attacks. Relative to a text-preserving baseline, TNAS guidance raises exact rationale preservation from 83.0% to 89.5% and cuts teacher-forced text drift by 68.4% at a comparable collision rate. In InstructVLA, TNAS guidance cuts text drift by 40.5% and preserves the exact response on 85.0% of examples alongside a 1.73-unit action-chunk shift. An unchanged rationale therefore cannot certify a safe action, so monitors for reasoning VLAs must verify the predicted action channel directly.
[CV-125] What Remains in Sight? Autoregressive Video Decoding as Representation-Guided Context Rewriting
链接: https://arxiv.org/abs/2611.04164
作者: Lixuan He,Jihyeon Je,Yang You,Leonidas Guibas
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Long-horizon video generation is increasingly moving toward autoregressive rollout, where each newly generated segment becomes part of the evidence used for future prediction. Recent causal decoders, long-context training, and KV-cache mechanisms have extended feasible duration, but most inference pipelines still decide the visible past implicitly through recent windows, compressed caches, or external memory. This raises a direct question: \textbfcan the choice of which past frames or segments to show to the decoder be treated as an explicit test-time decision? We answer this question with \textscReCR, a training-free method that formulates autoregressive video decoding as representation-guided context rewriting. At each step, \textscReCR builds a selected visible context from the generated history by using internal decoder representations to favor context units that match the global rollout state, avoid redundancy with the recent continuation, preserve temporal boundary coverage, and widen the range of spatial layouts in view. The selected units are then mapped onto a compact causal axis before decoding the next block. Across long-horizon text-to-video benchmarks, \textscReCR consistently improves multiple autoregressive backbones under matched visible-token budgets. Empirical analyses such as representation-space diagnostics and transition-count prompt switching further show that improving what remains visible is an effective path toward more stable long-video decoding.
[CV-126] Improving PI-RADS Prediction and Prostate Cancer Detection from Multiparametric MRI with Anatomical Guidance
链接: https://arxiv.org/abs/2611.04155
作者: Seungbin Park,Thibault Heintz,Rachit Saluja,Francesca Khani,Timothy McClure,Daniel Margolis,Mert R. Sabuncu,Heejong Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate prediction of Prostate Imaging Reporting and Data System (PI-RADS) scores from multiparametric MRI (mpMRI) remains challenging, more so than clinically significant prostate cancer (csPCa) detection. Given the importance of lesion location in PI-RADS scoring, we evaluated whether incorporating prostate zonal masks improves PI-RADS prediction and csPCa detection across multiple architectures (3D ResNet-18, ResNet-50, and ViT) using a large-scale internal dataset. Anatomical guidance generally improved performance across architectures, with the best results achieved by ResNet-18 with 2-zone guidance for PI-RADS prediction (QWK = 0.511) and ResNet-50 with 5-zone guidance for csPCa detection (AUC = 0.809). Fine-tuned results on external datasets further demonstrated the benefits of anatomical guidance. Furthermore, models with anatomical guidance improved the utility of model-predicted PI-RADS scores for PI-RADS-based csPCa detection (positive defined as PI-RADS=3). Combining model-predicted and expert-assigned PI-RADS scores significantly increased the AUC from 0.711 to 0.756 (p 0.05), suggesting that AI-derived PI-RADS scores may provide complementary information to expert assessment for csPCa detection. Overall, our findings highlight the value of incorporating prostate zonal anatomy into deep learning-based prostate MRI assessment.
[CV-127] Co-Occurrence Priors Override Visual Spatial Evidence: Understanding False Alarms in Vision-Language Models for Construction Struck-By Hazard Detection
链接: https://arxiv.org/abs/2611.04112
作者: Xiaoshan Zhou
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Vision-language models (VLMs) increasingly support construction hazard monitoring, yet their frequent false alarms remain poorly understood. This study investigates whether false struck-by hazard warnings arise from failures in object recognition, spatial relation encoding, or hazard decisions dominated by learned co-occurrence priors. We evaluated Qwen2.5-VL-7B and InternVL3.5-8B using 355 counterfactual worker-excavator scene pairs with controlled spatial proximity and worker-removal variants, combining behavioral analysis, internal representation probing, causal interventions, and decision correction. Both models recognized the entities and encoded their spatial relations (out-of-fold AUROC: 0.82 and 0.91), assigning higher hazard scores to near than far scenes in 79% and 73% of pairs. Nevertheless, 74% and 100% of far scenes were falsely classified as hazardous. Removing the worker reduced Qwen’s hazard rate to 11%, while worker presence exerted 2.4- and 3.2-times greater influence on hazard scores than spatial proximity in the respective models. Attention-head interventions, neuron-level interventions, and activation steering failed to eliminate this co-occurrence bias. However, conditioning hazard decisions on the model’s own spatial-relation representation improved Qwen’s specificity from 0.36 to 0.58 at matched sensitivity, outperforming threshold recalibration. These findings reveal that VLM struck-by false alarms stem primarily from decision-making failures rather than deficiencies in object recognition or spatial encoding, suggesting that future efforts should prioritize conditioning hazard decisions on encoded spatial evidence rather than further improving visual recognition alone.
[CV-128] Generalizable AI-Generated Image Detection with Embedding-Space Explanations WACV2027
链接: https://arxiv.org/abs/2611.04111
作者: Nikos Giakoumoglou,Paschalis Giakoumoglou,Tania Stathaki,Symeon Papadopoulos
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: WACV 2027
Abstract:AI-generated images have reached unprecedented realism, while instruction-driven AI editors re-synthesize images for local changes, leaving no splice boundary to localize. Most detectors are black boxes with unverifiable verdicts and generalize poorly because adapting a backbone on the detection distribution memorizes generator-specific fingerprints that quickly become obsolete. Specialist detectors built on pure-vision representations, particularly DINOv3, generalize well across generators, but explainable methods forgo this advantage: jointly training one multimodal model for classification and language generation subjects its visual representation to the language objective. We therefore require that explanation training cannot alter the verdict. We introduce FOX, which first builds and locks a specialist DINOv3 detector, then uses its representation to guide a lightweight VLM toward forensic explanation. A joint-embedding predictor maps locked visual evidence into a frozen text encoder’s semantic space, producing guidance that the VLM verbalizes on demand. FOX achieves 97.4 mean AUC across 14 unseen generators, 69.8 BERTScore on XPlainVerse, and 56.1 on LOKI.
[CV-129] Is This 3D Asset Usable? Beyond the Beauty Render for Asset Curation
链接: https://arxiv.org/abs/2611.04102
作者: Rushikesh Zawar,Vladimir Kim
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large 3D repositories make it easy to collect assets, but difficult to determine whether they can be reused reliably across scene assembly, editing, rendering, game production, and training-data construction. An asset can look plausible in RGB while hiding disconnected geometry, problematic UV organization, or materials that fail to resolve correctly after import. We introduce Diagnostic Rendered Evidence (DRE): aligned beauty, wireframe, and UV-seam-plus-wireframe views scored by pass-specific frozen ViT classifiers and combined through late fusion. On 1,234 human labeled TexVerse assets, nested grouped evaluation achieves 86.36% balanced accuracy and 88.32% bad-asset recall. On a separate, balanced 764 external reference test, DRE achieved 88.9% balanced reference-label accuracy. Our method requires only an imported asset and renderer-derived evidence, without repository identity, creator, popularity, text, or source metadata at inference. Our results support cross-corpus portability and show that purpose built diagnostic views expose defects that just the beauty views can miss. Webpage: this https URL
[CV-130] CHROMA-MILL: Joint Low-Light Enhancement and White Balance under Colored Illumination WACV2027
链接: https://arxiv.org/abs/2611.04092
作者: Pol Riubrogent-Comas,David Serrano-Lozano,Maria Pilligua,Michael S. Brown,Ramon Baldrich,Javier Vazquez-Corral
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027. 16 pages (main paper + supplementary material), 7 figures, 7 tables. Project page: this https URL
Abstract:Capturing an image in a low-light environment produces a weak, noisy sensor signal. Low-light image enhancement (LLIE) aims to restore it. Auto white balance (AWB) is also required for every image, and the same noisy signal degrades its ability to infer the scene illuminant. LLIE is typically performed first, followed by AWB, but this strategy remains largely unexamined because existing low-light datasets span a narrow range of illuminants and lack illuminant ground truth for evaluating AWB. We address this with CHROMA-MILL, a RAW dataset of 50 scenes captured with a DSLR and smartphone under four calibrated illuminants at eleven intensity levels. Because light is additive, we blend these captures in the linear RAW domain to synthesize physically valid illuminant mixtures with exact chromaticity ground truth. Benchmarking on this data, we find joint correction is best, and introduce CTI-Retinexformer, which supervises an illumination map, a chromaticity prediction, and an illumination-invariant scene embedding, outperforming the best cascade by 0.7 dB PSNR _L and 1.1 \Delta E_00 .
[CV-131] ScienceVision: Can AI Observe and Reason Like a Scientist?
链接: https://arxiv.org/abs/2611.04087
作者: Tianyi Ma,Jing Gu,Haozhe Zhao,Liang Chen,Yunfan Zhang,Haijun Su,Jialong Wu,Le Xue,Arnav Das,Dustin Tran,Yinfei Yang,Andrew Dai,Kuan Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Scientific discovery builds on accurate observations interpreted through domain knowledge and reasoning. For scientific 3D data, this requires understanding structures and spatial relationships, from protein contacts to anatomical organization. Existing multimodal benchmarks largely evaluate text and 2D images, while scientific 3D evaluations vary in representations and protocols across disciplines, limiting cross-disciplinary comparison. To assess these foundational capabilities, we introduce ScienceVision, a cross-domain benchmark for scientific 3D understanding and reasoning through expert-level visual question answering (VQA). We construct 250 questions, with 50 each from Biology, Chemistry, Medical Imaging, Mechanical CAD, and Architecture, through a pipeline combining synthetic question generation with domain-expert review and annotation. Each task pairs a native 3D artifact with a scientific question, a specified response format, and a task-specific reference answer, requiring domain knowledge to be applied to geometric or topological evidence. We evaluate 12 models on the same questions in three settings: visual question answering with native 3D artifacts accessed through an agent harness’s file and code-execution tools; the same workflow augmented with expert-inspired viewer inspection and measurement tools exposed through the Model Context Protocol (MCP); and visual question answering from prescribed rendered 2D images using an agent harness. All settings use the same task-specific deterministic final-answer scoring. The inspection environment supports view changes, target isolation, and slice inspection. In this main comparison, the best mean answer score is 0.53 out of 1. Mean scores in the native-artifact workflow without additional MCP tools exceed those in the rendered-image workflow for every model, by 17.2 percentage points on average.
[CV-132] From Visual Matching to Textual Conditioning: Cross-Domain Few-Shot Segmentation via Reference Inversion
链接: https://arxiv.org/abs/2611.04084
作者: Jiahao Nie,Yun Xing,Wenbin An,Qixin Zhang,Yap-Peng Tan,Alex C. Kot,Shijian Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-Domain Few-Shot Segmentation (CD-FSS) aims to segment objects from unseen domains using only a few annotated references. Recent Segment Anything Model (SAM)-based methods have advanced CD-FSS, yet their reference-target visual matching is highly sensitive to target appearance variations. SAM3 introduces text-conditioned segmentation, enabling natural-language descriptions to guide segmentation across target images, but directly using the category name as the condition still yields unstable performance under severe domain shifts. We propose Reference Inversion for SAM3 (RI-SAM3) that leverages SAM3’s large-scale vision-language pre-training to learn suitable textual tokens for each target category. RI-SAM3 comprises three core designs: (i) Bi-level Reference Inversion that learns complementary tokens before and after the frozen text encoder: the former remain anchored in SAM3’s text space while the latter represent the category more flexibly; (ii) Layer-adaptive Textual Conditioning combines these tokens differently as object queries evolve across decoder layers; and (iii) Presence-aware Confidence Supervision that jointly supervises query confidence and target presence for reliable predictions. With SAM3 frozen, RI-SAM3 optimizes around 12K parameters only per category and it efficiently segments each target in a single forward pass with reusable textual conditioning without per-sample reference-target visual matching. Extensive experiments over multiple widely adopted benchmarks show that RI-SAM3 achieves superior performance consistently in CD-FSS tasks, together with strong robustness, efficiency, and generalization.
[CV-133] Spatial and Temporal Super-Resolution and Denoising of Intracardiac 4D Flow MRI
链接: https://arxiv.org/abs/2611.04053
作者: Yusuf A. Ozturk,Jie Zheng,Fanwei Kong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Intracardiac flow is driven by complex, and time-varying cardiac motion and interaction with the heart valves, producing complex flow features that may provide valuable information for disease diagnosis and treatment planning. 4D Flow MRI enables non-invasive, time-resolved measurement of three-dimensional cardiovascular velocity fields. However, substantial noise and limited spatial and temporal resolution limit its ability to accurately resolve flow features. We propose 4D-MRAR (4D Magnetic Resonance Augmented Reconstruction), a deep learning (DL) method to super-resolve and denoise 4D Flow MRI data. Unlike prior DL methods that train and infer on static vascular regions, our method predicts intracardiac flow within hearts undergoing large deformation. Moreover, it uniquely allows for simultaneous super-resolution in 3D space and time. The method combines a residual convolutional neural network, a recurrent neural network, and an implicit neural field to predict high-resolution velocity and pressure fields while efficiently enforcing the governing equations of fluid flow during training. Trained on a synthetic dataset obtained from computational fluid dynamics (CFD) simulations of left ventricle (LV) flow, our method generalizes to synthetic whole-heart 4D flow MRI data, and demonstrated promising performance on real 4D Flow MRI. The produced super-resolved flow fields exhibit significantly improved accuracy and greater consistency with the governing equations of blood flow compared with prior methods.
[CV-134] Do Pretrained Backbones Transfer to Physical Sensors? A Systematic Study of Multimodal Initialization Across the Sensor Stack WACV2027
链接: https://arxiv.org/abs/2611.04034
作者: Fabio Montello,Ronja Güldenring,Lucas Mogensen Sandby,Christoffer Koo Øhrstrøm,Lazaros Nalpantidis
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027. 22 pages (10 main, including references, and 12 supplementary), 4 figures, 13 tables
Abstract:Transfer learning from pretrained backbones has become standard practice in multi-sensor computer vision—yet two questions remain open: which pretrained weights transfer best to multi-sensor setups, and can in-domain pretraining outperform off-the-shelf solutions? Frameworks like MultiMAE demonstrate the utility of masked autoencoding in a multi-modal context, but rely entirely on dense, model-derived modalities. It is unclear how robustly these methods translate to real-world applications that use physical sensors, where the aggregation of diverse public datasets for large-scale pretraining inherently introduces modality sparsity. To investigate this systematically, we curate a multi-sensor autonomous driving collection of 2.82M images from 16 publicly available datasets, incorporating both physical hardware modalities (RGB, LWIR, NIR, Event) and model-derived modalities (Stereo Depth, Segmentation). We use this collection for in-domain pretraining based on the MultiMAE approach. We adapt MultiMAE’s loss formulation to accommodate sparse samples, and study the effect of an enforced encoder bottleneck for modality alignment and of additional losses specific to dense reconstruction (SSIM and FFT). We then systematically compare against common pretrained backbones (ViT, MAE, DINO and MultiMAE) across downstream sensor fusion tasks. We find that MultiMAE is a surprisingly strong out-of-the-box baseline, transferring effectively even to modalities it has never seen, while our targeted modifications yield incremental but consistent improvements on multi-sensor transfer and overall robustness.
[CV-135] Compression Forcing: Hierarchical History Tokenization Enables Flexible World Models
链接: https://arxiv.org/abs/2611.04028
作者: Siqiao Huang,Partha Kaushik,Michael Chen,Hengkai Pan,Kaiwen Geng,Omar Chehab,Fernando Moreno-Pino,Max Simchowitz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages, 16 figures
Abstract:Effective world modeling requires compressing salient history information that facilitates future prediction. However, most existing approaches inherit tokenization and conditioning mechanisms from video generation, where objectives are centered around perceptual realism rather than predictive utility. This discrepancy limits the world model’s effectiveness, especially in environments dominated by visual nuisances or task-irrelevant background details. To this end, we introduce Compression Forcing (CF), a general framework that aligns history compression with the predictive objective of world modeling. Compression Forcing features Dynamics-Aware Tokenization (DAT), a novel tokenization method that compresses history observations into a sequence ordered by decreasing dynamics saliency. Built on a diffusion backbone, this structure enables Hierarchical History Guidance (HG-H), allowing selective amplification of dynamics-salient information during inference. Extensive experiments across various environments demonstrate improved prediction fidelity, physical realism, and downstream decision-making performance, establishing Compression Forcing as a highly flexible and effective approach for world modeling.
[CV-136] SurgEye: Instruction Tuning-Free Perception-Reasoning Framework for Surgical Scene Understanding
链接: https://arxiv.org/abs/2611.04012
作者: Qing Xu,Xiechen Shen,Xianjian He,Guqiao Liang,Zixiao Chai,Jiale Qin,Yifan Chen,Siyi Li,Tian Yuan,Yiwei Li,Ziheng Meng,Zhen Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Surgical scene understanding is pivotal for computer-assisted intervention, yet demands compositional reasoning across instruments, anatomy, and workflow. Existing surgical vision-language models rely on instruction tuning with large-scale question-answer pairs, entailing laborious data curation and costly retraining. To bridge this gap, we propose SurgEye, a perception-reasoning framework that grounds reasoning in a vision foundation model without instruction tuning. Specifically, we devise Routed Multi-Task Perception (RMP) that routes the vision foundation model through task-specific pathways learned from public perception labels, producing spatial and temporal evidence. We further introduce Agentic Compositional Reasoning (ACR) that employs a frozen large language model to coordinate RMP specialists and temporal memory as callable tools, composing evidence across tasks and time into grounded answers. Moreover, we construct SurgEye-Bench spanning eight surgical procedures, pairing each question with perception labels for verifiable evaluation. Extensive experiments demonstrate that SurgEye outperforms state-of-the-arts in surgical reasoning. The source code will be available.
[CV-137] Do We Still Need Rotation-Invariant Architectures? A Statistical Robustness Benchmark for Point Cloud Classification WACV2027
链接: https://arxiv.org/abs/2611.03952
作者: Abdulla Nasir Chowdhury,Gulpi Qorik Oktagalu Pratamasunu,Kazuhiro Fukui
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to 2nd Round of WACV 2027, 24 pages (8 main + appendix). Website: this https URL
Abstract:A substantial line of work builds rotation invariance directly into point cloud architectures. We benchmark 10 architectures (PointNet, PointNet++, DGCNN, RIConv, RIConv++, RISurConv, PointMLP, PointTransformer, and the equivariant VN-PointNet and VN-DGCNN) across 5 domains under pose, noise, and occlusion, and analyze the results with confirmatory hypothesis tests rather than point estimates. The three rotation-invariant (RI) architectures achieve the best pose robustness but rank among the lowest on noise robustness in 4 of 5 domains. We ask whether this cost is intrinsic to invariance or specific to how invariance is obtained. We retrain all 10 architectures unchanged, with a simple invariance loss on the embedding and three training seeds each, and evaluate all models on the independently designed ModelNet40-C. Invariance training transfers narrowly but reliably. It improves exactly the corruption families its perturbations target wherever the baseline has headroom (mean occlusion +10.5, lidar +13.4 percentage points of accuracy). At matched training budget the best standard backbone and the best RI architecture end on par on unseen corruptions (within 2.5 percentage points in every seed), and invariance-trained RIConv++ is the most seed-stable top model. Once rotation augmentation is widened, standard backbones concede only a flat, angle-independent accuracy offset under full-sphere rotation. We release the benchmark along with a package for analyzing point cloud robustness and reproducing our results.
[CV-138] Camera Radar LiDAR Fusion via Point-Cloud Projection for Multi-Task Learning
链接: https://arxiv.org/abs/2611.03942
作者: Kavin Chandrasekaran,Sorin Grigorescu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: IEEE International Workshop on Signal Processing Systems 2026
Abstract:Autonomous vehicles require robust perception systems to operate safely under diverse environmental conditions. While cameras provide high-resolution visual data, their performance significantly degrades in low-light and adverse weather. High Definition (HD) radar offers superior reliability and angular resolution under such conditions but lacks rich visual feature recognition. LiDAR, though expensive, delivers highly precise distance measurements and dense point clouds. Each sensor exhibits unique trade-offs, necessitating the development of advanced sensor fusion algorithms to harness complementary strengths while minimizing computational complexity and maintaining accuracy. In this study, the proposed fusion architecture performs early fusion by combining camera images with perspective-view point cloud data from HD radar and LiDAR sensors. The proposed model is trained to detect vehicles and segment free drivable space simultaneously. Experiments are performed on the RADIal dataset where we demonstrate that fusing point cloud data with camera images in front-view surpasses the performances of the most recent standalone and fusion models.
[CV-139] Coordinate Frame Recovery Per Class NMS and Null Return Modelling for Sim2Real V2X LiDAR Detection ECCV2026
链接: https://arxiv.org/abs/2611.03940
作者: Aman Upganlawar
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages. 1st place, Track 7 (UrbanTwin Sim2Real LiDAR, V2X-Real), DriveX Grand Challenge, ECCV 2026 Workshop. Code: this https URL
Abstract:We describe our approach to the V2X-Real track of the UrbanTwin Sim2Real LiDAR Challenge, in which a 3D object detector must be trained exclusively on synthetic data and evaluated on unseen real roadside LiDAR from an Ouster OS1-64 unit, while the synthetic point clouds are simultaneously scored for distributional realism. Our final entry scores 0.7457 combined, from a detection mAP of 0.4382 and a realism score of 0.9251. Three findings account for most of our result, and all three were diagnostic rather than architectural. First, a coordinate frame mismatch between our predictions and the evaluation ground truth was suppressing detection AP entirely; recovering it took our score from 0.3520 to 0.5147 in a single submission. Second, a single global non maximum suppression threshold was destroying genuine pedestrian detections, because pedestrians occur in groups and a 0.3 IoU threshold applied to a 0.7 m box suppresses distinct neighbouring people; correcting this per class raised overall mAP from 0.0751 to 0.4295. Third, approximately 31 percent of the real reference point cloud consists of null returns encoded as the literal point (0, 0, 0), a format feature entirely absent from our synthetic clouds, which pinned Earth Mover’s Distance near its floor until we modelled it. Negative results are reported at equal length, and Section 9 gives a full account of data provenance and compliance. That section also accounts for every mechanism contributing to the submitted score.
[CV-140] FlowBench: Counterfactual Imagination Training for Spatial Intelligence
链接: https://arxiv.org/abs/2611.03937
作者: Yifan Liu,Baihan Zhang,Khai Loong Aw,Klemen Kotar,Daniel L.K. Yamins
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Humans imagine scene changes under hypothetical motions, an ability central to spatial reasoning and robotic planning. However, this capability remains underexplored in vision-language models and world models. We formalize this capability as Counterfactual Spatial Imagination (CSI) and introduce FlowBench, a resource for training and evaluating both, with 428K training examples spanning simulated and real-world scenes, household environments and tabletop manipulation, and diverse question types. A scalable, fully automatic pipeline pairs each hypothetical motion with spatial questions and, in simulation, exact outcome frames and optical flow. For VLMs, proprietary models exhibit an imagination gap, performing worse when motion outcomes must be inferred than when observed, and open-source models perform near chance; fine-tuning Qwen3-VL-8B on FlowBench-Sim-Train raises its accuracy by 29.5 percentage points in simulation and 16.9 on real images, a strong sim-to-real transfer. For world models, training on FlowBench-Sim improves text-to-flow prediction for all four model families we test, raising the accuracy of Psi-0.6 from 52.1% to 61.7%. FlowBench thus serves as a training and evaluation resource for both. Code and datasets are released at this https URL.
[CV-141] Geometric Refinement and Distributional Alignment for Sim2Real Roadside LiDAR Detection ECCV2026
链接: https://arxiv.org/abs/2611.03936
作者: Aman Upganlawar
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 8 pages. 1st place, Track 6 (UrbanTwin Sim2Real LiDAR, LUMPI), DriveX Grand Challenge, ECCV 2026 Workshop. Code: this https URL
Abstract:We describe our approach to the LUMPI track of the UrbanTwin Sim2Real LiDAR Challenge, in which a 3D object detector must be trained exclusively on synthetic data and evaluated on unseen real roadside LiDAR, while the synthetic point clouds are simultaneously scored for distributional realism. Our final entry scores 0.4793 combined, from a detection mAP of 0.1333 and a realism score of 0.9126. Our contribution is not a new architecture. Working from a PointPillars baseline, we obtained most of our gains from three sources. First, a diagnosis that small object failure in roadside LiDAR is a localisation problem rather than a detection problem, which led to a parameter free geometric refinement worth 86 percent relative on pedestrian AP. Second, an audit of our own post processing which revealed that a shipped rule was destroying valid predictions, the repair of which produced our single largest detection gain at 335 percent relative on the Van class. Third, treating the realism metrics analytically rather than as a black box, which showed that one of the four components is a closed form function of nine summary statistics. We also report an experimental methodology that we believe is the more transferable result. Because mean AP decomposes into independent per class terms, a single submission can carry several mutually controlled experiments, and a submission assembled entirely from previously measured components has a score that is known before it is sent. We predicted our final score to four decimal places prior to submission. We report negative results at equal length, and give a full account of data provenance and compliance in Section 8, including a full account of every mechanism contributing to the submitted score.
[CV-142] CoReMatch: Recurrent Continuous Coordinate Refinement for Real-Time Robotic Feature Matching
链接: https://arxiv.org/abs/2611.03932
作者: Robert J. Wang,Charles Ling,Boyu Wang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CoRL 2026
Abstract:Feature matching underpins many robotic perception tasks, including visual localization, mapping, SLAM, and autonomous navigation. Existing semi-dense matchers predict confidence on fixed grids, limiting correspondences to predefined locations and requiring costly high-resolution grids for precise localization. We present CoReMatch (Coordinate Refinement Match), a recurrent framework that refines correspondences directly in continuous coordinate space rather than predicting discrete confidence maps. CoReMatch progressively improves correspondence estimates by propagating information across iterations. At the coarse stage, a Deep Aggregated Transformer captures global context and iteratively updates correspondence coordinates, while a lightweight regression head further sharpens their localization. Experiments on indoor and outdoor benchmarks show that CoReMatch strikes a strong balance between accuracy and efficiency, outperforming Efficient LoFTR in accuracy while running more than 10x faster on embedded edge hardware. These results demonstrate that recurrent coordinate refinement offers an efficient and scalable approach to real-time feature matching for robotic perception.
[CV-143] FlowRig: Unified Skeleton Generation for 3D and 4D Meshes via Flow Matching
链接: https://arxiv.org/abs/2611.03775
作者: Fengzhi Guo,Nate Morrill,Shiqi Li,Cheng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: Project page: this https URL
Abstract:Automatic rigging is a fundamental task for both static 3D and dynamic 4D mesh animation, yet existing methods do not provide a unified feedforward solution for these two settings. While static 3D riggers achieve strong quality, they often provide limited control over bone count and become temporally unstable when applied frame by frame to dynamic meshes. Existing 4D methods, in contrast, typically rely on expensive per-sequence optimization. We propose MoTWorld, a template-free, data-efficient framework for unified 3D and 4D skeleton generation. Built on flow matching, MoTWorld generates temporally aligned skeletons for an entire sequence in a single feedforward pass, while naturally handling single-mesh 3D rigging. The model further supports explicit skeleton-count control, uncertainty estimation for candidate selection, and masking-based interpolation, forecasting, and backcasting. To address limited paired 4D rigging data, we adopt a curriculum that initializes from large-scale 3D rigging data and adapts to mid-scale 4D mesh-skeleton data. Experiments show that MoTWorld achieves strong static 3D rigging performance with controllable bone complexity, as well as high-quality and temporally consistent 4D reconstruction and prediction results.
[CV-144] VagueBench: Benchmarking the Robustness of Embodied Agents on Ambient Context Illusions
链接: https://arxiv.org/abs/2611.03765
作者: Yang Qu,Qiang Fu,Zonghao Ying,Jiayi Zhao,Yaoyuan Zhang,Jinyang Guo,Jian Yang,Aishan Liu,Yingxun Wang,Xianglong Liu
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Embodied agents are increasingly deployed in open-world environments, yet current robustness evaluations remain biased toward hazardous instructions and jailbreak vulnerabilities. A more fundamental issue is largely overlooked: agents are rarely tested under semantically sufficient but contextually minimal instructions (i.e., common in real-world human communication), where auxiliary contextual cues are absent. For the first, we identify a novel phenomenon termed Ambient Context Illusion, where agents fail to execute tasks correctly when non-essential contextual details are omitted, despite unchanged task semantics. To systematically study this, we establish VagueBench, a benchmark built upon the AI2-THOR interactive simulation environment for the evaluation of context dependency in embodied agents. We systematically consider key semantic components of instructions (entities, procedures, constraints, and prior knowledge) and derive a unified taxonomy with four categories and nine subtypes to reduce auxiliary information. VagueBench contains 1,303 instruction pairs across diverse indoor environments, constructed via a human-agent co-generation framework that integrates semantic redundancy estimation, taxonomy-guided transformation, and rigorous semantic-invariance verification. Through extensive evaluation on 6 state-of-the-art embodied agents, we observe significant degradation on VagueBench in both task performance and executability (relatively -19.65% in task success rate and relatively +54.10% in empty plan rate) revealing a strong reliance on auxiliary linguistic signals rather than core task semantics. Furthermore, we demonstrate that this phenomenon persists in real-world robotic systems, where it can lead to incorrect decisions and potential safety risks.
[CV-145] Beyond Proposals: Region-Consistent Open-Set Object Detection
链接: https://arxiv.org/abs/2611.03741
作者: Jiehui Tang,Yunlong Yu,Zhao Wang,Jungong Han
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Open-set object detection (OSOD) suffers from a fundamental limitation under dense proposal mechanisms: highly overlapping proposals associated with the same object are often assigned contradictory known/unknown predictions. Existing methods perform open-set reasoning independently for each proposal, while ignoring the shared semantics among correlated proposals, resulting in fragmented detections and unstable open-set decisions. In this paper, we reformulate OSOD as an object-centric semantic inference problem, where correlated proposals jointly support a coherent region-level semantic decision. Based on this perspective, we propose OCSI-Net, a unified framework that combines object-centric semantic consistency with geometry-aware uncertainty stabilization. Specifically, OCSI-Net aggregates correlated proposals into structured region clusters and infers region-level semantics from aggregated semantic evidence, thereby enforcing coherent open-set predictions within the same object region. Furthermore, a geometry-aware uncertainty mechanism is introduced to stabilize optimization near open-set transition boundaries. Extensive experiments on multiple benchmarks demonstrate that OCSI-Net consistently achieves state-of-the-art performance, improving the harmonic mean of known and unknown detection by an average of 6.83% over the second-best method.
[CV-146] Physics-Informed Functional Tucker Method with RKHS Factors for Sparse Spatiotemporal Reconstruction NEURIPS2026
链接: https://arxiv.org/abs/2611.03717
作者: Zhengyang Liu,Jianguo Huang,Junxi Hu,Yucheng Mi,Haojie Cai,Wenxuan Shen,Zhikun Zhang,Renfeng Peng,Guangtao Zhang,Zhiqiang Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026
Abstract:Reconstructing physical dynamics from fixed sparse sensors and missing frames is a fundamental challenge in scientific research. Sparsity leaves large regions of the field unconstrained, making the inverse problem ill-posed: a model may fit the observed sensors while producing inaccurate off-sensor structures or unreliable predictions between support frames. To this end, we propose PhysFTM—a physics-informed functional Tucker model that parameterizes the Tucker mode factors in reproducing kernel Hilbert spaces (RKHSs) and incorporates PDE constraints through finite-difference residuals evaluated at off-sensor collocation points. These residuals provide additional physical information in regions without direct observational supervision. PhysFTM first reconstructs continuous spatial fields at support frames via an alternating minimization scheme, optimizing the Tucker core tensor and the RKHS-based factors separately. To enable missing-frame reconstruction, PhysFTM constructs a continuous trajectory in Tucker-core space, which is decoded by the learned spatial RKHS-FTM representation to recover the full spatiotemporal field. Experiments on Allen–Cahn, Navier–Stokes, three-dimensional Heat equations, and a three-dimensional gridded Copernicus ocean reanalysis demonstrate accurate 2D/3D reconstruction under two missing-frame protocols. Code is available at this https URL.
[CV-147] LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends
链接: https://arxiv.org/abs/2611.03685
作者: Zhaojian Yu
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native \emphLeague of Legends with 36 champion-specific tasks spanning resource management, combat, cooperation, and objectives. Agents interact through screenshots and mouse–keyboard actions; automated initialization and 82 executable subgoal checks distinguish partial progress from completion. Across 324 task evaluations of three models at three reasoning-effort settings, GPT-6 Astra achieves the highest task success rate of 25.0% at low and medium effort, and the highest subgoal success rate of 40.3% at medium effort. However, high effort improves neither metric over low effort for any model. For Astra, longer requests leave fewer interaction rounds, suggesting a cost to additional reasoning in real-time control. Trajectories reveal incomplete subgoal composition and actions executing after the observed scene changes, motivating agents that combine visual reasoning with timely action.
[CV-148] Label-Preserving Fourier Domain Adaptation with Generative-AI and Real Style Donors for 3D Mitral Valve Segmentation
链接: https://arxiv.org/abs/2611.05106
作者: Javeria Javed,Ahmed Salim,Muhammad Mahboob Ur Rahman
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 1 figure, 3 tables, submitted to IEEE for possible publication
Abstract:Accurate segmentation of the mitral valve from three-dimensional transesophageal echocardiography (3D TEE) is severely restricted by the scarcity of publicly available expert- annotated volumetric datasets. To address this critical limitation, we investigated Fourier Domain Adaptation (FDA), utilizing general-purpose generative-AI images (from platforms like Chat- GPT, Google Gemini, and Microsoft MAI) as annotation-free style donors for 3D mitral valve segmentation. We employed a DeepMitral residual U-Net model on the public MVSeg2023 dataset, comparing six conditions over three runs: real-only training, traditional augmentation, FDA with each generative source, and a non-AI FDA control using real-image style donors, plus an optimization-matched real-only control and a \beta sensitivity analysis. Case-level paired testing showed that FDA with these generative sources led to significant reductions in boundary- and surface-related errors across all three donor conditions relative to the primary real-only baseline. However, the impact on the core segmentation metric, Generalized Dice Score (GDS), proved generator-dependent. ChatGPT-FDA showed a significant GDS advantage over the optimization-matched control but not over the primary baseline, and Gemini-FDA showed no significant GDS difference. Notably, only MAI-FDA provided a statistically robust and significant increase in GDS compared to the primary baseline. The \beta sensitivity was not robust across training runs. These findings confirm that FDA remains an important augmentation mechanism. While generative-AI images offer a practical means of acquiring style diversity without new annotations, the non-AI control performed comparably to ChatGPT-FDA, so we do not conclude that this approach is superior to training with real-image donors.
[CV-149] LIPS-PC: Learning Illumination Patterns for Single-Pixel Classification
链接: https://arxiv.org/abs/2611.04780
作者: Tomer Porat,Talya Eden,Eliahu Cohen
类目: Optics (physics.optics); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 9 pages, 8 figures, comments are welcome
Abstract:In single-pixel (ghost) imaging, known illumination patterns probe an object and a detector with no spatial resolution returns one number per pattern. Each measurement costs acquisition time and photons and, for ionizing probes, contributes to delivered dose. Motivated by automated radiographic defect screening, where a class label suffices, we introduce Learning Illumination Patterns for Single-Pixel Classification (LIPS-PC). The patterns are learned end-to-end while the measurement operator remains one fixed linear matrix, identical for every input. We parameterize it as a residual correction to a two-dimensional discrete cosine transform (2D-DCT), D=D_\mathrmbase+\Delta , and jointly train the correction and a compact classifier using cross-entropy, without a reconstruction objective. This yields a task-driven linear embedding through optimization. We evaluate LIPS-PC numerically on MNIST and Fashion-MNIST under a controlled protocol, then on RIAWELC, a real industrial X-ray weld-radiograph dataset. Across five budgets, M\in8,16,32,64,128 , corresponding to sampling ratios from below 1% to 12.5%, the learned operator outperforms the best tested fixed transform by 9-15 percentage points at M=8 on all three datasets. Its eight-measurement accuracy requires roughly 16-32 measurements with the best fixed transform, indicating a 2\times to 4\times saving at comparable accuracy. The advantage generally shrinks as M increases, with a small reversal on RIAWELC at higher budgets. These results show that learning what to measure is especially valuable when measurements are scarce.
[CV-150] Sparse-View 3D Coronary Reconstruction from Vessel Segmentations Using a Diffusion Prior
链接: https://arxiv.org/abs/2611.04604
作者: Tianyuan Wang,Kenan Niu,Stefano Stramigioli
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, 2 tables
Abstract:Reconstructing three-dimensional (3D) coronary anatomy from sparse angiographic views could support percutaneous coronary intervention, but limited angular coverage and imperfect vessel segmentations introduce substantial geometric ambiguity. We propose a vessel-specific reconstruction framework combining a learned 3D diffusion prior, visual-hull initialization, and projection-based data consistency. The unconditional diffusion model is trained on coronary vessel masks without paired 2D–3D data and reused across different view counts without retraining. During inference, a scaled visual hull initializes reconstruction, while projection-based updates constrain diffusion refinement using patient-specific vessel observations. We evaluated the framework on simulated angiograms derived from ImageCAS computed tomography angiography volumes using two, four, and eight views at two contrast levels. Across 50 evaluation coronary-tree cases, the proposed method achieved higher mean Dice and clDice than visual hull, deep image prior (DIP), and hash-grid implicit neural representation (INR) at all view counts under reduced contrast. With eight views, improvements over DIP, the best-performing baseline at lower contrast, were approximately 0.13 in Dice and 0.22 in clDice. At higher contrast, performance remained competitive with hash-grid INR, the best-performing eight-view baseline. The preferred diffusion starting timestep depended on observation quality and view count, with excessive refinement reducing accuracy at higher contrast. Projection guidance improved both metrics across all conditions. Averaging stochastic reconstructions further improved accuracy, while sample disagreement localized errors with average-precision gains of 0.14–0.21 over error prevalence. These findings support diffusion-prior reconstruction from sparse vessel observations, although clinical validation remains necessary.
[CV-151] Hybrid Quantum–Classical Pauli-Correlator Architecture for Image Reconstruction
链接: https://arxiv.org/abs/2611.04442
作者: Sparsho Chakraborty,Siddhartha Patra
类目: Quantum Physics (quant-ph); Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages , 6 figures
Abstract:We introduce the Pauli-Correlator Autoencoder (PCAE), a hybrid quantum–classical architecture for image reconstruction. PCAE splits an image into patches and compresses each patch as a matrix product state (MPS). A small quantum circuit turns each compressed patch into a few measured numbers, and a classical neural network rebuilds the patch from these numbers and the patch position. A learnable energy term, taken from physics, is added to training and tracked as training proceeds. Two versions place the six qubits on a line (PCAE1D) or on a grid (PCAE2D). An averaging step makes the PCAE2D features follow rotations and reflections of the image exactly. Trained on one image at a time, PCAE2D rebuilds images from six public datasets with a peak signal-to-noise ratio of 25.8 to 41.6 decibels. On CIFAR-10 images not used in training, a formula-based stand-in for the PCAE2D features performs within 1 decibel of several simpler classical methods. Five trained models also run on real IBM quantum computers without retraining, and rebuild images at 25.90 to 31.52 decibels. A short argument and a synthetic test show that, with a linear final layer, measuring qubits in pairs is needed for targets built from products of inputs. These results show that PCAE works as a hybrid model that rebuilds varied images and runs on today’s quantum hardware at the six-qubit scale.
[CV-152] COSMIO: A Benchmark for Cross-Survey Modality Imputation NEURIPS2026
链接: https://arxiv.org/abs/2611.04139
作者: Dichang Zhang,Yixuan Shao,Jiaqi Deng,Jiali Cui,Haotian Yin,Yuanpeng Liu,Xiang Gao,Zhiqiang Lao,Heather Yu,Simon Birrer,Dimitris Samaras
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026. 34 pages, 8 figures
Abstract:Large-scale astronomical surveys define a heterogeneous, partially observed multi-survey learning setting: each object is observed by only a subset of surveys, while survey observations differ in resolutions, wavelength coverage, noise characteristics, and footprints. This setting gives rise to a natural machine learning problem: given observations of an object in some surveys, how to infer its appearance in other surveys. We formalize this as the problem of cross-survey modality imputation and introduce COSMIO, a multi-survey benchmark of aligned observations obtained by forward-rendered shared astrophysical scenes across LSST, Euclid, and Roman under survey-specific instrument characteristics. The benchmark enables systematic evaluation of imputation across source-target survey combinations and missing-survey patterns. We evaluate a broad range of machine learning methods for imputation, which reveal a clear domain gap: methods developed for existing imputation settings transfer poorly to the survey setting. We identify rare-event imputation as a central challenge. This regime arises in the early stages of new surveys, when rare but scientifically valuable phenomena may be available only from prior surveys. We use strong gravitational lensing, where light from a background galaxy is gravitationally deflected by a foreground galaxy, as a representative rare-event setting. We evaluate model performance on zero-shot imputation of gravitational lenses and on downstream scientific tasks using the imputed lens observations. On our constructed evaluation set of galaxy-galaxy strong lenses, performance degrades under rare-event distribution shift, and this degradation further propagates to the downstream lens-detection task. Code: this https URL.
[CV-153] An automated photometric pipeline for measuring the corotation radius of spiral galaxies
链接: https://arxiv.org/abs/2611.04123
作者: Kimia Yazdani,Sahel Mesforoush,Wayne B. Hayes
类目: Astrophysics of Galaxies (astro-ph.GA); Computer Vision and Pattern Recognition (cs.CV); Data Analysis, Statistics and Probability (physics.data-an)
备注:
Abstract:The corotation radius R_CR of a spiral galaxy marks the radius where the angular speed of the spiral pattern matches the orbital speed of the disk material. Despite its importance for understanding spiral structure and galaxy evolution, very few galaxies have corotation measurements, often derived using heterogeneous techniques. We present an automated photometric pipeline that identifies corotation radii by tracking the azimuthal phase offset between young and old stellar populations. Building on the color-gradient framework of \citetPuerari1997, we extract azimuthal brightness profiles from deprojected dual-band images, fit periodic models to the profiles, and identify corotation candidates at radii where the phase difference between the two stellar tracers changes sign. Each candidate is assigned an adaptive uncertainty interval and reported with a goodness-of-fit metric. We benchmark the method against a cross-method literature compilation, scoring every technique – including our own – against a reference built from the published measurements alone and evaluated leave-one-out. Over the 90 galaxies for which such a reference exists, the pipeline places a correct corotation radius in 80.0% of them, recovers 63.6% of their known corotation radii, and reaches a harmonic mean of recall and purity of 0.58 , the highest values of any method in the comparison. Unlike the published methods, each of which was applied to a small, curated set of galaxies, our pipeline is run on the entire reference sample without any pre-selection, a sample two to six times larger than existing methods. The pipeline is scalable to large surveys, flexible with respect to spiral mode, and naturally recovers multiple corotation radii in galaxies with complex spiral structure, providing a uniform framework for studying disk resonances across diverse galaxy populations.
[CV-154] DUNEDepth: Bronchoscopic Depth and Pose Estimation via Hybrid Adaptation of a 3D-Vision Foundation Model
链接: https://arxiv.org/abs/2611.04025
作者: Qi Chang,Austin Kao,Ange Lou,William E. Higgins
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, 3 tables. Accepted to MICAD 2026 (International Conference on Medical Imaging and Computer-Aided Diagnosis), Edinburgh, UK, Oct. 22-24, 2026. To appear in Springer Lecture Notes
Abstract:State-of-the-art bronchoscopy guidance systems and computer-aided diagnosis methods have emerged as important tools for early lung cancer detection. Effective use of these tools depends on being able to accurately link a patient’s bronchoscopic airway exam video to their pre-operative 3D chest CT scan. Unfortunately, the derivation of such links, which include bronchoscope depth and camera motion, from the exam video remains an open problem. Building on recently proposed 3D-vision foundation models, we propose DUNEDepth, a hybrid architecture that combines a shared DUNE ViT encoder, a twin MASt3R cross-attention decoder structure, and a prediction head that includes elements of the MASt3R and Reloc3r models. To fine-tune the architecture to our application, we utilize an integrated hybrid synthetic-supervised/self-supervised training strategy that draws on both bronchoscopic video and CT-derived virtual bronchoscopic views. Experiments drawing on bronchoscopic video sequences demonstrate that DUNEDepth more accurately derives depth information and camera motion trajectories than previously proposed self-supervised methods.
[CV-155] Evaluating the Impact of Nonlinear Registration on Longitudinal PET Analysis in a Rodent Stroke Model
链接: https://arxiv.org/abs/2611.04002
作者: Haytham Ziani,El Mostafa Kalmoun,Zohaib Atif,Hyuk-Sang Kwon
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation at the 2nd International Conference on Artificial Intelligence, Security and Networking (CAISN 2026)
Abstract:Longitudinal rodent positron emission tomography (PET) studies of stroke and neuromodulation rely on accurate registration to a common atlas space to detect group-level effects. Prior work applying ultra-high frequency low-intensity magnetic stimulation (UHF-LiMS) to a rat model of chronic capsule infarct used affine normalization to a Sprague Dawley MRI template without evaluating that choice. We compare a two-stage (affine and nonlinear) pipeline against an affine- only one for rat PET-to-atlas alignment on the same dataset. Across 32 longitudinal [18F]-FDG PET scans (8 rats, 4 timepoints), the nonlinear stage gave a higher Dice similarity coefficient on every scan, and the gain was significant at the rat level. Jacobian determinants showed no folding on the sampling grid. Group-level linear mixed-effects maps of change from baseline (AFNI 3dLME), computed identically under both pipelines, differ in the size, number and location of their suprathreshold clusters. In this dataset, the registration pipeline is therefore a variable that can change the interpretation of downstream group-level analyses.
[CV-156] A Novel Conditional Adversarial Diffusion Model for High-Fidelity OCT-to-OCT Image Translation and Precise Anti-VEGF Therapy Response Prediction in Diabetic Macular Edema ALT
链接: https://arxiv.org/abs/2611.03958
作者: Reza Rasti,Armin Biglari,Sobhan Mohammadi Hafshejani,Alireza Dehghani,Michael J. Allingham,Sina Farsiu
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 8 figures, 6 tables, Submitted to IEEE Journal of Biomedical and Health Informatics
Abstract:Diabetic macular edema (DME) is a leading cause of severe vision loss among working-age adults. Although intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections currently serve as the first-line therapy for DME, a notable subset of patients fails to respond despite the substantial cost and clinical burden, suggesting they may benefit more from alternative interventions. Automated, noninvasive pre-treatment response assessment could therefore spare non-responders from the current trial-and-error treatment paradigm. In this study, we propose a novel conditional adversarial diffusion framework that synthesizes post-treatment optical coherence tomography (OCT) images from baseline scans, enabling the derivation of each patient’s initial response to anti-VEGF therapy. The model was developed with subject-level 5-fold cross-validation on a US cohort (n = 127) and externally validated on an independent Iranian cohort (n = 44). Benchmarked against fourteen baseline and state-of-the-art translation models, the proposed model achieved the best treatment-response prediction performance among the methods evaluated for that task on both cohorts, with balanced accuracies of 85.7% and 93.0% on the US cohort and 85.2% and 95.3% on the external Iranian cohort at the 10% and 20% central subfield thickness reduction thresholds, respectively. Ultimately, this framework offers a decision-support tool to help ophthalmologists anticipate initial anti-VEGF outcomes in DME prior to treatment initiation, toward personalized therapy selection. The source code will be made publicly available upon acceptance.
人工智能
[AI-0] Adversarial Speculative Reasoning Improves Long Horizon Agents
链接: https://arxiv.org/abs/2611.05345
作者: Hongyin Luo,Wei Fang,Lijie Yang,Owen Stepan,Daniel Suit,Jeff Krapf,Dana Wensberg,Jack O’Brien
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Speculative decoding has been treated purely as a systems optimization that speeds up large language model decoding without changing the model behavior. Under this view, the growing effort to train better draft models has been focused solely on acceptance rate. We argue that this view does not hold true for long-horizon agent reasoning. Measuring frontier open-weight models on complex agentic tasks with different speculative decoding approaches, we found that numerical noise grows within verification windows, and the draft acceptance results of the thinking process can change the output distribution of the model after 30 steps, thus noticeably influencing the reasoning accuracy of long-horizon agents in random directions. In this work, we improve the reasoning capability of target models by controlling such noise. While we cannot change the overall token distribution verified by the target model, we can encode critical reasoning with highest precision by training the draft model to be rejected. We identify these points in agent traces and train the draft model to favor adversarial preference. At inference the trained draft and the target part ways precisely at these decision points, verification restarts, so that critical thinking tokens are sampled from the target model and encoded under its least noisy conditions. This approach makes critical reasoning better indexed and more durable over a long horizon. On complex agentic coding benchmarks, our approach raises end-to-end success rate with no change to the target model or its output distribution. Our results recast the speculative decoding mechanism from a pure accelerator into an active allocator for context precision that improves the accuracy of long-horizon agents by training 0.06% to 0.28% of hosted parameters.
[AI-1] PyRUA-DataFarm: LLM Agent as a Cost-Efficient Teleoperator for Real-Ready Robot Data
链接: https://arxiv.org/abs/2611.05320
作者: Kewei Zhang,Juncheng Ma,Guodong Lv,Duomin Wang,Feixiang Ruan,Ruiyang Si,Chuanrui Zhang,Yuxuan Chen,Hao Chen,Yichen Liu,Yintianrun Zhang,Yufan Deng,Enze Xie,Daquan Zhou
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 22 pages, 9 figures, 9 tables. Project page: this https URL Code: this https URL
Abstract:Teleoperation remains the default way to collect robot demonstrations, but every episode costs robot and operator time, and every new task starts from scratch. We ask whether a coding agent can take over the teleoperator’s job. In our DataFarm, the agent receives a data request and one calibration episode of the robot in the target scene and rebuilds the task in simulation: it writes the scene, an expert controller, and a success judge. The simulator supplies exact actions, and a video model conditioned on real images renders each rollout as photographic video, generating all camera views together consistently. The output is real-ready data: synchronized camera views labelled with the robot’s actions, on which a policy can be trained directly and deployed on the real robot. Independent judges certify the expert and every rendered episode before admission. On five real-robot tasks, policies trained only on farm data match policies trained on as many teleoperated episodes (93 vs. 84 successes in 100 trials), and under an unseen tablecloth and coloured light they keep 87 successes where the teleoperation-trained policies fall to 46. Adapting a task is fully automatic and costs \ 21 of API usage on average, paid once per task, plus \ 5 for a render contract; every further episode costs \ 0.80 on average for simulation, rendering, and review, with no robot or operator. One hour of three-view data for a task costs \ 107 on average, these costs included, slightly below a quote of \ 117 for teleoperation in China and below \ 290 in labour alone in North America.
[AI-2] You dont need long context
链接: https://arxiv.org/abs/2611.05306
作者: Peter Belcak,Saurav Muralidharan,Jindong Jiang,Yonggan Fu,Wonmin Byeon,Hanrong Ye,Ryan Angilly,Ali Taghibakhshi,Shih-Yang Liu,Siru Ouyang,Greg Heinrich,Pavlo Molchanov
类目: Artificial Intelligence (cs.AI)
备注: Technical report. 31 pages, 8 figures
Abstract:Later tokens are costlier to generate than earlier tokens. On frontier open weight models, the disparity in cost may rise to as much as 7x. Through frequent compaction, we show across a broad range of long-horizon and long-input tasks that frontier language models sacrifice little to no task accuracy when maintaining short contexts. We further demonstrate that LLM performance does not collapse on long-horizon software engineering tasks even when taking at most one action per context, and based on that we outline the concept of semantic state space models (SSSMs). Our findings question the need for long-context language models.
[AI-3] he Geometry of Ignorance: Detecting Suppressed Knowledge in LLM s through Activation Frag mentation EMNLP
链接: https://arxiv.org/abs/2611.05272
作者: Adar Peleg,Dvir Alsheich,Tomer Ashuach,Avi Mendelson
类目: Artificial Intelligence (cs.AI)
备注: Presented at EMNLP Findings 2026
Abstract:Evaluating whether a large language model (LLM) genuinely knows a concept remains a fundamental problem, since behavioral metrics can be fooled by suppression: a model that refuses to answer may still harbor recoverable knowledge. The framework measures the geometric coherence of \textitanon-vectors, the differences in hidden states between a concept-specific query and its anonymized paraphrase, and shows that genuine knowledge forms a tight, low-entropy cluster while ignorance forces geometric fragmentation. Across ten instruction-tuned LLMs from three model families, all fragmentation metrics reliably separate known from unknown concepts, and post-unlearning, fragmentation geometry correlates with adversarial recovery across four attack types and five unlearning algorithms, though the direction of that relationship is shown to depend on attack methodology. Furthermore, a layer-resolved analysis reveals that different adversarial mechanisms exploit this geometry at varying depths, with high attack-specific correlations that are invisible to behavioral evaluation. Together, these results constitute a single-phase, attack-free proxy for unlearning robustness that captures aspects of parametric knowledge inaccessible to output-level metrics. A reference implementation is available at this https URL
[AI-4] JHiMA-WebRL: A Joint Hierarchical Multi-Agent Reinforcement Learning Framework for Long-Horizon Web Tasks
链接: https://arxiv.org/abs/2611.05263
作者: Shanka Subhra Mondal,Jiayun Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal web agents have shown promising capabilities for interacting with websites through graphical user interfaces (GUIs), yet realistic web tasks often require long-horizon, multi-step planning that remains challenging for monolithic policies. Such agents must jointly infer high-level plans, navigate through intermediate states, track progress over extended interaction histories, and execute visually grounded actions. Although prior work has explored modular multi-agent systems with a separate planner, the planner is typically not updated jointly with the actor. We investigate whether explicitly separating and jointly training planning and execution provides a stronger inductive bias for long-horizon web navigation. We introduce JHiMA-WebRL, a joint hierarchical multi-agent reinforcement learning framework for web agents that couples a task decomposer with an actor. To train the decomposer, we use a strong teacher model to construct structured subtask supervision from multimodal GUI trajectories, and use these annotations to fine-tune both the decomposer and the subtask-conditioned actor. We then extend Group Relative Policy Optimization (GRPO) to jointly optimize the decomposer and actor in the interactive WebArena environment through hierarchical rollout grouping and role-specific advantages. On WebArena-Lite-v2, JHiMA-WebRL-14B, consisting of a 7B actor and a 7B decomposer, achieves an average success rate of 37.2%, compared with 24% for a ScaleCUA-32B agent that we fine-tune with RL on the same training tasks and evaluate under the same protocol. Ablations show that jointly updating the decomposer during RL outperforms updating the actor alone. These results suggest that jointly learned role factorization with smaller models can be a competitive alternative to scaling monolithic agents for long-horizon web tasks.
[AI-5] Going Deeper With Memories
链接: https://arxiv.org/abs/2611.05255
作者: Afshin Khadangi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Byte level language models avoid tokenizer dependence and provide universal coverage, but processing every byte at full resolution is expensive. We introduce DeepMem, a causal multiscale architecture that separates semantic compression from exact byte preservation. A learned invertible subband transform constructs a hierarchy of shorter sequences, while a lossless carrier and explicit detail sidebands preserve byte identity. Fast weight memory blocks operate along the encoder, with iterative shared state computation at the compressed bottleneck, while an attention based super resolution decoder restores byte resolution representations. DeepMem achieves stronger language modeling performance than a matched byte level Transformer on FineWeb-Edu and PG19, and obtains the best macro average performance across the evaluated FLORES languages. It also improves PIQA accuracy while remaining competitive on code modeling, multiple choice reasoning, and byte level corruption robustness. Bottleneck refinement requires approximately 7,600\times fewer counted FLOPs than equivalent full resolution reasoning, while maintaining competitive evaluation throughput.
[AI-6] Fairness Metrics based on the AUC in Clinical Predictive AI
链接: https://arxiv.org/abs/2611.05222
作者: João Matos,Ben Van Calster,Richard D. Riley,Paula Dhiman,Gary S. Collins
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Fairness evaluation of clinical prediction models for binary outcomes often involves fairness metrics that compare the area under the receiver operating characteristic curve (AUC, or c-statistic) across subgroups. However, the AUC has been shown to be a non-collapsible metric, meaning that its subgroup specific and overall values are not directly comparable as often implicitly assumed. This raises important questions about how existing AUC-based fairness metrics should be used and interpreted. We examine eight AUC-based fairness metrics proposed in the literature, including: within-group AUC parity; cross-group AUC (xAUC) parity; gap-to-overall AUC; equity-scaled AUC; minimum-based metrics; \mathrmxAUC^1 ; \mathrmxAUC^0 ; and \mathrmxAUC^1 or \mathrmxAUC^0 parity. We characterise their estimands, relate them to the decomposition of overall AUC (into within- and cross-group components), and critically appraise them. We then illustrate their behaviour using simulations based on the EuroSCORE II, a clinical prediction model for in-hospital mortality. We recommend reporting an AUC matrix, containing the overall AUC and all within- and cross-group AUC components, alongside population characteristics. When fairness metrics are needed, we recommend reporting the within-group AUC parity, with cross-group AUC parity providing supplementary information but requiring caution due to its potential conflict with calibration. Metrics involving \mathrmxAUC^1 and \mathrmxAUC^0 have merit, summarising within- and cross-group AUCs into single values, but their interpretation is non-trivial, and they should be used with caution. Gap-to-overall AUC, equity-scaled AUC, and minimum-based metrics compare quantities that should not be compared and it is unclear how to operationalise them, making them not fit for purpose and generally not recommended.
[AI-7] OperAItor: An Agent ic Framework for Fault-Tolerant Operation of Chemical Processes
链接: https://arxiv.org/abs/2611.05179
作者: Javal Vyas,Mehmet Mercang{ö}z
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注:
Abstract:OperAItor, an open-source framework for developing and evaluating large language model agents for supervisory fault management in chemical processes. The framework is demonstrated on a dynamic reactor-distillation process representing isopropanol dehydrogenation, with physically based thermodynamics and modified reaction kinetics to permit liquid-phase operation. The process is represented by a 113-variable differential-algebraic model and operates at a nominal feed rate of 100kmol/h, producing approximately 4t/h of acetone. The simulated plant includes 8 PI control loops, 22 process alarms, 5 duty-standby pump trains, and 11 independent safety instrumented functions. The agent architecture integrates multivariate statistical process monitoring, fault diagnosis, knowledge-graph retrieval, action planning, and supervisory action generation. Five action-validation configurations are investigated, comprising operation without validation, an LLM-based judge, digital-twin simulation, and deterministic admissibility checks with and without consequence projection. The framework is evaluated using four LLMs across 20 scenarios involving single and multiple simultaneous faults. Without agent intervention, 18 scenarios result in plant stops, corresponding to an economic loss of 399,000 relative to fault-free operation under the assumed benchmark economics. For GPT-5 nano, Qwen3 8B, and Mistral 7B, validated agent configurations reduce the number of stops to 5-8 and recover 265,000 of the loss. The study provides a reproducible benchmark for comparing agent architectures, language models, and action-validation methods for supervisory fault management in chemical processes.
[AI-8] Agent AM: An AI Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion
链接: https://arxiv.org/abs/2611.05171
作者: Olabode T. Ajenifujah,Amir Barati Farimani
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Predictive models for laser powder bed fusion (LPBF) are rarely used on the shop floor because querying them requires programming and model-specific knowledge. We present AgentAM, a large language model (LLM) agent that gives process engineers natural-language access to CFD-trained surrogates of spatter and melt pool geometry for SS316L through the Model Context Protocol (MCP). Polynomial surrogates trained on 279 single-track simulations predict melt pool dimensions ( R^2 \geq 0.95 ) and spatter volume ( R^2 = 0.83 on test data) at any power-velocity point in the trained range, and geometric criteria assign each point a geometry-derived defect regime (keyhole, lack of fusion, balling or stable) that matches the regime obtained from the simulated geometry in 93.4% of tracks under cross-validation. The agent selects and parameterizes tools for prediction, process-window scans and constrained optimization. Minimum-spatter optima often sat within 10 \mu m of the lack-of-fusion boundary; a margin-aware optimizer moved them away from all thresholds and raised the estimated probability of remaining classified Good from 0.94 to 1.00 under an assumed 2% process drift, and from 0.72 to 0.91 when jointly sampled cross-validated geometry residuals were added. About 40-50 well-spread simulations sufficed to reproduce the defect map. To evaluate the agent layer, we introduce a 145-prompt benchmark scored against direct tool calls, plus eight prompts (40 runs) on margin-aware requests. Over the 725 runs of the main benchmark the agent passed the intended inputs in 98.5% of parsing prompts, reported the scored spatter values and defect classes faithfully and met every stated constraint in the 65 feasible optimization runs, within a median 1.0% of the brute-force optimum.
[AI-9] Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings
链接: https://arxiv.org/abs/2611.05167
作者: Parastoo Azizeddin,Eray Erturk,Maryam M. Shanechi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Multimodal neural data can enable a more complete understanding of brain dynamics underlying behavior. Modalities such as spiking activity, local field potentials (LFPs), and behavior capture diverse spatiotemporal aspects of brain processes, yet prior neural-behavioral models that separate sources of variability rely on a single neural modality. By leveraging these complementary strengths, multimodal neural fusion can provide a unified, rich representation of brain-behavior processes and address the limitations of single-modality analyses. Here we develop BREM-NET, a nonlinear multimodal dynamical model that integrates behavior with multiple neural modalities having distinct statistical characteristics and temporal resolutions. BREM-NET performs multimodal neural fusion during inference while separating behaviorally relevant and residual neural dynamics via a structured learning framework. In a public multimodal non-human primate dataset with simultaneous spiking and local field potential recordings during a 2D reaching task, BREM-NET separates these two types of dynamics and learns them more accurately, as reflected in better behavior decoding, better neural prediction, and stronger latent alignment with behavior, and it retains these capabilities when neural modalities are asynchronous or suffer dropouts, which are major challenges in real-world recordings. This framework provides a new tool for studying behaviorally relevant neural computations across spatiotemporal scales of brain activity.
[AI-10] Basin2Vec: Learning Basin-Centric Embeddings for Scalable Hydrological Modeling
链接: https://arxiv.org/abs/2611.05164
作者: Muhammad Talha,Xiaofeng Liu,Yuan Yang,Pang-Ning Tan,A. Pouyan Nejadhashemi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliable streamflow prediction across time and in ungauged basins requires representations that capture hydrologically relevant basin characteristics. Existing methods rely on either fixed handcrafted attributes or general-purpose Earth observation embeddings not tailored to basin structure. We introduce \textbfBasin2Vec, a self-supervised framework that learns compact basin representations from hydroclimatic and physiographic data without observed streamflow. Basin2Vec organizes representation learning around basin scale and relationships across space and time. Across multiple forecasting architectures, it consistently outperforms predefined attributes and general-purpose geospatial embeddings in temporal generalization and zero-shot spatial transfer. Basin2Vec provides average relative gains of up to 8.3% temporally and achieves 3.5x the median predictive performance of the strongest baselines on unseen basins. These results demonstrate the effectiveness of basin-centric pretraining for transferable hydrologic prediction. Code is available at this https URL
[AI-11] RoboTryOn: Synthesizing Dexterous Robot Demonstrations from Human Videos via Virtual Try-On
链接: https://arxiv.org/abs/2611.05163
作者: Seungyong Lee,Suhyeok Jang,Minyoung Kim,Jinwoo Shin
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 29 pages, 15 figures, 13 tables. Project page: this https URL
Abstract:Robot foundation models have shown remarkable performance in robot control, yet adapting them to a new task still relies heavily on teleoperated demonstrations. Egocentric human videos offer a scalable substitute, but overlaying rendered robots on human videos or retargeting human poses into robot actions leaves significant visual or action gaps for high-DoF robot embodiments. To address this limitation, we propose RoboTryOn, which treats human-to-robot translation as virtual try-on: try-on keeps the person and swaps the garment, RoboTryOn keeps the demonstrated motion and swaps the human for the robot. Specifically, RoboTryOn converts a human video into a video of the target robot specified by a reference image, while preserving the original task, objects, and background. To maintain visual consistency, we translate selected keyframes autoregressively and use them to guide video synthesis with motion conditioned on the human hand skeleton. Additionally, we use an inverse dynamics model (IDM) that incorporates depth features to infer action labels from synthesized videos for policy learning. Our experiments across multiple humanoid embodiments show that policies trained with our synthetic data improve the average success rate on novel tasks, including challenging bimanual manipulation, from 2% to 60%, without any real robot demonstration of those tasks. Moreover, training with our synthetic data matches the performance of policies trained solely on teleoperated demonstrations at approximately a third of the collection cost, demonstrating the cost efficiency of our approach.
[AI-12] Asynchronous Robot Policies
链接: https://arxiv.org/abs/2611.05155
作者: Sombit Dey,Luc Van Gool,Danda Pani Paudel
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: For code, checkpoints and more results, check out this https URL
Abstract:Scaling vision-language foundation models has significantly advanced generalist robot manipulation, but the increased model scale introduces computational latency that degrades real-time physical reactivity. Existing Vision-Language-Action (VLA) architectures execute high-level semantic reasoning and action ex- ecution sequentially within the primary control loop, creating a fundamental tension between semantic-reasoning capability and robot agility. To address this, we propose Asynchronous Robot Policy (ARP), an architectural framework that decouples semantic reasoning from low-level action generation using asynchronous processes operating at independent frequencies. In ARP, a high-capacity Vision-Language Model (VLM) asynchronously updates a semantic memory at low fre- quency, while a lightweight Vision-Action Expert, conditioned on this semantic memory, generates robot actions at a much higher control frequency. Such a formulation, enables having a fast perception-action loop without compromising on the semantic capability. To overcome asynchronous memory staleness, ARP trains the vision-action expert on the semantic staleness and execution delay, with temporally mismatched observation across processes. On Franka manipulation tasks, ARP achieves an 18% higher success rate and 20% less time need for execution in quasi-static setups, while 3-5 times baseline performance in dynamic simulation environments. Furthermore, ARP enables operating the VLM at 10 times lower than its nominal frequency with no significant performance loss making it much more efficient than the baseline. These results highlight the architectural advantages of modeling robot policies as asynchronous processes, enabling real-world reactive deployment without sacrificing the scale of foundation models.
[AI-13] Catalyzing Interactive HTML Generation Capabilities via High-Fidelity Reinforcement Learning
链接: https://arxiv.org/abs/2611.05151
作者: Xujia Wang,Yixuan Liu,Shaohan Huang,Furu Wei,Lei Hou,Bin Xu,Juanzi Li
类目: Artificial Intelligence (cs.AI)
备注: 38 pages, 21 figures
Abstract:Generating HTML from natural-language specifications is an important application of code large language models (LLMs). Despite producing renderable pages, current models often fail to ensure functionality and visual quality during actual interaction. Existing methods typically target task-specific webpage supervision, leaving demanding interactive artifact optimization underexplored. Using mini-games as a challenging testbed, we expose limitations in interaction experience, reward signal, and credit assignment fidelity that are obscured by shallow webpage interactions. We introduce WebCatalyst, a browser-interaction reinforcement learning framework with three complementary components: a game-level GUI assessor for sustained interaction and evaluation, Visual Anchor for mitigating VLM reward hacking, and Action-Trace Credit for grounding token-level credit assignment in runtime execution traces. Trained on fewer than 1K requests, predominantly for games, WebCatalyst raises the Cookie-Bench Mini Games score from 68.8 to 75.3 and also the WebGen-Bench accuracy from 48.8 to 56.9 on Qwen3.6-35B-A3B, outperforming Claude Opus 4.7 in average rank across evaluated metrics. Further analysis shows that Visual Anchor prevents aesthetic degradation, while runtime-grounded credit assignment accelerates functional improvement. Together, our results show that improving feedback fidelity is the key to substantially advancing interactive HTML generation.
[AI-14] Creating Power Flexibility in Datacenters via Query Routing
链接: https://arxiv.org/abs/2611.05148
作者: Shivam Patel,Bingqing Chen,Jared Fernandez,Guannan Qu,Gauri Joshi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The growing energy requirements of large language model inference highlight the need for datacenters to support safe grid operation by providing power demand flexibility, while maintaining service for interactive workloads. This work investigates query routing as a mechanism to enable such capability by dynamically allocating queries across different language model instances without deferring requests. We develop a general analytical framework that connects query arrival rates and sequence length distributions to token iteration time, request residence time, serving capacity, and power consumption of model instances. Leveraging this framework, we characterize the feasible query routing allocations and the corresponding power demand flexibilities, enabling the selection of routing strategies to achieve desired power consumption behavior. We validate our framework across four GPU platforms on language models ranging from 1B to 70B parameters, obtaining 2.8% mean absolute percentage error in estimating achievable power flexibility. We evaluate our approach on a real trace-driven simulation of a 10,000 H100 GPUs datacenter, exhibiting flexibility up to 16% of total power consumption, with 90% of the desired power reduction achieved within approximately 10 seconds. These results establish query routing as an effective mechanism for datacenter power demand flexibility and demonstrate the broader applicability of our proposed framework to datacenter level planning and orchestration.
[AI-15] Meta2: Precomputing Reusable Knowledge for Online Harness Evolution in Long-Horizon Agents
链接: https://arxiv.org/abs/2611.05139
作者: Lingzhi Yuan,Yiguang Cai,Weize Liu,Siqi Yang,Feijiang Han,Yongkyun Lee,Udari Madhushani Sehwag,Furong Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Harness optimization improves long-horizon agents by refining the prompts, tools, memory, and hooks around a frozen model. Offline methods can learn from completed trajectories, but deploy one shared harness that may overfit training tasks and cannot react to evidence revealed during a new run. Online evolution can adapt before, during, or after one run through an evolver, but may have to reason about every intervention from scratch or rely on verifier feedback that is usually unavailable at test time. We introduce Meta ^2 , which moves offline optimization one level up: rather than optimizing a shared harness, it learns reusable knowledge for harness evolution and applies it within each task online. Offline, Meta ^2 learns a textual Meta-Skill M by contrasting training rollouts of the same task agent under three configurations: without an evolver, with an evolver guided by the current version of M , and with an evolver given privileged verifier information available only in training. Contrasts in trajectories and outcomes are aggregated across tasks to refine M into reusable guidance on when and how to evolve, when to leave the harness unchanged, and which interventions to avoid. Online, an evolver combines the frozen M with emerging trajectory evidence to decide whether to KEEP the harness unchanged or EVOLVE it through a task-local edit. Each edit applies to the rest of the same run without resetting its environment or progress. Thus, evolution guidance transfers across tasks while harness edits remain task-local, and held-out verifier feedback is never available to the evolver. On two long-horizon benchmarks, Terminal-Bench 2.1 and DeepSWE 1.1, Meta ^2 achieves the highest performance among evaluated methods with GPT-5.6-Terra, reaching 81.00% and 44.44%, respectively. Ablations support the value of contrastive evolution guidance and keeping online harness edits task-local.
[AI-16] From AutoResearch to Continual AutoResearch: Do Agents Learn Across Tasks?
链接: https://arxiv.org/abs/2611.05108
作者: Jeremy Qin,Ruizhi Deng,Mohamed Osama Ahmed,Frederick Tung,Leo Feng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Autoresearch has shown that agents can autonomously write code, run experiments, and iteratively improve machine learning solutions. Yet most evaluations start a new agent session from scratch for each new problem, making them costly and leaving unclear whether agents can retain and apply prior insights when adapting to new tasks. We introduce CONTINUAL AUTORESEARCH, an open-ended environment that compares persistent and reset instances of the same agent under matched task streams, data, and time budget. Continual AutoResearch covers nine task families, spanning synthetic tasks, symbolic regression, time-series forecasting, and tabular AutoML. Across five frontier agents, we measure first-solution quality, research efficiency, and final held-out performance. To distinguish insight transfer from session continuity, we run a memory ablation that vary what information agents retain from prior tasks. We show that persistent state improves research efficiency of all five agents, allowing them to find stronger solutions sooner. However, these gains do not consistently translate into better final solutions. Overall, our findings suggest that retaining experience and utilizing it effectively are distinct capabilities.
[AI-17] Workload-Static Optical Circuit Switching For Scalable LLM Inference Decode
链接: https://arxiv.org/abs/2611.05104
作者: Maximilian Thürmer,Nicolas Scotto di Perto,Eloise Stein,Natanijel Vasic,Mikaeil Bagherli,Zhenguo Wu,Benjamin Klenk,Holger Fröning,Johannes Feldmann
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Networking and Internet Architecture (cs.NI)
备注: 39 pages, 10 main text, 29 appendix, 28 figures
Abstract:LLM inference decode executes fine-grained collectives whose latency directly limits per-user interactivity, yet scale-up fabrics are still built from electronic packet switches designed for training. We observe that, for a given model deployment and parallelization, the communication structure of decode is fixed, and propose workload-static optical circuit switching (OCS): circuits are configured once per deployment, co-designed with model parallelism and with topology-aware collective schedules that turn spare accelerator ports into fewer communication phases or higher bandwidth. We evaluate this design with GXNET, a graph-based workload emulator coupled to analytical and flit-level network models grounded in hardware measurements. In a 576-accelerator scale-up domain serving 5T-parameter MoE models with up to 5M tokens of context, all-OCS fabrics improve interactivity by up to 65% and energy efficiency by up to 119% over 4:1 oversubscribed packet-switched fabrics.
[AI-18] Molecular Prior Networks NEURIPS2026
链接: https://arxiv.org/abs/2611.05103
作者: Pawel Gomoluch,Anthony Bradley
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: Accepted to the NeurIPS 2026 Workshop on AI for Drug Discovery: Bridging the Translation Gap
Abstract:Predicting affinity between small molecules and their protein targets remains one of the central problems in drug discovery. Due to the substantial cost of wet-lab experiments, machine learning models of drug-target affinity are usually trained on relatively small datasets, ranging from tens to hundreds of molecules. Given the inherent difficulty of generalization between different targets and chemical series, existing datasets of protein-ligand affinities are typically not used for new projects. At the same time, tabular foundation models, including Prior-data Fitted Networks, have recently shown how transformers can be used to solve machine learning problems in a single forward pass of the network, after being trained over a large corpus of, typically, synthetic datasets. Inspired by that line of work, we propose a novel affinity modelling approach, in which the labels of the query molecules are predicted from the labelled support molecules in a single forward pass of a transformer model. The model is trained on thousands of existing affinity datasets, enabling it to capture the general patterns of molecular activity across different targets, chemical series and dataset sizes. For the smallest support sets, our model matches the state of the art on the regression variant of the FS-Mol benchmark, while being faster than existing methods, thanks to the lack of task-specific training. A comparison against a reduced variant of our architecture, which amounts to kernel regression in a meta-learned feature space, shows that the predictive performance of the transformer model is only partially explained by simple molecular similarity.
[AI-19] he Platonic Brain: Representational Convergence in EEG Foundation Models
链接: https://arxiv.org/abs/2611.05097
作者: Nicolas Waehner,Enzo Tagliazucchi,Jacobo Sitt,Demian Wassermann,Giovanni Franco Gabriel Marraffini,Trinidad Borrell
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The Platonic Representation Hypothesis (PRH) predicts that artificial neural networks converge on shared representations as scale and performance increase. We test whether these predictions hold for representations of the human brain with EEG foundation models. Using EEG recordings from six participants watching a television series, together with aligned visual and language content, we tested whether EEG foundation-model representations converge within and across modalities as their embeddings become better at decoding an informative set of EEG markers. We found that EEG models with greater decodability showed stronger cross-family alignment in both local neighbourhood similarity and global structure, not consistently increasing with model parameter count. Cross-modal convergence was modality and metric dependent: greater EEG decodability was associated with stronger global alignment to language, but not consistently to vision models, while local alignment showed no consistent association. This dissociation may reflect local sensitivity to low-level sensory features, whereas global geometry may capture weaker shared semantic structure.
[AI-20] LatentTriad: Pre-Action Risk Representations for Malicious Agent Skill Detection
链接: https://arxiv.org/abs/2611.05088
作者: Zhe Yu,Yi Liu,Ying Zhang,Simin Chen
类目: Artificial Intelligence (cs.AI)
备注: Preprint. Under review. 44 pages, 16 figures, 37 tables. 9 pages of main text
Abstract:Large language model agents are increasingly extended with reusable skills, but untrusted skill content can induce operations outside the user’s task. Detecting this contextual risk requires more than suspicious-pattern matching, while dynamic analysis incurs the cost of skill execution. We find that hidden states formed during prefill contain readable information about task-operation consistency, including across held-out expression forms in controlled evaluations. Matched Operation-state interventions also affect later Boundary states and shift fixed risk readouts toward the donor label. Based on these findings, we introduce LatentTriad, which combines contextual Boundary features, group-adjusted Operation directions, and departures from benign state relationships. All branches share one prefill: detection generates no output tokens, invokes no tools, and executes no skill code. Across 5,830 skill packages from five sources and seven backbones, LatentTriad achieves 99.0-99.2% pooled F1 and 93.0-94.6% source-macro F1. A separately measured compact implementation completes prefill and detection in a median of 259-284 ms. These results connect pre-action risk readability and functional influence to execution-free skill screening.
[AI-21] ALBI: LLM -Guided Inductive Logic Programming via Iterative Generate-Test-Constrain Refineme
链接: https://arxiv.org/abs/2611.05039
作者: Aboubaker Seddiq Benterki,Jüri Vain,Gert Kanter,Leelo Aino Jürgenstein
类目: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
备注:
Abstract:We introduce ALBI (Agent for Learning By Induction), a neuro-symbolic ILP framework that replaces the ASP-based hypothesis generator in the Learning from Failures (LFF) paradigm with a Large Language Model (LLM), while retaining SWI-Prolog as a formal verifier. ALBI implements an iterative generate-test-constrain loop: the LLM proposes candidate hypotheses from structured prompts; Prolog tests each candidate; and diagnostic feedback drives refinement. We further introduce semantic anonymization, a metamorphic testing methodology, to distinguish structural symbolic induction from surface-level pattern matching on predicate names. Experiments on 30 standard ILP benchmarks show that ALBI achieves 100% success across three frontier LLMs (Gemini 3.1 Pro, GPT 5.4 Pro, Claude Opus 4.6), matching Popper while requiring only 1.4-1.7 iterations on average. Under anonymization, all models maintain 100% success with a moderate iteration increase (1.36-1.67 times), providing evidence of robust symbolic reasoning.
[AI-22] Rethinking GA2Ms: performance and out-of-distribution generalisation
链接: https://arxiv.org/abs/2611.05012
作者: George Dimitriadis,Damian Machlanski,Paul Clarke,Spyridon Samothrakis
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Generalised Additive Models (GAMs) are a cornerstone of interpretable machine learning, offering transparent predictions through structured additive decompositions. Despite advances from classical GAMs to modern neural and spline-based variants, two notable gaps remain. First, comprehensive benchmarking of spline-based GAMs on large-scale tabular learning tasks, the dominant modality in applied settings, is lacking. Second, their robustness under distribution shift, a setting where structured inductive biases should confer advantages, remains unexplored. We propose a multi-resolution, higher-order Composite Generalised Additive Model (CoGA2M) and evaluate it on the TALENT benchmark suite. CoGA2M ranks in the top third across all baselines (29 for regression, 32 for classification), establishing competitive performance with state-of-the-art tabular methods. We further extend TALENT with out-of-distribution (OOD) splits exhibiting train-test distributional shift and benchmark CoGA2M against LightGBM, a consistently top-performing model on TALENT. Under distribution shift, CoGA2M outperforms LightGBM, demonstrating a clear structural advantage. Our results position spline-based GAMs as strong contenders for tabular prediction and reveal a previously unrecognised benefit of additive structure in the OOD regime.
[AI-23] LLM s as MDP Designers: A Dependency-Aware Agent ic Framework for Automated Robot Policy Generation NEURIPS2026
链接: https://arxiv.org/abs/2611.04974
作者: Hong Je-Gal,Jinwoo Kim,Chanbin Yi,Hyun-Suk Lee
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted to the 40th Annual Conference on Neural Information Processing Systems (NeurIPS 2026)
Abstract:We investigate the automated generation of executable robot reinforcement learning (RL) policies from natural-language task descriptions. Rather than using large language models (LLMs) as execution-time decision makers, we use them as designers that construct RL pipelines before deployment, including Markov decision process (MDP) formulation and simulator implementation. We identify a phase-wise structure in this problem: MDP formulation is dependency-structured and sequential, whereas simulator implementation becomes modular once the formulation is fixed. Based on this structure, we propose ARPG, a dependency-aware agentic framework that performs sequential MDP modeling, localized verification and correction, and simulator-structured implementation while externalizing intermediate artifacts. ARPG targets a key failure mode of automated policy generation: executable code may still yield non-learnable policies when state, action, reward, and termination artifacts are not semantically aligned. Experiments in Isaac Sim show that ARPG improves modeling correctness, environment executability, and policy-generation success compared with monolithic LLM baselines and off-the-shelf agentic systems. Additional demonstrations on custom robotics, standard RL, and engineering-domain MDPs further suggest that the same modeling pipeline can be reused beyond the main benchmark suite.
[AI-24] Artifact-to-Capability: Diagnosing Breaks in Capability Formation of Self-Evolving Agents in a Controlled Environment
链接: https://arxiv.org/abs/2611.04952
作者: Ziqian Ban,Haoxiang Yang,Yanfei Zhang,Shixin Liang,Haoran Yu,Xu Lin,Shaobin Zhao,Xuran Li,Zhiyi Cheng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Self-evolving agents write interaction experience into artifacts and reuse them in later tasks. Most work supports such methods with three proxy signals: artifacts accumulate, behavior changes once they are loaded, and metrics rise. We call this signal-based evaluation. What these signals are meant to indicate, however, has not been defined. To fill this gap, we define capability formation from artifacts as five sequentially dependent properties, two of which form a checkpoint for capability formation, and introduce three-scale diagnosis, which judges these properties at the state, game, and lineage levels. In freestyle Gomoku, Reflexion, Voyager, and DGM evolve with the same base model and all show the three signals, yet each exhibits a distinct capability-formation break. Reflexion shows a legalization gain: its pass rate rises only because more of its moves are legal. Voyager shows a compositional override: components that are sound on their own combine to miss all 20 available one-move wins. DGM shows loss-only drift: after passing the checkpoint, later versions only lose passing states. These counterexamples show that signal-based evaluation cannot determine whether a capability has formed.
[AI-25] Fault-tolerant shallow recurrent decoders for sparse sensing with missing sensor measurements
链接: https://arxiv.org/abs/2611.04893
作者: Matteo Romilio Pasqual,Matteo Tomasetto,J. Nathan Kutz,Andrea Manzoni
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world sensing systems are inherently imperfect: sensors may fail, intermittently disconnect, operate with asynchronous sampling rates, and be subject to noise, leading to measurement streams that are irregular, incomplete, and heterogeneous. Nevertheless, sensing strategies for real-time state reconstruction typically assume synchronous and continuously active sensors, a constraint that substantially limits their applicability in operational environments. In this work, we introduce fault-tolerant sparse sensing strategies in the context of shallow recurrent decoders (SHRED) to enable high-dimensional state estimation from sparse, asynchronous, and missing sensor measurements. Specifically, we consider naive imputation strategies, fault-tolerant recurrent temporal encoders, and we introduce four different transformer-based masking mechanisms to make SHRED explicitly aware of sensor availability through the binary data-availability mask. Moreover, we further introduce a multi-step decoding training objective for SHRED, resulting in smoother latent trajectories and more robust predictions. Throughout a synthetic benchmark and three real-world problems in hydrodynamic and ocean modeling, we show that (i) transformer-based masking mechanisms outperform standard imputation techniques and fault-tolerant recurrent architectures, (ii) the multi-step decoding training objective enhances reconstruction fidelity and robustness in the presence of heterogeneous missing-data patterns, while (iii) the computational efficiency of SHRED is preserved, with no additional inference-time overhead.
[AI-26] FourSight: Stable FP4 RL Post-Training via Sequence Filtering and Sparse Channel Compensation
链接: https://arxiv.org/abs/2611.04887
作者: Yining Wei,Tianyuan Wu,Gu Gong,Yuechen Tao,Yinghao Yu,Xiaowen Chu,Wei Wang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 18 pages, 13 figures
Abstract:Reinforcement learning (RL) post-training increasingly targets low-bit deployment, with NVFP4 weight-and activation quantization (W4A4) becoming the state-of-the-practice format. However, integrating NVFP4 into RL degrades rollout quality and amplifies cross-engine training-inference mismatch (TIM), destabilizing training. We show that these failures are concentrated rather than uniform - severely mismatched tail tokens concentrate in a few outlier sequences, while policy weights remain largely intact and quantization error concentrates in a few high-variance activation channels. Guided by this, we present FourSight, an algorithm-system co-design with two targeted solutions: TIM-aware sequence filtering that excludes drifted trajectories from policy updates, and sparse precision compensation that keeps only top-variance channels in BF16 while retaining native NVFP4 elsewhere. We further design a fused SpliceGEMM kernel with specialized prefill and decode datapaths to minimize overhead. FourSight sustains near-BF16 accuracy and strong FP4 deployment quality across models and engines, while improving step-to-accuracy by 1.76-2.60x and staying within 2.3% of FP4 step time up to 120B-scale models.
[AI-27] Modelling lock-in of AI adoption through skill erosion
链接: https://arxiv.org/abs/2611.04886
作者: Renaud Lambiotte
类目: Artificial Intelligence (cs.AI); Physics and Society (physics.soc-ph)
备注: 10 pages, 4 figures
Abstract:Generative AI can improve immediate performance but may erode the human skills required for future work. We study a model in which individuals tend to imitate successful peers, while their independent skills grow with practice and erode with reliance on AI. Because each agent’s skill reflects their history, non-users and AI users develop different skill distributions, which we derive exactly. Skill erosion can produce bistability and hysteresis between a low-adoption, high-skill state and a high-adoption, low-skill state. When agents rarely change behaviour at random, the two transitions are asymmetric. A single AI user spreads through a population of experts only if AI-assisted work earns more than expert work, whatever the timescale of skill adaptation. A single non-user, by contrast, can lead an AI-dependent population back to independent work only if they regain enough skill before returning to AI, which becomes easier the more persistent behaviours are. As AI improves, adoption can jump abruptly, and a sufficiently long increase in its benefit, such as a period of low prices, can leave a population relying on AI after the benefit has returned to its initial value. Skill erosion thus creates a history-dependent switching cost that can make widespread AI adoption difficult to reverse.
[AI-28] ScamWorld: An Interactive Environment for Evaluating Web Agents Against Online Fraud
链接: https://arxiv.org/abs/2611.04879
作者: Yutao Wu,Xiao Liu,Ming Wen,Hanxun Huang,Jun Wang,Xingjun Ma
类目: Artificial Intelligence (cs.AI)
备注: 15 pages, 17 figures, 7 tables. Code: this https URL
Abstract:Web agents increasingly browse, shop, and manage accounts on behalf of their users. Prior work has shown that they can be misled by adversarial content, including prompt injections and deceptive interfaces. Beyond such attacks, agents operating on the open web also encounter online fraud designed to deceive humans, including fraudulent online stores, fake investment platforms, and advance-fee schemes. How web agents respond to such fraud during task execution remains underexplored, in part because scam websites are short-lived, making systematic evaluation difficult. To address this gap, we introduce ScamWorld, an interactive environment that reconstructs reported scam websites across diverse scenarios, fraud mechanisms, and languages. Each reconstructed website reproduces documented fraud mechanisms, making relevant signs of fraud observable before the agent’s final action and enabling controlled, repeatable evaluation. Through an extensive study of web agents powered by six frontier LLMs, we find that baseline deception rates range from 11% to 35% across models. Agents proceed more readily with payments than with requests for credentials or wallet information. A closer analysis of agent trajectories shows that some agents acknowledge suspicious conditions yet proceed, at times treating the user’s instructions as authorisation to accept additional terms imposed by the website. ScamWorld provides a reusable testbed for systematically evaluating and stress-testing web agents against real-world online fraud.
[AI-29] Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks
链接: https://arxiv.org/abs/2611.04848
作者: Jin Zhu,Junxian Zhu
类目: Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML)
备注:
Abstract:Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology. The importance of variable selection has attracted considerable attention in statistics and machine learning. In this paper, we are mainly interested in model-agnostic variable selection for identifying variables that have general nonlinear dependence on the response. We propose MambaPFN, short for Model-Agnostic Markov BlAnket selection with Prior-data Fitted Networks, which is built upon the Transformer, a building block of many popular large language models. Specifically, we consider a functional-mapping perspective on variable-selection learning, in which the Transformer is treated as a learnable mapping from a dataset to its relevant variables. To learn this mapping, MambaPFN introduces three new components: (i) a faithful, rich, and scalable scheme for generating pretraining data, (ii) an objective that targets each variable’s posterior inclusion probability, and (iii) a modified Transformer architecture tailored to variable selection. Once pretrained, MambaPFN is applied to new datasets without parameter updates. Empirically, MambaPFN achieves desirable variable-selection performance in simulation studies and exhibits a scaling law—more pretraining datasets and larger models can continuously improve its performance. Applied to a Facebook comments dataset, MambaPFN selects the fewest variables and achieves the highest computational efficiency and predictive performance, improving upon the best baselines by about 20%.
[AI-30] DGC-Net: Cross-Modal Dynamic Routing with Global-Guided Compensation in Multimodal Sentiment Analysis
链接: https://arxiv.org/abs/2611.04841
作者: Linan Zhu,Huaijing Wu,Xiao Han,Yuhao Wang
类目: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:User-generated videos, including opinion vlogs and video reviews, provide a rich source of people’s attitudes toward products, services, and social topics. Understanding the sentiment expressed by speakers is important for analyzing online opinions, as transcripts alone may not capture the attitude and intensity conveyed through vocal tone and facial expressions. Multimodal sentiment analysis (MSA) addresses this need by integrating textual, acoustic, and visual cues. However, these cues are often subtle, noisy, and inconsistent across modalities. Existing local affective cue modeling methods typically operate within individual modalities and overlook global contexts from other modalities. This may cause cross-modal affective cue inconsistency. Hierarchical global-local fusion methods integrate global semantics with fine-grained local affective cues. However, they commonly determine the relative contributions of the two representations implicitly, preventing the global-local contributions from adapting to the sample-dependent reliability of local cues and resulting in either insufficient use of informative local cues or excessive interference from unreliable ones. To address these issues, we propose DGC-Net, a Dynamic Gating and Global-Guided Compensation Network for MSA. DGC-Net mitigates cross-modal affective cue inconsistency through cross-modal contribution-aware dynamic expert routing. This mechanism uses auxiliary global contexts to guide local expert selection. Moreover, global-guided dynamic compensation fusion alleviates sample-dependent global-local contribution imbalance while preserving global semantic as the primary anchor and adaptively incorporating expert-refined local cues. Experiments on five multimodal benchmark datasets demonstrate the effectiveness and general applicability of DGC-Net, and ablation studies verify the contribution of each proposed component.
[AI-31] Found by Search Broken by a Shortcut: Transfer and Tampering of Searched Intrinsic Rewards
链接: https://arxiv.org/abs/2611.04838
作者: Chase J. Metoyer
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 7 figures, 11 tables. Code and data: this https URL
Abstract:Can a search over innate drives find intrinsic rewards that help a reinforcement-learning agent learn, transfer to a task it never saw, and resist an exploit that was absent during the search? Each candidate reward combines four generic events (a new view, a pickup, a drop and a toggle), each with a weight, a relief and a refill rate. We ran three pre-registered stages; the search protocol was locked in a public, timestamped commit before any search result existed. CMA-ES and an equal-budget random search used 1,560 PPO training runs, and the final evaluation and tamper test used 340. On the held-out task, KeyCorridorS3R2, the CMA-ES-selected reward (E) beat task-only PPO, episodic novelty and the random-search-selected reward ® in all 3 registered comparisons, all of them meaningful by the registered size rule. Among these generic rewards E transferred best; the earlier paper’s satiation reward, which on this task is given hand-wired key and locked-door events, did better (20 of 20 seeds solved against 4 of 20; descriptive). On UnlockPickup, where that reward uses generic events, E’s mean area exceeded it by 0.276 (descriptive). On the training tasks there was no registered difference between E and R: both E-over-R tests missed. A free reset action, absent during the search, collapsed E’s learning on DoorKey-8x8 (mean area 0.829 without it, 0.136 with it; registered) to below task-only PPO (descriptive). The searched drives helped on every task tested, but a reward given task-specific events did better on the held-out task, and selection gave E no protection against an exploit absent from the search.
[AI-32] HTCE: Homeomorphism-inspired Topological Cross-Scene Experience Canonicalization and Reuse for Object Navigation
链接: https://arxiv.org/abs/2611.04836
作者: Xiaoran Xu,Yupeng Wu,Tianyu Xue,Xuanran Dong,Yifan Xu,Xiaoshan Yang,Changsheng Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As embodied agents perform object-goal navigation across residential environments, they accumulate successful experiences whose semantic structure recurs across scenes even when geometric trajectories do not. Reusing such experience is challenging because differences in region naming and observation granularity produce distinct graph representations of similar approaches, while meaningful differences in anchors, branches, and endpoints must stay distinguishable. We introduce HTCE, a homeomorphism-inspired topological framework that canonicalizes and reuses navigation experience. Feature and topology subdivisions characterize representational variations, and their semantically constrained reductions produce comparable semantic sequences and structural signatures while preserving semantic anchors, branching relations, and navigation endpoints. HTCE then retrieves historical successful approaches through subdivision-aware matching as guidance for a frozen vision-language policy. Canonicalization compresses the experience index losslessly, merging 17.7–23.6% of distinct stored route descriptions while retaining full task performance. Under a single frozen vision-language backbone shared by all compared systems, HTCE attains the best SR and SPL in every setting—59.9%/31.8% on HM3D v0.1, 71.0%/31.5% on HM3D v0.2, and 46.6%/16.6% on MP3D—with SR gains of up to 4.9 points and structured retrieval contributing up to 2.8 SPL points. The code is available in this https URL
[AI-33] Reconstructing Fraudulent Strategies in Online Payments via Interpretable Inverse Reinforcement Learning
链接: https://arxiv.org/abs/2611.04819
作者: Huilin He,Weiyu Han,Linbo Shao,Ruobing Zuo,Xiao Tan,Shuai Chen,Dawei Cheng
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Payment fraud causes financial losses and undermines users’ trust in web payment services. Although fraud detection is a critical defense for payment security, complex and diverse fraudulent behaviors can still evade detection. Generating fraud samples for data augmentation can broaden detectors’ coverage of different fraud patterns. However, existing methods matching the distribution of transaction attributes alone do not ensure that generated sequences preserve the sequential relationships among transaction choices, payment feedback, and subsequent behavioral adjustments. To this end, we propose PaySeqGen, an interpretable inverse reinforcement learning framework that reconstructs fraud behavior policies from fraudsters’ observed payment trajectories. We design a mechanism for constructing candidate actions to support plausible payment sequence generation while enabling exploration. The framework also learns interpretable behavioral rewards that capture fraudsters’ preferences across different transaction contexts. By alternating between reward learning and policy optimization, the framework reconstructs policies that generate new fraud sequences for detector augmentation and learns interpretable rewards supporting sequential fraud-pattern analysis in real trajectories. Experiments in real-world payment scenarios at Ant International, a leading global digital payment provider, show that PaySeqGen outperforms state-of-the-art generative augmentation methods. Reward-based trajectory analysis also identifies fraud patterns of practical value, helping analyst locating suspicious segments and examine multistep fraud tactics. Evaluation in the deployed system of online payment further confirms its effectiveness.
[AI-34] he Pen is Mightier than the Pixel: Natural Textual Unlearnable Example for Large Vision Language Models
链接: https://arxiv.org/abs/2611.04817
作者: Chenyu Yi,Jiawen Zong,Sami Hormi,Bingquan Shen,Xudong Jiang,Alex Chichung Kot
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The proliferation of Large Vision Language Models (LVLMs), trained on vast and often sensitive datasets, raised concerns regarding data privacy and unauthorized usage. Unlearnable Examples (UEs), which subtly perturb training data to mislead the models, have emerged as a promising defense method. This paper presents the first exploration of UEs specifically on LVLMs, which poses unique challenges due to their inherent multimodal nature. We propose a novel text-based perturbation method, Natural Textual Unlearnable Examples (NTUE), which can effectively mislead LVLMs by subtly manipulating the image label, establishing a shortcut between an identity of interest and a separate target, without introducing factual misinformation or affecting readability. Comprehensive experiments compared with traditional UE methods demonstrate the superior performance of NTUE. Our findings show that NTUE achieves a high success rate with a poison rate as low as 5%, underscoring its potential as a practical and robust solution for protecting multimodal data in the era of LVLMs.
[AI-35] Agent icFinBench: Benchmarking Financial Agents on Diverse Tasks from Real-World Requests
链接: https://arxiv.org/abs/2611.04813
作者: Shu Zhou,Ziyang He,Yuxin Rao,Yudong Fang,Jiali Qian,Dongyuan Chen,Juntao Zhang,Yong Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Financial agents are becoming an important class of real-world AI systems. Financial tasks vary widely in type and difficulty, and many are open-ended with no ground-truth answers. However, existing benchmarks neither capture this breadth and open-endedness nor separate grounding from correctness. To bridge this gap, we introduce \textbfAgenticFinBench , a benchmark of 230 real-world financial tasks spanning 8 categories, 2 languages, and 4 complexity levels. We also propose a reference-free evaluation framework that combines question-specific rubrics with a two-stage claim audit that detects hallucinations, defined as claims absent from the agent’s trace and refuted by external evidence. This framework better matches how financial work is assessed in practice. Experiments across 18 agent configurations reveal three key findings: (1) Ignoring hallucinations can overstate agent performance: hallucination auditing lowers the baseline leader’s score by 7.58 points and moves it to fourth place. (2) Harness differences explain more variation in evidence grounding than model differences (28.6% vs 8.8%). (3) Quality scores fall from 89.44 on direct retrieval to 79.18 on the most complex tasks. Case studies show that agents struggle to explain how events affect company profits or stock prices, compare financial data on a consistent basis, and justify their conclusions. By releasing AgenticFinBench, we aim to advance financial reasoning, factual verification, and agent design for more reliable responses to diverse real-world financial requests. Our project page is at \textbfthis https URL .
[AI-36] RAHC: Rule-Augmented Hyper-Relational Knowledge Graph Completion for Materials Synthesis
链接: https://arxiv.org/abs/2611.04809
作者: Jiawei Sun,Sijia Xu,Zihan Chen,Zhengyi Yang
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 3 figures, including references and appendices
Abstract:Scientific discovery depends on reusing experimental knowledge, yet records scattered across papers and repositories are often incomplete. In materials synthesis, recovering missing conditions requires reasoning about the target material and the observed context of a specific experiment. Hyper-relational knowledge graphs (HKGs) represent this context through qualifiers, but existing completion methods leave domain knowledge and experimental regularities implicit. We propose RAHC, a rule-augmented HKG framework for single-slot and joint multi-slot synthesis-condition completion. RAHC builds a traceable rule catalog from training records and retrieved literature, retrieves rules for each query-candidate pair, and separately encodes graph structure and rule evidence. Hard rules constrain candidate validity, while soft rules guide ranking. RAHC-G aggregates rules using global reliability; RAHC-Q additionally learns query-specific relevance. Gated fusion combines structural and rule representations, while tuple-conclusion rules provide cross-condition evidence for joint completion. Experiments on five materials-synthesis datasets show improvements in single-slot and joint completion under Semi-inductive and Open-Answer Inductive settings, with Semi-inductive MRR gains of up to 15.6% over MAYPL. RAHC also improves performance on the entity-disjoint ILPC’22 Small benchmark, supporting transfer to fully inductive KG completion. These results support experimental condition selection, synthesis planning, and the reuse of scientific knowledge.
[AI-37] Density-Aware Reward Scaling: A Reinforcement Learning Framework for Reward-Proportional Sampling
链接: https://arxiv.org/abs/2611.04792
作者: Ria Shekhawat,Abhijeet Sinha,Yingtao Zhu,Dianbo Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning objectives that maximize expected return can concentrate probability mass on a small subset of high-reward outcomes, limiting diversity when multiple distinct solutions are desirable. We introduce Density-Aware Reward Scaling (DARS), a simple reward-processing mechanism that promotes reward-proportional sampling in continuous outcome spaces while retaining standard policy-gradient training. DARS estimates the local density of outcomes within each on-policy rollout group and inversely scales rewards by this density. The method requires no learned density model, auxiliary objective, critic, or modification to the underlying policy-gradient objective. In a controlled multimodal navigation task, DARS-GRPO avoids the single-mode collapse exhibited by GRPO and closely recovers the target mode allocation. Applied to text-to-image prompt adaptation, DARS-GRPO consistently improves image and prompt diversity across datasets, Policy LMs, and reward settings while maintaining good semantic alignment. These results show that density-aware reward scaling provides a lightweight way to improve coverage of diverse high-reward outcomes within existing reinforcement-learning pipelines.
[AI-38] Kierston KierstonBench: Simulation of Allentown PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks
链接: https://arxiv.org/abs/2611.04785
作者: Andrew Semenov,Svyatoslav Dorofeev
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance. Their capability and accuracy in this work cannot currently be measured. Real deployments lack the ground truth needed to score a model’s conclusions, and no benchmark evaluates these capabilities in a simulated environment. We present Kierston, a city-scale simulation engine, and KierstonBench, a benchmark that tests these capabilities against known simulated events. Kierston models approximately 125,000 residents of Allentown, Pennsylvania, generating their daily activities and the telecommunications, financial, administrative and license-plate records that surveillance and investigative systems collect about them. These records derive from a common behavioral timeline and are distributed across sources representing separate providers. Models investigate through a restricted records interface, linking observations across sources without access to the underlying simulation. The benchmark comprises 32 task families covering record linkage, routine reconstruction, incident investigation and assessment of deceptive accounts. In an evaluation of sixteen language models, most could not perform records investigation and submitted identifications the records did not support. A small number of frontier models solved most of the tasks and abstained when evidence was insufficient. In some cases these models still fell for common deceptions and assumptions, and none replaced an investigator.
[AI-39] Reducing Logprob Without Causal Verification: Diagnosing SAE-Based PII Suppression in LLM s AACL
链接: https://arxiv.org/abs/2611.04782
作者: Chaerin Lee,Wonho Lee,Sohee Park,Daeseon Choi
类目: Artificial Intelligence (cs.AI)
备注: Accepted to Findings of AACL-IJCNLP 2026
Abstract:SAE-based feature-level interventions to suppress PII leakage in LLMs are primarily evaluated by log-probability reduction. We show that this criterion cannot distinguish feature specific suppression from a magnitude artifact: a direction-agnostic effect of sufficiently large activation perturbations. Across 36 (model, layer, PII type) cases, we compare selected SAE feature interventions with coherencematched random direction controls using logprob and rank-aware metrics. While 17 cases (47%) appear successful under logprob-only evaluation, only 1 case (2.8%) passes our multimetric framework. The rest reveal two failure modes: random directions outperform selected features, and feature selection collapse across PII types. These results indicate that existing evaluations cannot verify whether interventions target causal pathways of PII leakage. A 9,000 run causal mediation feasibility study corroborates this: the verifier separates candidates reliably, but correlation-selected candidates rarely pass it. We therefore propose a new research mission for NLP: causally grounded PII suppression through circuit-level localization and causal mediation analysis.
[AI-40] Replica-Aware Split Inference for Edge IoT Systems
链接: https://arxiv.org/abs/2611.04779
作者: Zied Jenhani,Nasrin Razmi,Admela Jukan
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Split Inference (SI) addresses the hardware resource limitations of Internet of Things (IoT) devices by distributing tiny Machine Learning (TinyML) model segments across multiple IoT devices. However, a device failure can interrupt the inference pipeline. This paper proposes a replica-aware recovery approach that deploys replicas of TinyML model segments originally hosted on failed devices onto surviving devices. We formulate the joint selection of replica hosts and communication protocols as a mixed-integer linear programming (MILP) problem that minimizes post-reconfiguration inference latency, accounting for computation and transmission latencies, under flash memory and runtime memory constraints. Simulations using MobileNetV2 on heterogeneous IoT networks compare the proposed approach with four baselines. We implement the model in a network of 15 IoT devices with a maximum link-rate reduction of 75%, and show that MILP approach achieves an average modeled end-to-end inference latency that is 6.8% lower than that of Computation Greedy and 18.3% lower than that of Random. Index Terms TinyML, split inference, Internet of Things, fault tolerance, replica placement.
[AI-41] actiNavi: Serving and Revising an LLM Game Coach with Explicit Strategy Conditions
链接: https://arxiv.org/abs/2611.04772
作者: Qi Liu,Xiaoyang Yuan,Zhuomeng Zhang,Yubin Ruan,Xinyi Mou,Wenjin Wang,Hanying Jiao,Di Wu,Jinke Li,Jingxuan Hong,Ke Feng,Zixun Sun
类目: Artificial Intelligence (cs.AI)
备注: 17 pages, 1 figure
Abstract:A deployed LLM coaching agent answers requests whose correct response depends on player state and game version, so advice that is right for one player can be wrong for the next. We describe TactiNavi, the serving system behind the LLM coach of a commercial open-world game, which makes strategy applicability an explicit artifact shared by offline compilation, online execution, failure diagnosis, and release. An offline compiler turns versioned game evidence and expert guidance into strategy packages with explicit prerequisites, the serving plane evaluates those prerequisites as three-valued conditions over structured player state and records decision traces, and a control plane uses the resulting condition-rule dependencies to validate revisions on held-out traffic before release. In a retrospective evaluation of 1,122 logged coaching turns from three deployment periods, condition-bearing packages raise correctness from 57.9% to 82.0% over atomic facts and reduce strategy misapplication under changed player state from 18.1% to 10.4%, while trace-based claim verification reaches 86.4% precision at a median reply latency of 2.07 s. The deployment also showed that most apparent fixes are generation noise, because an unchanged-system rerun recovers 38.4% of failing turns and only one of 15 shipped revision units passes the release checks retrospectively. We distill these results into lessons on treating applicability as a first-class serving object, sizing traces for later diagnosis, and building the null model into the release path.
[AI-42] When Should an LLM Steering Edit Depend on the Input?
链接: https://arxiv.org/abs/2611.04757
作者: Rachael Harkness,Konrad Rawlik
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:An activation steering edit should depend on the input exactly when the destination varies \emphinside the class a query requests, and that is measurable before any rule is fitted or applied. Contrastive Activation Addition (CAA) adds one fixed direction to every input, the difference of the class means. Conditioning it on the requested class accounts for the between-class variation in the required displacement and leaves the within-class variation untouched, which no rule that reads only the source state can express. We study the impact of increasing input-dependence in the steering edit, moving from a difference of means to a destination retrieved per query — the mean of the k stored states nearest the query, inside the requested class — in which the two baselines are endpoints: at k equal to the class size the retrieved rule \emphis the class-conditional difference of means. We measure input-dependent steering on six substitution corpora that vary in whether the edit changes a word or a clause, and on an external benchmark, and find that input-dependent steering outperforms fixed-direction steering when the destination varies within the class. Retrieval wins at every layer of every substitution corpus in two models, and the level of input-dependence needed governs how to apply it: word-level edits are best made in the first tenth of the network from a few neighbours, clause-level edits at any depth from the class mean. Where the class already determines the answer the class mean suffices and retrieval only matches it, though a fixed direction still fails there. Four quantities computed from the states alone identify the regime in which the edit should be applied.
[AI-43] APES: Task-Aligned Joint Predictive Pretraining for Transaction Event Sequences
链接: https://arxiv.org/abs/2611.04747
作者: Ruobing Zuo,Weiyu Han,Huilin He,Haoyu Jiang,Ze Zhao,Jie Sheng,Xiao Tan,Shuai Chen,Tianyi Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Transaction event sequences support diverse prediction tasks in online commerce and digital financial services. The same transaction records can yield prediction targets with different semantics, including values, changes, and relations to past events. These targets impose different constraints on history representations, but their predictability alone does not establish the downstream utility of a target set. Moreover, target-wise prediction losses do not explicitly model conditional co-occurrence among targets. To address these challenges, we propose TAPES, a task-aligned joint predictive pretraining framework for transaction sequences. TAPES constructs a finite collection of candidate target sets from admissible field-operator combinations. It selects a target set according to the downstream utility of representations learned through short joint pretraining trials under matched budgets. It then reinitializes the encoder and pretrains it on the selected targets through a joint likelihood parameterized by a shared discrete latent variable, before downstream supervised adaptation. Experiments in a real-world transaction-risk setting and on multiple public financial benchmarks spanning diverse downstream tasks demonstrate that TAPES outperforms representative baselines. Ablation studies further validate the contributions of predictive target selection and joint modeling.
[AI-44] CLARA: A Cognitively-Driven Clarification Framework for Grounding Ambiguous Requests
链接: https://arxiv.org/abs/2611.04708
作者: Dimitrios Bekris,Marios Glytsos,Panagiotis P. Filntisis,Niki Efthymiou,George Retsinas,Petros Maragos
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Work conducted in early 2025
Abstract:Robotics and AI systems are increasingly deploying large vision-language models (LVLMs) to follow high-level instructions, from routine household chores to more complex organizational tasks. Yet, broad directives like “clean the room” or “organize the desk” frequently omit crucial details about user preferences and specific arrangement of objects that influence the desired outcome. Without any further guidance, such models tend to overestimate their grasp of ambiguous instructions and frequently assume they have sufficient understanding despite missing important details. In this paper, we propose CLARA (Conversational cLArification for Robotic Arrangement), a cognitively-driven clarification framework that proactively elicits key information from users, in a similar fashion as humans’ clarification seeking behavior. Through targeted questioning, CLARA refines ambiguous requests into structured, detailed instructions describing precisely how objects should be organized or rearranged, according to the user’s preferences. These clarifications then guide a diffusion-based image generation pipeline, producing realistic visual renderings of the final, desired scene. This final rendering also serves as a visual confirmation of the system’s understanding from the user. Experimental results and user studies demonstrate that CLARA leads to more accurate and consistent outcomes compared to another State-of-the-Art LVLM system, highlighting the importance of interactive clarification for bridging the gap between vague directives and fully grounded task specifications.
[AI-45] Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation
链接: https://arxiv.org/abs/2611.04689
作者: Jinwu Hu,Langyu Bian,Jian Sun,Dongjin Yang,Chao Shuai,Yufeng Wang,Kai Zhou,Bin Xiao,Mingkui Tan,Hongmin Cai,Wei Luo
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Open-set test-time adaptation (TTA) aims to adapt source-trained models to unlabeled test streams where covariate-shifted in-distribution (csID) samples and covariate-shifted out-of-distribution (csOOD) samples may coexist. Existing TTA methods are typically designed under a closed-set assumption and therefore lack explicit mechanisms for reliably distinguishing csID from csOOD samples. Consequently, csOOD samples may be mistakenly included in confidence-enhancing adaptation, producing harmful gradients that degrade known-class performance. To address this, we propose Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation (UAPA), a plug-and-play method for reliable open-set adaptation. Specifically, we first introduce Uncertainty-Driven ID/OOD Separation, which combines a classifier-prototype score with a mini-batch-normalized evidence-derived uncertainty score to distinguish csID from csOOD samples. We then propose a Dynamic Prototype Refinement strategy that periodically refines class-specific target prototypes using buffered csID features to progressively follow the estimated class directions. Lastly, we introduce Contrastive Prototype Entropy Adaptation, which applies entropy minimization to csID samples and entropy maximization to csOOD samples, while prototype-level contrastive learning aligns csID features with their corresponding target prototypes and reduces the maximum similarity between csOOD features and known-class prototypes. Empirically, UAPA outperforms the strongest baselines on the CIFAR benchmarks and Tiny-ImageNet-C, improving average OSCR by 2.08 and 1.28 percentage points, respectively.
[AI-46] Best-of-Depth: Learning Exploratory Dynamics in Looped Transformers
链接: https://arxiv.org/abs/2611.04663
作者: Shota Takashiro,Takeshi Kojima,Masanori Koyama,Kohei Hayashi,Yusuke Iwasawa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Looped Transformers extend inference-time computation through repeated latent-state updates, but final-depth training does not directly encourage useful intermediate alternatives. We introduce Best-of-Depth (BoD), which trains a recurrent trajectory as a candidate set by minimizing prediction loss across depths. Different valid targets can be matched at different depths without architectural changes or depth-specific labels. An exact-match analysis connects this objective to solution coverage. On N-Queens completion and graph coloring, BoD improves coverage over final-depth and intermediate-supervision objectives. N-Queens coverage reaches 91.03%, versus 45.75% for final-depth training. Further experiments show discovery beyond the training depth and recovery of solutions withheld from teacher pools on familiar inputs. Fine-tuning a pretrained looped language model with BoD yields higher pooled code-generation success than final-depth training when programs are generated separately at evaluated fixed depths. These results support training recurrent computation for solution discovery along its trajectory.
[AI-47] Does LinUCB Need Valid Confidence Sets?
链接: https://arxiv.org/abs/2611.04641
作者: Ahmed Ben Yahmed,Marc Abeille,David Janz,Clément Calauzènes
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We show that replacing LinUCB’s usual confidence width \beta by any fixed \alpha0 preserves sublinear regret when the action set is M -smooth and m -strongly convex. Our high-probability regret bound for this generalisation, \alpha -LinUCB, establishes that valid confidence sets are not necessary for the success of LinUCB. We identify the mechanism behind this result: action-set curvature drives \alpha -LinUCB to explore every direction, even when optimism is not guaranteed. To analyse this mechanism, we introduce a logarithmically capped spectral potential that, unlike the standard log-determinant potential, caps contributions from well-explored directions. This yields lower bounds on the smallest eigenvalue of the design matrix. At the standard confidence width \alpha=\beta\asymp\sqrtd\log T , our T -round regret bound in d dimensions is O\bigl(\fracMmd\sqrtT\log T\bigr) , up to iterated-logarithmic factors, improving the horizon dependence of the classical LinUCB guarantee by a factor of \sqrt\log T .
[AI-48] SAT Encoding for Set of At-Most-K Cardinality Constraints with Ladder Shape
链接: https://arxiv.org/abs/2611.04637
作者: Hieu Truong Xuan,Tuyen Kieu Van,Khanh To Van
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents a novel SAT encoding designed to represent a sequence of At-Most-K cardinality constraints arranged in a ladder-like structure, referred to as the Ladder AMK constraint. These AMK constraints feature a ladder-like arrangement, with shared common variables in the center and two variables differing at the beginning and the end. The proposed encoding first decomposes the sequence of AMK constraints into separated blocks, then encodes each block as a whole rather than encoding each AMK constraint individually. This approach improves the efficiency of constraint solving in SAT solvers by significantly reducing the size of the encoding, precisely the number of variables, and the generated clauses required. To achieve this, the encoding employs Sequential Counter to encode each block and constructs clauses that link adjacent blocks, ensuring equivalence with the input constraints. Compared to the previously proposed encoding that uses BDD for Ladder AMO constraint, our encoding requires fewer variables and clauses. Our study is the first study that develops SAT encoding for Ladder AMK constraints with k1. Experimental results on single Ladder AMO and Ladder AMK constraints indicate that our encoding significantly enhances both the size of the encoding and the efficiency of solving time compared to existing encodings. Furthermore, by evaluating on the two real-world combinatorial problems, namely Anti-bandwidth Problem and Nurse Rostering Problem, we found that the proposed encoding surpasses alternative encodings, as well as more advanced Constraint Programming and Mixed Integer Programming solvers. In summary, the SAT encoding technique presented in this paper provides a more efficient method for encoding sequence constraint, specifically Ladder AMK constraint, which could enhance the effectiveness of SAT solving for various problems involving this type of constraint.
[AI-49] ValQID: Learning the Net Value of Semantic Query Groups for Web Device Identification
链接: https://arxiv.org/abs/2611.04576
作者: Yifan Yang(1),Zhen Huang(1),Weijia Shi(1),Xichen Tan(1),Zhihuang Liu(2),Xionglve Li(1),Zhiping Cai(1) ((1) National University of Defense Technology, Changsha, China, (2) Xiamen University, Xiamen, China)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Web interfaces are widely used to access network services and manage networked devices. Identifying the vendors and types of devices behind Web interfaces is a critical step in asset inventory, security management, and risk assessment. Device identity cues are dispersed across page text, configuration fields, and management functions, and interpreting them often requires linking related Web objects. Responses and objects require different amounts of semantic analysis, so exhaustively processing all candidate content can introduce unnecessary computation. We present ValQID, which uses a relation graph to combine dispersed device identity cues, evaluates semantic questions efficiently with DSEJEV, and accounts for complementarity among semantic questions when estimating computation value. Based on the current identification state, ValQID weighs each question group’s expected identification benefit against its execution cost and adaptively allocates computation to identify device vendors and types. Evaluation on real Web response data shows that ValQID achieves relative gains of up to 47.1% in joint macro-F1 and up to 82.1% in macro-F1 for low-frequency vendors over a state-of-the-art method. DSEJEV speeds up semantic processing by up to 8.58 times compared with general-purpose language models.
[AI-50] A research world model of power engineering learns its dynamics and reveals what-ifs
链接: https://arxiv.org/abs/2611.04566
作者: Fanping Sui,Ying Zhang,Liwei Lin
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Decisions about the direction of research in power engineering determine which technologies mature and, through them, shape infrastructure that lasts decades, yet their consequences cannot be observed before they are taken, and existing forecasts of research fields extrapolate a single trajectory. Here we introduce research world models, which treat a research field as a dynamical system that can be acted upon, and establish one for power engineering from 1.1 million papers. A distilled language model reads each paper into role-typed concepts, two sophisticated, high-fidelity graph-temporal neural network cores learn how the resulting concept network evolves, and operators learned from natural experiments in the publication record estimate the effects of interventions. In temporally held-out tests, the model forecast years ahead which concepts would grow and which method-target pairs would be joined for the first time, outperforming tuned machine-learning baselines in most settings, and its estimated intervention effects tracked those realized in natural experiments it had not seen. Its training scheme and three of its operators replicated in two fields withheld from development. Questions posed in natural language are compiled by a domain-specialized 27-billion-parameter language model into executable actions and answered as paired simulations with and without the intervention. The answers were rated 4.1 of 5 for faithfulness by an independent language-model judge. By revealing the latent dynamics of a research field and its response to interventions, research world models offer a new way to simulate and test research decisions before they are made.
[AI-51] CANDO: Conservative Action-Noise Directional Optimization for Residual Reinforcement Learning
链接: https://arxiv.org/abs/2611.04543
作者: Zeyu Gao,Chengkai Hou,Chaoyue Li,Zhuangzhe Wu,Jiaming Liu,Yao Mu,Peng Jia,Li Zhang,Shanghang Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Pretrained generative policies provide strong behavior priors for robot control, but adapting them efficiently with online reinforcement learning remains difficult. A central challenge is how to improve a frozen generative policy without destroying the structure learned from offline demonstrations. Noise-space residual methods address this by modifying the policy’s input noise, but existing noise-space methods typically regulate adaptation through fixed latent feasible regions, which may exclude valuable noise edits. In contrast, action-space residual methods are more sample-efficient, but their efficiency can still be further improved. We propose Conservative Noise Directional Optimization (CNDO), a noise-space residual RL method that improves a frozen generative policy by learning state- and noise-conditioned directions around Gaussian latent samples. CNDO removes predefined latent feasible regions and instead regulates noise-space adaptation through the soft conservative constraint, which consists of conservative value estimation and gated execution. We further introduce Conservative Action-Noise Directional Optimization (CANDO), which adds an action-space residual, combining structured latent-space improvement with local action correction. Experiments show that, once CNDO narrows the gap between noise-space and action-space residual learning, CANDO further improves over single-space optimization, achieving 76.1 NAUC and a 91.4% final success rate, surpassing the best single-space results by 6.6 NAUC and 4.1% success rate. More demos and info are available on this https URL.
[AI-52] Learnings and Pitfalls from AI-Assisted A/B Experimentation at Scale
链接: https://arxiv.org/abs/2611.04515
作者: Brett Daley,Ilya Gorbachev,Anubhav Natani,Nicholas Brown,Athena Dai,Rachel Song,Marcin Pekalski,Josip Domazet,Anchal Choubey,Daniel Ting
类目: Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure, 2 tables. Accepted to the Conference on Digital Experimentation at MIT (CODE@MIT 2026)
Abstract:A/B testing underpins product decisions by providing trustworthy, reproducible quantification of proposed changes’ effects. Growing large language model (LLM) capabilities have motivated efforts to automate experimentation workflows. Yet LLMs are nondeterministic and prone to hallucination, properties that conflict with the trust experimentation systems must provide. Experiments are also operationally messy: diagnosing anomalies, debugging configurations, and conducting bespoke follow-up analyses require flexible reasoning and manual effort. These tasks let agents accelerate experimentation without replacing its deterministic statistical foundations. We report lessons from building and operating such an agent. Our central finding is that this tension is resolved not by making the model deterministic, but by bounding where its nondeterminism can reach; the model selects and sequences procedures, while deterministic code computes every quantity it reports. Three consequences surprised us: withholding information from the model improved its answers, demand concentrated on mundane retrieval rather than the autonomous decisions we anticipated, and the agent’s eagerness to help threatened statistical validity. Comments: 5 pages, 1 figure, 2 tables. Accepted to the Conference on Digital Experimentation at MIT (CODE@MIT 2026) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2611.04515 [cs.AI] (or arXiv:2611.04515v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.04515 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-53] Fast Cheap and Fair? Evaluating Decision Models
链接: https://arxiv.org/abs/2611.04510
作者: Muhammad Uzair Ul Haq,Davide Rigoni,Alessandro Sperduti,Flavia Fabris
类目: Artificial Intelligence (cs.AI)
备注: submitted for review
Abstract:Industries are increasingly adopting AI-driven systems to automate a wide range of tasks, including high-stakes decisions in human resources, such as candidate screening and recruitment. This makes evaluating their fairness essential, particularly when decisions directly affect individuals’ opportunities. Accordingly, the EU AI Act classifies recruitment AI systems as high-risk, imposing strict requirements on their deployment. In this context, compact ``System~1’’ decision models offer a fast and cost-efficient alternative to general-purpose LLMs, yet their fairness remains largely unexplored. This article presents the first systematic bias and fairness audit of \jev, a typed-decision model targeting recruitment applications. The study evaluates how explicit demographic attributes and implicit candidate cues influence screening scores, rankings, and shortlisting outcomes. These effects are compared against a noise floor derived from name substitutions and typographical variations. The evaluation also examines output validity, latency, and cost. Counterfactual CV datasets and evaluation code are released for reproducibility.
[AI-54] Signals: Continuous Diagnostics of AI Agents
链接: https://arxiv.org/abs/2611.04507
作者: Amey Rambatla(1),Vatsal Goel(1),Sagar Gaur(1),Siddhant Loya(1),Anurag Mishra(1),Asif Mohammad(1) ((1) Cisco)
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 1 figure, 6 tables
Abstract:AI agents can make incorrect decisions or waste resources even when their tools and infrastructure work normally. Their nondeterministic behavior makes these problems difficult to reproduce and diagnose. We developed Signals to identify such problems from agentic traces and suggest concrete remediations. We built the method around a taxonomy of 45 problem types across eight families, with definitions and evidence requirements for each type. The method distinguishes realized failures, risk exposures, and inefficiencies, groups related findings into engineering issues called Signals, and assesses their severity from the supporting evidence. Each Signal includes actions a developer can take and checks for evaluating the change. Further executions can add evidence to an existing Signal or reveal a new problem. We evaluated the method on public agent executions and achieved 91.43% F1 when matching Signals to reference issues, about 18 percentage points higher than a one-pass LLM control that used the same model and taxonomy.
[AI-55] Spatial Memory for AR Glasses: Hierarchical Visual Acquisition and Source-Linked Recall
链接: https://arxiv.org/abs/2611.04494
作者: Tianxing Zhu,Dan Huang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present a spatial-memory system that enables optical see-through AR glasses to recall earlier physical encounters. The dual-camera prototype combines hierarchical visual acquisition with source-linked storage and staged retrieval. Quality screen-ing, global and regional appearance novelty, and temporal support select monochrome observations for detailed RGB cap-ture. Semantic observations retain links to their source images, allowing retrieval to answer indexed historical questions direct-ly or inspect visual details omitted from scene descriptions. The architecture separates capture acceptance, interpretation out-come, and support for a particular question. This distinction motivates two core control mechanisms: bounded reassessment of familiar views when earlier processing leaves an evidence gap, and retention guided by source dependencies. In a 131.35 s egocentric replay, the implemented acquisition policy selected six RGB views from 648 monitoring observations, while feature extraction ran on 97.7% of observations. These results demon-strate selective RGB acquisition and identify frequent local inference as a remaining cost. Exposure duration and cross-view timing further constrain the evidence preserved from se-lected encounters. The system provides a practical acquisition-to-recall architecture for persistent AR assistance, with explicit boundaries between captured evidence, indexed interpretations, and supported historical claims.
[AI-56] Perseus: A Full-Stack System for Homomorphic Model Inference
链接: https://arxiv.org/abs/2611.04488
作者: Alessandro Zirilli,Davide Marincione,Evgenios M. Kornaropoulos,Giuseppe Ateniese,Emanuele Rodolà
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 4 pages, 5 tables
Abstract:Large Language Models (LLMs) have made it exceptionally convenient for users to send their private data to model providers, which process them in plaintext. Fully Homomorphic Encryption (FHE) promises to secure remote inference by computing directly on encrypted data, yet it has remained prohibitively slow. The effort to make it practical remains fragmented, with most existing frameworks being closed-source, which keeps progress from compounding. We present Perseus, a free and open Python software library that supports the full stack of encrypted model inference, from the cryptographic backend and data packing, to encrypted nonlinearities and end-to-end model execution. Perseus ships with built-in novel optimizations, one at each level of the stack: in-kernel regeneration of key-switching keys at the (i) backend level, a complex variant of Cachemir packing at the (ii) data level, and a state-of-the-art bootstrap placer that is both packing- and magnitude-aware at the (iii) model level. Perseus achieves state-of-the-art latency across multiple models and modalities, within 3% of the plaintext models’ accuracy and perplexity. Remarkably, it is 3.96\times faster at single-token decoding than any other implementation on Llama-3-8B. We initiate, for the first time, a public speedrun on encrypted GPT-2, so that progress can compound.
[AI-57] Reconstruction-Based Diagnosis through Gödel-Regularized Virtual Observability
链接: https://arxiv.org/abs/2611.04487
作者: Oliver Niggemann,Jonas Ehrhardt,Alexander Diedrich
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 1 figure
Abstract:Not every component in a technical system can be equipped with sensors. Hence, when a fault occurs, available sensors often detect it only after its effects have propagated. This makes the isolation of faults difficult. We approach this problem by introducing virtual sensors. For each combination of component and fault type, we let external knowledge describe the fault-sensitive variables. We introduce a novel neural observer that is only trained on normal data. For fault isolation, it uses available sensor measurements where it is possible and reconstructs missing variables as virtual sensors to predict normal behavior. It detects deviations between current values and nominal predictions and computes local symptoms, which are then compared by onset time. The result is a set of root causes describing the faulty behavior. We use Gödel logic to encode physical knowledge and guide training. We evaluate our approach on synthetic networks, the Tennessee Eastman Process, and datasets of two modular production plants.
[AI-58] CogniLife: Continuous Cognitive Distillation for Deployed Multi-Expert Agent Marketplaces
链接: https://arxiv.org/abs/2611.04483
作者: Xuwei Tan,Jingchun Huang,Ming Cai,Shuangquan Lyu,Tiange Wang,Yinzhi Wang,Ronghua Shi,Jingwei Song,Yang Jingsong,Yinghui Xia,Hehua Fan,Yifan Zhou,Hanxin Chen,Jiwei Song,Tianyu Shi
类目: Artificial Intelligence (cs.AI)
备注: 21 pages
Abstract:Multi-expert agent marketplaces – where creators publish domain-specific AI experts for end users to consult – are an emerging deployment pattern for language technology. A published expert can receive many requests yet little evidence about its creator, who remains active elsewhere: the cold-expert problem. We frame continuous cognitive distillation as converting attributed creator observations into revisable decision rules, communication patterns, and domain procedures. CogniLife proposes a cross-expert attribution design for this problem, with Creator Profiles for transferable creator patterns and Expert Journals for listing-specific knowledge; this design is not yet implemented or evaluated. A deployed platform partially realizes the surrounding lifecycle through creator-authorized authoring, capability-owned proposal filtering, and reversible consolidation of eligible owner-scoped memory. On ProfyBench-v1, an 85-task offline benchmark built from replay seeds and controlled internal tests, the Profy Full configuration as a whole achieves a mean overall score of 4.65/5 from the primary LLM judge, compared with 4.01 for Raw LLM and 4.28 for Expert Prompt, with the largest gains in completeness, specificity, and missing-information tasks. These configuration-level scores do not test cold-expert mitigation, cross-expert transfer, or longitudinal distillation.
[AI-59] A Parametric Linear Conic Projection for Structured Sparse Attention
链接: https://arxiv.org/abs/2611.04479
作者: Michel Barlaud,Clement Genninasca,Maxime Zanella,Benoit Macq,Benoit Legat
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In this paper, we introduce the Cone Alignment Index (CAI), a scale-invariant sparsity measure associated with a Lorentz-cone geometry. We provide a probabilistic analysis of the distribution of the CAI score, including a general theorem and explicit corollaries for Gaussian and uniform distributions. As a consequence, we show that, in the high-sparsity regime, the probability that the samples lie within the convex cone tends to one. Building upon this formulation, we derive two complementary projections: the \emphParametric Linear Conic Projection (PLCP), an efficient closed-form approximation, and the \emphParametric Euclidean Conic Projection (PECP), the exact Euclidean projection onto the cone boundary. Our analysis establishes several fundamental theoretical properties. We prove that PLCP and PECP are collinear, differ only by a positive radial scaling factor, and induce exactly the same sparsity threshold, thereby preserving an identical active support. Furthermore, we derive a support-identification theorem showing that, for any prescribed sparsity level ln , the number of coefficients discarded at each iteration is theoretically controlled. Building on this theoretical foundation, we develop a bilevel PLCP framework for structured matrix sparsification. The proposed method naturally generates hardware-friendly structured sparsity patterns, including row-wise, column-wise, and diagonal masks, making it particularly well suited for accelerating Transformer attention mechanisms. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2611.04479 [cs.AI] (or arXiv:2611.04479v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.04479 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-60] reating the Symptom Missing the Cause: Anatomy of a Scientific Reasoning Failure NEURIPS2026
链接: https://arxiv.org/abs/2611.04473
作者: Anna Gogleva,Thomas Charman,Martin Balla,Karl Gemayel
类目: Artificial Intelligence (cs.AI)
备注: 18 pages (8 pages main text, remainder appendix), 9 figures, 6 tables. NeurIPS 2026 Workshop on Failure Modes of AI in Biology (ICBINB-BIO); non-archival
Abstract:Scientific agents can find interventions that work without understanding why they do. We study this failure mode in controlled synthetic regulatory systems, where agents must experimentally identify a hidden fault and rescue the resulting phenotype. Across seven LLMs, every model restores the healthy phenotypes more often than it identifies the fault. Claude Opus 4.6 and Sonnet 4.6 rescue every organism while diagnosing only 55% and 35% of them. This failure is not simply due to insufficient exploration or unavailable evidence. In most cases, the cause is experimentally identifiable, and agents encounter evidence that distinguishes competing causes in 91% of relevant sessions, and still frequently misdiagnose the system. Targeted probes reveal where experimental reasoning breaks down. Models reliably interpret decisive evidence and revise hypotheses when the causal comparison is explicit, and can locate that evidence within a full experimental record. However, they struggle to predict the outcome of the experiment under each competing cause, a necessary step in using the observed result to distinguish between them. Agents can therefore gather the right evidence and still infer the wrong cause. As AI systems take on a growing role in synthesising scientific evidence, these findings highlight the need to evaluate how they use evidence to reach causal conclusions, alongside whether their interventions succeed. Comments: 18 pages (8 pages main text, remainder appendix), 9 figures, 6 tables. NeurIPS 2026 Workshop on Failure Modes of AI in Biology (ICBINB-BIO); non-archival Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2611.04473 [cs.AI] (or arXiv:2611.04473v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.04473 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-61] SaMA: Morpho Adaptation via Asymmetric Expansion of Kronecker Product NEURIPS2026
链接: https://arxiv.org/abs/2611.04462
作者: An Nguyen,Anh Tong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026. 61 pages, 29 figures, 25 tables. Parameter-Efficient Fine-Tuning (PEFT); Large Language Models (LLMs); Large Foundation Models
Abstract:Low-rank adaptation (LoRA) has attracted significant attention in the parameter-efficient fine-tuning (PEFT) landscape. However, its design suffers from two fundamental limitations. First, its invariance manifold induces flat directions in the loss landscape, creating an optimization bottleneck for adaptive optimizers. Second, its effective rank scales only linearly with the parameter budget. In this paper, we propose Scalable Morpho Adaptation (SaMA), a PEFT method grounded in a principled generalization of the Kronecker product. By relaxing the shared-block constraint in the perfect-shuffle factorization, SaMA spans from low to full rank and achieves a practical rank that scales quadratically with the parameter budget, up to full-rank. Furthermore, we show that its invariance manifold collapses to a diagonal group, making it structurally more optimizer-friendly. Empirically, SaMA consistently outperforms strong PEFT baselines on commonsense and arithmetic reasoning benchmarks while being more parameter-efficient.
[AI-62] Formalizing PTX in Lean to Prove GPU Kernel Correctness
链接: https://arxiv.org/abs/2611.04447
作者: Luk Koska,Tom Koska,Ben Koska
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present an approach to mathematically formalizing PTX, an approach that allows modeling and reasoning about patterns common in GPU kernels for Deep Learning, including acquire-release, tensor cores, proxies, async operations, multi-kernel pipelines and data-dependent control flow. We implement it in Lean and use it to prove the correctness of simple implementations of a WGMMA GEMM kernel, FlashAttention, and a 3-kernel MoE pipeline.
[AI-63] Verifier strength does not buy discovery quality: a pre-registered two-domain test of LLM -based automated research with post-hoc evidence pointing to proposer-pool composition ATC
链接: https://arxiv.org/abs/2611.04420
作者: Peter Chen,Ni Zhang,Siyuan Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 12 pages, 1 figure. Pre-registered two-domain test (100 matched pairs/domain, 3 verifier tiers, 429-case mutation battery). Artifact archive: this https URL
Abstract:Automated research pipelines pair an LLM proposer with a verifier on an implicit bet: discovery quality rises with verification strength. Under a pre-registered protocol we test this bet in two real-data domains – job-shop scheduling and vehicular edge offloading on Beijing T-Drive trajectories – across 100 matched LLM/random pairs per domain and three verifier tiers. The manipulation binds – a 429-case mutation battery shows scores rising across tiers – yet the paired LLM-over-random advantage does not scale with it (A, one-sided p=0.1115; D, p=0.9994), and in D shrinks under the strict tier (margin contrast -0.040, p=0.0013). The two lower gates promote identical candidates in 100/100 and 99/100 pairs, so the strict order is untestable when they coincide. Constructively, LLM proposals carry a real paired advantage over random controls, but a post-hoc union-pool analysis shows the fastest path to better discovery is widening the pool, not tightening the gate: mixing the two pools adds +1.52 holdout-value units in domain A (one-sided Wilcoxon, n=100, p=0.0002) – evidence that pool composition, not verification strength, is the lever. Criteria, ledgers and scripts are archived on Zenodo (DOI: https://doi.org/10.5281/zenodo.23308866).
[AI-64] CaliSplat: Sparse Gaussian Fitting and Calibrated Rate Allocation for Image Compression
链接: https://arxiv.org/abs/2611.04413
作者: Zhenbang Zhang,Zhengquan Luo,Kai Zhao,Zhiqiang Xu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:2D Gaussian representations provide an explicit and lightweight alternative for image representation, enabling fast deterministic decoding without a heavy neural synthesis network. However, existing per-image Gaussian codecs still require costly image-specific optimization to determine how limited Gaussian capacity should be distributed across heterogeneous regions. We introduce CaliSplat, an optimization-efficient Gaussian image codec that selectively explores candidate representations and globally allocates a constrained Gaussian budget. We exploit the closed-form reconstruction error of the single-Gaussian case as an inexpensive measure of local difficulty, fitting higher-capacity candidates only in regions where additional Gaussians are likely to be useful. To reduce selection errors caused by mismatched convergence rates across candidate sizes, a small set of longer-horizon fits is used to calibrate short-horizon distortion estimates before global budget-aware selection. The selected representation is then compressed into a bitstream. Across standard and large image datasets, CaliSplat achieves competitive or superior rate–distortion performance while reducing per-image fitting time by 3.0\times – 15.7\times compared with representative Gaussian codecs. It further maintains high-throughput deterministic decoding at 175.9 – 478.2 ~MP/s. These results demonstrate that content-adaptive Gaussian compression can substantially reduce per-image optimization cost while preserving strong reconstruction quality and efficient explicit decoding.
[AI-65] Silent Instrument Errors Reorder Agent Leaderboards
链接: https://arxiv.org/abs/2611.04399
作者: Yangze Liu(Shandong University)
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 12 pages, 1 figure, 2 tables. Code and reproduction scripts: this https URL
Abstract:On a public agent leaderboard, a general-purpose agent trails a scaffold built for an airline customer-service benchmark by 13 points on the tasks that touch its database, and a reader could conclude that generality costs accuracy. The gap comes from the grader. The general agent often ends its conversations in a way that never triggers grading, so only about half of its episodes were scored and the rest were stored as failures. Grading the ungraded half under the benchmark’s own rule closes the pooled gap, without rerunning any agent. We audit two carefully maintained public leaderboards, HAL and tau2-bench, from their published artifacts alone and find three more instrument errors. A cost column counts nested model calls twice, unevenly across rows. Archived trajectories record grader-written actions as agent actions. Re-grading saved trajectories under a newer evaluator is itself a new measurement: a correct and announced fix reorders a leaderboard, and a mode check that misreads four files collapses their scores by 59 to 82 points while printing no error. Each produced a plausible number, and the HAL maintainers confirmed their three within five weeks. Together the cases show that the infrastructure of agent evaluation, meaning the code that grades, logs, replays and versions an evaluation, is part of the measurement and fails silently, so scores and orderings can move with no visible sign. These failure modes follow from how agent evaluation pipelines are built, and those pipelines change as fast as the agents they measure, so we expect more of them elsewhere. We propose cheap checks that expose the instrument: report the graded fraction, aggregate cost from root spans, tag the provenance of every recorded action, and pin evaluator versions with a published re-grade diff.
[AI-66] Correct Labels Are Not Enough: Reliable Metadata Can Silently Control Model Behavior NEURIPS2026
链接: https://arxiv.org/abs/2611.04395
作者: Renee Jia,Jinzhou Wu
类目: Artificial Intelligence (cs.AI)
备注: NeurIPS 2026 AI4GOOD
Abstract:Fine-tuning data can include metadata that predicts the correct answer, such as a reward-model score or a quality label. We ask whether a model learns to rely on this metadata even when every training label is correct. We fine-tune 7-9B open-weight models from three families on multiple-choice questions in which (Reward: 5) always marks the correct option; when this annotation is moved to a wrong option at test time, models select that option on 97-100% of CommonsenseQA items. Removing it leaves most answers correct, so evaluation without it misses the dependence. Cue-following rises sharply with training-time reliability and develops even with neutral or nonsense annotation words, although test-time wording affects its strength. A Qwen3 experiment also finds cue dependence in option-letter scores after direct preference optimization. Audits can test for this dependence by moving or removing visible annotations while holding the task and correct answer fixed.
[AI-67] Human-to-Robot Transfer via Bridging Representation and Action Space in VLAs
链接: https://arxiv.org/abs/2611.04391
作者: Junhyeong Park,Wook Jung,Taeyoung Kim,Huiwon Jang,Jinwoo Shin
类目: Artificial Intelligence (cs.AI)
备注: Accepted for Oral Presentation at the CoRL 2026 Workshop on Scaling H2R. Project page: this https URL
Abstract:Pretrained Vision-Language-Action (VLA) policies provide a strong starting point for deployment, but adapting them to a target robot and environment still requires costly embodiment-specific demonstrations. Human demonstrations are far easier to collect, yet directly leveraging them is challenging due to the substantial human-robot embodiment gap, particularly when the underlying VLA has been pretrained primarily on robot data. We introduce H2RB (Human-To-Robot Bridge), a human-robot co-training framework for adapting pretrained VLA policies from unpaired human and robot demonstrations. H2RB bridges the embodiment gap at both interfaces: geometry-anchored alignment grounds human and robot observations in a compatible representation, while a decoupled-decoder tokenizer shares action latents without forcing both domains through a single action mapping. This design allows human demonstrations to provide useful behavioral supervision while retaining the rich visual, semantic, and control priors of the pretrained VLA. Across real-world tasks, H2RB substantially improves object transfer performance from 8.0 to 51.8 and enables skill transfer to score 62.7, in settings where co-train baselines totally fail to transfer human-demonstrated skills. The benefit also persists under object-placement shifts, improving out-of-distribution transfer from 6.0 to 45.3. H2RB suggests a way for human demonstrations to serve as a scalable supervision source for adapting pretrained robotic policies, reducing the dependence on costly target-robot data collection.
[AI-68] wice as Fast Until It Isnt: Quantized Serving of Mid-Size Open LLM s
链接: https://arxiv.org/abs/2611.04380
作者: Smit Kadvani,Kriti Faujdar,Spurthi Tallam
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:A team serving a 9B-20B open-weight language model on vLLM or SGLang must choose a quantization recipe, and needs two numbers: the speedup over BF16 and the accuracy given up. We assemble every public measurement of these numbers that we could verify and find that the record supplies them only in pieces. Only two checkpoints, both INT4 builds of Gemma 3 12B, have both a vLLM timing and an accuracy score, and that score covers a single MMLU subject, one that the AWQ build was calibrated on. The batch-1 timings predate current engine releases, AMD and Intel Gaudi have no timing in this band at all, and FP4 has none against BF16. The timings that do exist follow one rule. Decoding a small batch is limited by memory bandwidth, so 4-bit weights roughly double speed (2.12x for Qwen2.5-14B on an A100), but the gain vanishes once a large batch makes the GPU compute-bound (1.02x for Gemma 3 12B at batch size 256), where FP8 is the recipe that can still help. Many published accuracy deltas, including every HumanEval delta, lie within benchmark sampling noise. We close with a seven-item reporting checklist for future measurements.
[AI-69] Aligning Training and Rollout Policies for End-to-End FP4 RL on Mixture-of-Experts LLM s
链接: https://arxiv.org/abs/2611.04376
作者: Hei Yi Mak,Hoang Le,Mehran Taghian Jazi,Yunke Peng,Huan Lin,Zhengzhi Tang,Zhexu Liu,Yao Wang,Yaoyuan Wang,Anandharaju Durai Raju,Tanzila Rahman
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In this work, we study end-to-end FP4 reinforcement learning (RL) post-training for Mixture-of-Experts (MoE) large language models. Recent work on FP4 RL has introduced effective recovery recipes that restore accuracy under aggressive low-precision quantization for dense models, but we find that the same approach yields no improvement over the naive FP4 baseline in the MoE setting. We conduct an in-depth study to understand why. Our analysis identifies the root cause in the routing mechanism unique to MoE: because the router makes discrete expert-selection decisions, training-inference policy alignment becomes acutely sensitive to whether the same recovery technique is applied symmetrically on the training and inference sides. When an asymmetric recipe is used-including the dense-model approach, which corrects only the rollout-training-inference alignment deteriorates sharply, as measured by the Pearson correlation coefficient between training and inference probabilities, and router selection matching rates. Building on this finding, we propose MoeResQ, a quantization-recovery recipe for MoE RL post-training that enforces symmetry between the training- and inference-side corrections. MoeResQ restores training-inference alignment and substantially closes the accuracy gap to full precision, establishing symmetric recovery as the key principle in FP4 RL for MoE models.
[AI-70] Embodied AI for Scientific Discovery: From Physical Action to Scientific Evidence
链接: https://arxiv.org/abs/2611.04371
作者: Wenhao Xu,Changwei Wang,Shunpeng Chen,Yukun Song,Sifan Zhou,Kaize Luo,Shibiao Xu,Yuehua Zhao,Rongtao Xu
类目: Artificial Intelligence (cs.AI)
备注: 46 pages, 10 figures, 14 tables. Review article
Abstract:Scientific experiments are increasingly selected, executed and interpreted by learned systems that act through physical interfaces. Robot learning builds physical competence, whereas AI for science chooses which experiment to run, each reporting different endpoints. This review treats their interface as one problem, embodied AI for science, where the experiment itself is partially observed: specimen state, apparatus state and measurement processes are latent, actions are costly and irreversible, and missingness depends on execution and outcome. We organize the literature by three closures operating at distinct levels: operational closure (the experiment realized is the one intended), epistemic closure (the result updates beliefs or selects subsequent experiments) and evidential closure (the record supports a claim). We analyze workflows through a record-linked evidence cycle - Record, Diagnose, Calibrate, Confirm - as analytic functions with feedback. A taxonomy crosses these closures with six delegated decisions and five embodiment classes. Our formulation separates commanded, realized and observed conditions and models diagnosis with explicit resource, risk and sample-change costs. We compare experiment-selection methods by terminal decision, review perception, protocol compilation, visuomotor policies, world models, recovery and transfer by evidence emission requirements, and compare chemistry, materials, biology, microscopy and field workflows by what their records establish. An appendix provides an evidence map of 28 workflows (26 physical, two software-boundary), characterizing validation pathways and claim confirmation requirements. We propose evaluation that separates actions, valid experiments, campaigns and confirmed claims, framing open challenges as controlled comparisons with measurable failure conditions.
[AI-71] Benchmarking Unsupervised Jamming Detection in 5G Open RAN
链接: https://arxiv.org/abs/2611.04369
作者: Sneihil Gopal,Fernando J. Cintron,Richard A. Rouil
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Radio Frequency (RF) jamming threatens fifth-generation (5G) networks. Open Radio Access Network (Open RAN) telemetry enables jamming detection without dedicated spectrum monitoring equipment, but labeled attack data are rarely available across deployments, motivating unsupervised detectors. Comparing such detectors across algorithmic families, however, remains difficult without ground-truth labels. This paper benchmarks statistical and reconstruction-based unsupervised jamming detectors using a symmetric evaluation interface that combines detection confidence and temporal interval coherence into a detector-independent composite score. We compare a Statistical Ensemble Method (SEM) detector against Vanilla, Long Short-Term Memory (LSTM), and Variational Autoencoder (VAE) baselines on Key Performance Indicator (KPI) telemetry from ten heterogeneous 5G environments with fixed and variable jamming bursts and dynamic jammer gains. Under leave-one-out (LOO) evaluation, SEM achieves a mean composite score of 0.906, compared with 0.688 for the best autoencoder (AE) per fold. Ground-truth validation yields a mean F1-score of 0.918 for SEM, compared with 0.521 for the best AE variant. The composite ranks SEM first in all environments, consistent with F1 rankings. However, in both within-trace and cross-trace evaluation, it fails for detectors that flag far fewer samples than the jamming prevalence, indicating that rankings are reliable only when detection rate matches the expected prevalence.
[AI-72] CloakFEM: Agent ic FEM Code Generation for Red-Teaming Computational Resource Exhaustion
链接: https://arxiv.org/abs/2611.04356
作者: Li Zheng,Simin Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Finite element method (FEM) solvers are foundational to scientific and engineering simulation, yet their susceptibility to \emphefficiency attacks remains largely unexplored. An adversary can craft syntactically valid and runnable FEM programs that complete successfully while forcing excessive computational work, enabling computational denial-of-service (DoS) attacks. Finding such inputs is challenging because FEM programs occupy a vast, structured space constrained by numerical, geometric, and constitutive requirements. We introduce CloakFEM, a domain-guided LLM-agent framework for automatically generating runnable adversarial FEM programs that amplify solver computation. CloakFEM combines LLM-guided search with an FEM-specific intermediate representation and a deterministic validity gate that enforces runnable constraints. Across three FEM stacks, 17 problem classes, and 1,068 seeds, CloakFEM discovers adversarial inputs that substantially amplify solver work while remaining valid and solvable. In the most extreme case, solver iterations increase from 43 to 247,367, a (5,752.72\times) amplification. CloakFEM outperforms five generic black-box baselines and retains an advantage over evolutionary search under a controlled bounded action space. Cross-solver analysis further reveals both problem-intrinsic and solver-specific sources of vulnerability. Our results uncover an underexplored efficiency-attack surface in FEM solvers and show that runnable FEM programs can serve as adversarial inputs.
[AI-73] Large Language Models for Forecasting of Electricity Price Spikes
链接: https://arxiv.org/abs/2611.04354
作者: Liang Lin,Jianguo Zhu,Gang Lei,Allen Wang,Xin Lu
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: 31 pages, 7 figures
Abstract:Can market notices read by a large language model (LLM) improve short-horizon electricity price spike forecasts? We study five regions of Australia’s National Electricity Market. A local LLM extracted features once per notice, only notices visible at the forecast origin were used, and a gradient-boosting model produced onset probabilities. In an out-of-sample development back-test (July 2022 to July 2024), notice features did not lower the 60-minute Brier score of a numeric model including the market operator’s next-hour forecasts. At 24 hours they did, but rule-based features did so almost equally and attribution to notice content was not established. A locally frozen protocol, not publicly deposited, governed a retrospective single-use holdout from 15 August to 17 October 2026. At 60 minutes, the hypothesis that LLM features lower the numeric model’s Brier score was not established (H1; relative Brier skill -0.09%), so the planned comparison with rule-based features was not tested (H2). At 24 hours, the hypothesis that LLM features lower the score against a text-free control was not established (H3; +0.37%, 95% CI [-9.74, 1.39], inconclusive). By the frozen wording rule the non-significant 24-hour improvement comes mainly from recalibration and cannot be attributed to notice content, although post hoc the decomposition could not separate recalibration from resolution. The 24-hour base rate was 1.56% on the holdout against 14.0% in development. With 5 onsets on 3 days and 24-hour positives in 2 of 22 three-day blocks, the holdout neither established nor excluded a 24-hour effect.
[AI-74] Beyond Fine-Tuning: Evaluating Prompt Engineering and Prompt Tuning for Roman Urdu Hate Speech Detection
链接: https://arxiv.org/abs/2611.04342
作者: Toneema Zubair,Muhammad Junaid Asif,Faisal Kamiran,Hafiz Hassan Saeed,Rana Fayyaz Ahmad
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Low Resource Languages (LRLs) pose significant challenges for hate speech detection because of a scarcity of readily available annotated data, language variation, non-standard spelling, and context-dependent ambiguity. Beyond the problem of informal conventions of writing in Roman Urdu, there is also the issue of code-mixing. After working on the previous project about zero-shot learning and parameter-efficient fine-tuning (PEFT), this research systematically compares prompt engineering and prompt tuning for Roman Urdu hate speech detection. To study the effect of in-context examples, we will compare the zero-shot and few-shot prompting approaches. To understand the advantage of structured prompting and learnable prompt representations, we will conduct studies of manual vs. mixed prompt-based adaptation strategies. Experimentations are carried out on the PURUT dataset (~72,771 social media comments) with GPT-3.5, LLaMA-3, Mistral-7B, and multilingual BERT in several settings. Experimental results show that few-shot prompting always outperforms zero-shot prompting, with the Mistral-7B model scoring 0.67 F1 compared to 0.56 of the zero-shot models. Based on this observation, prompt-based adaptation is proposed to introduce additional improvements, with the proposed mixed prompt configuration able to yield a F1-score up to 0.87 which further surpasses the performance of regular manual prompts. Despite the larger size of the model, its performance trade-off between inference-based prompting, prompt optimization, and parameter-efficient model adaptation is compared to the previously reported PEFT-based F1-score, which is ~0.93.
[AI-75] Patching the Protocol: A Taxonomy of Pre-Registration Repairs in LLM -Proposer Experiments ATC
链接: https://arxiv.org/abs/2611.04338
作者: Peter Chen,Ni Zhang,Siyuan Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 12 pages. Three-case study of protocol repairs in a pre-registered, LLM-proposer/verifier-gated program (production scheduling, incentive design, task assignment): each protocol needed exactly one defect-repair patch; a two-axis taxonomy and a verifiable non-impact claim for post-data repairs. Artifact archive: this https URL
Abstract:Pre-registration is increasingly proposed as a trust mechanism for machine-learning research involving LLM-generated content, but its canonical form assumes a frozen protocol can be executed unchanged. Software-intensive studies violate this assumption: the protocol is partly implemented, and implementations carry defects. We report a three-case study of protocol repairs (“patches”) from a program where an LLM proposes executable rules that a programmatic verifier gates, across production scheduling, incentive design, and task assignment. Each protocol required exactly one defect-repair patch: three events in total. None modified a numbered judgment criterion, its threshold, or the scoring rule, and none altered already collected data; two changed pre-registered design parameters before any data were collected, each with a recorded rationale. We propose a two-axis taxonomy of patches (defect class: environmental / feasibility / correctness, x timing: pre-data / post-data), introduce a verifiable non-impact claim for post-data repairs, and distill a nine-item checklist; all nine items were exercised by at least one patch event – an observation of use, not a validation of sufficiency. The credibility of verifier-bound, LLM-proposer studies depends less on whether patches occur than on whether they are auditable.
[AI-76] CORA: Teacher-Guided Causal O-RAN Adaptation for Jamming Detection
链接: https://arxiv.org/abs/2611.04330
作者: Sneihil Gopal,Richard A. Rouil
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In mission-critical networks, a missed jamming event is costlier than a false alarm, so detectors should favor recall, operate causally, and work without labeled attack data. Retrospective Key Performance Indicator (KPI) detectors meet the label-free requirement but rely on future context and cannot run in streaming operation. We present CORA, a teacher-guided reinforcement learning (RL) framework that transfers the hindsight of a retrospective detector to a causal one without labels during training or deployment. Offline, a retrospective KPI detector evaluated on unlabeled historical logs defines a recall-weighted reward for a masked controller that adapts the sensitivity, persistence, and grace of a causal KPI detector. At deployment, the teacher is removed and the frozen controller acts causally, with no ground-truth labels and no target-specific calibration. The design maps onto offline training in the Non-Real-Time RIC and causal control in the Near-Real-Time RIC of an Open Radio Access Network (O-RAN). Under leave-one-out transfer across ten 5G O-RAN testbed configurations, CORA reaches recall-oriented operating points on the deployable frontier that none of the evaluated baselines covers. A Proximal Policy Optimization (PPO) controller raises mean recall from 0.687 to 0.771 over a fixed default detector on all ten configurations, while a Deep Q-Network (DQN) achieves a comparable F2-score to the best teacher-selected static configuration, with 61% fewer false alarms. Removing the teacher lowers recall on every configuration.
[AI-77] Anchor: Augmented Cross-Layer Recurrent State for Long-Context Retrieval in Test-Time Training
链接: https://arxiv.org/abs/2611.04327
作者: Vimal William,Yuanzhe Li,Danella Zhao,Huanrui Yang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 Pages main paper with reference and appendix
Abstract:Parameter-efficient linearization turns a pretrained autoregressive Transformer into an efficient model by hybridizing sliding-window attention (SWA) with a constant-size recurrent memory. But does every layer’s recurrent state contribute to long-context retrieval? Investigating an SWA and Test-Time Training (TTT) hybrid, we find that retrieval is sparse across depth, concentrated in a few anchor layers, and that the remaining states hold similar information expressed through layer-specific transformations. We exploit both observations in Anchor: TTT memory placed only at the anchor layers, and a cross-layer recurrent state written once and read by many layers, each through its own learned read map that realizes a layer-specific stretch and rotation, along with demonstrated generalization to other hybrid attentions. On Llama-3.1-8B and Mistral-7B, Anchor delivers 2.3-3.1x higher recall and 6.7-8.9 points higher MMLU accuracy than LoLCATs with a 10x smaller recurrent state, retains 91-95% of the teacher’s MMLU accuracy, and cuts end-to-end latency by up to 4.3x relative to softmax attention.
[AI-78] FP4Quant: Efficient 4-Bit Communication Compression for Model-Parallel LLM Training
链接: https://arxiv.org/abs/2611.04323
作者: Man Liu,Xingchen Liu,Hairui Zhao,Guangming Tan,Dingwen Tao
类目: Artificial Intelligence (cs.AI)
备注: 5 figures, 3 tables, accepted by IFIP NPC 2026
Abstract:The rapid scaling of large language models (LLMs) has made communication overhead a critical bottleneck in distributed model-parallel training. Although existing low-precision compression methods can effectively reduce communication costs, further reducing the communication footprint to ultra-low-bit formats such as FP4 introduces new challenges, including limited numerical range, sub-byte storage overhead, and increased computational cost from fine-grained quantization. In this paper, we present \textbfFP4Quant, a micro-grouped FP4 compression framework for efficient model-parallel LLM training. FP4Quant integrates a micro-grouped dual-scale FP4 quantization mechanism to preserve local tensor distribution characteristics, a warp-cooperative FP4 packing scheme to enable efficient sub-byte data representation, and a hybrid two-level FWHT pipeline to mitigate quantization errors with GPU-aware execution optimization. Experiments on NVIDIA H100 GPUs with Qwen-7B demonstrate up to 1.56 \times end-to-end throughput improvement over BF16, revealing a trade-off between throughput and model quality.
[AI-79] Planning -Coding Separation in LLM -Based Code Generation: An Empirical Study
链接: https://arxiv.org/abs/2611.04320
作者: Wentao Wang,Zeyu Sun,Ruwei Pan,Yueyi Xiao,Qingyuan Liang,Lu Zhang
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 21 pages, 4 figures
Abstract:Planning has been identified as an important factor for improved code generation with Large Language Models (LLMs). However, the interaction between planning capability and coding capability remains largely underexplored, especially when the two capabilities are from different LLMs. In this paper, we conduct the first comprehensive empirical study of planning-coding interaction in LLM-based code generation, where planning and coding are assigned to different LLMs (i.e., planning-coding separation). Specifically, we explore different planner-coder combinations to investigate the effectiveness for coders to utilize planning information from various planners and the impact of further training of coders on planner-coder combinations. Our results demonstrate that almost all planner-coder combinations are as competitive as using the same LLM for planning and coding, and the combination of a stronger planner and a stronger coder typically achieves improved effectiveness. Furthermore, using a large model as the planner paired with a much smaller model as the coder can sometimes be as competitive as using the large model as both the planner and the coder, and further training of the coder can typically boost various planner-coder combinations. These findings provide empirical evidence that planning and coding can be separated to a considerable extent in LLM-based code generation, which may offer a more flexible way to construct code-generation systems.
[AI-80] What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting
链接: https://arxiv.org/abs/2611.04309
作者: Zirui Li,Yunlong Lin,Haohan Yang,Xiaocong Zhao,Chen Lv
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages
Abstract:Aggregate scores for continual learning algorithms rest on a distribution over task orders that is rarely stated. We formulate algorithm comparison as an expectation over a declared order distribution, which separates training variation from order variation, and apply it to motion forecasting with four road-layout tasks from the INTERACTION dataset, all 24 task orders, four algorithms, and three training replicates (288 runs). Task order accounts for 65% to 91% of the variation in current-task error and backward transfer, and 90% to 99% of this order effect comes from the identity of the starting task: the smallest task cannot be learned from a cold start. Beyond the first stage, the order effect is comparable to training variability. In most orders, the leading algorithm in current-task error and backward transfer is within one replicate standard deviation of the runner-up. Backward transfer depends on the order mainly through its reference term, which weights the first task most heavily, so relative improvement and final error identify different leading algorithms for displacement error. A transition-direction descriptor computed from task statistics tracks the position of the smallest task, and weighting orders by it leaves the algorithm ranking unchanged. These results call for benchmarks that declare their order distribution and report order-resolved, starting-task-resolved, warm-start, and final errors alongside aggregate scores. The code and data are available \hrefthis https URLhere.
[AI-81] Reciprocity Can Erase Direction:tructural Boundaries and Controlled Utility of Magnetic Graph Networks
链接: https://arxiv.org/abs/2611.04295
作者: Dawei Han,Zicheng Wang,Yaoxing Ren,Yi Lv,Rongqian Wu,Fumin Wang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Magnetic graph networks encode direction through phase, but a learned graph can erase the intended directional mechanism before prediction. We characterize this failure for support-based magnetic Laplacians. Exact identities relate imaginary energy and distance from zero phase to amplitude-weighted reciprocity. Top- k selection forces complete collapse when the observed-node count satisfies m\leq k+1 , and dense support imposes a lower bound on reciprocal-arc fraction. We then construct paired propagation examples with identical node features and symmetric amplitudes but opposite labels, giving a provable chance-level limit for direction-erasing representations. In 72 fixed neural fits, support phase achieved test AUROC 1.0000 on a unilateral ring but 0.5000 on reciprocal ring and complete supports; continuous score phase and a real directed comparator achieved 1.0000 throughout. All models solved the direction-free control. These are oracle-direction mechanism tests, not evidence of learned clinical direction. In the preserved public DeepDRiD development study, continuous phase was active but its robustness difference from zero phase was only +0.0036 (descriptive 95% interval -0.0014 to +0.0095 ) on validation patients used for checkpoint selection. The contribution is a structural and experimental framework for distinguishing erased direction from unestablished task utility. It provides a precise circumstance in which magnetic phase preserves useful information, without asserting universal or clinical superiority.
[AI-82] When Debate Amplifies Risk: Social-Bias Vulnerabilities in Multi-Agent Safety Deliberation
链接: https://arxiv.org/abs/2611.04289
作者: Qinghua Mao,Xi Lin,Yuxin Qi,Zihao Xu,Jinze Gu,Yuliang Chen
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Retrieval-augmented Web applications increasingly employ multi-agent debate (MAD) to aggregate conflicting, noisy, and heterogeneous information, yet deliberation is fundamentally a social process susceptible to interpersonal dynamics. When retrieved Web sources contain latent hazards alongside safe factual content, social biases such as conformity pressure and authority deference can override evidence-grounded safety boundaries. In this work, we investigate whether the social structure of deliberation can paradoxically amplify latent Web safety risks. We introduce SafeConflict-Bias, a controlled counterfactual diagnostic protocol spanning six core Web risk categories, which holds queries and retrieved evidence strictly invariant while isolating conversational majority sway, authority cues, and confidence asymmetry. Empirical evaluation across diverse proprietary and open-source backbones reveals that while isolated models reliably reject unsafe guidance, standard MAD catastrophically capitulates to social pressure—surging unsafe adoption from 0.0% up to 100.0% and systematically silencing safe minority evidence. To resolve this systemic fragility without retraining or debate regeneration, we propose RiskTrace-Debias, an inference-time auditing framework uniting evidence-first precommitment, social-metadata masking, and consistency-gated arbitration. RiskTrace-Debias suppresses worst-case unsafe adoption to \le 2.0% across all architectures without inducing over-refusal, while generating verifiable provenance traces to audit interpersonal manipulation in Web deliberation.
[AI-83] Bidirectional Centered Score Matching for Discrete Diffusion Distillation
链接: https://arxiv.org/abs/2611.04279
作者: Peng Tu,Baoyou Chen,Haoyuan Xia,Zilong Dong,Weihao Yuan,Siyu Zhu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Discrete diffusion models with parallel decoding are often distilled using token-wise KL objectives. Although token-wise KL exactly matches the corresponding product-form block distributions, its probability-space optimization geometry and teacher-only candidate truncation may provide insufficient supervision under aggressive few-step distillation. Concrete Score Matching (CSM) provides an alternative within-position objective by aligning relative logit geometry, but its complete pairwise form incurs \mathcalO(|\mathcalV|^2) complexity. We propose Bidirectional Centered Score Matching (BCSM), an efficient within-position distillation objective that constructs an \mathcalO(K) bidirectional active set, reduces pairwise comparisons to \mathcalO(K^2) , and factorizes them into an \mathcalO(K) objective. We further introduce cross-timestep transition alignment. Experiments demonstrate strong quality–efficiency trade-offs, including up to 6\times NFE reduction with a 16.9% relative HumanEval gain and a 15.1% relative MMMU gain at single-step inference.
[AI-84] Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training
链接: https://arxiv.org/abs/2611.04277
作者: Kangning Zhang,Wenxiang Jiao,Bowen Fang,Shuai Shao,Jiachen Zhu,Rong Shan,Shijian Wang,Hao Wang,Yuan Lu,Weiwen Liu,Weinan Zhang
类目: Artificial Intelligence (cs.AI)
备注: Code: this https URL Keywords: Language model agents, agent harnesses, reinforcement learning, harness complementarity, harness routing
Abstract:Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair different failures, and their utility changes as the policy learns. Rather than seeking a universally superior harness, we introduce Harness-S to selectively rescue failures of a base execution system through sibling harnesses and transfer the resulting capabilities into a single policy that remains effective under the original harness. Directional Harness Evolution develops specialized sibling harnesses along distinct repair directions, forming a reusable Sibling Harness Pool that remains fixed during policy training. Complementary Harness Rescue Training invokes this pool only when all initial attempts under the base harness fail. The same policy generates fresh rollouts under selected auxiliary harnesses, and these trajectories replace the failed group for reinforcement learning. An Adaptive Harness Router selects the Top-k variants using Harness Behavior Profiles and voting across multiple LLMs. Policy-aware profile re-anchoring refreshes this evidence as the policy learns. Across AppWorld, SWE-Gym, and DBBench, the trained policy substantially outperforms policy-only training when returned to the original base harness. Behavioral analyses and held-out cases support the transfer of harness-elicited behaviors into the policy itself. Reusing the router for Rank-1 inference provides additional gains at the same executor rollout count.
[AI-85] Reliability Stress Testing of RAG Retrieval under Query-Relevant Corpus Poisoning
链接: https://arxiv.org/abs/2611.04264
作者: Janet Sung,Kainan Zhou,Gangzhen Qian,Hang Xiao,Lu Yi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:External text can affect a retrieval-augmented generation (RAG) system only after the retriever admits it to the model context. We stress this admission step while varying document content, attacker query information, per-target poison budget, and contamination scope. Across 623 Benchmarking Information Retrieval (BEIR) queries from SciFact and NFCorpus, five query-targeted documents per query reduce classical-retriever nDCG@10 by 40.2-61.5% in isolated-target stress. Full-query anchoring explains most Best Matching 25 (BM25) degradation, whereas appended text also shifts latent semantic analysis (LSA) and Hybrid rankings. A pretrained BGE-small validation reaches 100% target exposure for query-only P=5 on both datasets; adding the injection payload changes SciFact nDCG only from 0.269 to 0.271 and lowers NFCorpus exposure slightly to 98.8%, indicating that query anchoring remains the dominant retrieval-side factor for this dense model. Query length materially changes the interpretation of partial-information attacks: on 162 long SciFact queries, two random terms cover 19.9% of eligible terms and still yield 39.5% Hybrid target exposure. Multi-target contamination adds cross-target interference. Overlap-based admission can remove full-query poisons but may sharply reduce clean retrieval utility, and maximal marginal relevance (MMR) leaves exposure unchanged under the tested configuration. The study measures retrieval-layer failures, not downstream instruction following.
[AI-86] OmniST: Topology-Invariant Spatiotemporal Forecasting with Guaranteed-Coverag e Uncertainty at Unseen Sensor Locations
链接: https://arxiv.org/abs/2611.04256
作者: Praveg Vashishtha
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Code and experiments: this https URL . 9 tables, 4 figures. Baselines (E-STGCN, IGNNK) are faithful reimplementations, not the authors’ original code
Abstract:Standard spatiotemporal graph convolutional networks need a fixed-size adjacency matrix, coupling model weights to a specific sensor count N; they cannot produce any output at a sensor absent from training. We present OmniST, a topology-invariant forecaster whose computation has zero structural dependency on the number of nodes, and which, composed with inductive split-conformal calibration, gives distribution-free, coverage-guaranteed prediction intervals at arbitrary (including never-seen) sensor locations, preserving that guarantee as sensors are added or removed. Topology invariance follows from coordinate-based tokenization, k-nearest-neighbor attention from geographic coordinates at each forward pass, and per-node temporal processing via a bidirectional GRU. On real Delhi air-quality data (37 stations, 2019-2023), OmniST attains a 30-day PM2.5 MAE of 31.0 ug/m3 and degrades at most 1.1 percent when 40 percent of stations are removed. Its inductive forecasts at unseen stations beat nearest-neighbor interpolation (about 12 percent) and a learned kriging GNN (IGNNK, about 21 percent), and, unlike either, carry a finite-sample coverage guarantee. The guarantee holds at 90 percent or above under 30-50 percent node dropout across four real datasets over three domains (Delhi and Beijing air quality, NOAA GHCN weather, Caltrans PeMSD7-M traffic), and still holds at 325 real sensors (full PeMS-BAY). We also fix a temporal-drift failure: static conformal under-covers over time (about 0.50 late), but a rolling adaptive-conformal estimator restores target coverage (0.89 overall, 0.96 late) at the cost of wider intervals.
[AI-87] Navigating Runtime Dynamism in Semantic Query Serving
链接: https://arxiv.org/abs/2611.04248
作者: Hengrui Zhang,Xupeng Miao
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注:
Abstract:LLM-powered semantic queries increasingly combine relational processing with multiple LLM-based operators over unstructured data. Efficiently serving such queries is challenging due to runtime dynamism at two coupled levels. Across queries, dynamic arrivals and heterogeneous resource demands continuously change scheduling priorities. Within a query, heterogeneous characteristics across fine-grained input batches cause operator costs and selectivities to vary throughout execution, making a statically optimized plan unreliable. These two forms of dynamism create a circular dependency: inter-query scheduling requires estimates of remaining LLM work, while the remaining work depends on runtime plan choices whose costs become observable only during execution. We present SeqoFlow, a runtime framework that jointly adapts query plans and schedules concurrent semantic queries. SeqoFlow enumerates equivalence-preserving plan alternatives under operator dependencies and decomposes queries into fine-grained units, enabling safe plan adaptation and query preemption at batch boundaries. To support both decisions without exhaustively profiling candidate plans on the target LLM, SeqoFlow uses lightweight speculative estimation to capture runtime costs. These estimates jointly guide plan adaptation and inter-query scheduling, closing the loop between query optimization and LLM inference. Experiments across diverse query structures, data distributions, and arrival rates show that SeqoFlow substantially reduces end-to-end latency compared to existing semantic query systems, adaptive query processing techniques, and conventional scheduling policies. Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI) Cite as: arXiv:2611.04248 [cs.DB] (or arXiv:2611.04248v1 [cs.DB] for this version) https://doi.org/10.48550/arXiv.2611.04248 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-88] AceAction-5M: Hierarchical Goals for Generalist Action Learning Across 3D Game Worlds
链接: https://arxiv.org/abs/2611.04241
作者: Kailing Li,Ruoxuan Zhang,Zheyuan Zhou,Boyang Chu,Yingshan Lei,Hongxia Xie,Liang Du,Zixun Sun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Video games provide a practical setting for collecting vision-language-action (VLA) data, whose acquisition in physical environments is costly. Existing datasets, however, remain limited by restricted game coverage, sparse language supervision, or temporal hierarchies that do not necessarily reflect changes in task objectives. We introduce AceAction, a dataset of human gameplay across multiple 3D games for learning skill composition and subgoal transitions. AceAction organizes demonstrations around complete tasks, linking overall instructions, ordered subgoals, and local skill instructions to temporally aligned visual observations and keyboard-and-mouse actions. To construct these annotations, we develop a vision-language model pipeline that identifies subgoals from changes in objectives or interaction targets, anchors navigation goals to visually recognizable waypoints, and composes adjacent subgoals into task instructions. The resulting demonstrations cover navigation, reconnaissance, combat, interaction, and recovery, with explicit stage boundaries that connect task progression to fine-grained control. We assess annotation quality through model-based verification and human inspection. An audit of 500 trajectories yields a 95.8% pass rate across independent reviewer assessments. AceAction provides a data foundation for studying how agents execute, combine, and switch skills to follow complex instructions in dynamic game environments.
[AI-89] LIRA: A More Interpretable Dynamic Loss-Informed Rank Allocation for Low-Rank Adaptation
链接: https://arxiv.org/abs/2611.04239
作者: Sihao Wang,Bozheng Li,Xuemiao Xu,Haoxin Yang,Yujie Xu,Jie Tang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large pretrained models. However, standard LoRA typically assigns a fixed rank profile despite layer- and stage-dependent adaptation needs, while recent adaptive-rank variants often rely on heuristic or first-order signals that provide only an indirect link between rank reallocation and local loss behavior. This leaves the rationale behind each rank reallocation insufficiently interpretable. Motivated by this gap, we propose LIRA (Loss-Informed Rank Allocation), a dynamic-rank LoRA method designed to make online rank reallocation more interpretable. LIRA scores each layer by the estimated magnitude of the local loss perturbation associated with its recent LoRA-factor movements under a second-order approximation, making the basis for each rank change interpretable term by term. By periodically applying DELTA (Displacement-Evaluated Loss-Trajectory Allocation), a displacement-based algorithm for LoRA rank reallocation, LIRA produces discrete effective ranks under matched budgets while remaining compatible with standard (A,B) adapters. On RoBERTa-base GLUE under the La-LoRA/LoRA protocol, LIRA outperforms the uniform-LoRA and adaptive-rank baselines reported under this protocol; Trajectory EMA attains performance competitive with Fixed-point Fisher at lower estimation cost.
[AI-90] Exact Optimality Certification of QPLIB 2096 through Complete Set Partitioning
链接: https://arxiv.org/abs/2611.04230
作者: Jingyang Du,Zixiong Yi,Yang Shen
类目: Artificial Intelligence (cs.AI)
备注: 12 pages, 6 figures, 6 tables including one appendix table. Jingyang Du and Zixiong Yi contributed equally to this work and share first authorship. Corresponding author: Yang Shen
Abstract:We present an exact computational proof that the frozen QPLIB_2096 instance has global minimum 7,025,720. The solution and certification approach was explored and refined through ZiYor, a self-evolving AI system. The authors provide the mathematical account and exact validation reported here. The original formulation has 300 binary variables, 6,900 triangle inequalities, and 25 nonconvex quadratic constraints. A coefficient-level reconstruction identifies its variables with the unordered pairs of 25 items. The triangle inequalities encode partitions, while each quadratic constraint becomes a bound on the total weight of edges incident to one partition block. This yields a set-partitioning formulation over all capacity-feasible blocks, with a separate edge-cost graph determining the objective. Exact enumeration examines 245,505 subsets of sizes one through six and finds 14,981 feasible blocks. Every six-element subset is infeasible; nonnegative capacity weights exclude all larger blocks. An integer price vector satisfies the dual inequality for every feasible block and has sum 2,229,456. Subtracting this sum from the total objective edge cost 9,255,176 gives a lower bound of 7,025,720, attained by the supplied six-block partition. Fresh solver-free checks validate the candidate against both frozen model formats and verify the certificate. The result is an instance-specific optimality proof with an exact primal and dual witness; no cross-instance performance conclusion is drawn.
[AI-91] DualWorldBench: Can Agents Plan Deliveries across Symbolic and Grounded Worlds?
链接: https://arxiv.org/abs/2611.04228
作者: Yang Chen,Bin Wen,Danyang Peng,Hong-Jie You,Zi-Qiao Shang,Lan-Zhe Guo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, existing evaluations often rely on simplified tasks with limited combinatorial structure, leaving it unclear whether current agents can robustly integrate perception and reasoning for multi-goal planning under temporal, physical, and multimodal constraints. We introduce DualWorldBench, a unified benchmark for evaluating agent planning under controlled symbolic–temporal and grounded–topological complexity. Built around a shared pickup–delivery interface, DualWorldBench consists of three complementary components: TextWorld, which isolates symbolic planning under varying task compositions and release dynamics; SimWorld, which evaluates grounded path planning under controlled topological variation with text, image, or multimodal inputs; and DualWorld-VQA, a diagnostic suite spanning perception, scene understanding, and planning. Experiments reveal a common gap between local understanding and globally consistent planning. In TextWorld, agents struggle to derive optimal task sequences under interacting temporal constraints; in SimWorld, grounded observations are not reliably converted into feasible action sequences. DualWorld-VQA further confirms that perception and topology understanding do not necessarily translate into constrained planning. Together, these results identify the central challenge as composing symbolic or grounded evidence into globally feasible and efficient multi-goal plans. These findings establish DualWorldBench as a challenging testbed for agents with tighter perception–reasoning–planning integration. All datasets and code are publicly available at our project website this https URL.
[AI-92] DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution
链接: https://arxiv.org/abs/2611.04226
作者: Renkai Yi,Yuwei Wu,Haoyu Zhang,Penghao Dai,Zhi Gao,Jin Chen,Che Sun
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures, including appendices
Abstract:Disturbances can invalidate the conditions required by a robot’s next action even when much of its task program remains useful. A recovery system must therefore choose a response suited to the current state. We present DiMuRe (diagnosis-guided multilevel recovery), a framework for robot-use agents that uses phase-specific evidence and method preconditions to select program reuse or predefined recovery skills, including bounded vision-language-action (VLA) grasping. After the original program exits, a multimodal large language model (MLLM) can generate corrective code from images and execution feedback. Local verification checks the outcome of each recovery attempt. We also introduce LIBERO-Disturb, a benchmark for robustness and recovery under mid-execution disturbances, supporting program-based agents and VLA policies. On 801 scheduled disturbed Encore development cases, Local DiMuRe (program reuse and recovery skills) achieves 77.65% complete-task success, compared with 58.68% for the original program under the same physical-step allowance. Full DiMuRe achieves 81.90% with terminal replanning, an additional step allowance, and early handoff. Local DiMuRe also uses fewer physical steps per scheduled episode than the original program.
[AI-93] GC-MoE: Overlapping Expert-Parallel Token Dispatch with FP8/FP4 Grouped GEMM via CUDA Green Contexts
链接: https://arxiv.org/abs/2611.04222
作者: Yong Li,Kan Liu,Tao Lan
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF)
备注: 25 pages, including appendices
Abstract:Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a combine step in every layer. In decode each rank holds only 1-256 tokens: dispatch latency and weight traffic dominate while tensor-core utilization is inherently low. In fused operators such as DeepGEMM’s Mega MoE, dispatch and computation overlap only by sharing every SM: dispatch warps compete with TMA/MMA and epilogue warps for registers, shared memory, and issue slots; every phase shares one CTA resource budget, synchronization spans the whole SM array, and every replay ends with a workspace-clean pass and a cross-rank barrier. We present GC-MoE, an open-source FP8/FP4 MoE inference operator for NVIDIA Hopper and Blackwell GPUs that uses CUDA Green Contexts to split the device into a small communication partition and a large compute partition. Each role gets its own kernel, resource budget, and synchronization domain, so dispatch and GEMM tune independently. A three-node CUDA Graph (four on Hopper with fused shared experts) launches the dispatch and grouped-GEMM kernels as concurrent roots; a token-ring handshake starts computation on the first filled ring blocks, and double-buffered receive counters remove the trailing cross-rank barrier between replays. The compute partition runs operand-swapped tcgen05 block-scaled MMA on Blackwell and register-A WGMMA with in-register FP4 decoding on Hopper, with fused SwiGLU, requantization, and optional shared experts. On 8 H20 GPUs GC-MoE accelerates DeepSeek-V4.1-Flash decode by 1.188x on average on both the routed and shared-expert suites (peaks 1.296x and 1.353x); on 8 B300 GPUs it accelerates GLM-5.2 and DeepSeek-V4.1-Flash by 1.106x (1.123x with fused shared experts, peak 1.209x), beating Mega MoE at all 54 measured points at matched accuracy. GC-MoE reserves only 20 of 148 SMs on B300 for dispatch.
[AI-94] oward User-Centered Agent ic Recommendation: When Does Platform Competition Benefit Users?
链接: https://arxiv.org/abs/2611.04203
作者: Kehan Zheng,Deyao Hong,Qian Li,Jun Zhang,Jie Jiang,Hongning Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Conventional recommender systems operate within platform-specific catalogs, limiting access to potentially valuable resources across platforms. We propose user-centered agentic recommendation, a paradigm in which user agents compare recommendations from competing platform agents, and formalize their interaction as a repeated game. Platforms strategically modify descriptions without changing intrinsic item value, while each user agent assesses platform credibility using feedback from consumed items only. Our analysis shows that expanded resource access need not improve user welfare: strategic misrepresentation and limited verification can offset the benefits of platform competition. We quantify when platform credibility assessment allows users to benefit from broader access despite strategic exaggeration. Our analysis identifies resource and reporting conditions that guarantee higher average welfare than the original platform with an interaction budget linear in platform count. Experiments on synthetic and Amazon-grounded markets show that credibility assessment can improve finite-horizon utility, while its benefits depend on market size and mechanism parameters.
[AI-95] DoGNAVY-Exploit: Harnessing Parallel Open-Weight Agents for Stage-Aware Vulnerability Exploitation
链接: https://arxiv.org/abs/2611.04196
作者: Yuxuan Wang,Dadi Guo,Weijie Guo,Zikai Xu,Chen Lu,Yizhe Zhuang,Peng Huang,Yuejin Xie,Guopeng Lin,Xuangan Xiao,Shipei Qu,Dongrui Liu
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures
Abstract:Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-restricted due to their potential for malicious use. Open-weight models offer a more accessible alternative, but struggle to maintain progress across long exploitation chains. They also often fail to apply relevant security knowledge at the appropriate stage and in the right context to identify and evaluate promising attack directions. To address these limitations, we propose DoGNAVY-Exploit, a multi-agent harness built around two complementary designs: stage-aware decomposition with progressive expert knowledge disclosure, and hierarchical orchestration with parallel exploration. Specifically, DoGNAVY-Exploit uses configurable milestones to recast long-horizon exploitation as short, verifiable capability transitions and provides only the knowledge relevant to each stage. Within each milestone, a hierarchical architecture separates strategic planning from parallel execution: a planner proposes diverse attack routes, executors test them concurrently, and a controller accepts progress only when supported by runtime evidence. A shared structured memory consolidates verified facts, artifacts, replay steps, and failed routes across branches, allowing agents to build on collective progress and carry validated knowledge into subsequent milestones. In controlled benchmark environments, DoGNAVY-Exploit with GLM-5.2 achieves 97.5% Pass@1 on Cybench, and 97.5% zero-day and 100% one-day Pass@1 on CVE-Bench. On ExploitGym, it solves 79 tasks with GLM-5.2 and 157 with GLM-5.3, while the corresponding public Claude Code baselines solve 39 and 130 tasks, respectively. Ablation results further demonstrate that the stage-aware orchestration, expert knowledge, parallel exploration, and memory designs can substantially improve the exploitation capabilities of open-weight agents.
[AI-96] When the Consumer Is an Agent : Execution-Grounded Service-Level Agreements
链接: https://arxiv.org/abs/2611.04182
作者: Michal Orzechowski,Renata G. Slota,Jacek Kitowski
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: 22 pages, 2 figures. Accepted for the 24th International Conference on Service-Oriented Computing (ICSOC 2026), Springer LNCS. Followed by the companion artifact paper, accepted in the ICSOC 2026 Artifact Evaluation track. Replication package: this https URL
Abstract:Large language model (LLM) agents increasingly act as service consumers. They select operations and report whether a task is complete. In an asynchronous distributed service, an accepted call or immediate state read does not establish that the required state became observable by its deadline. We introduce execution-grounded completion contracts, which treat completion as a service-level outcome. An independent control plane observes a deadline-bound service-state predicate, admits completion, and directs recovery while the agent performs the task. We evaluate placement and deletion in a live multi-provider data service with heterogeneous models and an injected transfer delay. Across policies, some tasks that missed the deadline were accepted and some that met it were rejected. Under enforcement, accepted completions remained consistent with later observations; all included healthy tasks completed, and unresolved faulted tasks terminated as unverified. The results show that completion of these tasks can be governed by independent observation of service state rather than by the agent’s self-assessment.
[AI-97] Clocks all the way down: does sequence-processing AI need endogenous temporal structure?
链接: https://arxiv.org/abs/2611.04181
作者: Meysam Hashemi,Alireza Valizadeh,Viktor Jirsa
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Mainstream sequence-processing AI is temporally reactive: each update is driven by input and learned state transitions, rather than by an independently evolving internal temporal reference. Biological brains, by contrast, continuously generate rhythmic activity that structures perception, memory, and cognition, and this rhythmicity reaches all the way to behavior. We ask whether this difference is merely implementational or genuinely computational. Four lines of evidence suggest that endogenous temporal structure supports putative functions that input-driven architectures may not readily replicate: 1) phase-dependent routing of information, 2) delay-structured combinatorial representation, 3) cross-frequency multiplexing of items, and 4) oscillation-gated offline consolidation. A proof-of-concept delay-ablation test dissociates representational capacity from memory retrieval. In recurrent and spatial spiking networks with fixed synaptic weights and connectivity, shuffling delays preserves part of the capacity but substantially impairs the sequence retrieval. The relative contrast is more pronounced for spatial networks and longer propagations. Instantaneous transmission collapses capacity itself. We then outline a cross-frequency multiplexing scheme that combines item identity and serial order with alternative mechanisms for selective memory retrieval. Finally, we state one falsifiable hypothesis, conditions for matched-resource tests, and three research directions: the components already exist; the disciplined comparison does not.
[AI-98] Validation-Gated ML Pipeline Synthesis with Expert-Grounded Self-Evolving Skills
链接: https://arxiv.org/abs/2611.04180
作者: Zixiao Zhao,Jing Sun,Zhe Hou,Mengze Li,Qian Liu,Cheng-Hao Cai,Zijian Zhang,Xin Wang,Gillian Dobbie,Jin Song Dong
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 49th International Conference on Software Engineering
Abstract:Recent advances in large language models have enabled agentic systems that automate machine learning pipeline construction from minimal user input. However, existing agentic AutoML methods either depend on rigid templates that limit adaptability, target narrow task domains, or rely on free-form code generation that lacks reliability guarantees, leaving intermediate errors to propagate silently and degrade the final model. We present EvoML, an agentic framework for end-to-end ML pipeline synthesis built on expert-grounded skills that evolve across runs, executed through a validation-gated harness. The framework decomposes pipeline construction into four phases coordinated by three agents: an Orchestrator that enforces validation gates, advancing each phase only when its outputs pass structural, execution, and semantic checks, which precludes error propagation and keeps every artifact auditable; an Executor that runs expert-grounded skills, grounded in the nnU-Net workflow and pairing reusable templates with online adaptation; and a Curator that performs post-run evolution, consolidating the scripts and guidance recovered during a run into the skill library so it improves across datasets. We evaluate EvoML on eight datasets spanning six sophisticated, real-world ML task types, against agentic AutoML systems and command-line coding tools. EvoML achieves full automation and the best final model quality on all eight, spanning segmentation (up to 0.94 Dice), classification (0.99 accuracy), reconstruction (0.95 SSIM), and denoising (0.79 normalized PSNR), while transferring to broader ML tasks.
[AI-99] GlucoAgent : Personalized Glucose Forecasting from Multimodal Personal Context
链接: https://arxiv.org/abs/2611.04126
作者: Xingzhi Sun,Hyo Jin Kim,Zhiyuan Wang,Hao Chen
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Continuous glucose monitors, smartwatches, meal logs, and health profiles form a rich multimodal personal context that can significantly enhance glucose forecasting. However, existing methods struggle to effectively utilize these signals to improve glucose prediction because the relevance of different modalities varies drastically across individuals and time. We present GlucoAgent, an LLM-based agentic system that addresses this challenge through dynamic, case-by-case analysis at inference time. For each forecast, GlucoAgent writes and runs code over an individual’s multimodal context and returns predictions with a reason and supporting evidence. The generated programs are highly diverse, ranging from retrieving similar past meals to fitting and back-testing physiological and statistical models. On CGMacros, a multimodal glucose dataset, GlucoAgent outperforms time series foundation models on every forecasting horizon, achieving an average RMSE of 22.5 mg/dL compared to 23.5 mg/dL of Chronos-2, the leading multimodal time series foundation model. Furthermore, GlucoAgent is highly effective at leveraging multimodal data. The addition of multimodal data reduces its average RMSE by 1.4 mg/dL (24.0 mg/dL to 22.5 mg/dL), compared to a marginal 0.3 mg/dL (23.8 mg/dL to 23.5 mg/dL) improvement for Chronos-2. This advantage is most pronounced in challenging post-meal windows, where GlucoAgent uses multimodal context to reduce 60-minute forecast RMSE by 4.6 mg/dL (31.0 mg/dL to 26.5 mg/dL), far exceeding the 0.8 mg/dL (31.9 mg/dL to 31.1 mg/dL) reduction achieved by Chronos-2.
[AI-100] he Evolution Strategies Paradox: Better Models from Worse Perturbations
链接: https://arxiv.org/abs/2611.04113
作者: Roberto Dailey,Kajetan Schweighofer,Jamieson Warner,Conor F. Hayes,Elliot Meyerson,Babak Hodjat,Risto Miikkulainen,Xin Qiu
类目: Artificial Intelligence (cs.AI)
备注: 20 pages, 9 figures
Abstract:How is it possible for perturbation-based methods like Evolution Strategies (ES) to optimize billion-parameter large language models (LLMs)? Classical search methods are highly dependent on finding good candidates within generated populations. However, when using ES to fine-tune LLMs, a phenomenon can occur where all offspring models consistently perform worse than the parent model, yet during training, the parent model consistently improves. This paradox occurs across models, tasks, and even modalities. This paper characterizes the paradox, then examines how the local reward landscape and properties of the ES estimator contribute to it: in high-dimensional spaces, exploration leads to worse performance, yet still highlights good directions; aggregating those directions then allows for making local improvements. The result can be seen as a new optimization paradigm where a principled interaction of noise and curvature produces the paradox as a byproduct of effective optimization. These findings provide guidance for taking advantage of perturbation-based methods in LLM fine-tuning and in high-dimensional optimization in general.
[AI-101] Separating Common and Unique Directions for Data-Free Model Merging NEURIPS2026
链接: https://arxiv.org/abs/2611.04108
作者: Shiting Wang,Yingjie Zou,Ke li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by NeurIPS 2026
Abstract:Model merging combines multiple fine-tuned models without additional training, and arithmetic methods have emerged as a simple yet effective way to merge models without data. However, existing arithmetic methods treat task vectors in parameter space and apply a single element-wise rule to directions that can differ in mergeability. In a shared directional basis, we find a consistent two-regime structure: a low-dispersion common bulk shared across tasks and a high-dispersion task-dominated tail that carries disproportionate energy. These findings motivate Common–Unique Subspace Decomposition (CUSD), a data-free pre-merging method that separates task vectors into common and unique directional components, merges the common components with downstream arithmetic operator, and retains unique residuals separately. Experiments across 11 base models, four model families, and four arithmetic-based merging operators show that CUSD improves 42 of 44 model–operator combinations and achieves up to 22.5% improvement over the corresponding baselines. Our code is publicly available at this https URL
[AI-102] Structured Clinical Memory as Context for Probabilistic Decision Models: A Synthetic Evaluation with Jev
链接: https://arxiv.org/abs/2611.04101
作者: Jash Sayani,Gaurang Karwande,Achinth Bharadwaj
类目: Artificial Intelligence (cs.AI)
备注: 4 pages, 1 figure, 8 tables
Abstract:Longitudinal clinical context is often distributed across noisy, repetitive forms and narrative records. We study whether a compact structured memory can improve a probabilistic decision model’s answers when the underlying patient vignette is already available. We created 30 fully synthetic cases spanning twelve clinical domains, paired each with synthetically generated structured memory, and evaluated 120 typed questions with Jev. The raw-vignette arm achieved 90.0% accuracy (95% normal-approximation confidence interval: 84.6%-95.4%); adding memory achieved 93.3% (88.9%-97.8%). Five questions changed from incorrect to correct and one changed in the opposite direction (exact McNemar p=0.219 ). Choice log loss decreased from 0.258 to 0.175 and binary Brier score from 0.054 to 0.022. Memory added 310 input tokens per call on average, while observed mean latency was unchanged at approximately 0.5 seconds. These results are directionally positive rather than clinically validating: the cases are synthetic, the labels are single-author judgments, and the memory is an oracle upper bound rather than extracted production output. The pilot supports evaluating structured clinical memory as a reusable state interface for decision models.
[AI-103] he Agent Board: Kanban for Machines; Durable Coordination for Agents That Outlive the Conversation
链接: https://arxiv.org/abs/2611.04100
作者: Tyler Hunt,Donald Nguyen,Gurbinder Gill
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one machine, one sitting. That shape fails work that spans many agents, outlives any one model call, or waits on systems that are not agents. We describe the Agent Board, the coordination substrate in the Corvic harness. The board is a durable, shared table of semi-semantic tasks: a few exact fields the system routes on, and a free-form body that only language models read. Agents post tasks, watch tasks, and block on them, ending their turn and holding no process until a matching post wakes them. A system agent, the Board Keeper, acts as the interrupt handler for the board: it carries events from non-agent parts of the harness onto the board, matches them to waiting tasks by reading their prose, and allocates new agents for tasks that name a kind of agent rather than an instance. Loadable coordination policy kernels give statuses and roles meaning for a given pattern of work, without the board enforcing them. The design mirrors the kanban board a human team puts on a wall. We argue it is what persistent agent products such as Meta Muse, OpenAI dots and SpaceXAI Grok Bot need once an agent must wait, hand off work, or work with agents from another vendor. We view the board as an operating-system scheduler for agents and state where the analogy breaks. We separate what the mechanism guarantees from what it leaves to convention, and report a trace from a live board and three small simulated pilots. In the pilots, the wait cost of the board was flat in wait length while naive polling grew about 165-fold, but a plain external watcher was cheaper than the board. The Board Keeper matched prose waiters with no false wakes but missed 3.6 percent of real wakes.
[AI-104] On the Primacy Bias in RLVR Training
链接: https://arxiv.org/abs/2611.04061
作者: Phuc Minh Nguyen,Ngoc-Hieu Nguyen,Parshin Shojaee,Chinh Duc La,Duy Minh Ho Nguyen,Rui Zhang,Khoa D. Doan,Ting Hua,Nitesh V. Chawl
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a promising approach for enhancing the reasoning capabilities of large language models. However, it still remains unclear whether RLVR can extend reasoning capabilities beyond what is already encoded in the base model. In this paper, we study this question through the lens of primacy bias: the tendency of models to over-rely on already-learned representations, disproportionately improving performance on problems they already handle well while underexploring harder ones that require departing from those representations. Through our controlled experiments and large-scale analysis, we show that RLVR training on mixed data tends to further sharpen performance on easier problems while yielding only marginal gains on harder ones. We find that although hard problems lead to modest gains overall, they are actually the ones that help most in expanding the model’s reasoning capabilities and pushing the representations. However, prolonged training on these hard problems may also hurt and lead to forgetting on easy problems. These findings help to better understand some of the prior conflicting observations about RLVR, specifically, why on (OOD) domains where the base model performs poorly, improvements on hard problems can expand reasoning; while on well-trained domains, forgetting can reduce performance and mask genuine gains obtained from hard problems.
[AI-105] Query-based Submodular Data Selection for Efficient Instruction Tuning
链接: https://arxiv.org/abs/2611.04059
作者: Zhenghao Zhao,Yasutomo Kawanishi,Huihuo Zheng,Michael E. Papka,Yan Yan
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models are fine-tuned for increasingly specialized tasks, their performance depends more on which training examples are selected than on how many. Given a large heterogeneous training pool and a small target set specifying the desired capability, targeted data selection aims to find a compact subset that maximizes downstream performance. Existing methods score each candidate independently against the target and select the top-ranked examples, yet this point-wise strategy ignores inter-example interactions: at tight budgets the selected subset is dominated by near-redundant points that waste capacity on overlapping information. We propose Query-based Submodular functions with Gradient-guided Synthesis (QSGS), which formulates targeted selection as a submodular graph cut over a gradient similarity graph. A modular relevance term pulls selected examples toward the target task, while a pairwise dispersion penalty suppresses intra-subset redundancy. The resulting objective balances target relevance and intra-subset redundancy using the same formulation across different selection budgets. Empirically, the benefit of diversity-aware selection becomes increasingly pronounced as the data budget decreases. To further close coverage gaps that no selection from existing data can bridge, we introduce a gradient-guided synthesis step that identifies under-covered target directions via residual subspace analysis and generates synthetic examples to fill them. Experiments on MMLU, BBH, and TydiQA across Llama-2, Llama-3, and Mistral, show that QSGS at a 0.5% budget matches or exceeds full-data performance on Mistral-7B, outperforms the strongest baseline across all benchmarks, and yields increasing gains as the budget shrinks.
[AI-106] Look Before You Launch: Learned Throughput Prediction for Hardware Selection in a Production Training Fleet
链接: https://arxiv.org/abs/2611.04057
作者: Hossein Askari,Wei Sun,Chenguang Zhu,Bin Zan,Sreen Tallam,Keyu Man
类目: Artificial Intelligence (cs.AI)
备注: 16 pages, 8 figures, 4 tables
Abstract:In mixed-vendor accelerator fleets, many training jobs can run on several hardware types, and the best choice depends on each job’s throughput on each type. Routing a job needs a throughput estimate for every candidate type before the job runs. We present LEAP, a machine learning performance prediction service that predicts each training job’s throughput on every candidate hardware type at submission time and ranks the candidates by predicted throughput per unit cost. It routes tens of thousands of training job attempts a day across hundreds of different model types and more than 20 hardware types in a production fleet. LEAP predicts throughput from submission-time training configuration, including each job’s prior throughput. LEAP reaches an accuracy of 77% over 30 nightly retrains, higher than the lookup table it replaced and at least as high as every alternative model we tested, including fine-tuned language models. Over six months, the share of eligible training GPU-hours that received a ranking by LEAP grew from 58% to 95%, and we estimate that it saves about 3% of training computational cost. In this paper, we identify the drivers that actually move accuracy and those that do not. We offer these as lessons for teams building performance predictors at fleet scale.
[AI-107] HybridSpec: Throughput-Aware Speculative Decoding for Hybrid GDN-MoE Models
链接: https://arxiv.org/abs/2611.04055
作者: Haiyu Wang,Sai Qian Zhang,Zechun Liu,Steven Li
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In speculative decoding, parallel drafters such as DFlash make drafting cheap, so target verification dominates each round. A central choice is the verification length, the number of tokens that the target processes in each round. Verifying more draft tokens can accept more of them, but work after the first rejection is wasted. For hybrid target models that combine Gated DeltaNet (GDN) and mixture-of-experts (MoE) layers, verification time grows much faster with this length than for dense models. In a single-request profile of Qwen3.6-35B-A3B with DFlash, it grew by 71% from length 4 to 16, almost entirely in GDN and MoE layers, compared with 4%-6% for a dense Qwen3-4B target. Each GDN layer must update a recurrent state at every token and recover it at the end of the accepted prefix. The MoE cost depends on the experts that the tokens activate, which are known only once verification runs. We present HybridSpec, which selects the verification length on the GPU from draft-side acceptance estimates and measured target latency. Verification then runs this length plus one extra token up to the first MoE router, before any expert runs. There, a lightweight routing correction uses the observed expert assignments to decide whether to keep that token. The rest of verification runs only on the selected tokens. Block-parallel GDN kernels avoid storing a state after every token and reconstruct the accepted state of every GDN layer in one launch. In isolated benchmarks at verification lengths 4 to 16 with every draft token accepted, these kernels, including accepted-state reconstruction, are 1.54\times - 4.04\times faster than the default kernels. With three GDN-MoE target models in single-request greedy decoding, HybridSpec is 1.75\times - 1.88\times faster than autoregressive decoding and 1.30\times - 1.33\times faster than DFlash.
[AI-108] Forgetting to Improve: Principled Data Removal in Active Learning
链接: https://arxiv.org/abs/2611.04054
作者: Manuel Wendl,Erik Englesson,Andreas Krause,Carl Henrik Ek
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The uncertainty of a statistical model is most commonly factorized into an aleatoric and an epistemic part. This factorization changes how predictions are interpreted in downstream decision tasks. Importantly, except for the idealistic scenario with no model mismatch, the quantification is a characteristic of the model and not the data generating process. In this paper, we propose Forgetting to Improve a method that reduces this discrepancy by incorporating the task into the modeling framework. Our key insight is to acknowledge that in scenarios of model mismatch, data can have a detrimental effect on the modeling for a specific task. Based on this insight, we propose an influence function for Gaussian process models that allows for principled removal of detrimental data samples. We showcase the flexibility of this approach by demonstrating significant improvements across a range of tasks, including Bayesian optimization, model-based reinforcement learning, and transductive learning.
[AI-109] Exploit or conform? How norm utility prompting changes the way LLM s use community beliefs
链接: https://arxiv.org/abs/2611.04048
作者: Leon Klingborg,Theodor Cimpeanu,Alexander J. Stewart
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) serve both as tools for aiding human decision-making in everyday life and as models of human agents in experimental simulation studies. Alignment on prototypical social decisions – such as whether to cooperate on a collective action – therefore has immediate practical consequences for researchers and for society at large. Here we systematically explore the impact of ``norm utility’’ – the non-material components of utility that account for an individual’s desire to conform to social norms and to be internally consistent – on the strategic decision-making of three open-weight LLMs in one-shot, two-player social dilemmas. We find that when a social dilemma is presented together with information about community beliefs – but without any indication of how those beliefs should matter – models tend to treat community belief as strategic information, so that in the Snowdrift game cooperation falls as community belief in cooperation increases. When we explicitly prompt the components of norm utility this relationship inverts, and cooperation increases with community belief, in broad agreement with the predictions of a simple norm utility model. We then show that prompting norm utility reduces the volatility of strategic decisions with respect to changes in the underlying social context, and generates more consistent reasoning habits across models. Taken together, our results suggest that prompting norm utility can help generate more human-like and consistent behavior across models and lower volatility within models.
[AI-110] Recursa: Toward Recursively Self-Improving Foundation Model Systems
链接: https://arxiv.org/abs/2611.04035
作者: Erkang Zhu,Yin Lin,Xiaorong Shi,Xuanrui Lin,Jingren Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Foundation models in changing environments can encounter unfamiliar domain knowledge that their original training did not cover. However, new domains rarely provide large amounts of expert-created training data or reliable supervisory signals for further training. We introduce Recursa, a foundation model system that adapts to new domains by learning from its own experience without human experts or a stronger teacher model. Recursa uses a knowledge repository to store what it learns: domain-specific skills and instructions on when to apply them, along with memories of past task experiences and reflections on what to improve. Recursa bootstraps the repository from domain materials and then refines it through recursive self-improvement. It uses parallel learners to execute tasks and synthesize local improvements, then validates and merges the improvements into the knowledge repository. On Harvey LAB’s Firm Knowledge benchmark, Recursa raises the all-pass rate on held-out test tasks from 30.0% to 55.0% with DeepSeek-V4.1-Flash and from 35.0% to 46.0% with Qwen3.8-Flash.
[AI-111] ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
链接: https://arxiv.org/abs/2611.04032
作者: Anshu Raina,Yao Fu,Zhang Jiejing,Peyman Razaghi,Shiksha Patel,Ravi Gupta,Deval Shah,Adeem Jassani,Subhajit Dutta Chowdhury,Lorri Rao,Kailash Gogineni,Zhenyu Gu,Onil Gunawardana,Emad Barsoum
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 37 pages, 11 figures
Abstract:Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode disaggregation. The best configuration depends on the model, hardware, workload, and objective. Operators usually find it by trial and error. Exhaustively measuring the candidates in our study would take about 25,000 GPU-hours. ROCm InferaSim predicts serving performance from a few short measurements. Choices such as batch size, prompt length, and GPU count change the amount of work per GPU without changing the kernels. ROCm InferaSim benchmarks each distinct set of kernels once, on at most one node. We call this measurement an anchor. Analytical laws carry that anchor to other deployments that run the same kernels. For multi-node deployments, ROCm InferaSim reduces the configuration to a single-node equivalent, measures it, and scales the result analytically. A simulator replays multi-turn traffic, and a tuning agent searches configurations using the predictions. On AMD Instinct MI355X GPUs with vLLM, SGLang, and ATOM, 11 anchors took 9.1 GPU-hours, against 204 GPU-hours to measure the 226 fixed-length configurations they cover. On 172 configurations, time per output token has 8.1% median error. On a multi-turn coding workload, throughput is within 8%. For 159 disaggregated deployments, none used as an anchor, throughput is within 5.5%. A five-candidate short list contains a deployment within 12% of the best feasible pairing of model and latency target.
[AI-112] A Practical Causal Framework for Autonomous Mobile Robots: From Simulation to Real-World Human Environments
链接: https://arxiv.org/abs/2611.04027
作者: Luca Castri,Gloria Beraldo,Nicola Bellotto
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Accepted in IEEE Robotics and Automation Letters (RA-L)
Abstract:Causal inference has emerged as a powerful paradigm enabling robots to make safe, efficient decisions by reasoning about the ``why’’ behind events. However, its real-world application is hindered by the difficulty of collecting causal data, especially in human-populated environments where training takes time, while users expect immediate operation. To this end, we tackle the challenge of transferring simulation-learnt causal relations to the real world with minimal effort. While retaining the invariant high-level causal structure, we empirically demonstrate the system adapts efficiently to real-world situations by updating only the causal model parameters with a limited set of new observations. Our results show that the parameter update enables rapid sim-to-real transfer. The framework runs efficiently onboard of a mobile robot and significantly outperforms a non-causal baselines navigating successfully and safely in complex human-shared scenarios.
[AI-113] Controlling Action-Head Co-Adaptation to VLM Representation Anisotropy
链接: https://arxiv.org/abs/2611.04006
作者: Ruijie Zhang,Jiayin Zheng,Yequan Zhao,Liyan Tan,Ziyue Liu,Zhengyang Wang,Kunpeng Song,Nathan Zhao,Jian Yao,Yin Zhang,Zheng Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Modern learning systems increasingly combine pretrained models with downstream heads. Despite the growing importance of such modular designs, how the downstream model co-adapts to the geometry of pretrained representations remains relatively underexplored. We study this problem through vision-language-action models (VLAs), where a pretrained vision-language model (VLM) conditions an action model for control. We find that the action model progressively concentrates its sensitivity on high-variance dimensions of the VLM outputs. Through controlled experiments across different backbone-head models, we further show that counteracting this sensitivity concentration can be beneficial under representation–task mismatch settings. Motivated by these observations, we introduce \emphAdaptive Interface Equalizer (AdaIE), a parameter-free mechanism that reweights pretrained representations according to the evolving downstream sensitivity during training. AdaIE improves VLA success rates on simulation benchmarks, with gains of up to +5.28 percentage points (pp) on RoboCasa-GR1 and +1.68 pp on RoboTwin 2.0, and achieves an average gain of +19 pp across five real-world manipulation tasks. It further generalizes to diffusion policies and world models, improving downstream performance in both settings. Our results highlight interface co-adaptation as an important optimization problem when coupling pretrained representations with downstream models.
[AI-114] When Music Needs Meaning: Semantic Communication as a Transformative Enabler for the Musical Metaverse
链接: https://arxiv.org/abs/2611.03995
作者: Ali Al Housseini,Jaime Llorca,Cristina Rottondi,Luca Turchet,Tiziano Leidi,Omran Ayoub
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures. Accepted and Presented at IEEE International Symposium on the Internet of Sounds (IS2) Conference 2026
Abstract:The Musical Metaverse (MM) enables geographically distributed musicians, audiences, sound engineers, and AI agents to interact through synchronized audio, visual, kinematic, and haptic streams, imposing latency, synchronization, bandwidth, and scalability requirements that are difficult to satisfy with conventional bit-level communication. This paper argues that semantic communication, understood here as the transmission of musically meaningful information rather than raw audio-visual-haptic samples, provides a principled communication paradigm for the MM. We define the MM from a communication perspective and derive modality-specific require ments; propose a three-pillar framework for musical semantic communication comprising semantics extraction, semantic-aware orchestration, and cross-modal semantic synchronization; survey representative work across semantic communication, music AI and edge orchestration, identifying the gaps that prevent their integration; and propose a semantic-first reference architecture based on device-side semantic encoders, edge semantic routers, and cross-modal semantic synchronization. The resulting framework positions the MM as a concrete and demanding use case for 6G semantic communication.
[AI-115] Measuring Model-Specific Alignment Drift in LLM -Based Opinion Dynamics
链接: https://arxiv.org/abs/2611.03980
作者: Nataša Djurdjevac Conrad,Sebastian Pokutta,Christof Schütte
类目: Artificial Intelligence (cs.AI); Physics and Society (physics.soc-ph)
备注:
Abstract:Large language model (LLM) agents are increasingly used as synthetic participants in opinion-dynamics experiments, often assuming that an agent’s behaviour is fully determined by its prompt. We test this assumption with a framework in which LLM agents discuss a contested policy issue over repeated rounds while their stance is quantified by a fixed psychometric instrument independent of any dynamics model. Across six LLMs, committed personas are intrinsically stable: in each of the five models tested without peer input, every agent keeps its assigned side for 20 rounds. The observed loss of alignment under discussion is therefore interaction-induced, we call it alignment drift, and model-specific: gpt-4.1 keeps opinion clusters apart, claude-sonnet-5 holds them partially, and gpt-5.6-sol and claude-opus-5 abandon them. Under selective attention, one LLM maintains separated opinion clusters under stance-homophilous attention. Qualitative profiles persist at doubled population and horizon and on a second issue. Such heterogeneity makes the LLM choice a first-class validity variable for social simulation.
[AI-116] Stacking-Based Weighting for Large Language Bayes in M-Open Settings
链接: https://arxiv.org/abs/2611.03966
作者: Anant Bhide,Akarsh Gupta,Edmond Cunningham,Justin Domke
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 Pages,4 Figures
Abstract:Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate probabilistic programs and performs inference within each model, combining their predictions using Bayesian model averaging. While this provides a flexible framework for probabilistic inference without manual model specification, the resulting candidate model set is often misspecified in practice, and likelihood-based aggregation can concentrate on a single model, under-representing uncertainty across the sampled models. We propose a stacking-based aggregation method for LLB that selects model weights by optimizing predictive performance rather than marginal likelihood, while preserving the original language-model-driven model generation and within-model inference pipeline. Because leave-one-out predictive quantities are intractable in latent-variable models, we derive a variational lower bound that yields a tractable ELBO-based stacking objective. Across real-world probabilistic inference tasks, our method achieves comparable predictive performance to Bayesian model averaging while yielding more dispersed model weights, reducing concentration on a single candidate model under misspecification.
[AI-117] ProteoCraft: Protein Design as Proposal-Verification Search
链接: https://arxiv.org/abs/2611.03963
作者: Fang Wu,Hongbin Lin,Bingchen Duan,Yuchen Wang,Li Erran Li,Yanjie Huang,Mingxuan Cao,Yijun Ma,Yunxiang Mo,Yanchao Li,Kuan Pang,Juangui Xu,Hanqun Cao,Dongwei Jiang,Xinyue Xu,Xiangru Tang,Zehong Wang,Jian Ma,Kejun Ying,Guanlue Li,Mingran Cao,Fanjin Wang,Tianfan Fu,Shuangjia Zheng,Yao Shu,Jure Leskovec,Yejin Choi
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
备注:
Abstract:Protein-protein interactions underpin nearly all biological processes. Yet, designing proteins with desired interactions remains challenging due to the structural complexity governing molecular recognition. This study views protein design as a search optimization problem over a combinatorial sequence space under expensive structural evaluation. Toward this goal, we introduce a novel tree-based search framework, ProteoCraft, that employs approximate structural gradients from diffusion-based structure predictors as effective proposal policies to specify where the search should explore. Complete no-gradient inference of folding models separately assigns values to the resulting sequences, determining what is good. ProteoCraft allocates this verification budget through a coarse-to-fine paradigm: multi-site edits first explore distinct sequence basins, then verified promising states undergo strict single-site refinement. Across fourteen de novo binder-design settings spanning eight target proteins, five immune-relevant TCR-facing targets, and four localized CDR redesign tasks, ProteoCraft improves design quality and high-confidence yield. These improvements persist under independent AF2-IG and Rosetta-based interface evaluation. Prospective experimental validation further yielded an 82.9% binding hit rate for PD-L1 miniproteins, with a best measured KD of 0.879 nM, and confirmed binding of designed IFNAR2 VHH. Our code is available at this https URL.
[AI-118] P2-MOPD: Accelerating Multi-Teacher On-Policy Distillation via Parameter Priors
链接: https://arxiv.org/abs/2611.03953
作者: Sinuo Wang,Jiacheng Lu,Zeyu Zhang,Maojia Song,Rui Sun,Beidi Luan,Yutong Xie,Jing Li,Daxin Jiang,Qi Wu,Zuo Bai
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-teacher on-policy distillation consolidates expert capabilities by supervising student-generated trajectories, whereas task arithmetic offers a training-free alternative through linear parameter combinations. We view the linear combination of student and expert parameters as a single differentiable model and propose P ^2 -MOPD, which jointly optimizes the mixing coefficients and the student weights under the same on-policy distillation objective. Unlike task arithmetic, which stays within the span of the frozen student and expert parameters for any choice of merging coefficients, P ^2 -MOPD keeps the student trainable, so the experts act as parameter priors without confining it to this span. Across four domains at 1.7B and 4B scales, P ^2 -MOPD achieves faster and more balanced recovery than MOPD from the same initialization, surpassing its final average recovery in half the training steps. At the end of training, our 1.7B model reaches 94.7% average recovery, versus 80.9% for MOPD.
[AI-119] We Ran a Research Sprint With AI. The AI Worked. The Research Didnt
链接: https://arxiv.org/abs/2611.03946
作者: Anush Sankaran,Arijit Banerjee
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Generative AI tools for research are widely deployed, yet individual productivity gains do not guarantee better team research. We argue that how AI use is structured matters alongside access. Personal AI tools may amplify two failures under deadline pressure: a consumption bottleneck, in which generation outpaces team-visible evaluation, and AI-induced research silos, in which private human-AI loops displace shared decision-making. We motivate this position with a mechanism-generating pilot: a 100-minute research sprint with twelve researchers in three independently operating teams, within a seventeen-person session. Both patterns recurred across teams and evidence sources. Of 33 logged AI interactions, one was accepted outright and 27 lacked a recorded disposition; missing records alone do not establish absent evaluation. A parallel autonomous pipeline produced a complete draft without in-session human evaluation, illustrating production capacity rather than comparative quality. We propose Human-AI-Human collaboration: an interaction topology in which AI connects researchers’ contexts and supports inspectable collective evaluation and decision-making. The pilot does not isolate AI-specific effects from time pressure or workflow practices. We close with seven modular, falsifiable design hypotheses, prioritizing shared context and team-visible evaluation checkpoints for controlled follow-up.
[AI-120] What Is This Memory Useful For? Measuring Task-Conditioned Utility in Long-Term Agent Memory AAMAS2027
链接: https://arxiv.org/abs/2611.03930
作者: Chih-Hsuan Yang,Anjir Ahmed Chowdhury,Feng Yan,Rajeev Thakur
类目: Artificial Intelligence (cs.AI)
备注: 18 pages, including supplementary material. Project website: this https URL
Abstract:A stored experience may answer one future question and offer little help with another. We measure this task-conditioned utility rather than assigning each memory a single importance score. A task-blind writer stores a memory without seeing the later task; a frozen reader then answers that task with and without the memory, using identical filler. A cost ledger records the resources used to form, store and expose the memory. On single-call factual-recall tasks built from LongMemEval-V2 web-agent memories, the same memory helps more when it contains information needed for the answer. Its mean paired reward gain on these answer-bearing task pairs exceeds that on matched-control pairs by +0.33 (95% interval +0.21 to +0.46 ). The tested task-blind summaries and structured records yield smaller gains than Raw, including at fixed total context; a budget sweep is consistent with retention of answer-bearing content contributing to the difference. The bearing–control contrast remains positive in a new session with randomized memory position and under a second reader, although individual memory–task labels change. Generic retrieval performs above chance but misses most screen-defined answer-bearing memories. The factual-recall gains do not extend to the tested software-engineering and scientific-coding benchmarks, where even raw memories have no measurable effect. We provide a public, versioned Conditional Memory Utility Dataset of paired measurements and costs. The practical takeaway is to evaluate memory compression by the task utility it preserves alongside its costs. Task-conditioned measurements make this comparison possible; their stability must also be checked. Comments: 18 pages, including supplementary material. Project website: this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2611.03930 [cs.AI] (or arXiv:2611.03930v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.03930 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-121] ProofPaths: An AI Agent Framework for Assessing Student Reasoning
链接: https://arxiv.org/abs/2611.03882
作者: Mohsen Hariri,Vipin Chaudhary
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 8th Workshop on Education for High Performance Computing (EduHiPC 2026). 6 pages, 3 figures. this https URL
Abstract:Students can use large language models to complete assignments, but a correct solution does not establish whether they understand the reasoning behind it. Individualized interviews can probe that understanding but require recurring staff effort. We introduce ProofPaths, an AI agentic framework that combines rubric-based grading with individualized and interactive interviews. Multiple grading agents assess a submission against instructor guidance, exchange critiques, and revise their judgments. Their scores determine which review supplies the grade and feedback. An interview agent asks students to justify steps in their submission and uses their answers to guide follow-up questions. Each question is linked to a retrieved submission excerpt, so instructors can compare the student’s explanation with the cited passage. The system tracks assessment progress and stores grading reviews and interview transcripts for each submission. Several deterministic tests of the assessment controllers check source handling, score aggregation, and limits on correction attempts. Instructors can inspect all reviews and transcripts and override grades within the assignment workflow.
[AI-122] Predictive gains causal losses: why interventional competence cannot be learned from data alone
链接: https://arxiv.org/abs/2611.03861
作者: Marcus Thomas
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Retaining predictive gains can require persistent errors in a model’s response to interventions. For deterministic predictors with finite second moments under squared-error loss, any observational advantage over the best predictor using only an outcome’s direct causes requires dependence on other inputs, regardless of the learning rule. When admissible interventions vary inputs while preserving the worldly outcome, this dependence yields a positive lower bound on root-mean-square intervention-response error. We identify conditions under which population gradient updates introduce this dependence from a parent-only baseline or damage a previously correct mechanism through shared parameters while improving other predictions. These dependencies can also make model behavior vary with how the same intervention is realized. We characterize interventional alignment through a common output mapping across internal states and physical processes realizing the same intervention target and relevant context. Correction is limited by the causal hypotheses currently admitted within inquiry. The relevant hypothesis class comprises these causal alternatives, rather than the function class the computational system could in principle represent. For predictions restricted to these alternatives, interventional mean error cannot fall below the best approximation they permit, whether evidence is observational or interventional. Reducing error below a positive limit requires admitting better hypotheses, which may entail revising the causal variables, mechanism structure, or intervention interpretation. Interventional criticism constrains replacements without determining them. We conjecture that reliably acquiring and maintaining interventional competence when causal commitments fail requires autonomous participation in proposing alternatives, testing their consequences, and revising them in response to error.
[AI-123] Performance Scalability and Maintenance: An Experimental Study of RPA vs. Autonomous Agents in the Healthcare Domain
链接: https://arxiv.org/abs/2611.03853
作者: Daniel Schulz,Pedro Cabral Buchaim,Bernardo Soares,Fernando Rafael Ensslin Schwengber,Lorenzo Farias,Daniela Duarte da Silva Bagatini,Ana Paula Wernz da Cunha Müller
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 19 pages, 3 figures, 6 tables
Abstract:Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need. Robotic Process Automation (RPA) is deterministic but sensitive to interface changes, whereas autonomous agents based on large language models (Agent Process Automation, APA) promise adaptability at a higher computational cost. Following Design Science Research, we developed and compared an RPA solution (Playwright) and an APA solution (browser-use, LangGraph and Gemini 2.5 Flash-lite) to retrieve mammography and breast ultrasound reports from a laboratory portal, treating reports solely as informational artifacts. Across two rounds (T and T+7), RPA reached 98.90% accuracy and 98.27% F1-score versus 96.49% and 94.48% for APA, was about 13 times faster (16.07 s vs. 207.21 s per patient) and cost about six times less per report (USD 0.030 vs. 0.187). Both reached 100% step success rates; APA varied more over time. For a stable interface, RPA was superior.
[AI-124] RoboScope Kit: Unified Interfaces for Fast and Scalable Embodied-AI Evaluation
链接: https://arxiv.org/abs/2611.03850
作者: Deng-Ke Wu,Yifan Jin,Yida Yang,Lei Qian,Zhaoye Zhou,Hongru Zhang,Kai Ye,Shuyuan Hu,Yiran Mao,Zewei Ye,Bo Zhao
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 29 pages, 9 figures. Project page: this https URL Code: this https URL
Abstract:Simulation benchmarks are the standard way to compare robot policies, but each benchmark brings its own evaluator, observation layout, and action convention, and evaluation is slow: 500 RoboTwin Easy episodes with one simulator and one model on one GPU take 359.7 minutes. Profiling the official StarVLA evaluator shows that simulation occupies CPU cores while the GPU averages 14.3% utilization. RoboScope Kit wraps each model once in a Python class called a Policy, and each benchmark once in an adapter around the benchmark’s own evaluator, so the benchmark’s rules and scoring stay unchanged. Scheduling runs simulator workers and Policy replicas as separate pools, sizes both from measured probes, and shards work deterministically across GPU instances; runtime optimization renders only the observations an action-chunk Policy reads. Adapters cover six benchmark families. On LIBERO-Long with a StarVLA checkpoint on one 48 GiB RTX 4090, the concurrency search selects twelve simulator workers sharing three Policy replicas, which evaluate 500 episodes 4.45 \times faster than the official StarVLA evaluation with equivalent success rates, 2.78 \times faster than RLinf, and 1.52 \times faster than vla-eval at its tuned configuration. With CUDA MPS, which lets the replicas run their kernels concurrently, the search selects sixteen workers sharing four replicas, 6.16 \times faster than StarVLA, 3.85 \times faster than RLinf, and 2.10 \times faster than vla-eval. On RoboTwin Easy, four GPUs with chunk-boundary observation complete the 500 episodes in 14.4 minutes, 25.04 \times faster than the single-GPU baseline, with identical success outcomes and terminal steps.
[AI-125] LaplaceDiag: Fault Diagnosis through Changes in Learned Actuator Effects
链接: https://arxiv.org/abs/2611.03849
作者: Oliver Niggemann
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a plant and different faults can produce similar observations. Detailed component models and labeled fault examples may also be unavailable. We introduce LaplaceDiag, a data-driven method that uses changes in actuator effects to narrow the search for faulty equipment. Actuators act on specific devices and mechanisms, so their changed effects can provide clues to root causes. LaplaceDiag learns a healthy plant model with LaplaceNet, a neural network that operates in Laplace space. It separates nonlinear prediction based on measured history from an explicit matrix that describes how actuator values affect sensor values. Keeping the history predictor fixed allows current actuator effects to be estimated by linear regression without retraining the neural network. Comparison with a healthy reference accounts for estimation uncertainty and normal variation to detect changes and generate actuator-related diagnostic hypotheses. The method supports online and offline diagnosis, providing an intermediate step between anomaly detection and identifying faulty components. We successfully evaluate LaplaceDiag on two datasets.
[AI-126] PianoBench: Evaluating Musical Perception and Reasoning in Language Models
链接: https://arxiv.org/abs/2611.03839
作者: Louis Bradshaw,Irmak Bukey,Daniel Chenyu Lin,John Thickstun,Chris Donahue
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Multimodal language models continue to improve on broad music understanding benchmarks, but which musical features can they perceive and reason about? We investigate this question by introducing PianoBench, a benchmark that tasks language models with reasoning about musical performances across congruent acoustic and symbolic input representations. PianoBench is derived from our new multimodal, training-scale dataset comprising more than 20,000 hours of interleaved transcripts of music and spoken musical commentary from instructional piano videos. Open-weight audio-language models achieve at most 52.8% accuracy on PianoBench, and at best only modestly outperform their accuracy on a no-audio control. In contrast, frontier models using chain of thought achieve up to 93.2% accuracy when provided transcripts of music as text, exposing a gap between perceiving musical features from audio and reasoning about musical events when they are given explicitly. We then test whether mid-training on interleaved musical instruction can improve question answering without chain of thought, raising Qwen 3.5 9B Base’s zero-shot accuracy on PianoBench with music transcripts from 42.8% to 56.0% after instruction tuning. These experiments suggest that instructional videos may represent an overlooked source of multimodal supervision for music. Available: this https URL.
[AI-127] Low-Rank Hierarchical Merging for Efficient Long-to-Short Reasoning NEURIPS2026
链接: https://arxiv.org/abs/2611.03834
作者: Zeqiu Yu,Xuesheng Zhang,Wenxiao Zhao,Shixiao Wang
类目: Artificial Intelligence (cs.AI)
备注: Neural Information Processing Systems (NeurIPS 2026)
Abstract:Long-to-Short (L2S) model merging seeks to combine the strong reasoning capability of long chain-of-thought models with the concise style of base models, but existing methods that rely on linear interpolation and first-order activation statistics fail to capture how the loss landscape responds to parameter displacement, leading to degraded accuracy on hard tasks. We propose Low-Rank Hierarchical Merging (LHM), a training-free framework that replaces heuristic statistics with lightweight second-order curvature information. LHM consists of three components: (1) a low-rank Hessian approximation that efficiently estimates per-layer Hessian traces via Hutchinson’s estimator on random inputs, eliminating the need for task-specific data; (2) a Layer Second-Order Sensitivity (LSOS) index that combines Hessian trace with task-vector norm to quantify per-layer fusion difficulty; and (3) a Dual-Constraint Coefficient Mapping (DCCM) that converts LSOS scores into layer-adaptive merge coefficients while jointly satisfying a fusion-error bound and a global length-reduction target. Experiments across six mathematical benchmarks on 1.5B and 7B models show that LHM outperforms state-of-the-art methods such as ACM-TIES and ORCA, achieving over 4% accuracy gain and up to 93.1% reduction in output length relative to the long-CoT model.
[AI-128] AIRoA-0: Long-Horizon Precise Cross-Embodiment Manipulation from Handheld Data
链接: https://arxiv.org/abs/2611.03815
作者: Yueh-Hua Wu,Kei Ota,Hanlong Li,Hayata Yuasa,Jason Wang,Kengo Ikeuchi,Kensuke Chiyoma,Makoto Sugiura,Masafumi Takahashi,Masatoshi Tateno,Naoaki Kanazawa,Nicola Scianca,Petr Khrapchenkov,Satoshi Otsubo,Takehiko Ohkawa,Takuya Okubo,Tatsuya Matsushima,Yuki Noguchi,Yuma Shin,Zixuan Zhong
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 18 pages. Project page: this https URL
Abstract:AIRoA-0 (A-0) is a bimanual manipulation system learned from 16,151 hours of handheld demonstrations, with no robot data collected at any stage. The demonstrations are recorded with YUBI, our open-source handheld gripper, which lets people record manipulation data anywhere with no robot present. From a single instruction, A-0 sorts more than a dozen small parts into their numbered cells and changes the mop pads and brush of a robot vacuum; among other tasks, it unzips a pencil case and folds a towel. It performs these tasks using only a camera on each wrist, with no head or scene camera, at a site where none of its training data was recorded. Across the ten tasks of our evaluation, it averages a success rate of 78%, against 27% for the strongest of four other vision-language-action models post-trained on the same task demonstrations. We collect no teleoperation data and use no reinforcement learning or on-robot corrections, and the robot appears only at test time. The system learns which regions of each wrist view matter for the task, predicts the stage the task has reached, and decodes actions as a continuous stream. A-0 is embodiment-agnostic: it runs on robots that mount the YUBI gripper, such as a bimanual Franka, TX-G2, OpenArm and UR. We are releasing the dataset, to our knowledge the largest open dataset of handheld-gripper demonstrations with task and subtask instructions, together with the pre-trained weights and the post-training recipe. Videos of A-0 on every task are on the project page, this https URL. Anyone can build the gripper, record a task of their own and post-train the model on it in under four hours on a single node of eight H200 GPUs.
[AI-129] What Malware Actually Does: An Empirical Analysis of Post-Infection Behavior Across Malware Families
链接: https://arxiv.org/abs/2611.03774
作者: Emmanuela Andam,Emanuel S. Grant,Naima Kaabouch
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at 2026 Cyber Awareness and Research Symposium (CARS). This is the author’s accepted manuscript
Abstract:Most malware detection research focuses on identifying whether a file is malicious before it executes. Far less attention goes to what happens after infection. This paper presents an empirical analysis of post-infection malware behavior across 37 families, grounded in dynamic sandbox execution using CAPEv2. Behavioral traces are modeled as heterogeneous graphs with five node types and seven typed edge relationships, preserving relational structure that flat feature vectors discard. A voting ensemble trained on graph-derived structural features achieves 94.44% accuracy and MCC of 0.863, outperforming a flat behavioral baseline by 7.4 percentage points in MCC. A multiclass classifier distinguishes 24 malware families, and a per-family behavioral fingerprint analysis characterizes post-infection activity across registry, network, process, and API dimensions. An LLM explanation layer generates analyst-readable reports with MITRE ATTCK mappings grounded in observed behavioral evidence. The full pipeline is deployed as HeteroScan at this https URL.
[AI-130] RuntimeTickets: Source-Grounded Security Obligations for Agent Skills ICLR2027
链接: https://arxiv.org/abs/2611.03772
作者: Kuiyi Gao,Xue Liu,Bowei He
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 27 Pages, Submitted to ICLR 2027 (Under review)
Abstract:Third-party Agent Skills extend what an autonomous agent can do, but a malicious Skill can hide data exposure or output manipulation inside a legitimate workflow. Neither reading a Skill’s description nor granting it tool permissions tells whether its execution meets the user’s security requirements. We introduce RuntimeTickets, a protocol that enforces and audits these requirements while a Skill runs. From released malicious-Skill datasets we derive a risk model and build adversarial-benign Skill pairs. From the task’s delivery contract, a language model proposes security obligations for that task. Each obligation cites the Skill lines it answers, and the ticket tightens the host’s approvals into fine-grained allowed actions but never loosens them. A runtime gate checks the recipient, object and amount of every outbound send before forwarding it, and trusted monitors record the effects within their observation scope. The ticket links these checks and observations to the approved obligations of each Skill run, and a deterministic decoder turns the ticket’s status into governable data flows and verifies each obligation. Neither enforcement nor decoding involves a language model, so text injected by a Skill can change the proposal but never the rule that decides a send. We evaluate RuntimeTickets at the network egress boundary of three unmodified agent frameworks against production Skill scanners and runtime baselines, among them an AgentBound-derived allow-list and SkillGuard. RuntimeTickets eliminated both failure modes of current Skill defenses, overlook (malicious Skills let through) and overkill (benign Skills blocked): its overlook rate was 0.0% and its overkill rate 0.0%, while the scanners erred on both sides. RuntimeTickets ties approved requirements to verifiable execution evidence and offers a common foundation for the safe and auditable deployment of third-party Skills.
[AI-131] Metadata-Conditioned Audio Transformers for Adaptive Respiratory Sound Classification
链接: https://arxiv.org/abs/2611.03769
作者: George Kontogiannis,Pantelis Tzamalis,Sotiris Nikoletseas
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注:
Abstract:Respiratory sound classification from auscultation recordings is confounded by heterogeneous acquisition conditions, including recording device, auscultation site, and patient characteristics, that introduce systematic variability unrelated to the underlying pathology. Although such metadata is often available, existing approaches either ignore it or rely on naïve fusion strategies that do not model how distinct metadata sources influence different components of the acoustic representation. We propose a family of metadata-conditioning mechanisms for the Audio Spectrogram Transformer (AST), progressing from gated residual fusion to Feature-wise Linear Modulation (FiLM) adapted to the Transformer architecture. We further introduce two novel variants: Token-Aware FiLM (TAFiLM), which applies distinct modulation to classification and patch tokens, and Soft-Factorized FiLM (SoftFiLM), which learns soft masks to align metadata factors with disentangled feature subspaces. Experiments on the ICBHI benchmark demonstrate consistent gains across all conditioning strategies. SoftFiLM achieves 64.11% on the official 4-class task (+3.94% over the baseline) and 72.40% on the 2-class task, establishing a new state-of-the-art on the 2-class task and competitive performance on the 4-class benchmark. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) Cite as: arXiv:2611.03769 [cs.SD] (or arXiv:2611.03769v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2611.03769 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-132] Multi-layer Indexer: Overlapping Selection and Attention in DeepSeek Sparse Attention KR NEURIPS2026
链接: https://arxiv.org/abs/2611.03768
作者: Serdarcan Dilbaz,Arjun Krishna,Kiran Kamble,Daniel M. Bikel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 20 pages (8 pages main text), 4 figures, 11 tables. Accepted to the Long-Context Foundation Models (LCFM) Workshop at NeurIPS 2026, Atlanta, December 12, 2026. Code: this https URL
Abstract:Sparse attention cuts the cost of long-context inference by making the attention matrix sparse: each query attends only a subset of the sequence, chosen by a fixed pattern or, in recent production LLMs, learned per query. In DeepSeek Sparse Attention (DSA), the attention of DeepSeek-V3.2 and the GLM-5 family, every layer is preceded by a lightning indexer that scores the whole cache and keeps the k=2048 highest-scoring tokens for the layer to attend. At long context this indexer scan, not the sparse attention it feeds, is where the decode time goes. But adjacent layers select nearly the same tokens, so a layer can attend the selection of the previous indexer layer instead of its own. This removes the dependency: each indexer then runs concurrently with the attention of its layer. The resulting Multi-layer Indexer (MLI) needs no retraining and no new weights. Its default realization changes no kernel, running inside SGLang’s production decode graph; an optional fused decode kernel accelerates it further on DeepSeek-V3.2. MLI composes with IndexCache (the cross-layer reuse GLM-5.2 ships as IndexShare): IndexCache drops the indexer from some layers, and MLI hides the scan on the rest. We serve DeepSeek-V3.2 and GLM-5 under IndexCache’s protocol at retentions 1/2 to 1/32 (the fraction of layers that keep an indexer) and contexts 10K to 195K. At equal retention the composition decodes faster than IndexCache: up to 14.5% faster on DeepSeek-V3.2 and 9.8% on GLM-5 at 1/2, and 7.9% and 5.1% at 1/4, largest where IndexCache keeps the most indexers and growing with context. At retention 1/r it is faster still than IndexCache at 1/(2r) while keeping twice the fresh selections: at 128K it reaches 1.32x the unmodified model’s decode throughput at 1/4, above IndexCache’s 1.28x at 1/8. Its LongBench, RULER and LongBench v2 accuracy stays within a few percentage points of IndexCache’s.
[AI-133] he role of injected noise in diffusion model generalization NEURIPS2026
链接: https://arxiv.org/abs/2611.03764
作者: Vincent Emmerling,Felix Günther,David Kappel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages, 11 figures, published at the NeurIPS 2026 Workshop: 2nd Workshop on Principles of Generative Modeling (PriGM)
Abstract:Diffusion models trained on distinct datasets can produce highly similar samples when prompted with the same noise input, suggesting that they learn a generalized data distribution. We investigate the role of noise injected during the sampling process, and show that it plays an important role in making or breaking this strong generalization effect. Based on this finding we investigate the robustness of sample similarity to changes in the injected noise, and find that its impact is largest in the sampling regime where broad structure already exists but the precise identity of the sample is not yet determined.
[AI-134] JevBreak: Your Model Their Decision
链接: https://arxiv.org/abs/2611.03759
作者: Nitin Aravind Birur,Divyanshu Kumar,Sahil Agarwal,Prashanth Harshangi
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Restricting what a model can say is often mistaken for restricting an attacker’s influence over its decision. An attacker can add text to the evidence and steer the model toward a wrong answer that still belongs to the allowed set. We evaluate seven decision models spanning hosted APIs, open-weight models and an encoder on 16 tasks in seven domains from Decision Bench, targeting every wrong option across 240 text examples (990 targets per model). We present Slot Attack, a black-box attack designed for decision models that appends text assembled from named components to steer predictions toward an attacker-selected incorrect option. The method relies solely on returned label probabilities, achieves success rates of 32–99% on initially correct decisions, and identifies the effective component combinations with a median of 17–72 queries per successful attack. Accuracy and confidence do not reliably reveal this vulnerability. Models with near-identical accuracy on the sampled tasks differ by about 20%age points in attack success. We demonstrate that an attacker can force not just one wrong answer but every available one: on about 20% of correct decisions for the JEV models, 77% for gpt-6-luna and 98% for GLiNER2.5; a confidence threshold does not reliably screen the attack out. A fixed answer set constrains a decision’s form, not its source; robustness must be measured per model, per task and per version rather than assumed from output format.
[AI-135] he Web Doesnt Sit Still: Adversarial Self-Evolving Attacks on Search Agents NEURIPS2026
链接: https://arxiv.org/abs/2611.03754
作者: Geyuan Wang,Shunyu Liu,Siyuan Liang,Chenyang Lyu,Dacheng Tao
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026
Abstract:Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-seeking tasks. However, since acquiring external information necessitates web search, these agents are highly susceptible to various adversarial attacks (e.g., malicious information injection or stealthy context manipulation). Existing attack strategies inevitably rely on static and human-crafted templates, failing to emulate the dynamic and complex nature of real-world threats. In this paper, we propose an adversarial self-evolution framework that integrates bi-level optimization between the attacker’s strategy evolution and the defender’s adaptive mitigation, to expose the vulnerabilities of search agents. Specifically, we introduce the Contrastive Rollout Evolutionary Optimization (CREO) method to drive the directional evolution of the attack strategy population via contrastive evolutionary signals. To provide a stable environment for this continuous evolution, we further construct a controlled sandbox and design fine-grained metrics to quantify internal vulnerabilities. Extensive experiments across various multi-hop QA benchmarks and frontier LLMs demonstrate that the proposed framework yields attack efficacy superior to static baselines, underscoring the urgent need to develop more robust defense frameworks.
[AI-136] LoRA-Patch: Defending Against Backdoored LoRA Adapters via Directional Steering
链接: https://arxiv.org/abs/2611.03752
作者: Shengyan Huo,Peihai Jiang,Xixiang Lyu,Yige Li
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Third-party LoRA adapters introduce serious backdoor risks: malicious adapters can maintain normal task performance while embedding hidden, trigger-activated behaviors, making them difficult to detect through standard utility evaluations. Existing removal-based defenses typically sanitize backdoored models by updating, pruning, distilling, or mixing model parameters. However, these approaches are misaligned with adapter-based deployment, where the trusted base model is expected to remain frozen. In this work, we propose LoRA-Patch, a lightweight backdoor defense tailored to LoRA adapters. Our key insight is that backdoor injection distorts the directional geometry of a poisoned adapter relative to its trusted base model. Building on this insight, LoRA-Patch suppresses trigger-activated behaviors by realigning adapter directions through layer-wise steering, without modifying the base model weights. Extensive experiments across five backdoor attacks and three models show that LoRA-Patch effectively mitigates trigger-activated backdoors, outperforming existing baselines while preserving benign task performance. Moreover, LoRA-Patch is computationally efficient, constructing patches within 20 seconds across all evaluated models and adapters. Our code is publicly available at this https URL.
[AI-137] Reasoning is topologically simple
链接: https://arxiv.org/abs/2611.03739
作者: Chris Kapulkin,Nathan Kershaw
类目: Artificial Intelligence (cs.AI)
备注: 15 pages + 14 pages of appendices
Abstract:We formulate and investigate the hypothesis that reasoning text has simpler co-occurrence topology than non-reasoning text, and that higher-quality reasoning has simpler co-occurrence topology than lower-quality reasoning. We discuss and test applications to AI safety and AI evaluation.
[AI-138] HyVox: Rethinking Low-Power Voice Intelligence towards Neuromorphic-von Neumann Edge Computing
链接: https://arxiv.org/abs/2611.03735
作者: Snehasis Addy,Eduardo Calle Ortiz,Prashant Shenoy,Phuc Nguyen
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注:
Abstract:We present HyVox, a hybrid neuromorphic–von Neumann system for low-energy open-set speaker identification under tight energy budgets. HyVox splits the workload at the speaker embedding. A neuromorphic processor embeds the speech with sparse, event-driven computation, and a von Neumann microcontroller holds the speaker gallery and makes every decision: it scores queries by cosine similarity, rejects unknown speakers, and enrolls new users by writing a prototype, with no weight update. On the neuromorphic processor, a hierarchical temporal abstraction (HTA) SNN backbone learns a synaptic time constant per leaky integrate-and-fire (LIF) neuron, allocating 832 neurons across fast, medium, and slow timescales. A 32-neuron spiking embedding extractor (SEE), pretrained on episodes that each enroll new speakers and reject outsiders, turns the backbone output into an embedding for speakers unseen in training; its 32 spike counts are the only interface between the two processors. To fit the chip’s synapse and integer budgets, we compress the backbone 6.5x to 29.0k synapses, train it for integer bit-shift dynamics, and fine-tune it with chip-accurate spiking training (CAST). CAST trains through a differentiable replica of the chip’s integer neuron update, derived from its cycle-accurate simulator, so that gradient descent optimizes the exact weights and thresholds the chip runs; it doubles the top-1 accuracy of the mapped backbone on unseen speakers. On 20 Common Voice households of unseen speakers, HyVox reaches 99.6% top-1 and 5.5% rejection EER with 2 members and 94.9% and 13.2% with 20, and the network deployed on the neuromorphic chip matches its trained model. Each inference on a 3-s window takes 0.96 s and 1.63 mJ of dynamic energy, 42% lower latency than the fastest embedded ANN baseline and 13.5x less dynamic and 5.8x less total energy than the most energy-efficient one.
[AI-139] From Logs to Faults: Reference-Free Fault Localization for Retrieval-Augmented Generation Pipelines
链接: https://arxiv.org/abs/2611.03725
作者: Hira Naveed,Scott Barnett,Stefanus Kurniawan,John Grundy,Chetan Arora,Hourieh Khalajzadeh,Omar Haggag
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:Retrieval-Augmented Generation (RAG) pipelines are widely used for domain-specific question answering, yet locating faults remains manual and time-consuming. Fault localization is challenging because RAG pipelines consist of interdependent stages, where faults can propagate downstream, masking their origin, or remain undetected even when the final answer is correct. Existing evaluation approaches often focus on individual stages and rely on reference answers or code instrumentation. We present RAGLog, which detects and localizes faults across RAG stages using execution logs, without requiring reference answers or code instrumentation. RAGLog transforms heterogeneous API logs into a common RAG schema and combines rule-based, embedding-based, and LLM-based checks to identify faulty stages. We introduce a live fault injector and RAGLog Faults, a dataset of 3,134 runs from five RAG pipelines with stage-level fault labels. With Claude Haiku 5.5, RAGLog achieves a pooled F1 score of 0.73 on runs with naturally occurring faults, outperforming Claude Code (0.58) and Codex (0.52), and comparable effectiveness on runs with injected faults (0.84 versus 0.80 and 0.82, respectively). It analyzes runs in approximately five seconds at US\ 0.005 each, with overall lower cost and latency than agent baselines. RAGLog is available as an open-source tool for debugging, evaluating, and monitoring RAG pipelines.
[AI-140] EMBERS: Empirically Motivated Robustness Testing of Deep Research Systems via Defect-Guided Fuzzing
链接: https://arxiv.org/abs/2611.03723
作者: Shuochuan Li,Zan Wang,Dong Wang,Yuhang Jiang,Huajin Cui,Chen Zhang,Gang Wang,Junjie Chen
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Deep Research (DR) systems extend large language models into autonomous agents that decompose complex information needs, iteratively search the web, and synthesize evidence to answer challenging questions. However, their multi-stage execution may introduce robustness risks, as variations in user questions or intermediate search inputs may propagate across research stages and lead to incorrect answers. Existing evaluations focus on task-solving capabilities or robustness against external evidence disturbances, leaving intrinsic weaknesses in DR execution insufficiently explored. Thus, we conduct an empirical study of 1,932 execution trajectories from three representative DR systems and identify nine recurring defect categories across subquestion formulation, online search, and final reasoning. Guided by these findings, we propose EMBERS, an empirically motivated, defect-guided fuzzing framework for robustness testing of DR systems. Starting from correctly answered seed questions, EMBERS generates stage-targeted test inputs using defect-guided mutation, web-search-assisted fidelity assurance, and diversity-aware seed maintenance. Experiments across 16 framework–model–dataset settings show that EMBERS achieves 3.21 times the failure-discovery throughput of the strongest baseline under identical testing budgets. Human and LLM-assisted evaluations demonstrate that most generated inputs preserve the original information need and necessary contextual constraints. Moreover, model fine-tuning experiments show consistent-answer performance improvement, highlighting their value for robustness testing and model improvement.
[AI-141] Counterexample-driven autonomous research with application to excess radiated energy in a Z-pinch
链接: https://arxiv.org/abs/2611.03721
作者: Bowen Zhu,Jian Wu
类目: Artificial Intelligence (cs.AI); Plasma Physics (physics.plasm-ph)
备注: 25 pages, 9 figures, 5 tables; code at this https URL
Abstract:Recent breakthroughs in artificial-intelligence-assisted mathematics show that open-ended machine search can outperform human mathematicians, yet transferring this paradigm to physics requires refactoring the research workflow. We introduce Simjecture, an open-source framework in which language-model agents pursue human-defined physical conjectures. The framework tackles two central challenges in this transfer: first, because physics is empirical and no model is final, it structures inquiry around counterexample-driven falsification and prospective hypothesis repair; second, because physical theories must interact with the real world, it uses computational physics as an executable testbed while separating the qualification of numerical instruments from scientific claim review so solvers and diagnostics can be adapted within a recorded investigation. We apply the framework in an 82.6 h investigation to tackle the long-standing problem of radiated energy exceeding the imploding-shell kinetic energy in a Z-pinch. The investigation reveals competing effects of energy conversion, retention, and release timing. Stronger coherent helical deformation increases resistive conversion while reducing radiation and increasing retained energy. A broader trailing-plasma profile delays the later radiation maximum and recovers much of its early bolometric deficit, with a spectral response that changes during the pulse. These results provide new insight into energy conversion in high-energy-density plasma physics and demonstrate a new paradigm for physics research.
[AI-142] Unsatz: Evaluating Scientific Discovery Without a Given Ansatz
链接: https://arxiv.org/abs/2611.03713
作者: Ethan Ji,Wei Wang,Yizhou Sun
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Before a scientist fits any parameter, they must decide what the model contains: which quantities exist, which are hidden, how they interact, and how they are measured. Physicists call such a trial form an ansatz. Benchmarks for automated equation discovery mostly supply it, by fixing the variables, a library of candidate terms, or a law to be modified. We introduce Unsatz, a benchmark in which an LLM agent must construct the ansatz itself. Each task is a simulated system, generated from a private seed as a program in a small modeling language: ordinary, delay, or partial differential equations, discrete maps, static and causal models, or stochastic processes, possibly with hidden states, distorted sensors, and uninformative measurement channels. The agent buys experiments within a budget, writes candidate programs in the same language, obtains their coefficients from the benchmark’s fitter, and is scored only on how its final program predicts experiments it never saw: conditions outside the range it could explore, interventions, and longer runs. Across 80 systems in 20 domains, nine language models solve between 21% and 60%. On 44 of these systems, classical equation-discovery methods almost never succeed, although the same data usually suffice once the true equations are given with unknown coefficients; the difficulty lies in finding the structure, not in estimating it. Because mechanisms recur across systems, we also give agents the program they discovered for a related system; it helped only when the new system shared the mechanism and the first investigation had succeeded. Stronger agents make fewer unscientific errors; beyond these, agents find which variables a system has but not how they act, and prefer revisions that lower the training error to revisions that predict better.
[AI-143] A Compact Formula-Aware Spreadsheet Abstraction for Large Language Models
链接: https://arxiv.org/abs/2611.03705
作者: Tie Cheng
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are increasingly used to assist spreadsheet users with tasks such as spreadsheet model construction, formula generation, and data analysis, yet their interaction with real-world spreadsheets remains limited by the mismatch between the structure of spreadsheets and the sequential textual representations typically provided to LLMs. We propose a compact formula-aware spreadsheet abstraction that represents large homogeneous ranges through their positions, types, R1C1 formula patterns, and selected boundary values. An efficient algorithm detects and structures these ranges before a textual serialization is supplied to an LLM. The abstraction is provided to the LLM before detailed spreadsheet content is submitted, improving the accuracy and efficiency of spreadsheet processing while reducing token consumption. We evaluate the method on 50 spreadsheet-query cases. The evaluation shows that the abstraction alone was sufficient to answer some queries. When additional spreadsheet content is required, the abstraction reduces the amount of data that must be submitted to the LLM, thereby reducing token consumption. The abstraction layer also improves the reliability and accuracy of spreadsheet processing. We conclude by discussing further techniques for combining spreadsheet static analysis with artificial intelligence to improve the reliability, accuracy, and efficiency of AI assisted spreadsheet tasks.
[AI-144] raining LLM Agents LLM Agents to Automate LLM Post-Training with RSI-Dojo
链接: https://arxiv.org/abs/2611.03698
作者: Shuzheng Gao,Zhaojian Yu,Penghao Yin,Yuling Shi,Kai Cai,Shilin He,Michael R. Lyu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recursive Self-Improvement (RSI) aims to enable AI systems to autonomously build and improve AI systems themselves, including models, harnesses, and underlying infrastructure. Within this broader agenda, a central capability is enabling agents to train and improve base models. However, existing studies on automated model training primarily focused on workflow- or harness-level optimizations, rather than internalizing the ability to conduct post-training into the model itself. Developing this capability requires executable model training environments that can produce high-quality trajectories demonstrating how to select data, train models, diagnose failures, and iteratively improve results. In this paper, we introduce RSI-Dojo, a collection of executable model training environments designed to teach LLM agents to conduct post-training. To construct RSI-Dojo, we develop RSI-Craft, an automatic pipeline that converts human research artifacts into executable model training environments. RSI-Craft automatically collects research papers from top venues, instantiates datasets and metrics into a unified interface, and reproduces evaluation dependencies. It then filters tasks by data size, execution reliability, and discriminability, yielding RSI-Dojo with 281 seed model training tasks. Building on RSI-Dojo, we employ a trajectory collection method with an improved harness to collect high-quality model-training trajectories from frontier models, then retain examples through rubric-based and outcome-based filtering. We fine-tune Qwen3.5-9B and Qwen3.6-35B-A3B on these trajectories. The resulting RSI-Dojo-9B and RSI-Dojo-35B-A3B improve over their base models by 8.74 and 16.87 PostTrainBench points, respectively. RSI-Dojo-35B-A3B reaches 29.95 on PostTrainBench and 53.29 on PostTrainBench-Lite, matching the performance of frontier models such as GLM-5.2 and GPT 5.5.
[AI-145] Learning Directional Randers Geometry for Latent-Space Exploration
链接: https://arxiv.org/abs/2611.03675
作者: Théau Blanchard(HeKA | U1346, GE Healthcare),Stéphanie Allassonnière(CMAP)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Differential Geometry (math.DG); Quantitative Methods (q-bio.QM)
备注:
Abstract:Deep neural networks extract powerful latent representations from images, enabling fast and efficient downstream analysis. However, these latent spaces lack explicit geometric structure, leading to possibly poor interpolation fidelity and misleading extrapolation. Latent interpolation techniques assume symmetric transition costs which are often not the case for real world data. Their symmetric nature cannot capture directional processes, such as disease progression, where certain transitions are impossible (e.g. tumor growth dynamics or cognitive improvement in Alzheimer’s disease). Although Riemannian metrics extend Euclidean geometry by incorporating curvature, they remain symmetric. We show that understanding asymmetry and not curvature alone directly addresses these problems. To do so we propose a new Finsler-Randers metric learning framework. It incorporates direction-dependent distances, thus enabling the computation of trajectories respecting monotonic sequential constraints. We demonstrate its potential for interpolation and extrapolation on controlled cross-sectional datasets.
[AI-146] ReVi: Recursive Recap Video Generation
链接: https://arxiv.org/abs/2611.03674
作者: Kyrie You Wu,Chaozhuo Li,Songyang Liu,Xueyang Feng,Ziluowen Luo,Chenxu Wang,Ji Qi,Jinfu Zhu,Litian Zhang
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI)
备注: 26 pages, 7 figures, 17 tables
Abstract:Novels tell rich stories, but their textual form cannot directly present the sights and sounds of the worlds they describe. Film adaptations offer an audiovisual experience but require substantial production effort and viewing time; film recaps are shorter but depend on existing footage. Neither route directly provides a concise audiovisual account of a novel. We therefore study novel-to-recap video generation: producing a self-contained, source-grounded audiovisual retelling directly from a novel within a fixed viewing-time budget. This task requires compressing the story while preserving causal connections, allocating sufficient time to retained events, and aligning narration with newly generated visuals. We propose ReVi, a dual-loop multi-agent framework guided by edit first, generate second. Its Inner Loop plans story content, narration time, and corresponding shots before video generation. Across novels, its Outer Loop performs recursive self-improvement by revising both the editing policy and the meta policy that governs its revision. Experiments on two evaluation sets show that ReVi improves recap quality and duration compliance over representative baselines, with human ratings and multi-round improvement results providing further support.
[AI-147] MCPGuard: Finding Conformance Gaps in MCP SDKs via Dual-Track Analysis and Dynamic Adjudication
链接: https://arxiv.org/abs/2611.03662
作者: Xikai Sun,Kebin Liu,Fan Dang,Yawen Zheng,Zihao Yang,Yunhao Liu
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:The Model Context Protocol (MCP) connects large language model agents to external tools and services through official SDKs that implement message validation and session logic. Specification constraints omitted by these SDKs may propagate to downstream applications, highlighting the importance of SDK-level conformance testing. Yet existing MCP conformance tools mainly exercise running endpoints and reveal little about whether SDK models enforce specification requirements. We present MCPGuard, an automated LLM-based framework for detecting specification-to-SDK conformance gaps. Its structural track aligns JSON Schema constraints with normalized SDK models and produces constraint-specific evidence. Its behavioral track extracts normative prose rules, retrieves bounded function-level slices, and audits them with LLMs for candidate omissions. An evidence-aware dynamic adjudicator then inspects complete source context and builds PoCs to validate candidates. A shared kernel with language-specific adapters enables analysis across SDKs. On the official Python 1.26.0 and TypeScript 1.29.0 SDKs, MCPGuard confirms 13 and 19 findings, respectively, none of which are exposed by two black-box baselines. Tests on open MCP servers further show that these gaps can systematically propagate downstream. By making SDK conformance of specification constraints directly testable, MCPGuard complements conventional endpoint-oriented conformance suites.
[AI-148] CARD: A Proof-Carrying Certificate-to-Action Interface for Relative-Measurement Graphs
链接: https://arxiv.org/abs/2611.03651
作者: Yushan Li
类目: Artificial Intelligence (cs.AI)
备注: 19 pages, 5 figures, 8 tables
Abstract:Geometric certificates make mathematical claims about specified estimator objects, while robotic action requires a physical transform, a service decision, and timely proof support. We present CARD, a proof-carrying interface that keeps these objects distinct and records how they combine. Its lifted-fiber formulation separates semantic motion from witness repair and characterizes when projected and proof-carrying costs coincide. Its actionability result gives an exact useful-service law: uniform sampling without replacement produces a hypergeometric yield, while sampling from the eligible population gives one useful service per request under the observed live-confirmation condition. The primary study contains 404 Full16 cases from 20 sorties of an AA15-X500 airborne survey system. All 404 checker-confirmed optimizer points are reference-discrepant, with median rotation and translation discrepancies of 1.9891 degrees and 21.6322 m. The paired causal-navigation transforms are reference-consistent in all 404 cases, with medians of 0.0036566 degrees and 0.0104519 m. Across five matched request budgets, the largest difference between the hypergeometric prediction and the observed ordinary-random mean is 0.8412 services. Ordinary matched random reaches 0/50 target successes, while actionability-matched random reaches 50/50 and matches the CARD policy. Public downstream evaluation on 11 KITTI, EuRoC, and TUM SLAM cases measures optimizer wall-time ratios of 1.128x-67.65x for random versus CARD graph interventions. Native solver records locate this difference in elimination fill, Cholesky factor size, and numeric factorization. Together, these results connect certificate semantics, physical execution, proof acquisition, and graph computation through explicit system objects.
[AI-149] A Year in LLM Serving: Workload Evolution Caching and Load-Balancing
链接: https://arxiv.org/abs/2608.13573
作者: William Nixon,Jon Durbin,Florian Standhartinger,Haryadi S. Gunawi,Juncheng Yang
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注:
Abstract:Large Language Model (LLM) serving has become a critical cloud workload, and realistic traces are essential for motivating and benchmarking serving systems. However, existing LLM serving workload studies remain limited in scale and scope. They often observe short time periods and provide limited visibility into how users interact with models in production. As a result, they do not fully capture how LLM serving workloads evolve over time or how user-model interactions shape production traffic. In this work, we further the understanding of real-world LLM serving workloads through both a global characterization and a longitudinal study of a one-year production trace from CompanyX. Unlike prior studies, our trace captures full production behavior across many models and users, including both popular and long-tail models. We analyze the workload from aggregate, temporal, model-level, and user-level perspectives, revealing workload evolution and user-model structure that are typically hidden behind aggregate views. To support future research, we publicly release the full one-year trace, enabling downstream studies of production behavior without relying on sampled or synthetically generated workloads. The trace is available at this https URL.
[AI-150] On Discovering Algorithms for Adversarial Imitation Learning ICLR2026
链接: https://arxiv.org/abs/2510.00922
作者: Shashank Reddy Chirra,Jayden Teoh,Praveen Paruchuri,Pradeep Varakantham
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ICLR 2026 (Poster)
Abstract:Adversarial Imitation Learning (AIL) methods, while effective in settings with limited expert demonstrations, are often considered unstable. These approaches typically decompose into two components: Density Ratio (DR) estimation \frac\rho_E\rho_\pi , where a discriminator estimates the relative occupancy of state-action pairs under the policy versus the expert; and Reward Assignment (RA), where this ratio is transformed into a reward signal used to train the policy. While significant research has focused on improving density estimation, the role of reward assignment in influencing training dynamics and final policy performance has been largely overlooked. RA functions in AIL are typically derived from divergence minimization objectives, relying heavily on human design and ingenuity. In this work, we take a different approach: we investigate the discovery of data-driven RA functions, i.e, based directly on the performance of the resulting imitation policy. To this end, we leverage an LLM-guided evolutionary framework that efficiently explores the space of RA functions, yielding \emphDiscovered Adversarial Imitation Learning (DAIL), the first meta-learnt AIL algorithm. Remarkably, DAIL generalises across unseen environments and policy optimization algorithms, outperforming the current state-of-the-art of \emphhuman-designed baselines. Finally, we analyse why DAIL leads to more stable training, offering novel insights into the role of RA functions in the stability of AIL. Code is publicly available: this https URL.
[AI-151] Preserving the Privacy of Reward Functions in MDPs through Deception ECAI2024
链接: https://arxiv.org/abs/2407.09809
作者: Shashank Reddy Chirra,Pradeep Varakantham,Praveen Paruchuri
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ECAI 2024
Abstract:Preserving the privacy of preferences (or rewards) of a sequential decision-making agent when decisions are observable is crucial in many physical and cybersecurity domains. For instance, in wildlife monitoring, agents must allocate patrolling resources without revealing animal locations to poachers. This paper addresses privacy preservation in planning over a sequence of actions in MDPs, where the reward function represents the preference structure to be protected. Observers can use Inverse RL (IRL) to learn these preferences, making this a challenging task. Current research on differential privacy in reward functions fails to ensure guarantee on the minimum expected reward and offers theoretical guarantees that are inadequate against IRL-based observers. To bridge this gap, we propose a novel approach rooted in the theory of deception. Deception includes two models: dissimulation (hiding the truth) and simulation (showing the wrong). Our first contribution theoretically demonstrates significant privacy leaks in existing dissimulation-based methods. Our second contribution is a novel RL-based planning algorithm that uses simulation to effectively address these privacy concerns while ensuring a guarantee on the expected reward. Experiments on multiple benchmark problems show that our approach outperforms previous methods in preserving reward function privacy. Comments: ECAI 2024 Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2407.09809 [cs.AI] (or arXiv:2407.09809v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2407.09809 Focus to learn more arXiv-issued DOI via DataCite
[AI-152] Weight-space programming for auditing clinical decision networks
链接: https://arxiv.org/abs/2611.05341
作者: Humza Nusrat,Omar Nusrat
类目: Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 3 tables. Code, networks and every model answer: this https URL . A reading guide for language models ( this http URL ), the paper in Markdown and the data behind each figure and table are in the ancillary files
Abstract:Machine learning models in radiotherapy are commissioned by testing them on cases with known answers, which may not reveal a shortcut that the test cases share with the training data. Frontier language models have recently been reported to read and write the raw weights of small neural networks, an ability called weight-space programming. The purpose of this study was to determine whether such a model, given only a clinical protocol and a network’s weights, can audit and repair the network. Networks with 433 to 4,801 parameters were trained to screen head-and-neck planning requests against four rules. Flaws were planted through the training data: confounds that hid each rule, a 3 Gy shift in the cord threshold, and a hidden trigger. Claude Opus 5.5 audited each network blind, without tools, data or code execution, and every claim and repair was tested on about 420,000 requests. The language model named the broken rule in 16 of 18 flawed networks (95% CI 67% to 97%), including all 15 with a confound or a threshold shift. It repaired each of the 16 by changing a median of 3 parameters. Fine-tuning on protocol-labeled requests also repaired every network, but changed nearly every parameter. Each confound and shift it found was visible in single units, and a simple statistic chosen for each flaw type found the same flaws. It cleared 2 of 5 healthy networks without being forced and found 1 of 3 triggers. Testing against the protocol detected every flaw, and black-box testing found the confounds and the shift faster. The results indicate that, in small networks, weight-space programming can locate and repair flaws that silence an input or shift a threshold, but cannot yet certify that a network is correct.
[AI-153] A simple adaptive accelerated optimistic dual averag ing method for unconstrained convex optimization in p-norm geometry
链接: https://arxiv.org/abs/2611.05334
作者: Bowen Li,Danqing Zhou,Chuan He
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:Efficient preconditioning plays an important role in large-scale first-order optimization. In this paper, we propose an adaptive accelerated optimistic dual averaging ( \textA^2 ODA) method for unconstrained convex optimization that adopts low-cost preconditioning based on p -norm geometry for p\in[2,\infty] . The method is line-search-free and employs a new stepsize selection technique based on an aggregated affine lower model. Its update structure is simple and unified across p\in[2,\infty] , with the case p2 requiring no more elaborate updates than the Euclidean case p=2 . Under a generalized local smoothness condition, we derive a convergence bound that separates the effects of constant and gap-dependent curvature, with the latter potentially yielding faster-decaying terms. In the special case of local smoothness, this bound recovers a nearly optimal convergence rate.
[AI-154] UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation
链接: https://arxiv.org/abs/2611.04825
作者: Shibo Feng,Wei Li,Min Wu,Chunyan Miao,Peilin Zhao
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI)
备注:
Abstract:Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without increasing generative complexity. We propose UniFlow, a unified framework that integrates multi-scale representation learning with efficient non-autoregressive generation through a shared-semantic discrete interface. Specifically, a multi-scale tokenizer employs shared-index voting to consolidate complementary temporal information into a single public-ID sequence, while scale-specific codebooks preserve the information required for reconstruction. Building on this unified representation, we develop geometry-aware categorical flow matching, which initializes transport from learned low-rank anchors and generates public identities non-autoregressively. This design captures multi-scale temporal structure without requiring multiple generative token streams or sequential token decoding. Experiments across four benchmarks and both short- and long-term settings demonstrate improved generative fidelity, strong cross-scale consistency, and substantial inference speedups over representative autoregressive and diffusion-based baselines. Code is available at this https URL
[AI-155] Asymptotic Optimality of cμ-Rule for LLM Inference Scheduling under Memory Constraint
链接: https://arxiv.org/abs/2611.04807
作者: Zhenghao Li,Shengbo Wang,Nian Si
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language model (LLM) inference, the process by which a pre-trained model receives an input prompt and generates output tokens sequentially, has become increasingly important with the widespread deployment of LLM-based applications. As inference workloads continue to scale, GPU memory has emerged as a critical bottleneck: the long input prompts and generation sequences common in agentic and reasoning workloads lead to substantial key-value (KV) cache usage. In this paper, we study the resulting online scheduling problem from a queueing perspective. Requests arrive stochastically with heterogeneous prompt and output lengths, undergo prefill and autoregressive decode, and compete for a finite KV-cache capacity. Unlike classical queueing models, the duration of a processing step depends on both prefill computation and KV-cache I/O, so service capacity is jointly determined by computation, memory access, and memory occupancy. We develop a fluid model that reduces the system to a multiclass single-server queue with effective service rates capturing computation, memory access, and memory capacity. This fluid analysis yields a c\mu priority rule, where the effective service rate \mu is jointly determined by computational speed, memory bandwidth, and memory capacity. We prove that this rule minimizes cumulative holding cost in the fluid model and, for a finite workload, also minimizes completion time and maximizes full-run output-token throughput. We then construct a feasible scheduling policy for the original finite system and prove that its scaled dynamics converge to the optimal fluid policy. Finally, experiments on a real LLM serving engine using an NVIDIA H800 show that practical implementations of the proposed priority rule substantially reduce average latency while maintaining high throughput relative to existing scheduling policies.
[AI-156] Spectral Quantum Memory for Implicit Neural Representations NEURIPS2026
链接: https://arxiv.org/abs/2611.04574
作者: Daniel A. Moroz-Broitman,Eliya Nachmani
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to Neurips 2026
Abstract:Implicit neural representations (INRs) model signals as continuous functions from coordinates to values, but their performance depends strongly on how spectral capacity is allocated. Yet most Quantum INR (QINR) architectures treat spectral components as independent outputs of separate quantum blocks. We introduce the Spectral Quantum Memory Model (SQMM), a memory coupled quantum INR in which sequential hybrid data reuploading layers (bands) communicate through a shared coherent quantum memory register. Each band reads the memory, computes on its own coordinate upload, and writes part of its state back, so later bands can build on the computation of earlier ones. When memory read and write layers are input independent, we prove that the memory state forms an operator valued Fourier series whose support is contained in the Minkowski sum of the initial memory spectrum and the local band spectra. Downstream bands then condition their Fourier coefficients on the spectral content stored by earlier bands, and the memory coupling creates no new primitive frequencies. This theory motivates a spectral memory alignment loss that encourages memory to encode the spectrum expressed by the deployed prefix of the model. Across audio and image reconstruction benchmarks, SQMM improves over compact QINR and INR baselines.
[AI-157] CosmosAlpha: Multi-Relational Graph Search with Pareto-Guided Exploration for LLM -Driven Alpha Mining
链接: https://arxiv.org/abs/2611.04555
作者: Zhan-He Wang,Zi-Yang Lang,Yi-Yang Li,Qing-Yu Lan,Yun-Song Piao
类目: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 41 pages, 3 figures
Abstract:Discovering predictive alpha factors from noisy financial data is a fundamental yet challenging problem in quantitative finance. LLM-driven formulaic alpha mining offers an attractive paradigm by combining interpretable symbolic expressions with iterative generation and refinement guided by quantitative feedback. However, existing generation-centric and tree-based search strategies primarily organize factors through generation lineage, leaving structural and statistical relationships across search branches underutilized. Consequently, information remains localized along parent–child trajectories, which can confine exploration to redundant regions. Moreover, effective factor discovery must account for multiple distinct objectives that cannot be adequately captured by a single search criterion. We introduce CosmosAlpha, a graph-based LLM factor-mining framework that represents discovered factors as a multi-relational graph encoding generation lineage, expression-structure similarity, and cross-sectional exposure correlation. A Graph Flow mechanism propagates search utilities across the graph, allowing each factor to be assessed using relational evidence from nearby factors rather than in isolation. Pareto-guided selection jointly considers quality, fertility and diversity, explicitly modeling their trade-offs within a multi-objective search framework. Across CSI 300 and CSI 500, CosmosAlpha-Graph achieves the best mean rank over seven predictive and portfolio metrics and outperforms its controlled CosmosAlpha-MCTS counterpart on every reported metric, showing a consistent advantage for relational graph search in LLM-driven factor mining. The source code is available at this https URL
[AI-158] Predicting protein structures at any folding stage from sequence
链接: https://arxiv.org/abs/2611.04287
作者: Lisheng Zhang,Qingsong Li,Zinan Xie,Yanting Yang,Qikui Yang,Peng Lu,Zhican Fu,Wentao Cui,Runtong Hou,Zhengwei Xie
类目: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI)
备注: 47 pages, 10 figures. The first five authors contributed equally
Abstract:Protein structure predictors reliably identify native conformations, but do not describe the continuum connecting unfolded ensembles to folded states. Here we present FoldScape, a sequence-conditioned deep learning framework that generates C \alpha conformational ensembles at a specified folding fraction Q . FoldScape reconstructs three-dimensional chains through hierarchical, sliding-window regressions of one-dimensional inter-residue distances, followed by chain assembly and sampling with a rectified-flow decoder. It is trained on multi-scale coarse-grained folding trajectories and all-atom mechanical-unfolding trajectories. Across 20 human proteins (36-857 residues), at matched sample size, FoldScape covers about an order of magnitude more of the folding coordinate than seed-varied AlphaFold 3 ensembles, robustly across proteins, order parameters and binning choices. AlphaFold 3 better anchors the immediate native vicinity, which FoldScape does not reach, making the approaches complementary. FoldScape reconstruction accuracy remains approximately constant along Q , from collapsed coil to near-native globule, rather than deteriorating away from the native state. A stride-one crop-and-superpose scheme extends inference to chains several times longer than a single chain-level forward pass. We also show that training trajectories resolve mechanical unfolding at residue resolution, that elongation-conditioned ensembles exhibit reproducible continuous observables across pulling velocities, and that partially unfolded chains form geometrically coherent segments. FoldScape thus combines broad coverage of folding stages with stable reconstruction accuracy, without per-protein simulation.
[AI-159] When Model Complexity Matters in Cross-Subject Naturalistic fMRI Prediction
链接: https://arxiv.org/abs/2611.04041
作者: Junhao Zhao,Jacob Kang,Colin Kurniawan,David Simberg,Nan Xu
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注: 43 pages
Abstract:Increasingly complex neural models predict blood-oxygen-level-dependent (BOLD) responses to naturalistic stimuli, but the predictive value of this added complexity remains unclear. We ask when pretrained stimulus representations help and how much downstream complexity is needed, with and without recent measured BOLD history. We compare linear diagnostic references and nonlinear predictors across HCP movie fMRI (154 subjects), BOLD Moments (10 subjects), and Algonauts 2025 (4 subjects). Under a shared offline protocol, models are fitted on training subjects and evaluated on held-out subjects viewing largely familiar stimuli, without refitting. Pretrained visual features generally improve prediction over raw pixels; for linear autoregression, this advantage becomes more pronounced over longer rollouts (recursive predictions without additional measured BOLD). Measured BOLD history provides substantial gains that generally diminish as rollouts lengthen. Nonlinear stimulus-only predictors can improve on linear readouts, whereas linear autoregression provides a strong history-conditioned baseline. Controls examine context length, regularization, fitting procedures, and model capacity. Regional response consistency across subjects is associated with held-out prediction accuracy, while less consistent regions tend to gain more from measured history. In paired simulations with fixed linear neural dynamics, slower hemodynamic responses generally sustain high temporal prediction correlations over longer rollouts, illustrating how the observation process can contribute to predictive persistence. Together, these findings support evaluating stimulus representations and downstream complexity separately, using history-conditioned linear prediction as a diagnostic reference under matched inputs and measurement availability.
[AI-160] Measure Before You Repair: An Explainable Agent for Blind Restoration of Multi-Artifact 3-D MRI
链接: https://arxiv.org/abs/2611.03996
作者: Necati Sefercioglu,Mehmet Ozan Unal,Metin Ertas,Altan Cakir,Isa Yildirim
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
备注: 5 pages, 3 figures, 4 tables. Submitted to ISBI 2027
Abstract:A brain MRI scan can carry an unknown mix of artifacts, so restoration has to be blind, yet most deep-learning methods are built to correct one known artifact. We present Dx-Rx, an agent in which a multimodal large language model (MLLM) measures suspected artifacts before it repairs them. To our knowledge, it is the first agent to restore whole 3-D MRI volumes, whereas earlier MRI agents restore 2-D slices. In a loop, it looks at slices of the 3-D volume, calls five diagnostic tools (noise, spike, ghosting, bias field, motion) and four restoration tools in any order it chooses, checks each repair, can return to any earlier version to change the order, and ends with a radiology-style report of its diagnostic reasons, the repairs it applied and their results. We simulated up to five artifacts on 148 whole 3-D T1-weighted volumes, at levels calibrated to radiologists’ grades of real scans. An MLLM that only looked at the image, as earlier MRI agents do, removed at most 19.7 % of the artifact error. With the best of five MLLMs, Dx-Rx removed a mean 64.5 %, nearly matching the 64.6 % of the same tools given the ground-truth artifacts, which are never known for a clinical scan. With all five models, it made no volume worse and was on par with or better than an expert rule-based pipeline built from the same tools (62.3 %). Code and demo are public at this https URL.
[AI-161] Learning to read residual images for single-pixel photography
链接: https://arxiv.org/abs/2611.03729
作者: Hongjun An,Shuaizhe An,Tong Tian,Zhe Sun
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
备注: first submit
Abstract:Continuous video scales in cost with camera count, resolution and recording duration, yet many monitoring tasks seek events rather than complete images and can operate on a compact single-pixel measurement stream. Recovering a chosen interval as video is then desirable, but at 0.24% of the pixel count the scene correlation image looks like noise and per-clip optimization takes minutes. Correlating the residual of an estimate with the coding patterns nevertheless gives a residual image, the gradient of a centered measurement loss. Lifted through a pretrained video decoder, its agreement with the true error exceeds that of control decoders. Video-latent inversion (VLI) then outperforms frame-wise image-latent reconstruction at 1,000 bucket values. Measurement-Gradient-Conditioned Video Flow (MGV-Flow), trained once to read the lifted residual image with preceding frames, reconstructs five frames in 1.55,s at 22.9,dB median PSNR, against 398,s and 18.1,dB for VLI, with the largest gains in high-motion scenes. Directional fidelity ranks 30 coding configurations and selects one needing a third fewer bucket values. On a physical system, MGV-Flow outperforms per-frame reconstruction without retraining. A sliding window over the bucket-value stream yields 3.2 frames per second on one RTX~4090 and, in theory, 20 frames per second on a data-centre accelerator. Demonstrations are available at this https URL.
[AI-162] NepTTS-Bench: Native-Speaker Benchmarking and NepaliMOS for Nepali Text-to-Speech
链接: https://arxiv.org/abs/2611.03718
作者: Ashish Thapa,Rupak Ghimire,Lekhnath Sharma Pathak
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure, 2 tables. Dataset: this https URL ; code: this https URL
Abstract:Nepali TTS has reached a point where multiple systems are available, but there is no shared evaluation to compare them. NepTTS-Bench was created to address what existing automated and manual evaluation systems lack for Nepali TTS. A controlled benchmark with phonological grounding which contains 205 sentences (77 minimal pairs targeting aspiration, retroflex/dental contrasts, nasalization, gemination, and schwa deletion) which is rated by 180 speakers. There are 6,962 ratings for 10 TTS configurations and two human references. Along with this 7 ASR systems were evaluated for round-trip CER. SCOREQ was also run as an automated baseline. The key finding is that automated evaluation like SCOREQ prefers proprietary systems like ElevenLabs but native listeners prefer Piper. This is contributed by the fact that most of the systems not properly tuned for Nepali tend to show Hindi phonological behaviour and our minimal pairs are designed to surface such contrasts. We also present NepaliMOS predictor which is fine tuned on collected ratings. NepaliMOS achieves system-level rho = 0.90 on the dense panel but drops to utterance-level rho = 0.43 under leave-one-system-out evaluation. This is statistically significant and confirms that NepaliMOS captures genuine signal but cross-provider transfer is limited. A human calibration is recommended for new systems for which we release a Python evaluation package for practitioners to run the benchmark against new systems.
[AI-163] From Forecasting to Feedback: Evaluating Learned Dynamics for Tokamak Control
链接: https://arxiv.org/abs/2611.03690
作者: Guoyang Shi,Siqi Ding,Xuanhe Wang,Pei Guo,Changquan Yu,Jiale Hu,Jiayi Zhi,Zhengyuan Chen,Jianguo Chen,Tianyuan Liu
类目: Plasma Physics (physics.plasm-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Learned temporal models are moving beyond offline forecasting toward interactive environments for decision making, yet logged-trajectory accuracy need not predict closed-loop behavior under policies optimized in the learned model. This forecasting-control gap matters in tokamak magnetic control, where coupled plasma-circuit dynamics and actuator constraints can amplify errors through feedback. Using a calibrated free-boundary Grad-Shafranov solver, we collect four datasets in a 2x2 design over physics-parameter and current-reference coverage. At matched budgets of 500, 1k, 3k, and 5k rollouts, we train 16 action-conditioned Transformers; PPO policies optimized in the 5k surrogates are then evaluated without adaptation on six reference-solver conditions. Only joint-coverage D4 shows monotonically decreasing cross-condition mean error, whereas D1 improves in distribution but degrades out of distribution. Binary completion masks substantial closed-loop differences: D2-D4 each complete 83.3% of conditions, but reach on average 99.0%, 94.7%, and 90.5% of the evaluation horizon, while their worst conditions reach 94.2%, 68.2%, and 43.2%. Policies trained with action noise complete 5/24 matched conditions, compared with 19/24 under nominal training, and have a lower mean horizon-capped episode length. Based on one trained policy per configuration and one evaluation realization per condition, the results support combining prediction error, episode completion, condition-level duration, and tracking quality.
[AI-164] Development of tools for all-cause mortality prediction in heart failure patients: a systematic review
链接: https://arxiv.org/abs/2611.03665
作者: Erik Aerts,Annika Rosengren,Martin Lindgren,Martin Adiels,Yinan Yu,Helen Söland
类目: Applications (stat.AP); Artificial Intelligence (cs.AI)
备注: 26 pages and 5 figures, preprint and submitted for review
Abstract:Heart failure is a serious medical condition associated with risk of acute deterioration, recurrent hospitalizations and high mortality making accurate mortality prediction potentially valuable for patient care and healthcare planning. Numerous prognostic models have been developed using conventional statistics (CS), machine learning (ML) and deep learning (DL), but variation in study populations, heart failure definitions, prediction horizons and development practices may limit interpretation of applicability. We conducted a systematic review of mortality prediction models published between 2009 to the start of 2026 searching three databases (PubMed, IEEE and Scopus) for studies predicting all-cause mortality over a 1-60 months prediction horizon among patients with heart failure. Data extracted were cohort characteristics, heart failure definitions, model development, cohort size, validation practices, mortality, computational model design and reported performance. The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Of 1516 publications identified, 56 met the inclusion criteria. Publication numbers increased over the observation period, alongside increasing use of ML/DL models and decreasing use of CS approaches. Most development cohorts (64%) were classified as Unknown/Undefined heart failure, reflecting inconsistent reporting and terminology that did not correspond with established heart failure guidelines. External validation was reported in 23% of studies. Overall, research in modeling heart failure mortality has grown alongside increasing adoption of ML/DL approaches. Greater standardization of study design and reporting is needed for meaningful comparison, reproducibility and evaluation of models for clinical application.
[AI-165] Behavioral Prior Elicitation via LLM for Heterogeneous-Agent Bitcoin Flash-Crash Simulation
链接: https://arxiv.org/abs/2611.03660
作者: Tran Phuong Linh Pham,Thi Minh Phuong Ngo,Phuong Thao Nguyen,Quang Minh Nguyen,Thai Hoa Ngo,Trong Nghia Nguyen
类目: Applications (stat.AP); Artificial Intelligence (cs.AI); Trading and Market Microstructure (q-fin.TR)
备注: Accepted on Conference on Optimization, Modeling, Simulation, and Analytics (COMOSA 2026)
Abstract:Bitcoin perpetual futures trade continuously with shallow liquidity and high price impact, making them prone to millisecond-scale flash crashes. Agent-based limit order book simulators are a natural tool for studying such events, but their behavioral parameters are usually hand-crafted or require full order book histories that are rarely available at scale. We propose a four-stage pipeline that constructs a reproducible 100 ms BTCUSDT flash-crash event panel from 66 detected events, elicits behavioral priors for four agent archetypes using an offline large language model conditioned on empirical microstructure anchors, instantiates a heterogeneous limit order book simulator with the validated priors, and applies causal discovery plus counterfactual analysis to the resulting synthetic panel. Across 500 simulation runs, LLM-elicited priors produce a crash rate of 0.176 versus 0.008 under uninformative uniform priors and generate heav- ier return tails. In the frozen counterfactual analysis, reducing leverage amplification by one within-sample standard deviation lowers the predicted crash rate from 0.176 to 0.136, whereas reducing directional pressure yields only a marginal change. These find- ings suggest that empirically anchored LLM elicitation can improve simulator fidelity and help identify plausible crash-amplification mechanisms in a controlled synthetic environment
[AI-166] he Digital-Energy Recomposition of the Kitchin Cycle: Exploratory Evidence from U.S. IT and Irish Data-Centre Series (1995-2025)
链接: https://arxiv.org/abs/2610.33122
作者: Bartosz Ziollko,Adam Niedzialkowski,Bartosz Szyma
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); General Economics (econ.GN); Computational Finance (q-fin.CP); General Finance (q-fin.GN)
备注:
Abstract:Using quarterly FRED and CSO Ireland data over 1995Q1-2025Q4, we examine whether U.S.\ IT investment and Irish data-centre electricity are recomposing the Kitchin cycle around energy- and compute-backed assets. Three findings stand out. First, a Quandt-Andrews sup-Wald test locates a significant break in the U.S.\ IT-GDP correlation at 2014Q3 (sup-W = 9.69 ; r falls from +0.45 to -0.12 ); the Fisher r -to- z test survives Bonferroni adjustment ( z = +3.11 , p_\textBonf = 0.006 ), whereas the analogous test on the imposed Cloud-vs-AI boundary does not ( p_\textBonf = 0.164 ), so ``Strategic Decoupling’’ pre-dates the AI Era by approximately six years. Second, in Ireland the synchronous AI-Era correlation between metered data-centre electricity and Real GDP is large ( r = +0.41 , N = 16 ) but \emphcollapses to r \approx 0 when Real GDP is replaced by Real GNP, suggesting the headline is largely an artefact of the shared multinational tax-structure footprint between hyperscaler operations and Irish gross output. Third, the Irish cross-correlation profile is asymmetric in the opposite direction to a leading-indicator reading: the largest positive coefficients cluster on the k 0 side (GDP precedes data-centre load), so compute capacity is better read as coincident or moderately lagging. Given the small AI-Era samples and the absence of a quarterly multinational-adjusted aggregate such as GNI*, all coefficients are exploratory; subject to these caveats, the U.S.\ Kitchin cycle is best dated to 2014Q3, and the Irish Energy-Data Nexus is dominated by multinational firm activity.
机器学习
[LG-0] PolicyIK: High-Precision Inverse Kinematics for Robot Arms with Reinforcement Learning
链接: https://arxiv.org/abs/2611.05372
作者: Yuhan Liu,Xinqi Liu,Zixuan Dong,Keith W. Ross,Shenji Wan
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Inverse kinematics (IK) converts end-effector pose commands into joint motion, and many vision-language-action (VLA) policies rely on it to act on a given arm and to transfer across embodiments. Precise IK remains laborious: redundancy and singularities complicate the inverse mapping, execution depends on hand-tuned controllers, and training data on physical robots are costly to collect. We present PolicyIK, a goal-conditioned reinforcement learning policy for high-precision IK. It updates joint-position references from pose feedback and observes its accumulated reference relative to the measured joints, so it can retain the offset that the position controller needs at rest. Two-scale pose errors with an error-dependent action gain resolve millimeter residuals while preserving large motions. Progress and dwell rewards teach it to both reach the tolerance and stay within it. In simulation, a single attempt by the trained PPO policy keeps the mean errors over the final second within 1 mm and 0.2° on 93.5% of held-out tasks, and a deployment protocol raises success to 98.9%. When trained directly on a physical arm with an off-policy RL learner, PolicyIK achieves higher reaching and holding success than all four classical IK controllers under the same time budgets. In simulated pick-and-place, PolicyIK executes the end-effector commands of the \pi_0.5 VLA policy with 95.7% success. These results show that reinforcement learning can provide high-precision inverse kinematics for robot arms. Code is available at this https URL.
[LG-1] GUILD: Guided Iterative Data Collection for Imitation Learning
链接: https://arxiv.org/abs/2611.05368
作者: Shivin Dass,Kevin Nguyen,Rohan Yelandur,Junhong Xu,Roberto Martín-Martín
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Imitation learning policies in robotics are trained on datasets collected through costly real-world teleoperation. Often, the data-collection process is unguided, relying on the teleoperator’s intuition, resulting in data that contributes little to overall policy performance. Recent work has shown that curating such unguided datasets can substantially improve policy performance, but these approaches operate by filtering existing datasets after collection and therefore do not prevent the acquisition of uninformative data. In this work, we propose GUILD, a novel Guided Data Collection (GDC) method that actively reasons about which data should be collected to maximize downstream policy performance. We introduce an iterative framework that alternates between 1) task proposal and data collection and 2) data evaluation to close the loop. For task proposal, we leverage semantic priors from vision-language models to identify relevant environments, object configurations, and goals for data collection. However, since semantic relevance does not imply policy improvement, we perform data evaluation. By leveraging predictive data attribution we approximate how the collected trajectories influence policy performance on a target deployment setting. This signal is then used as feedback to guide subsequent data collection, closing the data collection loop. We evaluate our approach in simulation and real robot settings, demonstrating that guided data collection substantially improves sample efficiency and deployment performance compared to passive data collection strategies, multiple baselines and experienced roboticists. More information at this https URL
[LG-2] Agnostic Learning by Imitating
链接: https://arxiv.org/abs/2611.05346
作者: Sílvia Casacuberta,Parikshit Gopalan,Varun Kanade,Omer Reingold,Konstantinos Stavropoulos,Pranay Tankala
类目: Machine Learning (cs.LG); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Given a class of machine learning models and sample access to a data distribution with binary labels, which computational task is harder: training a discriminative model that distinguishes positive and negative labels as well as the best member of the class, or training a generative model whose outputs are indistinguishable from real data by all members of the class? We prove that these two fundamental tasks in computational learning theory are not just closely related, but actually equivalent to each other in complexity. One direction of this equivalence was previously known. Indeed, the powerful regularity lemma in complexity theory gives an efficient oracle reduction from the indistinguishability task (a.k.a. multiaccuracy) to the distinguishing task (a.k.a. agnostic learning) in the style of boosting algorithms. Our main contribution is an efficient oracle reduction in the other direction. Taking this idea further, we show that a multiaccuracy oracle is a remarkably powerful primitive, achieving not just agnostic learning but also a wide range of seemingly more demanding learning objectives via similarly efficient and direct constructions. Our results crucially use the power of making adaptive queries to the oracle, and stand in stark contrast to prior work, which showed that post-processing a single multiaccurate predictor is insufficient for even weak forms of learning. On a technical level, we draw a connection between our (offline) problem and a version of no-regret learning in the online setting, which allows us to use follow-the-leader type algorithms to modify the label distribution and aggregate the predictions of the multiaccuracy oracle.
[LG-3] CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
链接: https://arxiv.org/abs/2611.05325
作者: Yueqiao Chen,Dongyu Liu
类目: Machine Learning (cs.LG)
*备注: 21 pages of main text (including references), with 4 figures and 3 tables, plus 16 pages of appendices
Abstract:We study context-aware time-series forecasting, in which a single target variable is predicted from its own history and a set of contextual variables, such as weather and calendar information. As the context becomes more diverse, a single model struggles to capture all of its regimes, while ensemble learning and Mixture-of-Experts approaches rely on manual data partitioning or soft routing, which increases the inference cost and hinders interpretability. We propose CATF, a Context-Aware Time-Series Forecasting framework built on a Manager-Worker architecture: a Transformer-based Manager routes each input to a single Worker based on the context, using hard routing during both training and inference. The Workers thus specialize without manual data partitioning, and any single-model forecaster can serve as a Worker. On five context-rich datasets, four horizons, and two normalization schemes, CATF improves over its standalone counterpart in 162 of 200 comparisons, the best CATF-enhanced model outperforms the best standalone baseline in 19 of 20 settings, and it outperforms the zero-shot Time-MoE foundation model in 15 of 20 settings. A case study on bike-sharing data shows that the Manager routes inputs by the calendar and that each Worker specializes in a different weekly pattern.
[LG-4] ΦasmaNet: A Staged Spectral Token-Refinement Architecture for Fast Gravitational-Wave Signal-to-Noise Ratio Prediction
链接: https://arxiv.org/abs/2611.05309
作者: Ioannis Tsioulis,Anastasios Tefas,Nikolaos Stergioulas
类目: Machine Learning (cs.LG)
*备注: 33 pages, 12 figures
Abstract:Quantifying selection effects in gravitational-wave population studies requires millions of costly signal-to-noise ratio (SNR) calculations. We present \Phi asmaNet, a compact neural architecture for structured tabular regression, with staged spectral refinement as an explicit architectural prior. Learned input tokens are refined through ordered attention-based \Phi -Stages, each augmented by a \Phi -Refractor subspace-mixing module, then revisited through gated top-down \Phi -Feedback. Applied to the 11-dimensional parameters of precessing, higher-mode binary black hole signals (IMRPhenomXPHM), it predicts phase-averaged optimal SNR for a single detector at fixed Advanced LIGO design sensitivity. With approximately 732k parameters, it outperforms MLP, 1D-ResNet and Transformer-encoder baselines under matched training across 10^5 – 10^6 samples. At 10^6 samples, it achieves 100-\mathrmMAPE=98.73% on the held-out test set, 99.29% after rank-preserving monotonic (PCHIP) calibration, and 99.39% ( R^2=0.9998 ) in the detection-relevant regime \rho8 , with stable accuracy across independent dataset realizations. On a single GPU, it sustains approximately 1.2\times10^5 SNR evaluations per second and processes a 10^6 -injection catalogue end-to-end in approximately 8.3 s, about two orders of magnitude faster than conventional waveform-and-inner-product calculations on a 16-core CPU. Exact distance rescaling through \rho\propto1/d_L supports repeated catalogue-wide detectability evaluations. The fully documented pipeline is intended as a reproducible benchmark, while results on public tabular datasets indicate competitiveness beyond gravitational-wave applications. Although demonstrated for a fixed single-detector configuration, the construction supports extensions to detector networks and epoch-specific sensitivities. (Abridged)
[LG-5] Universal Hardness From Masking Channels
链接: https://arxiv.org/abs/2611.05296
作者: Parikshit Gopalan,Manoj Prabhakaran,Pranay Tankala
类目: Machine Learning (cs.LG); Computational Complexity (cs.CC)
*备注:
Abstract:We prove a broad learning-theoretic generalization of the celebrated hardcore lemma concerning the average-case complexity of computing a Boolean function (Impagliazzo, 1995). Our starting point is the observation that the classic notion of a hardcore measure, which reweights a function’s input distribution, is equivalent to an input-dependent masking channel that injects randomness into the function’s output distribution until it appears fully pseudorandom. The analogue of density of the measure is the fidelity of the channel. From this viewpoint, we develop a general framework for certifying hardness that allows for non-Boolean functions f and a wide range of notions of hardness besides the classic 0-1 loss, by choosing the right notion of fidelity via convex duality. We show the difficulty of learning f using a class \mathcalC to minimize a convex loss is tightly characterized by the maximum fidelity possible for a masking channel, which measures the least amount of noise the channel must add to hide the structure of f and make it \mathcalC -indistinguishable from random noise. Using our framework, we derive a number of multiclass generalizations of the hardcore lemma, and reveal some unexpected subtleties in going to larger alphabets. We construct a universal masking channel that applies to many natural notions of hardness simultaneously. Such channels are dual to omnipredictors, which optimize multiple losses at once (Gopalan, Kalai, Reingold, Sharan, and Wieder, 2022). We observe a symmetry between masking channels and generalizations of the hardcore lemma using pseudoentropy. We present a tight characterization of pseudoentropy using the MaxEnt algorithm from machine learning. A long line of research has shown that learning and hardness are two sides of the same coin; our framework gives a crisp and general formulation of this principle via convex duality.
[LG-6] GSBR: Geometric Support Behavior Regularization for Online RL
链接: https://arxiv.org/abs/2611.05295
作者: Charles Xu,Jacob Berg,Liyiming Ke,Sergey Levine
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Regularizing online reinforcement learning (RL) toward a pretrained base policy can improve sample efficiency and learning stability, but regularization that is too restrictive limits how much the policy can improve. Base policies – typically diffusion or flow-matching models trained by imitation learning on large, diverse datasets – capture many behaviors that are reasonable but suboptimal. Existing regularizers using such base policies are too restrictive in one of two ways: behavioral cloning regularizers pull the actor toward the full base-policy distribution, penalizing it for discarding suboptimal modes, while hard support constraints forbid improvements that lie just outside the base policy’s support. We propose Geometric Support Behavior Regularization (GSBR), which penalizes actions by their distance from the base policy’s support. Actions within the support incur no penalty, so the actor can concentrate on high-value modes; outside the support, where value estimates are less reliable, the penalty grows smoothly with distance while still permitting improvement when the value gained outweighs the penalty. GSBR estimates this distance from a finite set of base-policy samples, so it requires only sample access to the base policy. Across 19 tasks on four robotic benchmarks, GSBR raises the average success rate from 33% for the base policy to 95%, achieving the highest average success rate and throughput among all compared methods, with about 23% higher normalized throughput than the next-best RL method. Project website and open-source code at this https URL .
[LG-7] De Rham cohomological architectures for PINNs and PDEs with topological holes
链接: https://arxiv.org/abs/2611.05276
作者: Andrew Gracyk
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: First version
Abstract:We attempt to alleviate failure modes of classical physics-informed neural networks (PINNs) in scenarios of topological pathologies corresponding to punctures, holes, poles, and voids by introducing de Rham cohomological augmentations, or Hodge-de Rham PINNs. Vanilla PINNs often encounter pitfalls in hole-based settings due to discontinuous interfaces, nonzero closed path integration, path dependence, correspondence to exact forms via the exterior derivative, etc., therefore we absolve traditional failures via parameterizing a 1-form by the Hodge-Morrey-Friedrichs (HMF) decomposition for manifolds with boundary, drawing a connection to de Rham cohomology such as through the HMF isomorphism theorem. 1-forms can act as representations of scalar-valued PDE solutions through integral operator mappings. We propose performing a PINN-based loss on the parameterized 1-form through an associated PDE, which subsequently solves the potential-based PINN problem and moreover remedies the failures of vanilla PINNs via the Sobolev-regular operator under a Fréchet differentiation interconnection. The operator relationship often has a nontrivial closed-form and often requires integration or a case-by-case analysis, which we address thoroughly in our experiments. When an integrability condition is not met on our losses, we can equip exhaustion functions to train the networks. We demonstrate our methods on the Aharonov-Bohm effect, which, to our understanding, is largely nonexistent in PINN literature. We develop theoretical guarantees of our methods and demonstrate our pathological safeguards on a broad selection of topologically-adverse problems, including scenarios of inexact forms, and those of nontrivial harmonics and coexact terms.
[LG-8] GC: Antibody Optimization via Reinforcement Learning over a Learned Somatic Hypermutation Prior
链接: https://arxiv.org/abs/2611.05270
作者: Thao Nguyen,Robert G. Alberstein,Ewa Nowara,Homa MohammadiPeyhani,Holly R. Steach,Sarah A. Robinson,Yuval Shechter,Donald W. Lee,Jason A. Vander Heiden,Franziska Seeger,Andrew Leaver-Fay,Richard Bonneau,Joshua Yao-Yu Lin
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: 10 pages with supplementary documents
Abstract:Antibody affinity maturation is a natural optimization process in which B cells acquire mutations through somatic hypermutation (SHM), undergo selection for improved antigen binding, and iteratively evolve toward higher affinity. Computationally modeling this process requires capturing both biologically plausible mutations and antigen-specific selection. However, existing mutation models do not directly optimize binding, while affinity optimization often neglects realistic mutation trajectories. We introduce iGC (in silico Germinal Centers), a two-stage reinforcement learning (RL) framework for antigen-specific antibody optimization. Stage 1 learns an SHM mutation prior from parent-child pairs in B-cell lineage trees and adapts it to a target affinity-maturation dataset. Stage 2 initializes an RL policy from this prior and optimizes antigen binding with Kullback-Leibler (KL) regularization toward the frozen prior. To prevent reward exploitation, including repetitive high-reward substitutions and structurally tolerated framework mutations, we introduce a Reward-Tax Meta-Gradient objective. RewardNet smooths the affinity landscape, while TaxNet penalizes reward overexploitation, jointly defining a learned meta-reward for policy optimization. We evaluate iGC on mutation prediction, SHM consistency, affinity optimization, and mutation diversity. The mutation prior more than doubles the strongest baseline in mutation-site prediction. Following RL optimization, iGC maintains SHM sequence structure. It also achieves a strong exploration-exploitation balance, yielding competitive reward-diversity products across candidate-selection cutoffs. These results demonstrate that iGC integrates biologically plausible mutation generation with antigen-specific affinity selection, providing an in silico framework for iterative antibody affinity maturation. Comments: 10 pages with supplementary documents Subjects: Machine Learning (cs.LG); Biomolecules (q-bio.BM) Cite as: arXiv:2611.05270 [cs.LG] (or arXiv:2611.05270v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.05270 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-9] Stabilizing On-Policy Targeted Guidance in Agent ic Reinforcement Learning
链接: https://arxiv.org/abs/2611.05266
作者: Kevin Frans,Alexander M Rush,Sergey Levine,Pieter Abbeel,Ashvin Nair
类目: Machine Learning (cs.LG)
*备注:
Abstract:Naive on-policy RL is often bottlenecked by the capabilities of the base model — insufficient coverage can result in an inability to learn from certain rewards. At the same time, rewards in modern post-training systems often come from rich textual sources, e.g. judge models with a rubric of criteria. Can we utilize such structures to provide a rich directional signal that enables improving on otherwise difficult to optimize rewards? In this work, we examine a framework we refer to as targeted guidance that learns from per-turn textual feedback via an on-policy self-distillation objective. Our focus is not only on directly improving hard-to-optimize behaviors, but on how to do so throughout the training process without degrading baseline RL performance. Over a range of settings across an agentic multi-turn software engineering environment, we find success by combining targeted self-distillation with a per-criteria RL reward, and by limiting feedback to turns where the policy specifically fails. We further examine the design choices that lead to stable entropy and reduce oscillatory behavior throughout training.
[LG-10] Gaussian Partitioning for Reliable Learning of Multi-Index Models
链接: https://arxiv.org/abs/2611.05264
作者: Ilias Diakonikolas,Giannis Iakovidis,Daniel M. Kane,Mingchen Ma
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注: The abstract has been shortened for arXiv
Abstract:We introduce Gaussian partitioning, an algorithmic primitive for distributions smooth with respect to a Gaussian. It partitions space into polyhedral regions so that almost all mass lies in thin strips restricting a few orthogonal directions. Within each strip, the conditional distribution approximately matches low-degree Gaussian moments in the unrestricted directions. Our construction builds on logconcave partitioning of Diakonikolas, Kane, and Ma (FOCS 2026). Under input marginals smooth with respect to the standard Gaussian, we obtain agnostic learners with error (1+c)\mathrmopt+\epsilon for broad classes of Boolean multi-index models, including functions of halfspaces, low-dimensional convex concepts, and low-dimensional polynomial threshold functions. For functions of k halfspaces, both agnostic and testable agnostic learners achieve this error with dimension dependence d^\mathrmpoly(k/c) , improving the previous exponential dependence on k/c in the agnostic exponent. For learning from positive examples with imperfect unlabeled data (PUI), we give algorithms and Statistical Query (SQ) lower bounds that qualitatively characterize complexity through Gaussian moment matching. For fixed smoothness and positive-mass parameters, we learn intersections of k halfspaces with dimension dependence d^O(\log(k/\epsilon)) and matching SQ lower bounds, improving the previous d^\mathrmpoly(1/\epsilon) upper bound even for one halfspace. A general reduction from agnostic reliable learning to PUI yields negative reliable learners for these intersections with the same dimension dependence, for fixed smoothness. Finally, for PQ learning and testable learning with distribution shift, we prove qualitatively optimal SQ lower bounds of d^\Omega(1/\epsilon^2) , even for intersections of two halfspaces with Gaussian training data and arbitrary test distributions. Comments: The abstract has been shortened for arXiv Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG) Cite as: arXiv:2611.05264 [cs.DS] (or arXiv:2611.05264v1 [cs.DS] for this version) https://doi.org/10.48550/arXiv.2611.05264 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Giannis Iakovidis [view email] [v1] Tue, 3 Nov 2026 17:52:55 UTC (102 KB)
[LG-11] NovoCRF: Globally Normalized De Novo Peptide Sequencing on a Prefix-Mass Lattice
链接: https://arxiv.org/abs/2611.05260
作者: Rui Qiao,Lei Xin,Baozhen Shan
类目: Machine Learning (cs.LG)
*备注:
Abstract:De novo peptide sequencing has moved from dynamic programming over hand-designed or generative scores to large transformers that learn the task end to end. We present NovoCRF, a conditional random field (CRF) whose states are prefix masses, so that its paths are exactly the peptides within +/-0.06 Da of the precursor mass. A small network scores each edge from physics-derived ion-match features and is trained with its scores globally normalized over all mass-feasible peptides. To our knowledge, NovoCRF is the first de novo method trained with exact normalization over all mass-feasible peptides. Our software has three modules, each with exactly one job: a precursor mass finder recovers the accurate precursor mass from MS1, the CRF finds the peptides that fit the precursor mass by exact k-best decoding, and two BiGRU rerankers add sequence context and internal-fragment evidence. The model has 310k parameters and trains in about 17 GPU-hours. On seven held-out data-dependent acquisition datasets published in 2026 (126,262 labeled spectra), NovoCRF’s mean peptide recall is 42.4%, against 39.1% for Casanovo v5 with five beams, and its amino-acid recall 64.0% against 53.1%, at 1,746 spectra/s on 18 CPU threads, 55x Casanovo’s throughput on a GPU. Split by fragment evidence, NovoCRF leads on labels with up to three peptide bonds without a b or y ion, and Casanovo only beyond. NovoCRF can also search modifications it was never trained on at their exact mass: it finds 32.1% of the hydroxyproline peptides of a lion bone sample.
[LG-12] Logarithmic Regret for Alternating Mirror Descent
链接: https://arxiv.org/abs/2611.05243
作者: Tianlong Nan,Christian Kroer
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:
Abstract:Alternating online mirror descent (AltOMD) is a simple learning rule for zero-sum games, which improves upon simultaneous OMD numerically and theoretically. Previous work obtained O(T^1/3) and O(T^1/5) regret bounds for AltOMD, by leveraging modified-energy-function-based analyses. However, settling whether AltOMD achieves near-constant regret has remained an open question. In this paper we resolve the question of whether AltOMD achieves near-constant regret in the affirmative. In particular, we show an O(log(T)) regret bound on the sum of the players’ external regrets for any two-player zero-sum bilinear game with compact convex strategy sets, under mild regularity assumptions on the regularizers. We verify that our assumptions hold for the negative entropy regularizer, thereby obtaining logarithmic regret for the alternating multiplicative weights updates algorithm. The regret bound also yields an O(log(T)/T) duality gap bound for the averaged strategies.
[LG-13] Shrinkage prior for implicit generative models
链接: https://arxiv.org/abs/2611.05241
作者: Shreya Sinha-Roy,Christian P. Robert,Ritabrata Dutta
类目: Machine Learning (cs.LG); Computation (stat.CO)
*备注:
Abstract:Implicit generative models based on deep neural networks have revolutionized many scientific fields, from weather research to language generation. They successfully model complex random objects arising in real-world problems. In this work, we propose a sample-efficient training mechanism for these models. Our approach leverages regularized inference with shrinkage priors and generalized Bayes. In the large p (parameter dimension) and small n (sample size) setting, sparsity-inducing priors have been successfully used for linear regression when p \gg n . However, their application to implicit generative models remains challenging due to the intractability of the likelihood function. To address this, we adapt a scoring rule posterior based on a strictly proper scoring rule, following the generalized Bayes framework. We then propose a block SGMCMC within Gibbs sampling mechanism. This enables efficient exploration of high-dimensional parameter spaces by identifying a sparse representation of neural implicit generative models. We demonstrate the performance of our proposed method through experiments on p \gg n linear regression and an application of neural generative models.
[LG-14] Post-Inference Model Chaining: A Problem Statement and a Model-based Bootstrap for Training End-to-End Digital Twins
链接: https://arxiv.org/abs/2611.05225
作者: Lukas Madenach,Timo Bailer,Tobias Eilert
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:End-to-end digital twins can be constructed by fitting separate stage models and connecting them after inference. We investigate the Frequentist prediction properties of a commonly used Monte Carlo simulation scheme that forwards complete predictive draws through such a chain when measured stage outputs contain independent normal errors, but the latent stage means are deterministic. Analytical two-stage examples show persistent asymptotic interval and coverage inflation and, with quadratic propagation, mean bias dependent on the parameters to be estimated. Simulations of four- and eight-stage linear-quadratic univariate chains with 7, 15 or 30 training runs demonstrate pronounced input-dependent distortions. A diagnostic using error-free latent training predictors shows that the effect is mainly not attributable to errors in variables, but to the MCSim approach even showing underestimation of coverage. We propose a model-based bootstrap: an initial chain forwards fitted means, bootstrap chains are refitted on fixed-center bootstrap data while forwarding their mean prediction, and a target-stage future error enters a studentized prediction root. Across the investigated scenarios, relative mean bias is substantially reduced, and coverage is closer to the nominal 90%. At sample size 30, the eight-stage bootstrap has mean width ratios of 1.16-1.42 relative to the known-parameter normal reference, compared with 4.11-4.97 for observed-to-observed MCSim. These results support improved endpoint prediction in the tested setting. Further investigations of the bootstrap scheme in the supplement for a bivariate 6-stage process show similar satisfactory results.
[LG-15] Learning curves in natural and artificial systems
链接: https://arxiv.org/abs/2611.05224
作者: Dmitry A. Novikov
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注:
Abstract:A general mathematical model of experience formation is considered, in which the learning level is defined as the probability that the next value of the uncertainty factor has already been mastered and not forgotten. Within this model the shape of the learning curve is derived, and it turns out to be determined primarily by the distribution of situation occurrence: a uniform distribution yields an exponential curve, whereas a power-law distribution yields a power-law curve whose exponent equals the ratio of the distribution exponent to that same exponent increased by one. Both mechanisms generating power-law curves, established earlier in the analysis of debugging, assembly and repair activities, are shown to have been independently rediscovered in deep learning theory as the “frequency” and “spectral” mechanisms of scaling laws for artificial neural networks, while the exponential curves of machine learning (contraction operators, strongly convex optimization, stationary reinforcement) are generated by the same constancy of the contraction coefficient that was revealed half a century earlier in models of radioactive decay, the first-order lag element, and others. A solution is given for the problem of choosing a training distribution that differs from the deployment distribution. A methodological consequence is discussed: three distinct mechanisms generate curves indistinguishable in shape, so an observed learning curve does not allow one to judge the “internal” structure of the learning system.
[LG-16] Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks
链接: https://arxiv.org/abs/2611.05220
作者: Wen Zhou,Shaowen Wang,Upmanu Lall,Venkatesh Merwade,Dingqi Ye
类目: Machine Learning (cs.LG)
*备注:
Abstract:Flood peak depth on normally dry land governs damage and risk to life, but is observed only sparsely, by post-event high-water mark (HWM) surveys, and depth derived from satellite flood extent is produced after the event and inherits the vertical error of the digital elevation model (DEM) it is computed from. Deep-learning flood models have in turn been trained on hydrodynamic simulations rather than on observed depths. We ask whether flood peak depth can be learned from field observations and predicted before the peak from spaceborne terrain, geospatial foundation-model embeddings and antecedent meteorological forcing. We assembled 5689 USGS HWM surveys from 39 events (2011-2025) in 24 U.S. states and Puerto Rico, assigned each a peak time from nearby sensors, paired each with a rain-free window at the same site, and linked them to Copernicus GLO-30 terrain, AlphaEarth embeddings and 144 h of hourly forcing masked to the lead time. A cross-modal fusion network trained on these observations predicts depth at the surveyed marks with a mean absolute error of 0.39 m 6 h ahead of the peak. Supervision by surveyed depth corrects the DEM error implicitly: differencing the measured peak water level with the same DEM gives 0.93 m at the same marks, 2.4 times larger, although it uses the measured water level and the model does not. Forcing accounts for 77% of the attributed reduction in depth error; terrain and the embeddings are statistically equivalent to each other and contribute the remaining 23%. On eleven held-out events the model still separates flooded from rain-free conditions but no longer resolves depth at the marks (R2 = 0.03), so the skill reported here is confined to the events and regions sampled. Field-surveyed depths can thus supervise flood depth prediction and correct terrain error that extent-based estimation cannot, while transfer to unseen storms remains the open problem.
[LG-17] Physics-informed traditional and generative Mori-Zwanzig-reduced models for multiscale systems
链接: https://arxiv.org/abs/2611.05211
作者: Emily Williams,Saad Qadeer,Amanda Howard,Panos Stinis
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:Model reduction via the Mori-Zwanzig (MZ) formalism is well understood for coarse-grained representations constructed from standard basis functions such as Fourier functions. We show that the same formalism can be used to build reduced-order models in bases discovered by physics-informed machine learning. The fluctuation-dissipation theorem (FDT) is used to close the resulting non-Markovian evolution equations for the expansion coefficients of the learned basis. We further introduce a complementary, generative closure model. Relative to the Markovian dynamics alone, both the MZ and generative closures substantially improve predictions of the filtered states.
[LG-18] Privacy for Free in Efficient Agnostic Halfspace Learning
链接: https://arxiv.org/abs/2611.05208
作者: Giorgio Racca,Amartya Sanyal,Vikrant Singhal
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces. In particular, we study three different settings of agnostic learning: improper learning under Gaussian and log-concave marginals; proper learning under Gaussian marginals; and semi-agnostic learning under Gaussian marginals. In all three aforementioned settings, the added cost in the sample complexity due to privacy matches the respective non-private sample complexity, implying that we get privacy for free.
[LG-19] Multi-Objective Generative Optimization via Expected Hypervolume Fine-Tuning
链接: https://arxiv.org/abs/2611.05207
作者: Juan Guevara,Riccardo De Santi,Andreas Krause,Raul Astudillo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Scientific discovery requires plausible designs spanning useful trade-offs among competing properties: a molecule with strong binding affinity, for example, may be difficult to synthesize. Pretrained generative models, e.g., flow and diffusion generators, provide a foundation for proposing such designs, but steering them toward high-quality, complementary trade-offs remains challenging. Inference-time search incurs high per-sample costs, while fine-tuning typically relies on fixed per-candidate rewards that do not capture how designs complement one another. We introduce Hypervolume Fine-Tuning (HVFT), which adapts a single pretrained generator to maximize the expected hypervolume of its samples while controlling departure from the pretrained distribution. Expected hypervolume jointly rewards candidate quality and collective trade-off coverage. Nonetheless, we show that it cannot be expressed as the expectation of a fixed per-candidate reward and therefore cannot be optimized directly with standard techniques. A key observation of this work is that its first variation corresponds to the expected marginal hypervolume contribution of a candidate, which yields a tractable adaptive reward via an unbiased estimator based on model samples. HVFT uses this reward to decompose the objective into a sequence of KL-proximal scalar-reward subproblems. We establish concavity, relative smoothness, and convergence guarantees for the resulting distributional procedure. Across therapeutic-peptide and molecular design tasks spanning discrete diffusion and continuous flow-matching models, HVFT achieves state-of-the-art expected hypervolume and trade-off coverage.
[LG-20] Empirical Bayes Flow Matching for Continuous Cryo-EM Heterogeneity ATC NEURIPS2026
链接: https://arxiv.org/abs/2611.05202
作者: Daniel Aibinder,Azmi Haider,Dan Rosenbaum
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: Accepted at NeurIPS 2026. 20 pages, 6 figures, 5 tables. Code: this https URL
Abstract:We address the problem of learning generative priors over high-dimensional latent variables from indirect, noisy observations, with a focus on continuous heterogeneity in cryo-electron microscopy (cryo-EM). In this setting, 3D atomic structures x \in \mathbbR^3N_a are never directly observed and must be inferred from 2D projection images y generated by a complex, non-linear forward operator. We propose Empirical Bayes Flow Matching (EB-FM), a method that learns a flow matching prior p_\theta(x) directly from observations using a Monte Carlo stochastic approximation Expectation-Maximization framework. EB-FM alternates between an E-step that performs approximate posterior sampling from p_\theta(x \mid y) via guided flow-based generation while maintaining persistent latent estimates through stochastic approximation, and an M-step that updates the flow model using these stabilized latent variables as training targets. This approach eliminates the need for pretraining on clean data and naturally accommodates non-linear forward models. We validate our method on controlled inverse problems using two-moons and MNIST data, and demonstrate its effectiveness on simulated cryo-EM data with known poses, showing that EB-FM can recover continuous distributions of protein conformations directly in atomic-coordinate space.
[LG-21] Ensembles of Random Partition-Based Models under Covariate Shift
链接: https://arxiv.org/abs/2611.05195
作者: Mary Versa Clemens-Sewall,Eliza O’Reilly
类目: Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 29 pages, 3 figures
Abstract:Distribution shifts, including covariate shifts, are ubiquitous in real-world applications of machine learning. Empirical evidence suggests that, across a variety of models with and without downstream adaptation, ensembles tend to be more robust to covariate shift than single models. We extend the theory of covariate shift severity from single models to ensembles, providing theoretical justification for the empirical trends. We introduce new discrepancy measures of covariate shift severity to control the risk of partition-based kernel estimators. We then show that with sufficient ensemble size, these models can achieve minimax-optimal risk convergence rates under covariate shift. Our results suggest that modelers can expect to need larger ensemble sizes for more severe covariate shifts.
[LG-22] Stepsize modulation for cross entropy from local quadratic optimization
链接: https://arxiv.org/abs/2611.05191
作者: Bruno Bauwens,Viacheslav Krivodub,Bingyu Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Two methods are presented that perturb a given learning rate schedule in stochastic gradient descent with cross entropy loss. Both methods use an extra forward pass to estimate various derivatives of the minibatch loss. The first method selects a value \eta by linearising the softmax values and minimizing the squared error \tfrac 1 2 ||y - q - \eta q’||_F^2 , where q is the matrix representing the softmax values of the minibatch and y is the matrix with one-hot encodings of the labels. The second method considers a quadratic Taylor approximation of the minibatch loss under the outer approximation of the Hessian. Thus, it minimizes L(0) + \eta L’(0) + \tfrac 1 2 \eta^2 L’‘(0) . Here, the derivatives L’ and L’’ are computed from the derivatives of the logits (using the outer approximation of the Hessian of the loss). We observe that both methods give very similar values of \eta . Finally, these estimated \eta are used to slightly increase or decrease the learning rate of an existing schedule. The method is evaluated on Cifar10 and Cifar100 datasets for training Resnets, where they decrease the loss faster than the schedule that is perturbed and increase monotonicity. For Cifar10 it also leads to a small improvement of test accuracy, but in Cifar100 test accuracy remains the same. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.05191 [cs.LG] (or arXiv:2611.05191v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.05191 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bruno Bauwens [view email] [v1] Tue, 3 Nov 2026 16:45:29 UTC (443 KB) Full-text links: Access Paper: View a PDF of the paper titled Stepsize modulation for cross entropy from local quadratic optimization, by Bruno Bauwens and 2 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.LG prev | next new | recent | 2026-11 Change to browse by: cs 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?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); We gratefully acknowledge support from our major funders, member institutions, , and all contributors. About Help Contact Subscribe Copyright Privacy Accessibility Operational Status (opens in new tab)
[LG-23] Provably Learning Mixtures of Linear Experts
链接: https://arxiv.org/abs/2611.05184
作者: Adrian Candocia,Aamod Varma,Mark Davenport
类目: Machine Learning (cs.LG)
*备注:
Abstract:Mixture-of-Experts (MoE) models are widely deployed in modern machine learning. However, few convergence guarantees exist for gradient-based training of MoEs: the objective is nonconvex, and even simple mixtures trap first-order methods in spurious local optima. We analyze a canonical gated mixture featuring linear experts and a gate induced by Gaussian clusters in the input space, and give, to our knowledge, the first global convergence guarantee for jointly training the gate and the experts of such a model by gradient EM on its likelihood. Given a logarithmic overparameterization relative to the true number of experts m , gradient EM converges from randomly initialized gates and least-squares-initialized experts to any prescribed accuracy in polynomially many iterations, under a separation condition on the gate centers. Our analysis establishes a gradient-dominance inequality for the coupled dynamics by decoupling the loss into per-cluster subproblems. Empirical results validate the predicted m\log m phase transition and suggest that the least-squares warm start required by our analysis is not needed in practice: randomly initialized experts perform nearly as well. We also find that leaving the mixing weights unconstrained concentrates routing onto the true number of experts, whereas load-balancing penalties spread it, so that the fitted model can be pruned to a fraction of its width without loss of accuracy.
[LG-24] Macrocanonical Generator Networks: Data-Efficient Neural Surrogates for Amortized Physics Simulation NEURIPS2026
链接: https://arxiv.org/abs/2611.05157
作者: Niall Jeffrey,Benjamin D. Wandelt
类目: Machine Learning (cs.LG); Cosmology and Nongalactic Astrophysics (astro-ph.CO); Machine Learning (stat.ML)
*备注: Accepted at NeurIPS 2026. 20 pages total (inc. appendices), 4 figures
Abstract:Many scientific simulations are computationally expensive, limiting their use in simulation-based inference, uncertainty quantification, and decision making. We introduce macrocanonical generator networks, a framework for learning fast, data-efficient neural surrogates for amortized simulation of stationary physical processes by matching multiscale statistics of a target process. Inspired by microcanonical maximum-entropy synthesis, which matches feature statistics per sample by inner-loop optimization, our approach instead trains a neural generator to satisfy the same constraints in expectation, preserving physically realistic sample-to-sample fluctuations. We prove that gradient-descent training induces preconditioned gradient-descent dynamics in sample space that inherit the symmetry-preservation properties of the microcanonical gradient descent of Bruna and Mallat, amortizing its per-sample inner-loop optimization into a single training run after which every sample is generated in one forward pass. We further show that residual generator parameterizations initialized near the identity, trained with a small learning rate, preserve entropy locally during early optimization and mitigate mode collapse. Experiments on cosmology and fluid simulations show that macrocanonical generators recover realistic samples from extremely small training sets, in some cases a single training example, while generating new realizations more than 10^6 times faster per sample than the reference simulator and more than 10^3 times faster than microcanonical gradient descent. The trained generators can be reused for downstream pretraining, including simulation-based inference, providing a data-efficient route to multi-fidelity scientific machine learning workflows.
[LG-25] Beyond Foundation Models: PFN Priors Improve Tabular Hyperparameter Optimization
链接: https://arxiv.org/abs/2611.05141
作者: Artem Chistyakov,Renat Sergazinov,Artem Babenko
类目: Machine Learning (cs.LG)
*备注:
Abstract:Synthetic priors have so far served one purpose: pretraining tabular foundation models. We ask whether the same priors can improve conventional tabular models, which are trained from scratch on each dataset. We propose PriorTune, a method that meta-tunes hyperparameter configurations on synthetic tasks alone. Offline, for a given model, we fit a pool of configurations on datasets sampled from a published PFN prior and greedily select the subset with the highest winrate over the random baseline. Online, a new dataset fits only the selected configurations, so the gain comes at no extra cost. Empirically, we observe that scaling both the number of configurations and synthetic datasets has an equal scaling effect on the performance of the final subset of configurations, which we also explain theoretically. On the full BeyondArena benchmark, LightGBM with TabPrep features with our configurations ranks above TabPFN-3 and RealMLP, while being much more efficient at inference. Overall, our results establish existing PFN priors as a scalable source of meta-training data for hyperparameter selection for non-foundational tabular models.
[LG-26] PAC Learning with Multiple Correct Answers
链接: https://arxiv.org/abs/2611.05133
作者: Steve Hanneke,Farnam Mansouri,Alireza F. Pour,Amirreza Shaeiri,Kun Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study PAC learning when each example comes with a set of at most \ell correct labels and a prediction is correct if it belongs to this set. Training may reveal the full set, only whether a proposed label is correct (bandit feedback), or one correct label chosen by a demonstrator (emonstrations). Our main finding is that demonstrations can be much harder than full sets, and even than bandit feedback, both in which classes can be learned and in how many examples are needed. For finite label spaces, a generalized Natarajan dimension characterizes learnability from full sets and from bandit feedback, in both the realizable and the agnostic setting. From demonstrations, however, a class is realizably learnable if and only if the intersections of at most \ell+1 of its hypotheses are learnable from full sets. Already with three labels, this condition can fail for a class learnable from a single full-set example or even a single bandit answer. When the condition holds, we show that \widetilde O(\varepsilon^-\ell) demonstrations suffice, and the exponent \ell cannot be improved in general, even for classes learnable from \widetilde O(1/\varepsilon) full sets or bandit answers. For countable label spaces and \ell \geq 2 , our packed dimension, which asks for many disjoint groups of at most \ell+1 hypotheses with no common label, characterizes full-set learnability, and demonstrations still reduce to intersections. Agnostic learning from demonstrations, already for finite label spaces, is not determined by the dimensions of the intersection class that characterize realizable learning. Finally, we show that the uniform bound \ell on the number of correct labels is essential. Without this bound, it is impossible to characterize learnability by any dimension determined by finite subclasses. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.05133 [cs.LG] (or arXiv:2611.05133v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.05133 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kun Wang [view email] [v1] Tue, 3 Nov 2026 16:15:40 UTC (149 KB)
[LG-27] Do Ray-Traced Cities Make a Better Channel Prior for an Unseen Site? A Pre-Registered Study in Simulation and a Test on Measured Channels that Reverses It
链接: https://arxiv.org/abs/2611.05128
作者: Krish Patel
类目: Machine Learning (cs.LG); Information Theory (cs.IT); Signal Processing (eess.SP)
*备注: 11 pages, 3 figures, 3 tables
Abstract:Channel estimators at a newly deployed base station usually start from 3GPP stochastic channel models, which describe no particular street. We ask whether a channel prior pooled from many ray-traced cities serves an unseen site better than the 3GPP CDL models, and whether learned estimators make the difference. With Sionna RT on 14 city scenes, LMMSE estimation from covariance priors, leave-one-city-out evaluation, and pass/fail bars written down before each main experiment, we find in simulation that the pooled prior sits about 0.6 dB from a site-trained estimator with no site measurements, about what CDL reaches after 50 measurements. Learned models added little: at most 0.65 dB in estimation, and in channel prediction a simple classical method beat CNN predictors by 1.8-12 dB from 2 ms ahead. On measured channels from the DICHASUS outdoor dataset (1.272 GHz, three 2x8 arrays on one building) the result reversed: the pooled ray-traced prior was 3.2-4.1 dB worse than CDL. The simulated channels were too clean; with users at the real distances, even a scattering level tuned from 50 site measurements left the prior about 2 dB behind CDL. A pre-registered follow-on on two more measured environments found the generic CDL prior 2.1-4.7 dB from a site-trained one: no further in a factory hall than on the street, furthest in a small lab room. Finally, a hallway model calibrated to measurements by differentiable ray tracing reproduced delay spread, but as a prior in an unmeasured part of the hallway it was 4.5 dB worse than CDL on the full array. All measured results come from one sounder.
[LG-28] owards an Optimal Control Foundation of Early Exits - Turnpikes and ResNets NEURIPS2026
链接: https://arxiv.org/abs/2611.05120
作者: Jens Püttschneider,Simon Heilig,Asja Fischer,Timm Faulwasser
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Accepted at NeurIPS 2026
Abstract:The feed-forward propagation through neural networks is crucial for training and inference. To trade off efficiency and accuracy, it has been proposed to consider intermediate outputs of hidden states as data-dependent regularization during training and, ultimately, as early-exits during inference. In this work, we analyze the joint optimization of loss functions computed for the output layer and intermediate layers using optimal control theory. We prove that ResNets trained in this joint fashion exhibit the so-called turnpike phenomenon, this implies that the number of layers required to reach a prescribed loss level is bounded from above independently of the overall network depth. This induces a similarity property across the internal representations of early-exiting networks of varying depths. We connect the turnpike to the overthinking phenomenon, and provide theoretical insights into the success of early-exit criteria derived from softmax probabilities, specifically entropy and max-probability. In a range of experiments, we demonstrate the existence and effect of turnpikes and the resulting similarity in the early-exit performance for ResNets of varying depths.
[LG-29] Distributed Linear Bandits for Freshness-Aware Preprocessing in MEC-Enabled Digital Twin Networks
链接: https://arxiv.org/abs/2611.05115
作者: Mariam Yahya,Setareh Maghsudi
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Digital twins (DTs) require timely updates from Internet of Things (IoT) devices to accurately represent the state of the corresponding physical systems, but transmitting large data volumes can compromise freshness. Moreover, different task classes have distinct computational demands and freshness requirements, while stochastic task generation and sizes complicate resource allocation. In this paper, we propose a freshness-aware framework in which multi-access edge computing (MEC) servers preprocess IoT data, reducing transmission volume and DT-server processing load. The framework establishes mutual support between DT and MEC servers. The DTs determine feasible CPU-allocation strategies guaranteeing class-specific age-of-information (AoI) requirements under specified workload, delay, and update-generation bounds. MEC servers collaboratively identify the feasible strategy minimizing expected freshness-normalized preprocessing delay. We formulate CPU-strategy selection as a fixed-confidence distributed best-arm identification problem in linear bandits, with MEC servers as agents and CPU allocations across task classes as arms. We employ DistLinGapE, a distributed collaborative learning method, to coordinate exploration and share observations. Numerical results show that the method identifies the optimal feasible strategy and reduces preprocessing delay when deployed under stationary task-class assignments and workload statistics. Collaboration yields an identification speedup nearly proportional to the number of participating MEC servers in the evaluated settings.
[LG-30] Beyond Multi-Index Models: Learning Intersections of Degree-2 PTFs
链接: https://arxiv.org/abs/2611.05114
作者: Ilias Diakonikolas,Daniel M. Kane,Mingchen Ma,Lisheng Ren,Trung Tran
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:We give an algorithm for PAC learning intersections of k degree-2 polynomial threshold functions over R^d under a Gaussian distribution. Our algorithm achieves classification error \epsilon using d^O(k)2^O_k(\log^3(1/\epsilon)) time and samples, improving over the d^O(k^2/\epsilon^2) running time of the polynomial regression approach. To our knowledge, our result is the first to obtain a dimension exponent independent of the accuracy parameter, even for k=2 . We complement our algorithmic result with an SQ lower bound showing that the complexity dependence of d^\Omega(k) is unavoidable. Lifting the input to polynomial features turns the target into an intersection of k halfspaces. This suggests using the first two labeled moments for subspace recovery. In the lifted space, however, these moments can reveal nothing. Our starting point is a Gaussian comparison theorem showing that, for sufficiently regular defining quadratics, the labeled moments needed for subspace recovery are approximately preserved. To handle arbitrary defining quadratics, we establish a new structural result showing that every direction with significant resampling influence can be detected through degree- O(k) moments. These results yield an initial d^O(k)2^\mathrmpoly_k(1/\epsilon) -time learner using adaptive restrictions. To improve the exponential dependence on 1/\epsilon , we develop a two-stage dimension-reduction approach that avoids the costly adaptive partitioning. After identifying a subspace containing all directions with large influence, we use generalized regularity and Gaussian comparison to recover the remaining relevant quadratic features through a new derivative-based moment operator. The recovered features support an intersection close to the target. Combining them with robust hinge-loss minimization gives our quasipolynomial-time learner. Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS); Statistics Theory (math.ST); Machine Learning (stat.ML) Cite as: arXiv:2611.05114 [cs.LG] (or arXiv:2611.05114v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.05114 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-31] Dual-VaDE: Efficient and Robust Prototype Learning via Variational Dual-Domain Mixture Priors
链接: https://arxiv.org/abs/2611.05099
作者: Wugeng Zheng,Song Wang,Jian Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we propose Dual-VaDE, an unsupervised generative framework for learning interpretable anatomical prototypes from unlabeled medical images. In contrast to previous variational methods that rely on a single spatial latent code, which often entangles global geometry with local textures and fails to capture their cross-domain dependencies, Dual-VaDE introduces a dual-domain Gaussian mixture prior that explicitly couples spatial anatomy with phase-preserving Fourier spectral representations. We derive an exact, closed-form variational lower bound that fuses this joint prior into the decoder and optimizes the entire network end-to-end via an alternating EM formulation, avoiding heuristic two-stage clustering while ensuring superior training stability and robustness. Evaluated on OASIS-3 brain MRI, Dual-VaDE delivers substantially sharper and more plausible prototypes, achieves superior cluster separation with improvements of up to 94% over spatial-only baselines, exhibits markedly reduced variance across random seeds, and incurs negligible inference overhead (0.28 ms per image).
[LG-32] CRONOS: Benchmarking Non-Episodic Reinforcement Learning for Robotic Manipulation
链接: https://arxiv.org/abs/2611.05080
作者: Po-Yi Wu,Djengo Cyun-Jyun Fang,Dian Cheng,Tsung-Wei Ke
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning (RL) is promising for adapting robot policies to unstructured real-world environments. However, standard RL pipelines rely on episodic training with frequent scene resets, which is impractical for real-world deployment due to the need for substantial human intervention. While prior work has studied non-episodic RL in relatively small policies trained from scratch, its behavior when fine-tuning modern large-scale pre-trained vision-language-action (VLA) models remains underexplored. In this work, we introduce CRONOS, a simulation benchmark for RL fine-tuning of VLAs under non-episodic scene resets. CRONOS leverages high-fidelity physics simulators and adopts shared-scene multi-task settings. Using CRONOS, we show that naively fine-tuning pre-trained policies fails in non-episodic settings due to shifts in object configurations and end-effector poses; however, these challenges can be mitigated through targeted reset allocation and by addressing biases in pre-trained models. Moreover, we find that non-episodic training enhances long-horizon manipulation and improves generalization to previously unseen object configurations and task sequences. Project page: this https URL
[LG-33] Effects of Parton Pair Correlations on Direct-Photon Yields and Elliptic Flow in Heavy-Ion Collisions
链接: https://arxiv.org/abs/2611.05042
作者: Xingjian Lu,Shuzhe Shi
类目: Machine Learning (cs.LG)
*备注: 14 pages, 7 figures
Abstract:Direct photons escape heavy-ion collisions with little rescattering, carrying information from the full emission history. Yet describing the measured direct-photon yield and the sizable elliptic flow v_2 together remains a long-standing challenge. Here we explore how correlations between emitting partons may affect both observables. The local 2\to2 photon rate depends on the joint distribution of incoming partons, so a connected correlation between them can change photon emission without changing the one-particle distribution of medium particles. Once folded over the evolving medium in Au+Au and Pb+Pb collisions, the correlation raises the photon yield toward the fitted PHENIX and ALICE data and increases v_2 mainly at intermediate p_T . The fitted temperature dependence accounts for most of the net flow increase in this region, suggesting that the correlation changes the relative contributions between hotter and cooler regions.
[LG-34] Positional Encoding is All You Need for Scalable Equivariance Constraint Relaxation NEURIPS2026
链接: https://arxiv.org/abs/2611.05041
作者: Hagay Michaeli,Haggai Maron,Daniel Soudry
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Equivariant neural networks incorporate known symmetries of the data, such as permutations, rotations, or translations, directly into their architecture, often improving sample efficiency and generalization. However, strict equivariance can make optimization difficult, for example, when the data only approximately satisfy the assumed symmetry, or when the equivariance constraints induce a challenging loss landscape. Recent studies have shown that relaxing equivariance during training by introducing non-equivariant dense linear layers can ease optimization in such cases and improve the performance of the resulting strictly equivariant model, after removing the relaxation layers at test time. While effective, this approach incurs a significant computational overhead, limiting its applicability in scalable architectures. In this work, we propose a more efficient constraint-relaxation method based on positional encoding (PE). Our framework injects a positional encoding with learnable scale that breaks the model’s original symmetry. This positional signal is gradually annealed to zero, recovering the original equivariant model by the end of training. Empirically, we demonstrate significant improvements for translational and rotational equivariant models with only marginal compute and memory overhead.
[LG-35] Comparing model representations and fitting methods for port-Hamiltonian system identification
链接: https://arxiv.org/abs/2611.05031
作者: Sølve Eidnes,Håkon Noren Myhr
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注: 6 pages, 3 figures; submitted to the invited session “Learning and identification of port-Hamiltonian systems” at the 21st IFAC Symposium on System Identification (SYSID 2027)
Abstract:Port-Hamiltonian models describe physical systems by separating stored energy, dissipation and external inputs. Learning such models from data requires a choice of representation and fitting method. We compare using neural networks, analytic expressions and combinations of the two for representing the different components of the port-Hamiltonian system, and compare fitting the models with gradient-based optimisation or sparse regression. Testing on simulations of mechanical and electrical systems, with and without added noise, we present prediction accuracy and computational cost of the different approaches.
[LG-36] DRAP: Dynamics-Risk Adaptive Plasticity for Continual Learning in Physical AI
链接: https://arxiv.org/abs/2611.05029
作者: Oswaldo Ludwig
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Continual learning in physical AI differs from conventional continual learning because small per-step policy errors can accumulate over a trajectory. Each deviation may move the agent outside the training distribution, increase subsequent prediction errors, and ultimately cause closed-loop task failure. This work introduces Dynamics-Risk Adaptive Plasticity (DRAP), a replay-free method that estimates the historical importance of adaptable model tensors from sample-level action gradients weighted by local trajectory-amplification risk. A ridge-regularized local linear model is fitted to expert state-action transitions, and the largest singular value of its state-transition matrix provides a policy-independent, Lyapunov-inspired estimate of one-step perturbation amplification. After each task, risk-weighted squared gradients are accumulated in a tensor-level importance memory. During subsequent tasks, DRAP uses normalized cumulative importance to scale the effective AdamW learning rate of each tensor, immediately reducing the plasticity of components that supported previous behavior at dynamically sensitive trajectory points. We instantiate DRAP on a fixed KappaTune-selected subnetwork of OpenVLA and evaluate replay-free sequential adaptation across the LIBERO Spatial, Object, and Goal suites. Compared with standard AdamW fine-tuning and EWC, the results show substantially improved retention of previously learned behaviors while preserving strong new-suite acquisition.
[LG-37] Finite Spectral Depth Controls Gaussian Entrance Rank and Avoidance of the First Gradient-Flow Saddle
链接: https://arxiv.org/abs/2611.05022
作者: Yanlin Long
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 83 pages,1 figure
Abstract:Matrix factorization provides a simple model for studying the training dynamics of neural networks. Recent studies of gradient flow under small initialization have analyzed how trajectories escape from a neighborhood of the origin and approach their first nonzero stationary point. In this paper, we study continuous-time dynamics that applies a finite number of Newton-Schulz iterations to the normalized matrix gradient (Muon). We show that the number of Newton-Schulz steps can change the rank selected during the early stages of training and allow the resulting trajectory to bypass the first nonzero stationary point approached by gradient flow. Under an additional residual-inertia condition, we prove that bypass occurs for all sufficiently large finite Newton-Schulz depths and provide an explicit sufficient threshold. We also construct an example in which the bypass threshold is four.
[LG-38] Unifying and Beyond the Theory of Pervasive Capability Degradation in LLM Post-training SFT from Feature Interference
链接: https://arxiv.org/abs/2611.05003
作者: Hang Chen,Jiaying Zhu,Wenya Wang
类目: Machine Learning (cs.LG)
*备注: 20 pages, a very long paper :)
Abstract:Pervasive Capability Degradation (PCD) refers to the phenomenon in which supervised fine-tuning (SFT), while adapting a large language model (LLM) to a target task, often erodes the general capabilities acquired during pre-training. Prevailing explanations attribute PCD to macroscopic distribution shift and therefore lack microscopic manipulability, leaving existing mitigation methods without a complete theoretical foundation. In this paper, we first develop a bottom-up geometric theory of PCD grounded in feature interference under superposition: narrow-domain SFT data disrupt the almost-orthogonal feature structure formed during pre-training, and the resulting interference drives activations across decision hyperplanes in the output Laguerre-Voronoi geometry, flipping predictions on general tasks. This theory unifies existing methods into six paradigms and reveals both the source of their efficacy and their theoretical limits. We then prove that cross-entropy under one-hot supervision cannot resolve PCD on its own, and accordingly propose the AEGIS loss, which directly constrains the drift of activation margins along all competing token directions, thereby decoupling interference suppression from task adaptation. Across 3 model families and 14 SFT tasks, AEGIS outperforms representative methods of every paradigm on target tasks and pervasive capabilities simultaneously, at only about 1.3 times the computational cost of standard SFT. Its generality, scalability, and mechanistic interpretability offer a new perspective on fully resolving PCD.
[LG-39] Exact Selective Inference for Spectral Clustering via Piecewise-Constant Graph Perturbation ACML2026
链接: https://arxiv.org/abs/2611.04995
作者: Naotoshi Nakamura
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 31 pages, including supplementary material. Accepted at ACML 2026
Abstract:Cluster labels are often used twice: first to discover groups, and then to test whether the discovered groups differ. This double use of the data makes ordinary two-sample p -values anti-conservative. Selective inference fixes this by conditioning on the clustering event, but for spectral clustering the event appears difficult because the algorithm combines an affinity map, an eigendecomposition, and a rounding step. We show that, for spectral clustering based on unweighted \varepsilon - or k -nearest-neighbor graphs, the obstruction is the affinity construction rather than the eigendecomposition. Along the one-dimensional perturbation used by the truncated- \chi selective test, graph changes occur only at finitely many real roots of data-dependent quadratic distance comparisons. Between these breakpoints, the graph, Laplacian spectrum, embedding, rounding, and reported cluster pair are deterministic functions of a finite graph state. Consequently, in real arithmetic and under a known-scale spherical Gaussian model, the selection set is an exact finite union of intervals, yielding finite-sample selective p -values for selected spectral-cluster pairs. The same argument gives feature-wise truncated-normal tests. We also record direct extensions to known feature covariance, eigengap-selected K , and auxiliary-coordinate graphs. We validate null and partial-null calibration, operational power, numerical sensitivity, and model-scope diagnostics, and give a working-model PBMC algorithmic illustration. Smooth kernels such as RBF affinities fall outside the finite-state guarantee. Comments: 31 pages, including supplementary material. Accepted at ACML 2026 Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2611.04995 [cs.LG] (or arXiv:2611.04995v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.04995 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-40] ZEMA: confidence-calibrated signed descent with only one state NEURIPS2026
链接: https://arxiv.org/abs/2611.04987
作者: Ali Dadsetan,Domenic Rosati,Frank Rudzicz
类目: Machine Learning (cs.LG)
*备注: OPT @ NeurIPS 2026
Abstract:Recent work characterizes the Adam optimizer as sign descent modulated by gradient uncertainty: the update is the sign of the batch gradient, scaled by how strongly the batch supports that sign. We apply this view directly and introduce ZEMA (Z-score Evidence Momentum Adaptation), which measures that support with a per-coordinate z -score computed from the per-example gradients in the current batch, converts it into a sign probability, and smooths the resulting update with a single exponential moving average. Because the probability is formed before smoothing rather than after, ZEMA keeps one state per parameter instead of Adam’s two, at a 4.6 increase in per-step time; the per-example variance it requires can also be estimated from a small fraction of the batch. In fine-tuning and pretraining experiments, ZEMA is competitive with Adam, reduces the variability of the validation loss, and in some settings recovers the sign of the full-dataset gradient more consistently. We close with two directions this opens: extending confidence adaptation beyond the sign geometry, to methods such as Muon, and to differentially private training.
[LG-41] Deep Neural Operator Learning for stochastic heat equations and applications
链接: https://arxiv.org/abs/2611.04955
作者: Hengrong Du,Qi Feng,Weinan Wang
类目: Machine Learning (cs.LG); Analysis of PDEs (math.AP); Probability (math.PR)
*备注:
Abstract:In this paper, we establish a universal approximation theorem for solution operators of semilinear stochastic heat equations in spatial dimension (d\ge2), extending deep neural operator approximation results to SPDEs with distribution-valued Gaussian forcing. A smooth Fourier cutoff reduces the forcing to finitely many measurements, while Besov and Schauder estimates control the resulting solution error. The finite-dimensional solution map is then approximated by ReLU branch networks together with grid-based trunk functions, yielding explicit bounds on the numbers of forcing measurements and trunk functions and approximation in every finite stochastic moment. For linear equations, the reduction coincides with truncation of the first Wiener chaos. Lastly, as an application, we prove a high-probability local-in-time approximation theorem for stochastic Navier–Stokes equations, with finite measurements of both the initial condition and forcing and an explicit sensing rate.
[LG-42] Modulating Merging Strengths via Joint Loss Estimation for LoRA-based Continual Learning NEURIPS2026
链接: https://arxiv.org/abs/2611.04931
作者: Kun Gu,De Cheng,Zhipeng Xu,Lingfeng He,Di Xu,Nannan Wang
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Continual learning (CL) aims to adapt models to new tasks while preserving previously learned knowledge. Recently, Low-Rank Adaptation (LoRA), a representative Parameter-Efficient Fine-Tuning (PEFT) method, has gained increasing attention in CL due to its efficiency and scalability. Several LoRA-based merging methods maintain a single inference model by scaling the newly learned LoRA update with one coefficient and merging it into the previous weights after each task. However, they usually control the whole update with a shared scalar factor, although different update elements may affect previous and current tasks in different ways. To address this issue, we propose Merging Strength Modulation via Joint Loss Estimation (M2LE). It consists of Hessian Information-guided Adaptive Merging (HIM) and Task Simulation Perturbation for Merging (TSP). Instead of searching for a global merge coefficient, HIM directly solves for the target merged update under a joint objective over previous and current tasks. Solving this objective gives an analytic solution that naturally takes the form of element-wise modulation over the current task LoRA update. To improve the stability of the learned update under subsequent merging, we introduce TSP which simulates different tasks interference. Experiments on multiple benchmarks show that M2LE consistently outperforms existing PEFT-based continual learning methods. Code is available at this https URL.
[LG-43] Cool Graphs: Active Property Search Towards Quantum Nano-Refrigerators NEURIPS2026
链接: https://arxiv.org/abs/2611.04907
作者: Sofia Blyufer,Amir Kleiner,Uri Peskin
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026. 20 pages. Code: this https URL (archived at this https URL )
Abstract:Nanodevices that absorb energy from their thermal environment can act as quantum refrigerators. Finding suitable configurations, however, is a needle-in-a-haystack problem: the many-body state space scales exponentially with the size of the device, where each candidate configuration requires an expensive quantum-chemical computation. We introduce a search framework that couples a Graph Variational Autoencoder (GVAE) with an uncertainty-driven Active Learning (AL) loop to efficiently identify energy-absorbing configurations in electronic junctions with a 2D “working material” to be optimized. The GVAE maps physical parameters - site energies, coupling matrices, temperature - into a smooth latent space that distinguishes cooling from dissipative parameter regimes, achieving a classification discriminability of 0.920 \pm 0.004 on held-out configurations and 0.895 \pm 0.010 on unseen extrapolation topologies. An AL pipeline based on Monte Carlo dropout then targets the decision boundary, iteratively selecting the candidates where the model is least certain for simulation. Over 32 AL cycles, the uncertainty-driven strategy discovers energy absorbing systems at rates consistently exceeding the random baseline across all tested topologies (where only 1.9-8.4% of configurations exhibit the desired property), providing a data-efficient pathway for navigating a combinatorically large design space. The learned latent space organizes configurations along physically interpretable axes, that correlate with underlying microscopic model parameters. This hints at the existence of “cooling coordinates” - low-dimensional, physically interpretable quantities separating cooling from non-cooling regimes. Our framework, thus, paves a new way towards effective cooling of nanoscale electronic information processing devices, to reduce the environmental footprint of high performance computing.
[LG-44] Attending to Memory Writes Reduces Seed Variability in Recurrent Length Generalization
链接: https://arxiv.org/abs/2611.04902
作者: Sungwoo Goo,Hwi-yeol Yun,Sangkeun Jung
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recurrent memory requires learning useful updates, but attention added to assist learning can also compromise length generalization. We study bounded delta attention: causal attention over uncorrected update proposals whose bounded correction enters the recurrent update. Its implementation in TraceRelay combines additive phase recurrence with attention computed before a parallel scan. On prefix-supervised modulo-5, trained at lengths 1–40, three findings characterize its behavior at length 80. First, upper delta attention attains 70.81\pm8.26 % accuracy across seven seeds, versus 69.21\pm22.42 % for the local-attention recurrent baseline (mean \pm sample SD): a 63.2% reduction in SD with a similar mean. Upper state attention also reduces variation, reaching 74.75\pm6.24 %, so this benefit is not unique to update-level attention. Disabling the upper delta correction retains 70.62\pm8.60 %. Second, blocking gradients at the delta-attention inputs changes accuracy to 65.25\pm12.40 % in the matched seven-seed comparison, increasing SD by 50.2%. It also delays a 99% validation threshold on average among the same six seeds that reach it; the 95% threshold is reached earlier on average with input-gradient blocking across all seven seeds. Third, lower delta attention exhibits substantially less extrapolation degradation than lower state attention: 62.60% versus 19.40%, against a 71.06% baseline. Removing its correction reduces accuracy to 22.56%; its advantage over lower state attention also persists under input-gradient blocking. These observations motivate bounded update-level attention as a recurrent learning scaffold, combining reduced seed variation with less severe degradation in a placement where state attention fails.
[LG-45] DLER: Dual-Logit Experience Replay for Online Continual Learning
链接: https://arxiv.org/abs/2611.04885
作者: Huiwei Lin,Shanshan Feng,Kong Aik Lee
类目: Machine Learning (cs.LG)
*备注: 13 pages, 8 figures
Abstract:Online continual learning aims to continuously train neural networks on a non-stationary data stream in a single pass. Rehearsal-based methods alleviate catastrophic forgetting by replaying stored samples, but the imbalance between current and previous data often leads to predictions biased toward current classes. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in an online fashion. By decomposing the dot-product logits into an angle factor and a norm factor, our analysis reveals that the angle factor efficiently captures novel knowledge but is particularly susceptible to prediction bias, whereas the norm factor helps preserve historical knowledge. Based on these observations, we propose Dual-Logit Experience Replay (DLER), which exploits dual-logits for both model optimization and memory management. Specifically, DLER learns current samples using cosine logits and incorporates additional dot-product logits when replaying previous samples to mitigate prediction bias. To strengthen dual-logit learning under limited replay memory, an auxiliary supervised contrastive objective promotes angular compactness within each class, extending corrections learned from limited replay samples to other samples of the same class. Meanwhile, we introduce a memory update strategy that preferentially retains samples for which dot-product prediction assigns a higher probability to the correct class than cosine prediction, aligning sample selection with dual-logit training. Extensive experiments on three datasets demonstrate the effectiveness of DLER, particularly under limited memory budgets.
[LG-46] Anomaly Detection on Resource-Constrained Devices with Reservoir Computing
链接: https://arxiv.org/abs/2611.04835
作者: Marco Angioli,Mathias Persson,Hiroto Tamura,Hadi Banaee,Amy Loutfi,Gouhei Tanaka,Alberto Giaretta,Denis Kleyko
类目: Machine Learning (cs.LG)
*备注:
Abstract:Anomaly detection at the edge requires the deployment of models that achieve competitive performance, while minimizing computational cost and memory footprint. In this article, we investigate the suitability of the Mahalanobis Distance of Reservoir States (MDRS), an unsupervised time-series anomaly detection method, for resource-constrained devices and propose two optimizations to reduce its computational requirements. Specifically, we replace random reservoir connectivity (MDRS-Rand) with a circular shift (MDRS-Circ) and show that the anomaly score for an entire time series can be computed from the final reservoir state alone. Furthermore, we show that exploiting the reservoir’s temporal memory enables more efficient input preprocessing, further reducing computational cost. We evaluate the MDRS variants on the MLPerf Tiny anomaly detection benchmark against the reference autoencoder and other anomaly detection methods, and deploy their inference kernels on a NUCLEO-F401RE microcontroller to assess latency, estimate energy consumption, and measure memory footprint. Among the evaluated methods, only MDRS and the autoencoder meet the benchmark’s performance target, with MDRS requiring fewer operations and smaller models. With optimized preprocessing, MDRS achieves an ROC AUC of 0.869, a model size of 24 KB, an estimated energy consumption of 24 uJ per inference, and a latency of 0.32 ms, corresponding to a 75.9x speedup over the autoencoder with standard preprocessing.
[LG-47] Sleep-Disordered Breathing Detection with Quantum Recurrent Neural Networks
链接: https://arxiv.org/abs/2611.04818
作者: Alejandro Mayorga-Redondo,José Daniel Viqueira,Diego Alvarez-Estevez,Eduardo Mosqueira-Rey,Andrés Gómez
类目: Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: 16 pages, 4 figures
Abstract:This work investigates the application of Quantum Recurrent Neural Networks to the binary classification of sleep-disordered breathing events in time-series data, aiming to distinguish between apneic and normal breathing episodes. A variational classical–quantum architecture is proposed and evaluated against classical Recurrent Neural Network models. Experiments were conducted on multiple real-world datasets with varying levels of complexity to assess the strengths and limitations of the quantum approach. In the comparative analysis, both classical and quantum models showed comparable performance. The quantum model achieved superior performance on the smallest dataset, while yielding slightly weaker results in the most complex setting. However, the quantum approach exhibited notable strengths, such as competitive performance despite a drastically reduced parameter count and low-qubit regime, as well as good generalization capabilities. These results suggest that, despite current hardware and algorithmic limitations, variational quantum recurrent models represent a promising alternative for time-series classification as Quantum Computing technologies continue to mature.
[LG-48] On the Complexity of Finite-Sum Nonconvex-Strongly-Concave Minimax Optimization with Exact Proximal Oracles
链接: https://arxiv.org/abs/2611.04806
作者: Yuxing Peng,Zhiqing Tang,Weijia Jia
类目: Machine Learning (cs.LG)
*备注: 50 pages, including appendices and references. Lean 4 formalization of the lower bounds in the ancillary files
Abstract:Finite-sum nonconvex-strongly-concave minimax problems arise in adversarial training and distributionally robust learning. Their known lower bounds cover only linear-span algorithms, zero-respecting algorithms, or a restricted proximal protocol. They leave open the power of exact component proximal steps, whose responses are global saddle points of regularized component problems and can expose directions that gradients do not reveal. We show that neither unrestricted adaptivity nor exact proximal access improves the polynomial complexity. We consider n components with joint mean-squared smoothness L and condition number \kappa , started from a point with primal gap at most \Delta and joint gradient norm at most \sqrt2L\Delta . For \varepsilon^2/(L\Delta) below a universal constant, every randomized adaptive algorithm with exact component values, gradients, and proximal responses needs \Omega(n+\min\sqrt n,\kappa,n^3/4\sqrt\kappa\L\Delta/\varepsilon^2) component queries in the worst case to find an \varepsilon -stationary point of the value function. A method using only component gradients attains this rate up to a factor \log^2(2+\kappa) , including initialization. Zhou (arXiv:2610.04944) concurrently proves the same rate for zero-respecting first-order methods. In our hard instances, every component admits proximal steps with the same step sizes, and a response for a component reveals hidden directions only up to the first one assigned to another component.
[LG-49] raining Specialist LLM s on Your Phone: Forward-Only Optimization via Block-Spectral Coordinates
链接: https://arxiv.org/abs/2611.04797
作者: Liyan Tan,Yequan Zhao,Ziyue Liu,Yupeng Su,Ruijie Zhang,Zheng Zhang
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 27 pages, 11 figures, 15 tables
Abstract:Large language models (LLMs) on personal devices keep user data local, but limited memory restricts their general capability. Since most user-specific tasks do not require the breadth of frontier models, a practical solution is to adapt a shared on-device model into lightweight specialists. However, backpropagation is prohibitive in memory, and users rarely provide labeled data. We present MobileSpec, a system that trains specialists with forward passes alone and learns from automatically checked task outcomes, as in reinforcement learning with verifiable rewards (RLVR). Because forward-only updates are built from random perturbations, training is efficient only when these perturbations resemble the weight changes of RLVR, which largely preserve the singular values of pretrained weights and rotate their singular vectors. The core algorithm of MobileSpec, FuRA-S, produces such perturbations while training only the block-spectral coordinates of pretrained weights, namely the singular values of their column blocks. As we show theoretically and empirically, rescaling these blocks independently shifts their relative contributions to each weight matrix and thereby rotates its singular vectors. Since this update can be rewritten as a transformation of the layer input, FuRA-S also adapts INT4 models without modifying their weights. With specialists of only 1-5 MB that update 0.015-0.05% of the parameters, MobileSpec raises the success rate of a phone-use agent from 6-23% up to 89-100% and trains a personalized 8B model on an iPhone 16 Pro overnight. On RLVR benchmarks, FuRA-S is competitive with full-parameter evolution strategies (ES) and outperforms ES over LoRA adapters and quantized ES on INT4 models.
[LG-50] Accelerating Offloading-Based MoE Inference by Interleaving Concurrent Requests
链接: https://arxiv.org/abs/2611.04794
作者: Hongseung Yu,In Gim,Kyunghan Lee,Lin Zhong
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 12 pages, 13 figures
Abstract:Expert offloading enables large mixture-of-experts (MoE) language models to run locally, but conventional batching stalls the GPU while missing experts are fetched. A popular remedy, known as prefetching, is to predict what experts will be needed soon and fetch them using idle link bandwidth. Imprecise prediction, however, wastes bandwidth and GPU memory on fetching unnecessary experts. Our key finding is that prefetching can be harmful when there is an expert cache in GPU memory. This is because as the cache includes more necessary experts, expert prediction becomes less precise. Our key insight is that proper request scheduling beats prefetching. Building on this insight, we present Bevy, an MoE inference system based on runahead inference for concurrent requests. A request whose experts are available can run ahead to later layers while other requests wait for fetches, revealing the experts it needs next. Bevy can then fetch the missing experts so identified while computing ready requests, overlapping computation and fetching without predicting expert selections. Running requests separately, however, repeats reads of shared weights in GPU memory and increases compute time. To limit this cost, Bevy’s self-balancing scheduler batches ready requests at each layer and prioritizes lagging requests, adapting their grouping as expert availability changes. Across two local platforms, Bevy delivers up to 3.46 \times the decode throughput of this http URL and up to 1.55 \times that of the fastest prefetching baseline.
[LG-51] Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting NEURIPS2026
链接: https://arxiv.org/abs/2611.04760
作者: Raz Marshanski,Liran Nochumsohn,Mayank Jauhari,Boris N. Oreshkin,Omri Azencot
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient inference, yet existing approaches fall short of this goal: latent dynamical models impose structure but rely on restrictive generation mechanisms, while modern generative methods such as diffusion and flow matching achieve flexibility at the cost of iterative and computationally expensive sampling. We introduce the Koopman Generative Operator (KGO), a probabilistic forecasting method that conceptualizes prediction as the evolution of structured uncertainty. KGO integrates three core components: (1) Koopman Patch Embedding (KoPE) for temporally consistent latent trajectory extrapolation; (2) Koopman Flow Matching (KoFM), which enables fast, single-step generation through a closed-form matrix exponential in a Koopman latent space, bypassing iterative sampling; and (3) an Adaptive Uncertainty Gate (AUG), which provides probabilistic forecasts by adapting uncertainty per-variable and per-horizon, corresponding to a learned decomposition of aleatoric uncertainty without manual tuning. By unifying these components, KGO achieves state-of-the-art accuracy on 13/17 ProbTS benchmarks in CRPS and 16/17 in NMAE. Further, by eliminating iterative sampling, KGO delivers at least a 25 \times reduction in inference time compared to iterative generative models. These results position KGO as a practical and principled framework that advances the accuracy-efficiency trade-off in probabilistic time-series forecasting, combining leading accuracy on the evaluated benchmarks with fast, single-step generation.
[LG-52] Non-Interfering Asymmetric AdamW Updates for Constrained Exploration of Equifinality in Hybrid Models
链接: https://arxiv.org/abs/2611.04756
作者: Ekaterina Gikalo,Zavud Baghirov,Christian Reimers,Markus Reichstein,Martin Jung,Marco Körner
类目: Machine Learning (cs.LG)
*备注:
Abstract:Hybrid models let neural networks supply the unresolved parameters of a process-based simulator, and are fitted to observations that constrain aggregate fluxes and storages but not the internal partitioning that produces them. Such a fit is equifinal: configurations indistinguishable in the observed aggregates can disagree substantially about the unobserved internals, and therefore about the mechanism they attribute to the same data. We study evapotranspiration, whose total flux is observed while its split into transpiration and evaporation is not. A model ensemble built from random seeds alone spans only a narrow band of the partitionings the data admit. We produce the spread deliberately, training members under auxiliary repulsion on transpiration, subject to the fit to observations not degrading. Non-interference is usually arranged by making the auxiliary gradient orthogonal to the main one, but under AdamW the applied step is a preconditioned momentum direction, so orthogonality between raw gradients no longer implies it. We introduce Non-interfering Asymmetric Network Optimization (NANO), an update whose first-order effect on the main loss is exactly that of the main objective alone, whose auxiliary step is bounded, and which concentrates repulsion in the coordinates the main gradient leaves idle.
[LG-53] What Does It Mean to Remove an Edge? Intervention-Aware Fidelity for Graph Neural Network Explanations
链接: https://arxiv.org/abs/2611.04751
作者: Rocco Mattia Di Mauro,Marco Grassia,Giuseppe Mangioni
类目: Machine Learning (cs.LG)
*备注: 15 pages, 2 figures. Under review
Abstract:Graph neural network explanations are often evaluated via deletion fidelity, i.e., by deleting their top-ranked edges and measuring the resulting change in model output. Yet, in a message-passing GNN, “deleting an edge” does not specify a unique intervention. Structural deletion removes the edge from the graph and recomputes quantities such as degree normalization and attention, whereas message suppression leaves this structural context intact while blocking the message carried by the edge. The meaning of deletion fidelity therefore depends on what the intervention targets in the model computation. We formalize this distinction and derive sufficient conditions for exact equivalence. The two interventions coincide for additive message passing with edge-set-independent coefficients, but can diverge when aggregation depends on the presence of zero messages or when removing an edge changes the coefficients applied to surviving messages. We then construct intervention-specific edge attributions and evaluate each resulting ranking under both interventions, testing whether their relative fidelity changes with the evaluation intervention. Across nine datasets and a panel of GIN, GCN, and GATv2 models at depths 2–5, all nine GIN controls satisfy the equivalence predicted by the theory. On the target logit, the relative fidelity of the two rankings depends on the evaluation intervention in 10 of 11 reportable GCN and GATv2 cells, and in nine of them each ranking has the larger deletion area under the intervention it was built with. Deletion fidelity is thus not a property of an edge ranking alone. Evaluation protocols should explicitly report the intervention, output functional, target, and perturbation unit, and test alternative intervention semantics when the scientific question does not uniquely determine one.
[LG-54] Hybrid acoustic and pretrained embedding feature fusion for unmanned aerial vehicle detection using stacking ensemble learning under a leakage-aware evaluation protocol
链接: https://arxiv.org/abs/2611.04714
作者: Zahid Iqbal
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Small multirotor drones produce a characteristic acoustic signature, and microphones offer a low-cost way to detect them. High accuracies have been reported on public drone audio benchmarks, often with random clip-level splits of datasets built from a few long recordings and pieces of existing sound collections, so pieces of one source can appear in both training and test data. We study the binary task of the public DroneAudioDataset (11,704 one-second clips). Its 1,332 drone clips come from 257 five-second chunks of 7 recordings, and 96.4% of its non-drone clips are pieces of the 2,000 clips of the ESC-50 sound dataset. A grouped, source-disjoint protocol (five folds, three repeats) is compared with a random split. Each clip is described by 57 hand-crafted acoustic descriptors, reduced by a hybrid minimum-redundancy maximum-relevance and ReliefF selection or by NSGA-II multi-objective search, and by a 1024-dimensional embedding from the pretrained YAMNet network. Random Forest and XGBoost learners on both views form a stacking ensemble. Under the grouped protocol, the two-view stack reached a mean accuracy of 95.29% (SD 1.62 points over 15 test folds), a specificity of 98.29% and an area under the ROC curve of 0.990, and it correctly classified 98.78% of unseen ESC-50 source clips as non-drone. The random split overstated accuracy by 1.89 to 3.22 points for the four models. Adding YAMNet embeddings to the hand-crafted view raised accuracy by 1.11 points (higher in 12 of 15 folds). Hybrid selection matched the accuracy of all 44 baseline descriptors with 16 to 18 features, and NSGA-II retained 4 to 6 features at lower accuracy. The stack took 51.0 ms per clip on one CPU thread. Source-disjoint evaluation is needed for a fair benchmark on this dataset, and fusing hand-crafted descriptors with pretrained embeddings gives a small but consistent gain at modest computational cost.
[LG-55] Surrogate Models for Stochastic Al-Cu Phase-Field Solidification NEURIPS2026
链接: https://arxiv.org/abs/2611.04695
作者: Simon Savukoski,Tatu Pinomaa
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注: Accepted at the AI4Mat workshop at NeurIPS 2026. Includes supplementary material
Abstract:Phase-field models resolve microstructural evolution during rapid alloy solidification, but their computational cost limits long trajectories, stochastic ensembles, and studies across process conditions. We present a variance-preserving denoising diffusion bridge model (VP-DDBM) for state-conditioned prediction of two-dimensional Al-Cu solidification. Given the current phase, concentration, and prescribed temperature fields, the model predicts the subsequent state and is applied autoregressively. Across two held-out thermal gradients, VP-DDBM produces lower long-horizon errors than deterministic U-Net and SI-ConvNeXt baselines in domain-mean phase, domain-mean Cu concentration, and leading-tip position. It also retains fine dendritic structure over long rollouts, whereas the deterministic U-Net progressively smooths the morphology. VP-DDBM predicts the phase and concentration evolution approximately 73\times faster than the phase-field solver, showing potential for faster process-condition screening and ensemble-based microstructure studies.
[LG-56] Sheaf-MPNN: A Library for Sheaf Neural Networks as Message Passing
链接: https://arxiv.org/abs/2611.04670
作者: Alessio Borgi,Mario Severino,Luke Braithwaite,Steve Azzolin,Rémi Bourgerie,Olga Zaghen,Emanuele Mule,Riccardo Ali,Leo Minh Kustermann,Edoardo Coppola,Antonio Purificato,Stefano Fiorini,Christopher Irwin,Filippo Spiga,Fabrizio Silvestri,Michael Bronstein,Pietro Liò
类目: Machine Learning (cs.LG)
*备注:
Abstract:Standard Graph Neural Networks (GNNs) implicitly assume that neighboring nodes share the same feature space, which contributes to oversmoothing and poor performance on heterophilic graphs. Sheaf Neural Networks (SNNs) remove this assumption by giving each node and edge its own vector space and learning restriction maps that transform features as they move between neighbors. These maps define a sheaf Laplacian that drives diffusion over the graph, but existing implementations often assemble it explicitly as a global sparse block matrix, which adds memory and computational overhead and can hinder scalability. In this paper, we introduce Sheaf-MPNN, a PyTorch Geometric library that reformulates sheaf diffusion as local incidence-wise transport and aggregation, eliminating the need to construct the global Laplacian. Sheaf-MPNN implements fifteen families of SNNs under a unified message-passing framework, each supporting both homogeneous and heterogeneous graphs, and directed and undirected settings, with diagonal, general, orthogonal, and low-rank restriction maps and attention-based predictors. We further provide a unified benchmarking suite covering node- and graph-level tasks, heterogeneous graph learning, and computational efficiency, enabling systematic and reproducible comparisons across architectures. We establish theoretical equivalence between these message-passing formulations and their original counterparts and identify the architectural mechanisms needed to preserve their propagation dynamics. This unified perspective provides a principled foundation for implementing, comparing, and extending sheaf-based architectures within standard graph learning frameworks. Source code is available at this https URL.
[LG-57] FedCSGA: Client Selection Based on Genetic Algorithm for Federated Learning
链接: https://arxiv.org/abs/2611.04667
作者: Ghani Haider,Majid Kundroo,Leo Zhang,Jinchul Choi,Taehong Kim
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:Federated Learning (FL) enables collaborative training of machine learning models across distributed clients while preserving data privacy. In practical deployments, FL relies on selecting a subset of clients in each communication round to reduce communication cost, mitigate stragglers, and improve training efficiency under heterogeneous data and system conditions. In heterogeneous edge computing environments, both statistical heterogeneity and system heterogeneity significantly affect convergence efficiency and model performance. However, existing client selection methods often address these factors independently, overlooking their coupled and dynamic impact across communication rounds. To address this limitation, we propose FedCSGA, a Client Selection method for Federated learning based on a Genetic Algorithm. FedCSGA encodes client subsets as chromosomes and evaluates these subsets through a non-linear fitness function that jointly integrates resource-aware metrics and history-aware participation weighting. This design captures subset-level interactions, mitigates persistent selection bias, and balances statistical utility with system efficiency. Extensive experiments on FMNIST, CIFAR-10, CIFAR- 100, and Tiny-ImageNet demonstrate that FedCSGA consistently outperforms state-of-the-art baselines across diverse heterogeneity settings, achieving accuracy improvements of up to 7.0% and faster convergence with fewer communication rounds. These results validate the effectiveness of jointly modeling statistical and system factors to achieve robust and efficient learning in heterogeneous federated environments.
[LG-58] Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification
链接: https://arxiv.org/abs/2611.04664
作者: V. Baymakanov,Kh. Khamitov,A. Raimova,A. Isaeva,R. Taran,M. Zedan,M. Kornilov,M. Demianenko,K. Malanchev,M. Hushchyn
类目: Machine Learning (cs.LG); Instrumentation and Methods for Astrophysics (astro-ph.IM)
*备注: 14 pages, 5 figures. Submitted to Moscow University Physics Bulletin (DLCP-2026 special issue)
Abstract:We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects across seven imbalanced classes. Classical models trained on physically motivated features consistently outperform deep networks trained directly on raw light curves. We find that limited labeled data contributes to the performance gap, while the multivariate, irregularly sampled, and morphologically diverse nature of light curves poses additional challenges for deep learning models. While deep learning holds promise for learning representations directly from observations, our results indicate that current models fail to generalize with the available training set size. These findings motivate further investigation of the benefits of scaling labeled datasets and self-supervised pretraining on large unlabeled surveys. This work establishes a quantitative baseline for future research and highlights the need for further innovations to unlock the potential of deep learning in astronomical time series analysis.
[LG-59] Leakage-free evaluation of residual graph convolutional networks for network intrusion detection
链接: https://arxiv.org/abs/2611.04660
作者: Zahid Iqbal
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph neural networks are increasingly proposed for network intrusion detection, yet many reported results come from protocols in which test flows shape the graph used for training, duplicated flows sit on both sides of the split, or a single split is reported. This study measures what a residual graph convolutional network gains over simpler models once these sources of optimism are removed. CICIDS2017 and NF-UNSW-NB15-v2 were cleaned with label-aware removal of exact duplicates, removing 69% of NF-UNSW-NB15-v2 flows. Flows were linked in a nearest-neighbour graph built from training flows only, and validation and test flows were attached to their nearest training flows at prediction time. A two-layer residual network was compared with eight models under the same settings, including a nearest-neighbour vote over the same graph, gradient-boosted trees and a random forest, over three seeds that each draw a new split, with ablations, leave-one-family-out and feature-noise tests. On CICIDS2017 the residual network reached a binary F1 of 98.11% (SD 0.18) and on NF-UNSW-NB15-v2 90.57% (0.38), indistinguishable from a multilayer perceptron without the graph and from other graph networks. Tree ensembles were 1.48 to 1.57 F1 points higher on CICIDS2017 and led on the area under the precision-recall curve on both datasets; their CICIDS2017 multiclass macro-F1 reached 93.08% against 64.51%, a gap mainly from benign flows the residual network assigned to small attack classes. No ablation, including transductive graph construction, changed F1 beyond seed variation. Under Gaussian feature noise (SD 0.5), the residual network kept an F1 of 96.16%, while XGBoost fell to 0.67%. Under a leakage-controlled protocol, message passing over flow graphs adds no measurable accuracy over a perceptron, and tree ensembles remain stronger on both; the advantage of graph models lies in robustness to feature noise.
[LG-60] Exploiting Biases in Attention for Transformers WACV2027
链接: https://arxiv.org/abs/2611.04657
作者: Charles Herr,Gabriele Spadaro,Enzo Tartaglione
类目: Machine Learning (cs.LG)
*备注: 23 pages, 21 figures, accepted at WACV 2027
Abstract:Attention is known to be sparse in vision transformers. In particular, we analyze the unequal relevance of attention-matrix columns to the final computation and show that this phenomenon becomes increasingly pronounced with network depth. This can be interpreted as saying that tokens have an absolute importance for the attention process, independently of queries. Inspired by this, we propose a reformulation of the attention formula that makes this phenomenon explicit by adding an input-dependent bias to the query-key dot product. We show that this new term gives each token a scalar value indicating its anticipated influence on other tokens during the attention process, and can thus serve as a proxy for pruning attention maps. Across multiple transformer model sizes, this allows us to substantially reduce the computational cost of attention, in terms of both FLOPs and inference latency, while retaining most of the original classification accuracy. We publicly released the code and models at this https URL.
[LG-61] Policy Network Diffusion
链接: https://arxiv.org/abs/2611.04614
作者: Jiate Liu,Deheng Ye
类目: Machine Learning (cs.LG)
*备注:
Abstract:Diffusion models have shown remarkable progress in AI-generated content, including images and videos. In this paper, we explore whether diffusion models can generate the parameters of policy networks in reinforcement learning (RL), and whether the generated policies exhibit generalization capabilities. To achieve this, we propose Diffusion-based Conditional Policy Network Parameter Generation (DPPG), which consists of a Transformer-based autoencoder and a task-conditioned diffusion model. The autoencoder achieves a high compression rate by patchifying network parameters, while the task-conditioned diffusion model generates corresponding parameters conditioned on different environments. Our experiments demonstrate that, with a single unified DPPG model, we can synthesize high-quality policies across a diverse set of tasks from different environment domains, including Classic Control, Atari games, and MuJoCo. Additionally, our method can generate networks with a larger number of parameters compared to previous approaches. Furthermore, we find that in previously unseen environments, DPPG can generate parameters that achieve faster convergence and reach competitive performance, showcasing the potential future applications of parameter generation via diffusion.
[LG-62] CAPSkip: Cancellation-Aware Prefix Skipping for Selective Backpropagation
链接: https://arxiv.org/abs/2611.04611
作者: Heejin Choi
类目: Machine Learning (cs.LG)
*备注: 14 pages
Abstract:Per-step selective back-propagation forwards every example of a mini-batch but back-propagates only some of them. Most rules rank examples by loss or gradient magnitude, but such scalar scores ignore cancellation between per-example gradients: the gradient removed by a set can be much smaller than its individual magnitudes suggest. We introduce CAPSkip (Cancellation-Aware Prefix Skipping). Among the examples the model already classifies correctly, CAPSkip skips the longest prefix, in increasing order of last-layer gradient norm, whose vector sum stays within a relative budget b of the batch’s last-layer gradient. In exact arithmetic this bounds the change of the raw final-layer gradient, using rank-one contributions already available from the forward pass; the bound does not extend to deeper layers, optimizer updates, or the training trajectory. Because the cumulative distortion is not monotone in the prefix length, CAPSkip scans all prefixes; a factorised Gram matrix makes the scan cheap and keeps it on the device. With activation reuse and selected-row backward, CAPSkip fine-tunes DeiT-S on CIFAR-100 for 103 epochs in 0.576x the time of 103-epoch dense training, and is +0.25 pp [+0.08, +0.42] above an approximately time-matched 59-epoch dense run (n=3).
[LG-63] SCANO: Restoring Boundary-Condition Completeness in Sampled Meshes with a Sliced-Cross-Attention Neural Operator for 3D Thermal Fields
链接: https://arxiv.org/abs/2611.04603
作者: Tianyu Li,Zhiwei Cao,Qingang Zhang,Ruihang Wang,Binyang Song,Yonggang Wen
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 88 pages (46 main text + 42 supplementary material)
Abstract:Training neural operators on million-cell meshes can exceed GPU memory limits. Models are therefore trained on subsamples and deployed on whole meshes or selected regions. However, subsampling can distort boundary information, degrading accuracy. We ask when boundary conditioning under sampling stays consistent with whole-mesh training, and how to aggregate and inject boundary data. Representing the solution as an integral of boundary data against type-dependent measures, we show that subsampling distorts this conditioning in three ways: incomplete boundary coverage or a sample-dependent global state, face weighting by count rather than area, and compression of boundary inputs into a low-dimensional vector. This analysis yields four measurable consistency conditions checkable before training. We satisfy them with two backbone-agnostic modules. Boundary Quadrature Attention (BQA) aggregates all boundary faces, typed and area-weighted, into slice tokens that every query point reads at every layer. Anchored Global State (AGS) estimates the global state from anchors independent of the sampled points. Adding both to a scalable message-passing and slice-attention backbone yields SCANO. We evaluate the approach on controlled and public benchmarks and a data-centre thermal simulation dataset (DCTS) containing million-cell meshes. On both benchmarks, BQA improves every backbone, reducing whole-mesh error up to 15-fold. On all three datasets, AGS aligns regional with whole-mesh predictions, cutting regional error at least tenfold for most backbones on the public benchmark. Together, the modules nearly halve the best original backbone’s whole-mesh error on both benchmarks. SCANO predicts a room-scale field in under one second, over 100 times faster than whole-room anchor self-attention.
[LG-64] Ground-Wing: A Parametric 3D Wing-in-Ground-Effect Dataset for Benchmarking Neural Surrogate Models
链接: https://arxiv.org/abs/2611.04571
作者: Andrea Cenzato,Lukas Karch,Andre Luckow,Yoran Pichon,Francesco Pasqua,Abouzar Ghasemi,Rishikesh Ranade,Ira Shokar,Julia Kowalski
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper presents a CFD dataset of a highly parametrized 3D wing operating in ground effect, designed for benchmarking neural surrogate models for aerodynamic prediction, using a wing shape and flow conditions typically encountered in motorsport applications. The dataset comprises 770 RANS simulations of 194 unique wing configurations, sampled across 27 airfoil profile variants and 4 distances from the ground, spanning variations in wing sweep, chord, taper, angle of attack, and tip twist. Six neural network architectures representing distinct paradigms — convolutional (DoMINO), graph-based (X-MeshGraphNet), transformer-based (AB-UPT, GeoTransolver, GAOT), and JEPA-based (AeroJEPA) — are evaluated on their ability to predict surface pressure coefficient and wall shear stress distributions, from which integral drag and lift coefficients are recovered. The dataset is intended to be used as a challenging benchmark for surrogate CFD models, due to the uniqueness of the ground effect being absent from other wing and airfoil datasets.
[LG-65] Curriculum unlearning in humans and neural networks
链接: https://arxiv.org/abs/2611.04565
作者: Jirko Rubruck,Alice Zhang,Andrew Saxe,Christopher Summerfield
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:During learning, humans often benefit from curricula that teach simple rules before exceptions. However, we lack a computational explanation for this phenomenon. Here, using a canonical behavioural task and modelling with neural networks, we develop and test a theory for why simple-to-complex curricula succeed. We show that during learning, both humans and neural networks show simplicity biases, a tendency to learn simple rules that are underfit to the data. In neural networks this bias is accompanied by compression of neural representations. Simple-to-complex curricula help both types of learners overcome this compression, developing representations (and policy) with appropriate dimensionality for the task. We test and confirm a further prediction from this theory: learners that have been pre-exposed to simple tasks and with more compressed initial representations benefit more from curricula. These findings suggest that curricula help with ``unlearning’’ of simple rules.
[LG-66] he Price of List Replicability: Tight Bounds for Bias Estimation
链接: https://arxiv.org/abs/2611.04532
作者: Michael Chen,Atieh Bastan,A. Pavan,N. V. Vinodchandran
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the sample complexity of list replicable algorithms for estimating the biases of d coins. An algorithm is (k,\varepsilon,\delta) -list replicable if for every bias vector \mathbfp\in[0,1]^d , there is a set of outputs L_\mathbfp\subseteq \mathbbR^d of at most k elements such that every \mathbfy\in L_\mathbfp satisfies \lVert\mathbfy-\mathbfp\rVert_\infty\leq\varepsilon , and the algorithm output is in L_\mathbfp with probability at least 1-\delta . Prior work gave a (d+1,\varepsilon,\delta) -list replicable algorithm with sample complexity \widetildeO\left(\fracd^3\varepsilon^2\right) , but no nontrivial sample complexity lower bound was known. We establish the first non-trivial sample complexity lower bound for this problem and give an improved algorithm that matches it up to polylogarithmic factors. Together, these results determine the optimal sample complexity for list size d+1 to be \widetilde\Theta\left(\fracd^2\varepsilon^2\right) , showing that the quadratic dependence on dimension is inherent up to polylogarithmic factors. More generally, for every k\in[d+1,2^d] , we establish matching upper and lower bounds of \widetilde\Theta\left(\fracd^2\varepsilon^2\log k\right) on the total sample complexity. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.04532 [cs.LG] (or arXiv:2611.04532v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.04532 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-67] Variational Learning with Lie Group Exponential Families
链接: https://arxiv.org/abs/2611.04513
作者: Eren Mehmet Kıral,Thomas Möllenhoff,Koichi Tojo,Mohammad Emtiyaz Khan,Kenichi Bannai
类目: Machine Learning (cs.LG)
*备注: 35 pages, 7 figures
Abstract:Variational Bayesian learning for deep neural networks offers many potential benefits. The difficulty in implementation arises from constraints on the parameters of the variational distributions, as well as the need for low-variance gradient estimators. Our recent work proposes Lie group methods for multiplicative or affine groups that naturally overcome such difficulties. Lie groups are essentially parameter spaces with a binary operator such as addition or multiplication, which allows for natural updates within the parameter space. In this article, we streamline and extend our Lie group framework to any general Lie group, and give an explicit relation to standard natural-gradient variational learning methods such as the Bayesian learning rule (BLR). In particular, we prove that linear approximations of the Lie group algorithm give generalizations of the BLR. The key ingredient is the harmonic exponential family, a general class of Lie group exponential families associated to representations of Lie groups. Our work provides a systematic method to construct from such representations efficiently implementable algorithms for variational learning.
[LG-68] Sample-Grained Approximate Unlearning with Provable Per-Sample Bounds NEURIPS2026
链接: https://arxiv.org/abs/2611.04501
作者: Yaxin Xiao,Yulin Jin,Li Tang,Haoyang LI,Huadi Zheng,Qingqing Ye,Haibo Hu
类目: Machine Learning (cs.LG)
*备注: accepted by NeurIPS 2026
Abstract:Machine unlearning aims to erase the influence of a designated forget set from a trained model while preserving strong utility on the retain set. Approximate unlearning methods efficiently achieve this goal without full retraining, but their effectiveness is primarily assessed using dataset-level aggregate metrics. These aggregate metrics can mask deviations from retraining at the level of individual samples. In this work, we focus on per-sample unlearning effectiveness. We introduce SALA (Sample-grained Likelihoods ALignment), a plug-in optimization framework that enables approximate unlearning methods to operate at the level of individual samples by steering each example toward its correct target distribution. To quantitatively assess this behavior, we further propose the sample-grained distribution gap, a metric that captures per-sample discrepancies between approximate unlearning and exact retraining output distributions. We provide theoretical guarantees showing that SALA contracts this gap and yields a bounded per-sample discrepancy to retraining. To make this framework practical, we develop a low-cost estimation method for the member and non-member target distributions. Extensive experiments on classification and generation tasks demonstrate that SALA reduces the sample-grained distribution gap by up to 86.67% when paired with existing approximate unlearning baselines, while also improving robustness to unlearning hyperparameters.
[LG-69] SKILL2STEER: One-Pass Hypernetwork for Mapping Natural-Language Skills to Low-Rank Post-Block Activation Shifts
链接: https://arxiv.org/abs/2611.04497
作者: Ran Li,Lei Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Compiling a natural-language agent skill into model parameters removes the skill text from the prompt and leaves behind a small artifact that can be distributed and reused. Existing methods differ in where they inject the compiled quantity, and all of them assume that the artifact carries the content of the skill. We test that assumption. Through three controls on a retrieval agent, we find that a compiled skill shift carries a domain-general behavioural prior, not the content of the skill. A wrong skill document performs on par with the correct one, a single averaged bundle matches per-skill routing under a two-sided equivalence test, and a same-norm random direction collapses to the no-injection baseline, which shows that the trained direction stays load-bearing. We then introduce SKILL2STEER, a one-pass hypernetwork that reads a skill document once and maps it to low-rank activation shifts added at the output of every decoder block. Each skill compiles to 2.36M parameters, 9.25 times fewer than a weight-space adapter, while the base weights stay frozen and the generator runs only at compile time. The shift can be scheduled across retrieval steps and deployed as one routing-free bundle. On SearchQA, a seven-dataset benchmark, SKILL2STEER reaches 37.9 exact match, 9.8 points above the frozen model and 2.3 points above a weight-space method that ships 9.25 times more parameters per skill.
[LG-70] Hierarchical Intelligent Evolutionary Optimization for UAV-Assisted NOMA-MEC Systems
链接: https://arxiv.org/abs/2611.04474
作者: Yaxuan Jiao,Jiaxuan Lv,Jyoti Sahni,Wuyungerile Li,Winston K.G. Seah
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infrastructure-deficient environments. However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem. To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-based intelligent decision generation, communication-aware initialization, adaptive evolutionary exploration and successive convex approximation (SCA)-based precision refinement. Specifically, a novel communication-aware initialization mechanism, termed Channel Quality Indicator (CQI)-guided Load-Aware Trajectory Initialization (CQI-LATI), is designed to incorporate CQI and task workload characteristics into evolutionary population construction, generating high-quality and diversity-preserving initial populations. Theoretical analysis characterizes HIEA’s search-space reduction, the quality-diversity trade-off of CQI-guided initialization, and the accuracy-efficiency trade-off of SCA refinement, and extensive simulations demonstrate that HIEA outperforms benchmark schemes, with gains of up to 6.5% in throughput, 13.6% in task completion rate and 15.3% in energy efficiency.
[LG-71] heoretical Analysis of Muon on Transformers for Next-Token Prediction
链接: https://arxiv.org/abs/2611.04465
作者: Shengwei Wu,Yixuan Yang,Gao Huang,Song Li
类目: Machine Learning (cs.LG)
*备注: 62 pages, 5 figures
Abstract:Muon has recently shown strong empirical performance in training Transformer-based large language models (LLMs), yet its optimization dynamics remain poorly understood from a theoretical perspective. In this paper, we analyze the training dynamics of Muon for next-token prediction (NTP) in Transformers. The training procedure consists of two phases: the preprocessing phase learns the output-value component, while the main phase learns the key-query component. For the preprocessing phase, we characterize the optimization trajectory exactly and show that the normalized spectral margin is optimal after each update. For the main phase, we establish nonasymptotic convergence rates for the training loss and the normalized margin gap under softmax attention. These results suggest that Muon’s update rule can help models efficiently learn which tokens matter for prediction, offering a possible explanation for its strong performance in large language model training. The learned weight structure also yields a generalization guarantee for unseen contexts. Furthermore, we validate our theoretical insights with experiments on next-token prediction tasks.
[LG-72] Aero-VPA: Few-Step Flow-Matching Control over Non-Reconstructive JEPA Latents for Agile Flight on Edge Hardware
链接: https://arxiv.org/abs/2611.04392
作者: Yeonseok Lee
类目: Logic in Computer Science (cs.LO); Machine Learning (cs.LG); Robotics (cs.RO)
*备注: 38 pages, 3 figures, 22 tables. Simulation study (PX4 SITL + Gazebo Harmonic). Version 2 of a preprint first posted on Zenodo (doi: https://doi.org/10.5281/zenodo.23633375 ). Code: this https URL ; trained policy: this https URL ; benchmark dataset: this https URL
Abstract:Vision-Language-Action (VLA) models generalize across tasks, but their autoregressive decoders need sequential forward passes in proportion to the action horizon, each bounded below on embedded aerial platforms by streaming billions of parameters from memory. We present Aero-VPA, a lightweight Vision-Predictor-Action architecture for 3D navigation on edge hardware. A compact RGB-D Vision Transformer and a five-head ensemble predictor, trained without pixel reconstruction, forecast the next latent state; a Conditional Flow Matching solver generates a velocity and yaw-rate chunk in two Euler steps, so each decision takes five sequential network evaluations; and the ensemble dispersion drives an uncertainty fallback. Version 1 specified the architecture, deployment and protocol without results. This version reports the first closed-loop evaluation: PX4 software-in-the-loop simulation of quadrotor missions in procedurally generated forests. Behaviour cloning failed on all 60 missions despite open-loop errors of 0.02-0.04 m/s: the policy copies its last command, a copycat form of causal confusion. DAgger with a privileged, safety-gated supervisor gave the first successes but plateaued at 11.1-21.1% across six checkpoints. A map-free runtime shield with a 15 s depth memory, barrier constraints, a cap on motion outside the 73-degree field of view and backoff along the flown path raised the first DAgger policy’s success on 180 paired missions from 21.1% to 52.2% (95% CI [45.0, 59.4]; exact McNemar p = 1.2 x 10^-10) and cut its crashes from 72.2% to 22.2%, while timeouts rose from 2.8% to 23.3%. All six checkpoints gained 30.0-34.4 percentage points, at mean speeds of 1.16-1.36 m/s. The code (this https URL), the trained policy (72.6 MB, this https URL) and the full benchmark dataset are public.
[LG-73] Proper Agnostic Learning of Affine Halfspaces under Log-Concave Marginals
链接: https://arxiv.org/abs/2611.04344
作者: Zhijie Zheng
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 18 pages
Abstract:We give a proper constant-factor agnostic learner for affine halfspaces under full-dimensional log-concave marginals. For arbitrary binary labels, the algorithm uses independent labeled samples and returns a single affine halfspace with error at most C\mathrmOPT+\varepsilon . Its sample and real-arithmetic complexity are polynomial in dimension, inverse accuracy, and the logarithm of the reciprocal failure probability. The mean and covariance of the marginal need not be known, and the sample complexity can be made linear in this logarithmic confidence factor. The main geometric result compares global disagreement with disagreement on a balanced restriction: for a minority halfspace of mass p , global disagreement is within universal constant factors of p times conditional disagreement, provided the latter is sufficiently small. To construct the restriction from noisy labels, we estimate the minority cap’s mean and covariance robustly, approximately minimize the classical zonoid-depth dual objective, and expand a superlevel set to a marginal quantile. The disagreement comparison cancels the noise amplification caused by conditioning and allows the learned halfspace to be used on the entire space. The analysis combines degree-four sum-of-squares moment bounds for log-concave laws with a translation-based reduction from balanced affine learning to homogeneous halfspace learning.
[LG-74] Deep Learning Compilers: Architecture Optimization and Empirical Characterization
链接: https://arxiv.org/abs/2611.04273
作者: Jinghao Wen,Ruixuan Wang,Xun Jiao
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR)
*备注:
Abstract:As deep learning (DL) models become increasingly complex and hardware architectures grow more heterogeneous, DL compilers play a critical role in optimizing computational efficiency and enabling scalable deployment. This paper presents a systematic analysis of DL compilers, covering their architectural evolution, compilation workflows, optimization techniques, and emerging research directions. We organize DL compiler architectures into frontend, middle-end, and backend stages, emphasizing the role of the middle-end in connecting high-level model representations with hardware-specific optimizations. We further examine representative DL compilers and recent optimization techniques across different compilation stages. Beyond architectural and technical analysis, we conduct an empirical evaluation of compiler behaviors to investigate the effectiveness of graph optimization, compilation overhead, and dynamic-shape handling. Experiments using PyTorch/TorchInductor on an NVIDIA RTX A6000 demonstrate that compiler benefits depend strongly on workload characteristics, with speedups of up to 8.21\times for fusion-friendly computation graphs, while other workloads exhibit limited improvements. Our results further reveal that compilation overhead can require thousands of executions to amortize, and dynamic-shape compilation reduces cumulative shape-transition costs by approximately 70% at the expense of modest steady-state performance degradation. By combining systematic architectural analysis with quantitative experimental evidence, this work provides insights into the design, optimization, and practical deployment of modern DL compilers.
[LG-75] LENS: Reliability-Aware Stock Prediction via LLM -Extracted Priors and News-Driven Semantic Graphs CIKM2026
链接: https://arxiv.org/abs/2611.04217
作者: Hao Zhou,Tiejun Ma,Luis Felipe Costa Sperb
类目: Machine Learning (cs.LG)
*备注: Accepted at CIKM 2026. 12 pages, 3 figures
Abstract:Predicting stock returns requires modeling persistent company relations and time-varying market signals. Graph Neural Network (GNN) approaches combine static priors with dynamic relational graphs, but treat every extracted edge as a trustworthy binary fact, ignoring heterogeneous reliability across relational sources. We propose LENS (LLM-Extracted priors and News-driven Semantics), a static–dynamic graph framework for stock return prediction and ranking that models relational reliability as a first-class signal across the pipeline. On the static side, we extract a multi-relational company graph from a Large Language Model and aggregate it over multiple runs with confidence weighting to downweight low-consistency edges. On the dynamic side, we build a news-driven semantic graph with exponential-decay temporal aggregation to capture changing market context under uneven coverage. A context-dependent gated fusion module rebalances the two graphs at the per-stock, per-day level. On SP 500, LENS improves AR@5 by 16.4% and SR@5 by 22.6% over the strongest baseline, and on NASDAQ 100 it achieves comparable improvements (15.5% and 20.9% respectively). Ablations isolate each component’s contribution, and a human-validation study of sampled relations supports that the aggregation is grounded, providing evidence that reliability-aware LLM relations and news-based semantic graphs carry complementary information for graph-based financial prediction.
[LG-76] An Information Budget for Label-Noise Memorization and What Statistical Complexity Tracks: A Study with Multilayer Perceptrons
链接: https://arxiv.org/abs/2611.04199
作者: Nancy Baygorrea C.,Otávio da Fonseca Martins Gomes
类目: Machine Learning (cs.LG)
*备注:
Abstract:How much label noise has a network memorized, and when? We study a multilayer perceptron and what statistical complexity adds to a description of its predictions. Standard identities decompose predictive information into signal, memorization, and heterogeneity. The memorization term vanishes in the population under the stated noise-independence condition. A Fano-type bound relates it to training accuracies, but the memorized fraction M provides no upper bound. Across 89 MNIST runs with ten pre-specified hypotheses (five supported, four unsupported, one not testable), normalized memorization information rose with M on training samples, remained near zero on held-out samples with fresh noisy labels, and was close to its Fano bound. Unlike raw M, it has a zero population null under the independence condition and chance correction largely removed this difference. When every label was replaced by an incorrect class, test accuracy fell to 0.5%, yet predictions retained information about the true class. Mean statistical complexity peaked before memorization at corruption rates up to 40%. At higher rates, its peak fell within the memorization phase, near the epoch when mean training confidence reached 0.6 and the two could not be separated. As a stopping rule, it matched established rules only at low noise and detected corrupted samples less well than simple label-free scores. An exploratory extension of 280 runs covering Fashion-MNIST, pair-flip noise, and four training variants yielded similar results under uniform noise, but not pair-flip noise, where complexity peaked within two epochs. Conclusions are limited to this architecture, two small datasets, and synthetic noise.
[LG-77] Where Did the Update Go? Certifying Declared Change Envelopes for Model Updates
链接: https://arxiv.org/abs/2611.04192
作者: Muhammad Usama,Muhammad Saad Qadir
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deployed language models are updated continually, and regulators increasingly accept changes that were declared in advance. Showing that an update changed behavior only where it was declared to is a different statistical problem from bounding how much it changed: a useful update is meant to change some decisions, so global disagreement and regression bounds cannot certify it. We formalize the problem with declared change envelopes. Before training, the developer commits to a region of inputs, and every decision that changes outside it counts as leakage. Leakage compares two models, so it can be certified from unlabeled traffic with anytime-valid tests whose sample cost we bound from above and below, and per-update certificates compose into a bound on lifetime drift outside all declared scopes. We measure leakage for LoRA and full fine-tuning, preference optimization, reinforcement learning, and knowledge editing on models from 1.5B to 72B parameters. Narrow updates leak widely: domain and policy LoRA updates keep only about 7-36% of the decisions they change inside the declared scope. Across 256 trained variants, leakage tracks the update’s KL divergence to the incumbent on out-of-scope inputs (Spearman 0.94). None of the customary remedies we tested makes a rule-like update certifiable without giving up much of its intended change. Distilling the incumbent’s decisions on unlabeled prompts in the deployment format does: anchored LoRA, GRPO, and (at a small learning rate) full fine-tuning updates certify 5% leakage from a few hundred to a few thousand unlabeled samples, and the certificates replicate on held-out traffic and on written answers. The certificates cover decision-like outputs only; on open-ended chat, re-batching alone changes 11-21% of responses and no update certifies. Code: this https URL
[LG-78] What Does 4-Bit Microscaling Cost a Wearable Activity Recognizer?
链接: https://arxiv.org/abs/2611.04185
作者: Megan Healy,Hossein Valavi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Wearable sports sensors can recognize actions on the device itself, where lower-precision numbers reduce model storage and memory traffic. Two 4-bit block-scaled formats, MXFP4 from the Open Compute Project, introduced in 2023, and NVFP4 from NVIDIA, introduced in 2025, are drawing interest. Published evaluations cover general deep-learning benchmarks and large language models; to our knowledge, nobody has yet measured what they cost a small wearable-sensor model. We train a 159,492-parameter 1D CNN to recognize four basketball actions from wrist-accelerometer data in Hang-Time HAR and test it on players it has never seen, using five-fold cross-validation grouped by player. Every comparison between number formats is made fold by fold. In full precision the model reaches 69.26% macro-F1. Measured TFLite FP16 and INT8 models lose at most 0.16 percentage points. In simulation, 4-bit weights cost 1.45 points with MXFP4 and 0.72 points with NVFP4. When activations are quantized to 4 bits as well, MXFP4 loses 5.14 points and NVFP4 only 0.75, and NVFP4 comes out ahead in all five held-out-player folds. The ranking survives two alternative simulator settings, with NVFP4 leading by 3.49 to 4.40 points. A 2 \times 3 ablation with 4-bit weights and activations (two block sizes and three scale rules) points to scale selection as the main source of MXFP4’s loss. The reference MXFP4 conversion we simulate rounds the scale down, which can pick scales that are too small, so many blocks clip their largest values. Rounding the scale up recovers 3.18 points with 32-value blocks and 3.63 points with 16-value blocks, and switching to an 8-bit floating-point scale, as NVFP4 does, adds another 0.84 and 1.20 points. Block size matters less. Under this round-down rule, 16-value blocks score 0.44 points below 32-value blocks in all five folds; under the other two rules there is no consistent effect. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.04185 [cs.LG] (or arXiv:2611.04185v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.04185 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Megan Healy [view email] [v1] Tue, 3 Nov 2026 02:22:12 UTC (45 KB)
[LG-79] On the Feature Evolution of Temporal Difference Learning
链接: https://arxiv.org/abs/2611.04158
作者: Shangzhe Li,Dechen Zhang,Difan Zou,Weitong Zhang
类目: Machine Learning (cs.LG)
*备注: 76 pages, 1 figure
Abstract:Temporal-difference (TD) learning trains neural representations using bootstrapped targets generated by evolving value estimates, yet how predictive features emerge and propagate under incomplete neural updates remains poorly understood. We investigate this problem using finite-width two-layer ReLU critics on a constructed family of smooth stochastic horizon- H MDPs with unknown quadratic structure and d -dimensional observations. We first show that an informative TD update acquires the quadratic Bellman signal and yields a representation admitting a bounded predictive readout. The simultaneous hidden-weight and head update achieves a strict finite-sample prediction-risk improvement over matched head-only SGD, with a 2(d+1) -fold larger population quadratic response. We then establish that an imperfect learned continuation can already provide informative supervision to an earlier zero-reward stage. By tracking coupled neural updates with one SGD step per target copy, without requiring intermediate Bellman regressions to be solved, we obtain a uniform prediction-error transient \exp[-\Omega(K/(Hd))] , up to action-independent shifts and explicit sampling and finite-width errors. On the constructed signed-action MDP family whose total reward is bounded by 1, we show that an exploration-then-commit algorithm can achieve \widetildeO(d^3H^1/3T^2/3) regret over T episodes, using K=\widetildeO(Hd) global updates. Our analysis connects bootstrapped feature acquisition, concurrent neural refinement, and finite-budget policy performance.
[LG-80] RGDM: Robust Graph Diffusion Model NEURIPS2026
链接: https://arxiv.org/abs/2611.04146
作者: Yancheng Wang,Ping Li,Dongfang Sun,Yingzhen Yang
类目: Machine Learning (cs.LG)
*备注: Accepted to NeurIPS 2026
Abstract:Diffusion models represent a powerful class of generative models known for their solid theoretical foundations and remarkable performance across diverse tasks and domains. Existing node level diffusion methods synthesize nodes and edges within a graph, but are not designed to learn a robust generator from adversarially corrupted graphs. In this study, we introduce the Robust Graph Diffusion Model (RGDM), designed to generate labeled synthetic graph structures consisting of nodes and edges that integrate seamlessly into a given graph. The RGDM consists of a Robust Graph Autoencoder (RGAE) and a Latent Diffusion Model (LDM). Leveraging an edge selection mechanism and an innovative low-rank regularization on the latent feature, the RGDM produces clean and high-quality synthetic graph structures, even when trained on graphs subject to adversarial attacks. Comprehensive experimental evaluations reveal that Graph Neural Networks (GNNs) trained on the augmented graph, which is formed by merging the original attacked graph with the synthetic graph structures, exhibit significantly improved robustness against various graph adversarial attacks in the context of semi-supervised node classification. The code of the RGDM is available at this https URL.
[LG-81] ScrambleMark: Zero-Query Watermark-Agnostic Black-Box Evasion of Modern Speech Watermarking
链接: https://arxiv.org/abs/2611.04122
作者: Binhao Ma,Weikang Ding,Hanqing Guo,Wenwei Zhao,Xiaowen Li,Leah Ding,Zhuo Lu,Rui Duan
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:Speech watermarking is becoming a key defense for digital copyright and content authentication. Existing attacks treat evasion as a removal problem: they erase the watermark, relying on detector access or on speech data that is hard to collect, such as paired clean-watermarked or clean target speaker speech. Commercial platforms are moving to close off these assumptions. TrustedAudio, for instance, restricts detection to authenticated users and denies meaningful detection feedback to unauthorized parties. Adversaries thus cannot tell whether a sample is watermarked or which watermark it carries, nor reliably remove it without degrading speech quality. In this work, we show that evading a speech watermark does not require erasing it: detection relies on the structural consistency of the embedded pattern, and disrupting this consistency is enough to defeat it. Building on this observation, we identify harmonic structure as an effective attack surface, as perturbing it breaks watermark consistency while preserving perceptual quality. We therefore propose ScrambleMark, a zero-query, watermark-agnostic, black-box attack that evades detection through harmonic scrambling. ScrambleMark combines a deterministic engine for discovering transferable harmonic perturbations with a generative model that learns randomized scrambling patterns, avoiding reliance on fixed attack rules. Across three commercial platforms (Google SynthID, Resemble AI, and TrustedAudio), ScrambleMark is the only attack to both evade detection and keep speech natural, achieving nearly 97% evasion and a 4.2/5 human rating, far above the 1.1 to 1.7 of prior attacks. Beyond evasion, ScrambleMark withstands adaptive recovery designed to reverse it, evidence that structural evasion is hard to mitigate in practice. Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2611.04122 [cs.CR] (or arXiv:2611.04122v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2611.04122 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-82] Decomposing Rank Reversals under Temporal Nonstationarity: Alignment Aggressiveness and Instability
链接: https://arxiv.org/abs/2611.04116
作者: Deying Song,Xueying Ding
类目: Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注:
Abstract:Under temporal nonstationarity, model performance rankings reverse across training horizons due to heterogeneous sensitivities to memory. While existing literature addresses adaptive lookbacks, effective model complexity, and empirical forecast evaluation in isolation, no unified framework explains the structural mechanics driving these rank reversals. We bridge this gap by deriving a model-agnostic decomposition of out-of-sample R^2 into target alignment, miscalibrated aggressiveness, and predictive instability. Within this framework, we mathematically characterize complexity phase boundaries, establish closed-form optimal-memory equations for ridge regression under local drift, and extend these guarantees to complex model architectures via surrogate bounds. We validate these theoretical dynamics by benchmarking 14 linear, tree-based, and neural architectures across large-scale financial panels. In doing so, our framework demonstrates that empirical model rank reversals are driven by predictable surges in miscalibrated aggressiveness, providing a rigorous mechanistic diagnosis of generalization under temporal shift.
[LG-83] Structure Dimensionality and Distribution Shape - What Makes Pretraining Data Transferable and Why the Input Pipeline Decides Which
链接: https://arxiv.org/abs/2611.04106
作者: Sangyoon Bae,Jiook Cha
类目: Machine Learning (cs.LG)
*备注:
Abstract:What makes a pretraining corpus transferable? We describe a corpus by three properties: its structure, its dimensionality, and the distribution shape of each unit’s values. We propose the Transfer-Invariance Principle: the same property can be irrelevant to transfer under one input transform and decisive under another, so the model’s input transform, not the data modality, decides which properties can matter. To test it we build TransferForge, a generator that places a synthetic population at any prescribed structure, dimensionality, and distribution shape. KernelSynth, the synthetic prior of Chronos, cannot, because it draws independent series. When the model standardizes each input window (PatchTST on UCR), the distribution shape is inert and the structural setting governs. Synthetic pretraining then matches a real corpus of the same size and exceeds it once scaled. When the model encodes calcium fluorescence signals without normalization (POYO Plus), the shape becomes a governing constraint, and a shape-matched synthetic corpus replaces real pretraining with zero real neural activity. Flipping only the input normalization, with the model and data fixed, changes the effect of a shape manipulation by a factor of 5.3, so the pipeline, not the modality, sets the switch. The principle yields a recipe that chooses what to synthesize before any training. Pretraining data then becomes something to manufacture for the model, most valuably where real data is scarce, expensive, or private.
[LG-84] ModelBlaster: Closing the Performance Gap through Learning-Augmented Agents Across Models Kernels and Hardware Backends
链接: https://arxiv.org/abs/2611.04104
作者: Shengjun Kris Dong,Dima Nikiforov,Sahil Modi,Sana Damani,Edward C Lin,Siva Kumar Sastry Hari,Christos Kozyrakis
类目: Machine Learning (cs.LG)
*备注: 10 pages of main content and 23 pages of appendices
Abstract:Optimizing accelerator code across hardware architectures is difficult. ML compilers search predefined spaces of schedules and micro-kernels, while LLM-guided optimization can explore alternative algorithms, operator decompositions, and hardware-specific techniques. However, existing LLM agents typically target a single programming interface, toolchain, or accelerator family, focus on kernel-level optimization without model-level transforms, and offer limited reuse of experience across hardware targets. We present ModelBlaster, a Memory-Augmented In-Context Reinforcement Learning (MAIC-RL) framework for optimizing data-parallel kernels and whole models across heterogeneous SIMD and SIMT accelerators. Its mutable policy is a persistent knowledge base updated at inference time by Actor-Critic Textual Gradient Descent (AC-TGD). The knowledge base maps performance states to actions, outcomes, and constraints, enabling experience reuse across kernels, models, and hardware targets. A model-level optimizer uses kernel-level knowledge to inform whole-model decisions. The same optimization loop and knowledge schema support CUDA on NVIDIA GPUs, OpenCL on Qualcomm GPUs, and RVV intrinsics on RISC-V vector cores, with only lowering and measurement layers specific to each target. Compared with PyTorch, ModelBlaster achieves geometric-mean speedups of 1.85x, 5.58x, and 5.18x on KernelBench Levels 1, 2, and 3, and 1.69x on SOL-ExecBench Levels 1 and 2 on a B200 GPU. On Qualcomm Adreno GPUs, it achieves 5.8x over the vendor SDK and 1.7x over Apache TVM; on RISC-V vector cores, it achieves 2.7x over ExecuTorch with XNNPACK across 70 KernelBench problems. Comments: 10 pages of main content and 23 pages of appendices Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.04104 [cs.LG] (or arXiv:2611.04104v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.04104 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-85] ScaleTraj: Scale-Decomposed Human Trajectory Generation
链接: https://arxiv.org/abs/2611.04097
作者: Yulin Zeng,Yiheng Zhao,Jun Yan,Yuhong Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Individual-level trajectories are essential for applications that rely on user-level mobility behavior. Existing mobility-generation approaches face a fundamental tension between individual-level and population-level statistical fidelity: top-down models reproduce aggregate mobility statistics but struggle to guarantee realistic individual trajectories, while bottom-up generators synthesize individual trajectories yet often fail to preserve key population-level mobility regularities after aggregation. In particular, many bottom-up generated populations fail to reproduce the displacement distribution, whose heavy tail is well summarized by a truncated power law. We formalize this phenomenon as the \textbfPopulation-Level Displacement Gap. To investigate its prevalence, we conduct a systematic evaluation of representative model-driven and data-driven trajectory generators. Our results provide evidence that the Population-Level Displacement Gap is prevalent across diverse generation paradigms. To diagnose this gap, we introduce the \textbfScale Fidelity Profile (SFP), a scale-decomposed metric that quantifies discrepancies in trip allocations across spatial scales. SFP indicates that population-level displacement mismatches co-occur with systematic errors in how trips are allocated across spatial scales. Motivated by this observation, we develop \textbfScaleTraj, an autoregressive Transformer that decomposes location prediction into a Scale Selection Head that decides the movement scale and a Scale-Aware Cell Selection Head that predicts the destination conditioned on the selected scale. Experiments on two datasets show that ScaleTraj reduces the Population-Level Displacement Gap versus model-driven and data-driven baselines, while preserving fidelity. Our code is available at this https URL.
[LG-86] Follow the Features: SAE Feature-Guided Data-Efficient Instruction-Tuning of Large Language Models
链接: https://arxiv.org/abs/2611.04085
作者: Ryan Swift,Konstantinos Psounis
类目: Machine Learning (cs.LG)
*备注: 19 pages, 3 figures
Abstract:Selecting small, effective instruction-tuning datasets requires identifying which aspects of the original data to retain. We introduce Follow the Features (FtF), a data-selection method that uses sparse autoencoder (SAE) features to characterize training data and guide subset construction. FtF ranks features using dataset-level activation statistics, scores samples by their expression of highly ranked features, and incorporates feature resampling to encourage diversity. The LLM-SAE pair used for selection is independent of the model being fine-tuned, enabling reuse across target models. We evaluate FtF on two model-dataset settings at a training budget of 100 samples with a fixed sequence length of 256 tokens. FtF achieves performance competitive with GRAPE and S2L, with relative rankings varying across settings. On Qwen 3 8B trained on Alpaca, FtF exceeds full-dataset training by 1.59 percentage points in the harmonic mean of an AlpacaEval-based win rate and Open LLM Leaderboard performance, although full-dataset training retains a higher Leaderboard score. These results support SAE feature statistics as a useful signal for data-efficient instruction tuning.
[LG-87] RAPID: Increasing Efficiency of Internal Distillation via Randomized Methods
链接: https://arxiv.org/abs/2611.04081
作者: Ryan Swift,Konstantinos Psounis
类目: Machine Learning (cs.LG)
*备注: 24 pages, 5 figures
Abstract:Knowledge distillation is a technique used to transfer the expertise of large teacher models into small, efficient student models. Recent advances have integrated the distillation of internal activations, more directly training students to learn similar representations as the teacher. One of the core challenges of internal distillation is the dimensional incompatibility between the student and teacher internal representations. Existing methods add learnable matrices to project the larger teacher activations into the smaller representation space of the student. This is a notable point of inefficiency, as learning additional parameters for the projectors increases training costs, only for them to be discarded immediately after training. We introduce RAPID (RAndomized Projection for Internal Distillation) a novel distillation methodology which eliminates the wasteful projection learning by leveraging randomized methods. RAPID utilizes an efficient transform construction based on the Johnson-Lindenstrauss lemma to compress the teacher representations into the student embedding space with strong theoretical guarantees of structural preservation. RAPID also employs a combination of exponential moving average (EMA) and homoscedastic uncertainty weighting in a novel multi-objective function designed for internal distillation. Our experiments in post-training for reasoning demonstrate that RAPID increases training efficiency by as much as 21.1% while maintaining performance comparable to existing methods.
[LG-88] he Complexity of ReLU Network Synthesis from a Parameter Multiset
链接: https://arxiv.org/abs/2611.04074
作者: Dimitrios Myrisiotis
类目: Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注: 110 pages, 1 table
Abstract:We ask how hard it is to build a neural network when its parameter values are given in advance. An instance supplies a multiset of rational numbers, a target behavior, and a bound on the number of neurons, output included. The question is whether one can choose the graph of a ReLU network within the bound, and place the numbers on its edges and biases so that it meets the target. This separates the numbers from the architecture; permutation training instead fixes the hidden units and only rearranges the output weights. Matching two labeled examples exactly is already \mathsfNP -complete; we ask what changes when the target must hold on every input or on average. Agreeing with a Boolean circuit on every Boolean input is \Sigma_2^\mathsf P -complete, already for four neurons. Agreeing with a given ReLU network on every real input lies in \mathsfDP for every fixed neuron bound and is \mathsfDP -complete for three neurons. Approximating a ReLU network on the unit cube within absolute error one everywhere is \Sigma_2^\mathsf P -complete, even with no hidden neurons and all numbers equal and positive. Deciding whether some network has expected squared loss at most a threshold is \mathsfNP^\mathsfPP -complete for uniform Boolean inputs. For uniform inputs on the unit cube and for standard Gaussian inputs with a positive threshold, the same holds for two neurons when a positive risk gap, given in binary and possibly exponentially small, separates yes from no instances. Without a gap, the cube version is complete for guessing a polynomial-length string, then comparing a sum of exponentially many rationals with a threshold, all computable in polynomial time from the input and the guess. Since the graph is chosen too, the reductions must rule out every other network built from the same numbers.
[LG-89] Computer-Use Agents Gain Little Benefit from a World Model When Conditioned on Tutorials
链接: https://arxiv.org/abs/2611.04073
作者: Anh Nguyen,Tianyi Xu,Phat Tran,Harry Yu,Huazheng Wang,Stefan Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent work has improved computer-use agents (CUAs) by combining retrieved task-relevant tutorials and look-ahead planning using world models with MLLM judges. However, the contribution of each component – tutorials, the judge, and the world model – to overall improvement has not been evaluated in isolation. To fill this gap, we first construct OSWorld-Tutorial and ScienceBoard-Tutorial: two datasets for CUAs with curated multimodal tutorials, covering everyday and scientific domains. We then propose RAG-PolicyJudge (RAG-PJ), a policy-judge framework for CUAs conditioned on task-specific tutorials: the policy proposes candidate actions, and the judge selects among them. To isolate the effect of world models given tutorials, we compare RAG-PJ with and without common world models: using either zero-shot text-based descriptions of next states or an oracle that returns ground-truth visual observations. We find that RAG-PJ gains little benefit from these rollouts when tutorials are present, even with perfect visual futures. This suggests prior world-model gains in tutorial-conditioned methods come mainly from tutorial understanding and the judge’s ability to select the proposed actions, rather than from predicted rollouts. Error analysis reveals that the bottleneck is not rollout fidelity, but the judge’s ability to reason over future states and recognize task completion. This highlights the need to improve not just next-state prediction in world models, but also reasoning over their predicted rollouts. Datasets and code are available at this https URL.
[LG-90] Graph-Localized Coverag e for Conformal Prediction on Inductive Graphs
链接: https://arxiv.org/abs/2611.04066
作者: Jinwen Xu,Qin Lu,Georgios B. Giannakis
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Conformal prediction (CP) provides marginal coverage guarantees, but marginal coverage does not determine where prediction errors occur on a graph. Queries from different graph neighborhoods can exhibit substantially different levels of uncertainty even when aggregate coverage remains near its target. We introduce GLocalCP, an online CP framework for inductive node classification and edge prediction that combines marginal and graph-local calibration with uncertainty-adaptive prediction sets. Specifically, GLocalCP puts forth a novel uncertainty-adaptive nonconformality score that relies on the mutual information obtained from Monte-Carlo (MC) dropout based prediction. Then, it augments a global online threshold with a kernel-based local correction that propagates coverage feedback among similar graph queries. On the theoretical horizon, we establish pathwise O(N^-1/2) marginal calibration error without exchangeability or stationarity and, for the exact-kernel formulation, an explicit O(N^-1/2) graph-localized calibration guarantee. Experiments on seven node-classification and seven edge-prediction benchmarks show that similar marginal coverage can conceal substantially different local reliability. Our proposed GLocalCP maintains coverage near the target while consistently reducing graph-local calibration error. Uncertainty scaling in GLocalCP further reduces prediction-set size across all evaluated node and edge datasets while leaving marginal coverage essentially unchanged.
[LG-91] B-Transolver: Parallelized and Coherent Long-Horizon Rollouts via Temporal Bundling for Time-Dependent PDEs
链接: https://arxiv.org/abs/2611.04063
作者: Sidharth S. Menon,Ameya D. Jagtap
类目: Machine Learning (cs.LG)
*备注: 19 pages, 8 figures
Abstract:Transolver has recently gained widespread adoption for solving diverse physical problems on unstructured geometric meshes by leveraging its Physics-Attention mechanism, which seamlessly extends standard softmax attention to handle physical domains at scale. For time-dependent problems, existing literature relies on time-conditioned or autoregressive rollouts to preserve temporal causality while retaining Physics-Attention as the spatial backbone. However, a fundamental limitation of this formulation is its sequential execution model, wherein computational costs scale linearly with the temporal horizon, rendering standard Transolver inefficient for transient problems despite its exceptional spatial efficacy. This bottleneck has historically hindered its broader application in time-dependent physical modeling. To address this limitation, we introduce TB-Transolver (also referred to as Temporally Bundled Transolver), an efficient framework built on temporal bundling that scales Transolver for time-dependent PDEs. Furthermore, we propose \emphspatiotemporal coherence ( \rm C(K) ) as a general metric to systematically quantify snapshot correlation within a temporal bundle, enabling the \empha priori selection of optimal bundle widths using training data alone, thereby avoiding expensive grid searches. We demonstrate the computational efficacy of TB-Transolver across four physically distinct domains, namely, plasticity, fluid dynamics, reaction-diffusion, and global weather forecasting; achieving training speedups of up to 29\times over conventional autoregressive and time-conditioned baselines. Finally, these computational gains are further amplified through distributed multi-node and multi-GPU parallelism, yielding aggregated speedups of up to 45.6\times at optimal bundle widths for systems exhibiting high spatiotemporal coherence compared to single-GPU implementations.
[LG-92] StreamLTT: Risk Certification for Policy Selection under Stream Dependence
链接: https://arxiv.org/abs/2611.04062
作者: Jinwen Xu,Qin Lu
类目: Machine Learning (cs.LG); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:
Abstract:Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed risk target, but its standard formulation targets a fixed population risk estimated from calibration losses. In sequential deployment, losses may instead be dependent and nonstationary, so the risk of a fixed policy can evolve with the observed history. We introduce StreamLTT, which replaces this fixed population risk with average predictable risk, defined by conditioning each decision’s loss on the information available before its outcome is revealed. Conditional centering turns empirical deviations from this target into a martingale sum, enabling both a test that adapts to predictable variance and a finite-sample Hoeffding alternative. Combined with family-wise error rate control, StreamLTT yields a simultaneously certified policy region that supports subsequent deployment selection. Across seven forecasting datasets and seven deployment risks, StreamLTT places the certified frontier substantially closer to the prescribed risk target than IID, robust-variance, block-bootstrap, and concentration-based alternatives, while empirical family-wise error closely follows the prescribed level. A finite-sample ablation further shows that the predictable-risk formulation drives the tighter risk frontier, while adaptation to predictable variance makes more effective use of the available family-wise error budget. StreamLTT therefore extends risk-certified policy selection to dependent deployment streams by addressing both what risk should be certified and how it should be tested.
[LG-93] Structured Priors: Towards an algebra of component priors for prior-data fitted networks
链接: https://arxiv.org/abs/2611.04056
作者: Thomas Bartz-Beielstein
类目: Machine Learning (cs.LG)
*备注: Working paper, 5 pages
Abstract:Prior-data fitted networks are typically trained using synthetic data generated by a single, manually constructed generator. In cases where such a generator combines multiple components, e.g., in TabPFN, Mitra, or TabICLv2, this is achieved through a mixture using manually specified weights, the impact of which on the network only becomes apparent after pre-training. Instead of such a hand-built generator, this work proposes a structured prior: a small set of named component priors, linked by operators whose algebraic properties are formally proven and whose influence on the trained network has been validated. Within a measure-theoretic framework, the operators of mixture, convolution, amplitude scaling, local transform, and projection are defined. Their governing laws are proven. These operators are implemented in the macl2l software package. Experiments demonstrate that the implemented generators reproduce the identities within the resolution of a permutation test. Furthermore, it is shown that trained networks obey the distributive law; that a mixed prior composed of small components performs as well as or better than any individual component in seven out of eight open regression benchmarks; and that on two of these tables a structured prior reaches, with a ninth of the data budget, an error that a generic random-function prior does not reach even with the full budget. A theorem on model averaging demonstrates that networks trained on individual components constitute the Bayes predictor for the mixture, provided that the component networks are themselves the Bayes predictors for their respective priors. However, the pre-registered test of this averaged predictor with trained component networks failed, because the networks identify the generating component too rarely. The paper proposes a research program focused on the algebra of structured priors for prior-data fitted networks.
[LG-94] Denoising Is Stein: Moment Access and a Resource Hierarchy for Learning Hidden Structure in Diffusion Models
链接: https://arxiv.org/abs/2611.04043
作者: Rui Wang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 75 pages, 12 figures, jmlr
Abstract:We study score learning from denoising pairs when the data contain a hidden non-Gaussian component. If covariance reveals the component, its subspace can be recovered directly. If covariance is isotropic, the first distinguishing moment has order k^\ast , and the learner must extract that moment from clean points and their noisy views. Correlations in the denoising loss are Stein discrepancies against a baseline; for polynomial fields they determine exactly the corresponding moment differences. Averaging fresh noisy views conditional on a clean point gives an exact moment query on the clean distribution. In the covariance-matched regime, orientation-independent dictionaries satisfying an L^4/L^2 condition need order d^k^\ast-1 features, and the stated dot-product kernel ridge models need the same power in samples. Small-step online SGD on one unit has this exponent up to logarithms. A two-stage learner uses the curvature of a smoothed denoising loss to recover the hidden direction, then sharpens it and fits a scalar readout. For every even k^\ast , it obtains a fixed score gain over the Gaussian baseline from O(d^k^\ast/2\log d) clean points. In the covariance-visible regime, the frozen-feature threshold occurs at d^J/J! , while projection onto principal components makes the fitted-model, score-risk and generated-law distributions independent of ambient dimension. In the covariance-matched experiments, the measured partial-trace overlaps are consistent with the proposed spectral transition. A width-8 network trained with clean-point reuse shows a d^2 -like training-set threshold over d=64 – 192 , with fixed validation and calibration overhead. Comments: 75 pages, 12 figures, jmlr Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2611.04043 [cs.LG] (or arXiv:2611.04043v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.04043 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-95] sting Processor Contracts in Speculative Decoding Engines
链接: https://arxiv.org/abs/2611.04042
作者: Tianyu Ding,Yaocheng Tong,Muzhi Yang,Lei Yang,Fei Jiang
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Speculative sampling can accelerate decoding without changing the target distribution. Its standard guarantee assumes that logits processors, which enforce constraints or modify token scores, receive the appropriate token prefix and state at each verification position. We formalize this processor-prefix contract and present BADC (branch-aware differential conformance testing). BADC combines enumerated references on tiny random models, checks of processor inputs and state, and simultaneous confidence bounds on output-event probabilities. Preregistered, source-informed experiments on selected Transformers and llama-cpp-python releases confirm two causes: processors read unverified drafts or advance state without rollback. For one pilot-selected prompt on a trained checkpoint, fresh samples give a 60.26-percentage-point lower bound on the difference in the probability of a predefined first-token event between ordinary and prompt-lookup decoding, with nominal simultaneous 95% coverage under within-arm i.i.d. sampling. Controlled comparisons establish no cause-recovery advantage of directed selection over random selection, while permutation testing detects each tested family at least as often as BADC. These findings connect processor-interface failures to observable decoding differences without establishing diagnostic superiority or workload prevalence.
[LG-96] Decision Transformer with Task-Aware Representations for Offline Meta-Reinforcement Learning
链接: https://arxiv.org/abs/2611.04010
作者: Xinbo Zhao,Yuhang Liu,Xin Zhang,Lei Le,Zhaohan Xi,Yingxue Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:To enable effective generalization in offline meta-RL, it is essential to accurately infer the underlying task through latent task representations, which provide a compact and transferable summary of task-specific characteristics for guiding policy generation. In this paper, we propose Contrastive Task-Aware Decision Transformer (CoTA-DT), a DT-based offline meta-RL framework that learns task representations by jointly integrating four complementary components: (i) contrastive learning from offline trajectories, (ii) alignment with natural-language task descriptions, (iii) one-step supervision via a task-conditioned world model, and (iv) long-term supervision via a task-conditioned Q function. The learned task representations are used as conditioning signals for a DT-based policy, enabling task-aware meta-policy learning. To further support generalization in both zero-shot and few-shot settings, we design a language-grounded, self-adaptive strategy that enables effective and reliable adaptation to unseen tasks. Notably, in CoTA-DT, the pretrained LLM serves as a shared backbone for language encoding, world modeling, Q-function estimation, and policy learning. This unified design facilitates parameter sharing, improving both efficiency and generalization. Extensive experiments across diverse offline meta-RL benchmarks demonstrate that CoTA-DT consistently outperforms prior methods, particularly in challenging zero-shot generalization scenarios.
[LG-97] Physics-Informed Identification of Tensegrity Prestress across Taut-Slack Transitions
链接: https://arxiv.org/abs/2611.04007
作者: Sandesh Lamsal,Sumit Kumar,Rubi Bhandari,Subhamoy Sen,Andrzej Rutkiewicz,Leszek Małyszko,Landolf Rhode-Barbarigos
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Signal Processing (eess.SP); Fluid Dynamics (physics.flu-dyn)
*备注: 44 pages, 15 figures, 15 tables; supplementary material included
Abstract:Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service life. Under strong excitation, the cables may slacken and the dynamics become nonsmooth. Existing identification methods are limited to the taut regime, and physics-informed networks for tensegrities solve only the forward problem of predicting the response from known member parameters. This study formulates the inverse problem. Neural networks are trained on the equation-of-motion residual in constrained Cartesian coordinates, where the mass matrix is constant and the force densities enter linearly. The force densities, damping, and axial stiffness are trainable parameters, and the tension-only cable law is kept. A Fisher-information analysis shows, before inversion, which member parameters a record can determine. The method is tested on simulated planar tensegrity frames with up to five modules and a three-dimensional prism, and on measured records of a biomimetic leg and a full-scale tensegrity simplex. On the one-module frame, the identifiable force densities are recovered within 1.5% from noisy accelerations at half the free coordinates, on records with and without taut-slack transitions. Records with transitions determine members that taut records leave undetermined. On the five-module frame, a 16-cable two-span bridge, the median force-density error rises moderately, to 4.2%. On measured records, an identified spring stiffness is within 0.1% of its independently measured value on average, and the identified simplex prestress at 13 levels agrees with its cable force transducers to 2.9% root-mean-square.
[LG-98] Rapid Mean-Field Formation Transport with a Shared-Representation Neural Fokker-Planck Controller
链接: https://arxiv.org/abs/2611.03981
作者: Zejian Zhou,Hao Xu
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in the 2026 IEEE Conference on Decision and Control (CDC)
Abstract:This paper develops a mean-field formation transport method with low online computational cost for large-scale multi-agent systems. Classical mean-field planners often require repeated solutions of coupled forward and backward partial differential equations, making online replanning computationally demanding. Density and control are therefore approximated by a common parameterized nonlinear map with separate scalar density and vector control outputs. Parameters are optimized using Fokker–Planck and initial-condition residuals, formation-matching costs on numerically simulated closed-loop particle trajectories, and control effort. Numerical computations are restricted to a bounded region of the state space, with an additional penalty on probability flux across its boundary. For the periodic-domain model, we establish well-posedness of the induced density evolution and bound the density-approximation error in terms of the dynamics and initial-condition residuals. Parameter optimization precedes online operation, during which controller coefficients remain fixed. Experiments quantify the accuracy–runtime trade-off relative to a mean-field planner, show lower transport error than classical feedback baselines, and demonstrate the benefits of the common parameterization.
[LG-99] One Mistake Three Layers: Persistence Rules Under Depth Correlation
链接: https://arxiv.org/abs/2611.03968
作者: Maryam Rahimimovassagh,Michael Brown,Sanjeda Sara Jennifer,Niloofar Yousefi
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:Probing frozen representations layer by layer is now standard practice, but reporting when a signal first becomes decodable requires a rule for reading a depth curve, and the obvious rule is wrong. On synthetic curves with known ground truth, requiring a probe score to exceed a null-calibrated threshold for three consecutive stages reports an onset nineteen stages early under transformer-like depth correlation, with every resample returning an onset. The failure follows from correlation between neighboring layers, a hazard known in neuroimaging but not, to our knowledge, checked for depth curves. We give an estimator whose error rate is calibrated rather than assumed. Null trajectories are built by permuting labels within each evaluation unit and holding each permutation fixed across depth, preserving the correlation structure the rule must survive; thresholds are per-layer order statistics; and the persistence requirement is calibrated rather than chosen. Applied to pocket information in a frozen co-folding trunk, stratifying 1,820 post-cutoff targets by protein-family and pocket familiarity, the five-stage rule fires on 4 to 7 per cent of null trajectories against a nominal 5 per cent and the three-stage rule on 12 to 17: the synthetic failure reproduces on real data. Pocket membership is decodable above threshold at all 48 blocks in all four regimes, so the onset cannot discriminate them; the pre-declared accumulation depth does, and is latest for familiar families with structurally novel pockets in every feature set. That regime is also the weakest, by a margin surviving controls for length, pocket measure and alignment depth. Separately, a widely used public co-folding checkpoint loads against its own code with 128 trunk parameters missing; those would be randomly initialized, and every representation extracted would be partly noise while appearing normal. Subjects: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM) Cite as: arXiv:2611.03968 [cs.LG] (or arXiv:2611.03968v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.03968 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Maryam Rahimimovassagh [view email] [v1] Mon, 2 Nov 2026 21:11:15 UTC (1,814 KB)
[LG-100] Ermakov–Based embeddings as a parameter-free layer for physics-informed neural networks
链接: https://arxiv.org/abs/2611.03964
作者: Roberto Bernal-Jaquez,Roberto Romero Huerta,Alexander Schaum
类目: Machine Learning (cs.LG)
*备注: 12 pages, 2 figures, 6 tables, 2 appendices
Abstract:Evolution equations with time-dependent coefficients force a neural solver to spend capacity on a scale that the equation already determines. We show that a single classical object, the Ermakov-Pinney scale function \rho , can be written into the input layer of a physics-informed neural network as a parameter-free embedding. One construction covers three problem families through the two signs of the Pinney constant c : linear diffusion ( c=-1 , \omega\equiv0 ), diffusion in a time-dependent confining trap ( c=-1 , \omega\neq0 ), and the quantum oscillator with time-dependent frequency ( c=+1 ). An exact moment identity gives \rho in closed form from the initial data alone, so no auxiliary network or knowledge of the solution is needed. Our central finding is comparative: each family requires a different component of the construction and has a different honest control. In family I, the diffusive-time reparametrisation alone reproduces most of the gain, so that family is a poor test of the idea and shows why an ablation is mandatory. In family II no time reparametrisation exists, the control degrades accuracy, and the amplitude prefactor becomes essential (40.8x). In family III the adiabatic approximation also fails, and the quadratic phase factor is what matters (5.7x). The method does not help when the reduced solution does not converge to a fixed point: its extrapolation advantage then disappears. The reduction itself is the classical scaling-variable change of the self-similar-asymptotics literature; what is new is the moment identity that makes it architectural for time-dependent coefficients, and the comparative anatomy above.
[LG-101] Sampling Interventional Distributions with Orthogonal MMD Flow
链接: https://arxiv.org/abs/2611.03962
作者: Bariscan Bozkurt,Hugh Dance,Zonghao Chen,Alexandre Galashov,Arthur Gretton,Houssam Zenati
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many causal inference problems require sampling from interventional outcome distributions rather than estimating low-dimensional summaries. Existing approaches often rely on fitting generative models of the target distribution together with additional nuisance components, which can be computationally demanding for high-dimensional outcomes. We propose Interventional Maximum Mean Discrepancy Flow (IMMD-Flow), a particle-based method that instead represents the target interventional distribution through its kernel mean embedding. This kernel embedding admits a doubly robust, Neyman-orthogonal estimator with finite-sample guarantees and can be efficiently reused to sample from the interventional distribution via MMD gradient flow. IMMD-Flow accommodates both hard and soft interventions, admits theoretical guarantees for estimation and particle approximation, and extends to high-dimensional image outcomes through frozen encoders. Across image-based benchmarks, IMMD-Flow matches or improves upon state-of-the-art generative approaches while showing significant speed advantages.
[LG-102] Forget What Comes Next: LLM Unlearning via Statistical Independence
链接: https://arxiv.org/abs/2611.03955
作者: Andrii Shkabrii,Claudia Plant,Martin Pawelczyk
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) can memorize and reproduce fragments of their training data, raising privacy and legal concerns that motivate machine unlearning. Yet poisoned training examples can remain statistically detectable after unlearning, which raises the question of whether current unlearning objectives remove what the model has memorized at all. In this work, we introduce PINT, a novel algorithm for machine unlearning that uses the Hilbert-Schmidt Independence Criterion (HSIC) to reduce the statistical dependence between tokens to be forgotten and the internal representations used to predict them. We formally relate our HSIC-based objective to Exact Memorization through a computable upper bound, connecting the training objective directly to the model’s ability to reproduce the forgotten tokens. In extensive experiments, we compare forgetting-utility trade-offs across eight settings with models up to 7B parameters and demonstrate scalability to 70B parameter models. Our evaluations cover unlearning Gaussian poisons, personal information, hazardous knowledge in biology and cybersecurity, and copyrighted books and news articles. PINT achieves the strongest forgetting-utility trade-off on average and is the only method to make Gaussian poisons undetectable while maintaining near-original model utility. Code is available at: this https URL
[LG-103] Accelerating FastDEC Clustering Algorithm with GPUs
链接: https://arxiv.org/abs/2611.03934
作者: Mi Li,Eibe Frank,Bernhard Pfahringer
类目: Machine Learning (cs.LG)
*备注: submitted to DMKD
Abstract:Fast Dominance Estimation Clustering (FastDEC) is an efficient variant of Density Peak Clustering (DPC) that achieves high clustering quality through dominance-based density estimation, but remains constrained by the computational cost of exact k-nearest-neighbor (k-NN) construction and dominance component (DC) processing. We present SymWarp-FastDEC, a GPU-accelerated implementation of FastDEC. At its core is SymTrikNN, an exact self-k-NN kernel that exploits search symmetry using upper-triangular traversal and warp-cooperative top-k selection. We also introduce WP-DC, a warp-parallel DC reduction algorithm designed for modern GPUs. Combined with memory-adaptive execution planning and stream scheduling, WP-DC eliminates DC reduction as a runtime bottleneck. Experiments on synthetic and real-world datasets show that SymWarp-FastDEC produces clustering outputs identical to the original FastDEC on all evaluated equivalence benchmarks. Compared with a FastDEC variant using FAISS GpuIndexFlatL2 for exact k-NN search, our approach achieves approximately 1.7x to 4.9x higher end-to-end performance, with the advantage generally increasing with dimensionality. Compared with the original single-threaded Python FastDEC implementation, speedups reach approximately 2.5 x 10^4 in a very high-dimensional case where its k-d-tree-based search degrades; this extreme result is not representative of typical acceleration.
[LG-104] Sequential auditing of connected forecast errors under partial observations
链接: https://arxiv.org/abs/2611.03928
作者: Yuanbo Zhou,Junjie Xiong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph forecasting systems often rely on prediction intervals to quantify uncertainty across connected locations, but the true outcomes needed for evaluation may be only partially observed. We study how an auditor can selectively retrieve missing outcomes to determine whether there exists a connected group whose outcomes violate the same side of their respective forecast intervals. The audit stops when the observed labels determine the event or the query budget is exhausted. On three public graph time series, MOMS reduces mean queries by 19.6% to 38.5% relative to screened random acquisition when 30% of labels are hidden. Under localized masking on METR-LA, shortest witness finds more positive cases within ten queries, although the difference depends on node ordering. Direct component traversal reproduces all 9,696 screened random traces without enumerating connected subsets. The experiments show that reducing queries to a decision does not necessarily improve early event discovery or reduce local computation.
[LG-105] When Latent Dynamics Meet Data Assimilation: A Benchmark of Koopman-Type Representations
链接: https://arxiv.org/abs/2611.03916
作者: Zhiwei Zhang,Wenxuan Yuan,Xiaoyuan Cheng,Pouria Behnoudfar,Chuanqi Chen,Charlotte Moser,Yuta Tarumi,Jin-Long Wu,Rossella Arcucci,Takemasa Miyoshi,Nan Chen,Marc Bocquet,Sibo Cheng
类目: Machine Learning (cs.LG)
*备注:
Abstract:Koopman representations describe nonlinear dynamics through linear evolution in a transformed space, offering an attractive framework for their linear simplicity and generality. Yet these advantages do not necessarily translate into improved performance in deep latent data assimilation (LDA). We systematically compare seven representative methods and structural baselines across five systems spanning approximately periodic to strongly chaotic dynamics and a broad range of state dimensions. We evaluate forecast and assimilation accuracy, physical state recovery, latent-trajectory behavior, and dynamical consistency, and assess Gaussian error assumptions, objective convexity, and uniqueness of analysis solutions. Our results reveal a trade-off between representational flexibility and optimization tractability. Linear evolution of the latent state can stabilize short-term forecasts but restrict expressiveness in strongly chaotic and high-dimensional systems, without consistently improving assimilation accuracy. Parameterized linear operators offer greater flexibility, yet their joint optimization with the encoder–decoder remains nonconvex. Methods with closed-form analysis updates generally perform better and exhibit greater robustness in our experiments, although matrix inversion remains a computational bottleneck. These findings show that the practical benefits of Koopman structure depend on both sufficient observational information and the choice of assimilation procedure.
[LG-106] he Information Limits of Neural Controlled Differential Equations Under Sparse Observations
链接: https://arxiv.org/abs/2611.03887
作者: Rostislav Gusev,Evgenia Romanenkova,Alexey Zaytsev
类目: Machine Learning (cs.LG)
*备注: Preliminary version. Further theoretical extensions are under development
Abstract:Neural controlled differential equations (Neural CDEs) evolve hidden states along continuous paths constructed by interpolating discrete observations. Sparse measurements leave the input between observation times undetermined, and admissible continuous paths consistent with the same data can produce different terminal states. We characterize this ambiguity for planar drivers with bounded speed. The set of compatible terminal states admits a leading-order zonotopic approximation determined by feasible signed areas and transported Lie brackets. Its projection onto a scalar prediction yields a computable, target-specific ambiguity certificate. We also study the number of measurements needed to reduce the remaining uncertainty. For nondegenerate planar CDEs, the optimal worst-case radius over deterministic adaptive sampling policies scales as N^-1 when speed is bounded and N^-2 when both speed and acceleration are bounded, where N is the number of observation intervals. Synthetic experiments assess the certificate using admissible continuous completions and examine the role of path regularity.
[LG-107] Embedding Semantic Entropy: NLI-Free Semantic Uncertainty Estimation with Geometry-Aware Clustering
链接: https://arxiv.org/abs/2611.03876
作者: Akshiya Raveendran,Praveenasarma Baskarakurukkal,Pragatheeswaran Vipulanandan,Kamal Premaratne,Uthayasanker Thayasivam
类目: Machine Learning (cs.LG)
*备注: Large Language Models; Uncertainty Quantification; Semantic Entropy; Clus- tering; K-Medoids; Hallucination Detection
Abstract:Large Language Models (LLMs) often generate fluent yet incorrect responses, making un- certainty estimation important for reliable deployment. Semantic Entropy (SE) estimates uncertainty by clustering responses based on semantic equivalence, but it relies on hard entailment-based clustering. We propose Embedding Semantic Entropy, which replaces entailment-based clustering with K-Medoids in semantic embedding space and uses dynamic cluster selection to account for intra-cluster compactness and inter-cluster separation. We evaluate the method on TriviaQA, SQuAD, NQ-Open, SVAMP, and BioASQ using Falcon- 7B-Instruct, LLaMA-2-7B, LLaMA-2-7B-Chat, and Gemma-7B. Results show competitive AUROC and AUARC performance, suggesting that NLI-based entailment clustering can be replaced with embedding-space clustering with limited degradation while improving the inspectability of semantic grouping. This matters because semantic uncertainty scores depend on underlying semantic groups; by making these groups explicit, compact, and separable in embedding space, Emb-SE turns uncertainty estimation from a black-box en- tailment decision into an inspectable clustering problem.
[LG-108] INSIGHT-HPC: An Interpretable System Intelligence for Global HPC Telemetry
链接: https://arxiv.org/abs/2611.03875
作者: Rupak Kadel,Jie Li,Yong Chen
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: Accepted at the 2026 IEEE International Conference on Big Data (IEEE BigData 2026)
Abstract:Modern high-performance computing (HPC) clusters integrate multi-generation CPUs and GPUs, which generate rich, high-dimensional telemetry streams from out-of-band baseboard management controllers (BMC), job schedulers, and facility power distribution units. Detecting anomalies within this telemetry is critical to prevent energy waste and costly node failures. Existing approaches often rely primarily on statistical deviations and require stronger labels than actual production environments can provide. In addition, these detectors have a limited operational context for interpreting alerts. We present INSIGHT-HPC, a multi-plane anomaly-detection architecture that combines statistical, workload-aware, and physics-guided evidence into operator-interpretable episodes. INSIGHT-HPC integrates heterogeneous, large-scale telemetry streams and transforms noisy minute-resolution observations into de-duplicated fault episodes with operator-facing explanations. This design allows the system to distinguish workload-explained behavior from genuine infrastructure anomalies while producing interpretable alert summaries for operators. On production HPC telemetry with weak operational labels and controlled HPC-relevant fault injections, INSIGHT-HPC achieves a stronger precision-recall balance than all evaluated baselines. The system achieves a precision of 0.913, a recall of 0.817, an F1 score of 0.862, and an AUROC of 0.896. These results demonstrate the value of combining workload context, physical consistency, and explainable episode-level fusion.
[LG-109] ExaTune: Exhaustive Hyperparameter Landscape Exploration at Scale
链接: https://arxiv.org/abs/2611.03867
作者: Kevin Dick,Katie Lonsway
类目: Machine Learning (cs.LG)
*备注: 11 pages, 3 figures, includes supplementary materials
Abstract:Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explored performance landscape that encodes how results depend on configuration choices. Fitness landscape analysis comprises a robust collection of methods from evolutionary computation offering ruggedness indices, autocorrelation functions, and fitness-distance correlation that have never been applied to machine learning hyperparameter spaces at scale, given that fine-grained ground-truth enumeration data has not existed. We present ExaTune, an open-source HPC system to produce a new artifact class: the hyperparameter landscape map. ExaTune exhaustively evaluates every point on a discretized hyperparameter grid via K -fold cross-validation, aggregates results into reproducible parquet benchmarks, and applies a suite of five formalized landscape metrics: spatial autocorrelation (Moran’s I ), ruggedness, local optima density, fitness-distance correlation (FDC), and variance-based parameter importance. We release a benchmark collection of 8 complete landscape maps (totalling 285,080 exhaustively evaluated configurations) spanning five model families (SVMs, random forests, XGBoost, ridge regression, and k -nearest neighbors) across six classification and two regression benchmarks, hosted on HuggingFace. Empirical analysis demonstrates that Bayesian optimization, Optuna, Hyperband, and Random Search at standard trial budgets systematically miss instability regions, secondary optima, and parameter interaction effects that are visible in complete landscapes; information that is structurally unavailable to any optimizer by design.
[LG-110] Dr. No: Feature Steering for Abstention in Vision-Language Models
链接: https://arxiv.org/abs/2611.03862
作者: Farid Hasanov,Riccardo Renzulli,Bahram Zonooz,Mykola Pechenizkiy,Decebal Constantin Mocanu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large foundation models often answer even when the available visual or textual evidence is insufficient to support a reliable conclusion. This failure to abstain makes them hard to trust in high-stakes or information-seeking settings, where a confident but unsupported answer can be worse than no answer at all. It also raises a natural question: can the internal mechanisms behind this behaviour be identified and controlled at inference time? We present Dr. No, a feature-steering approach that mines sparse autoencoder (SAE) features associated with abstention and commitment, then modulates a small set of them during the prefill pass, without retraining the base model. Dr. No is evaluated on LLaVA-1.6 Mistral 7B across multimodal abstention benchmarks, including UNK-VQA and MM-UPD, and additionally benchmarked on language reasoning tasks using Qwen3.5-2B on AbstentionBench subsets. Against multiple uncertainty-based abstention baselines, feature steering achieves a more favourable balance between correct answering and appropriate abstention than post-hoc rejection methods. The gains are strongest on UNK-VQA and on MM-UPD cases involving image–question or question–answer incompatibility, and transfer most clearly to mathematical reasoning subsets of AbstentionBench. However, performance is more limited on absent-answer detection and retrieval-heavy multi-hop QA, such as MuSiQue. These results suggest that sparse internal features provide lightweight control handles for shaping uncertainty-aware behaviour in foundation models, while also showing that abstention control is source-dependent rather than governed by a single universal signal. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.03862 [cs.LG] (or arXiv:2611.03862v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.03862 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-111] Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization NEURIPS
链接: https://arxiv.org/abs/2611.03844
作者: Anisha Parsan,Sebastian Ober,Calvin McCarter,Nick Bhattacharya,Hunter Elliott
类目: Machine Learning (cs.LG)
*备注: Accepted to NeurIPS 26 GEM and NeurIPS 26 AI4DD
Abstract:Therapeutic antibody development is often framed as comprising two discrete stages: initial discovery of novel binders followed by iterative optimization to refine these hits into therapeutic-grade molecules. Models and methods often reflect these stages, with generative structure models applied to discovery and probabilistic black-box optimization methods applied to refinement. Here we propose to unify these stages by smoothly transitioning a generative discovery model into iterative optimization, allowing the handoff between the structure-based prior and supervision from wet lab data to be guided by the posterior uncertainty of a probabilistic surrogate. We show that this yields improvements in optimization efficiency in the low-N regime, as well as improved diversity amongst the optimized molecules.
[LG-112] Quantifying out-of-distribution data severity with Wasserstein distances: benchmarking large language models for alloy property prediction
链接: https://arxiv.org/abs/2611.03767
作者: YuJie Liu,Dezhen Xue,Jun Sun,Xiangdong Ding,Zhibin Gao,Turab Lookman
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注: 23 pages, 10 figures
Abstract:As general-purpose Large Language Models (LLMs), such as Gemini and DeepSeek, exhibit increasingly advanced reasoning capabilities, a critical question arises: how do their property prediction abilities compare to those of domain-specific pretrained materials models? In this study, we evaluate predictions for Ti, steels, high-entropy alloy (HEA) and Al, demonstrating that general-purpose LLMs surprisingly outperform SteelBERT, MatSciBERT, and SciBERT. To further enhance performance, we introduce a hybrid approach that utilizes numerical priors from a prior model, such as CatBoost or SteelBERT, to guide the general LLMs (Deepseek V3.1, Gemini-2.5-Flash). By inputting these initial estimates from a prior model as prompts, the LLM acts as a reasoning corrector, achieving accuracy superior to the prior model and “unguided mode”. Furthermore, we investigate the impact of data strategy using a similarity-based metric, revealing that data quality dominates quantity; using merely 5% of high-similarity reference data of training set as input yields performance similar to the results of using 100% of training set. Additionally, the LLMs demonstrate excellent uncertainty calibration, where self-reported “high” confidence strongly correlates with minimal deviation from ground truth. Our findings suggest that synergizing general-purpose LLMs with prior model numerical baselines and high-quality data curation creates a cost-effective and accurate paradigm for materials discovery, reducing the need for expensive domain-specific pretraining.
[LG-113] Language Models Can Benefit from Domain-Specific Inductive Biases NEURIPS2026
链接: https://arxiv.org/abs/2611.03756
作者: Damien Teney,Liangze Jiang,Zachary Shinnick,Hemanth Saratchandran,Simon Lucey
类目: Machine Learning (cs.LG)
*备注: 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Position Paper Track
Abstract:Language models are typically trained with a single choice of architecture on diverse domains and data sources. Modern transformers have been extensively refined for such general-purpose learning, but are they equally effective across different subsets of the pretraining data? We investigate this question by training small language models from scratch on representative subsets of pretraining data such as web text, code, and mathematics. We introduce variants of transformers in which standard activation functions are replaced with learned splines tuned on specific datasets, providing a simple way to modify their inductive biases. Using a mix-and-match of architectures and datasets, we evaluate cross-domain transfer, i.e. how well does an architecture tuned for one domain perform on data from another? We find that the tuned transformer variants improve training efficiency or generalization on all seven of our tested datasets, but the gains are often domain-specific. For example, architectures tuned on a natural language dataset such as FineWeb perform well on TinyStories, whereas those tuned on mathematics perform poorly on natural language. We identify several mechanisms underlying these improvements, including wider loss basins and more abundant “lottery tickets”. Our results show that different subsets of pretraining data can benefit from different learning mechanisms, highlighting a limitation of the prevailing trend towards a small set of general-purpose architectures and optimizers. This points to an opportunity to improve data efficiency by developing domain-specific learning strategies that retain the ability to train general-purpose models.
[LG-114] Honest Small Decision Models for Finance: Scale Specialization Collapse and Mixed-Corpus Remediation
链接: https://arxiv.org/abs/2611.03755
作者: Ajitabh Kumar
类目: Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注: ICAIF 2026: The 3rd Workshop on LLMs and Generative AI for Finance
Abstract:Financial workflows need fast, calibrated, auditable decisions, not just generated text. We train System-1 decision models, small language models fine-tuned to answer typed questions (choice/score/noul) with a probability in a single forward pass, across two model families (K2-Horizon and Qwen3.5/3.8) and nine rungs from 0.8B to 32B parameters, using one frozen LoRA recipe on a public decision benchmark and two new finance corpora: a programmatic-gold policy corpus (rule-reading with exact labels) and a reality-labeled outcome corpus (19,640 realized forward-return questions in walk-forward splits, plus a sealed 2026 test of 1,360 questions). Narrow fine-tuning costs general decision competence 9-45 percentage points, a data-mixture effect that shrinks with model scale and is associated with narrow-gradient volume. On reality-labeled questions, every trained rung from 0.8B to 32B lands at the horizon-conditioned prior baseline (accuracy 0.37-0.38, no detectable conditional edge under our controls); the models respond with flat, well-calibrated probabilities that track per-horizon base rates: naturally honest uncertainty, with a mid-scale calibration sweet spot (ECE 0.026 at 2B, degrading to 0.043-0.044 at 9-32B under prior drift). A mixed-corpus remediation carries all competencies in one adapter at modest cost: it matches or slightly exceeds the generalist specialist on the public benchmark while holding the policy ceiling and near-specialist outcome calibration, yielding a deployable artifact with per-domain temperature regimes and a band-dispatch escalation analysis: sharp, dispatchable bands where rules determine answers; flat, review-routing bands where reality does not.
[LG-115] CaRe-KT: Continuity-Aware Retrieval-Augmented Reasoning for Knowledge Tracing
链接: https://arxiv.org/abs/2611.03744
作者: Mehrnoush Mohammadi,Kamal Berahmand,Mohammad Morsali,Shazia Sadiq,Wojtek Tomaszewski,Hassan Khosravi
类目: Machine Learning (cs.LG)
*备注: 13 pages, 4 figures, 4 tables
Abstract:Knowledge tracing (KT) estimates learners’ evolving knowledge states from interaction histories to support adaptive learning. Retrieval-augmented KT improves these estimates using cross-learner behavioral evidence, but existing approaches typically separate evidence retrieval from downstream prediction. This creates a retrieval-inference alignment gap because evidence relevance varies with the learner’s evolving state and prediction context. We propose CaRe-KT, a query-adaptive framework that maintains relevance continuity between evidence acquisition and inference without equating retrieval relevance with predictive utility. CaRe-KT introduces an Evidence-Graph Cache (EGC) that combines persistent question-level relational compatibility with interaction-level temporal validity to construct query-specific evidence subgraphs. Historical learning trajectories retrieved through these subgraphs are encoded alongside the target learner’s history in a shared representation space using outcome-supervised contrastive learning. An attention mechanism combines acquisition relevance with learned query-evidence compatibility, allowing relevance to inform, rather than determine, each trajectory’s predictive influence. Experiments across five benchmarks show that CaRe-KT outperforms strong KT baselines and remains robust under sparse-history and cold-start conditions. These findings support relevance continuity as a design principle for retrieval-augmented knowledge tracing.
[LG-116] RATSIFT: Resolution-Adaptive Transformer for Solar Irradiance Forecasting and Temporal Downscaling
链接: https://arxiv.org/abs/2611.03738
作者: Jesto Peter,Timothy M. Hansen,Hossein Moradi Rekabdarkolaee
类目: Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
*备注:
Abstract:Accurate forecasting and temporal downscaling of solar irradiance is critical for reliable renewable energy integration and grid management. Traditional methods often struggle to capture rapid irradiance variability. In this work, we develop a Transformer-based architecture for simultaneous forecasting and temporal downscaling. The model takes input data at arbitrary resolutions and generates outputs at any resolutions represented within the training data, leveraging both global and local contextual information. To improve training efficiency, a genetic algorithm-based optimization framework is implemented to identify suitable model hyperparameters, while reducing the computational cost of exhaustive grid search. The effectiveness of the proposed model is shown by analyzing real data in Dallas, Texas. The proposed model achieved root mean square error (RMSE) values of 28.785, 29.544, 30.157, 32.132~W/m(^2) for 15-, 30-, 60-minute, and irregularly spaced global input and producing forecast at 5-minutes resolution, respectively. Furthermore, this model produced reliable uncertainty estimates, exhibiting wider prediction intervals for high-resolution inputs during volatile solar conditions, whereas coarser-resolution inputs result in narrower intervals and significantly lower training time and computational cost. Our approach provides a scalable and efficient framework for multi-resolution solar irradiance forecasting and temporal downscaling, suitable for practical energy forecasting and grid management applications.
[LG-117] SSPE: Robust Sequential Staypoint Extraction in Degraded Trajectory Data
链接: https://arxiv.org/abs/2611.03737
作者: Yueyang Liu,Lance Kennedy,Andreas Züfle
类目: Machine Learning (cs.LG); Databases (cs.DB)
*备注:
Abstract:Extracting semantically meaningful staypoints from raw trajectory streams is a foundational prerequisite for mobility analytics. However, real-world trajectory data is frequently degraded by severe spatial noise and protracted temporal dropouts. Classic threshold-based extraction methods, such as the widely adopted sliding-window algorithm, are highly sensitive to these anomalies, routinely fracturing continuous stationary episodes into disjointed fragments. To overcome this limitation, we propose the Sequential Staypoint Extraction (SSPE) algorithm. SSPE employs a resilient, state-driven architecture that replaces static spatial anchors with a dynamic centroid and introduces an n -step lookahead heuristic to structurally tolerate transient coordinate drift. Concurrently, it utilizes temporal bridging mechanisms to prevent artificial fragmentation during prolonged signal loss. Since optimal extraction parameters vary significantly across datasets, we further introduce a fully unsupervised, consensus-based parameter estimation framework. This framework derives baseline statistical scales directly from the raw trajectory data and identifies the optimal parameter configuration by maximizing structural agreement, thereby eliminating the reliance on annotated ground truth. We evaluate SSPE on the ExtraSensory dataset, a real-world mobility corpus with manually verified ground-truth staypoints, against the sliding-window implementation of Trackintel, one of the most widely adopted extraction baselines in the field. Empirical results demonstrate that SSPE achieves superior accuracy, yielding higher F1-scores while simultaneously improving computational efficiency over this standard.
[LG-118] Minimal Superior Penalties: Characterising and Calibrating Decision-Aware Proper Scoring Rules
链接: https://arxiv.org/abs/2611.03734
作者: Rouhollah Ahmadian
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Training a classifier means letting a validation metric decide which model is kept and when training stops. A natural requirement on that metric is superiority: every correctly classified forecast should score better than every incorrectly classified one, however close the underlying probabilities are. Neither the Brier score nor the logarithmic score satisfies this on its own. We show that superiority is governed by the separation between two endpoints – the supremum of the score on correct predictions and the infimum on incorrect predictions – and derive both in closed form for each score. Their difference is the infimal penalty, the greatest lower bound on the penalty needed to restore superiority. Prior work restores superiority with a penalty calibrated from the first endpoint alone; for the Brier base, our infimal penalty is smaller than that published penalty by a factor ranging from 4.00 at three classes to 2.11 at twenty. We further embed the published and infimal penalties in a one-parameter family and show that, under a single-valued tie convention, every member of the family is strictly proper. In a pre-specified two-dataset case study, mean selected-model macro-F1 rises with the penalty in the reported ten-class regimes, though not distinguishably from noise once seed/selection-mode nesting is accounted for; a fifth regime at forty-seven classes does not reproduce even this descriptive pattern. The intended no-superiority baseline is algebraically a rescaling of the family under test, so the experiment cannot attribute the observed pattern specifically to superiority. When superiority itself is the objective, any penalty strictly above the infimal value suffices; choosing a larger penalty is a separate calibration trade-off rather than a requirement for the guarantee.
[LG-119] Auditing a Model-Hub Benchmark: Data Quality Training Budgets and Side Information in HuggingBench
链接: https://arxiv.org/abs/2611.03727
作者: Michal Podstawski
类目: Machine Learning (cs.LG)
*备注: 12 pages, 10 tables, 2 figures. Code and data: this https URL
Abstract:Benchmarks built from model hubs combine interaction data, knowledge graphs (KGs) and resource documentation, but valid files do not ensure that these artifacts describe the same objects. Treating the release as a data product, we audit HuggingBench (SIGIR 2025), a benchmark for recommending Hugging Face resources, through data validation, a replication of its eleven recommendation methods and controlled experiments with three training seeds. In three of its four external KGs, the files pass key and range checks but violate the item-entity identity constraint on which KG recommenders rely. Correcting this join improves every KG-based method in every seed, by up to 73% in mean Recall@10, and consistently reverses ten of 27 comparisons between KG configurations. The protocol matters as well: the 100-epoch budget ends before early stopping in seven to eight of 29 validation-selected settings, and 21 of 23 such setting-seed pairs improve with a longer budget. The released item text, which no reported method uses, is the strongest signal: closed-form linear models with text and the item graph exceed the best reported Recall@10 by 18% while fitting in minutes on a CPU. Checks of other recommendation releases show that individually valid releases of one dataset can use incompatible identifiers. We release corrected files, mappings, validators and logs, and derive practices for preserving identity and provenance across the artifacts of benchmarks and model lakes.
[LG-120] Does the Trust Boundary Hold over Time? A Long-Horizon Benchmark for Receiver-Directed Federated Learning
链接: https://arxiv.org/abs/2611.03724
作者: Ting Xu,Henry Leung
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: 8 pages, 2 figures, 7 tables. Code: this https URL (available upon acceptance)
Abstract:In receiver-directed federated learning, each site draws a trust boundary. For every source and model component it judges, on its own data, whether the transfer helps (admit), harms (reject) or cannot yet be told (abstain). We ask whether this boundary holds over hundreds of rounds. Our benchmark has a federation of thirteen semi-synthetic sites, an exact oracle for every evaluated transfer, paired seeds, an attack protocol, fourteen methods over 600 rounds and Qwen3-1.7B LoRA sites over 500. The answer depends on which boundary is measured. Across 20 paired CPU seeds, a gate judging against a fixed reference better identified transfers useful relative to the site’s starting model. Yet it admitted 29% of transfers harmful to the model the site now has. A gate judging against the current model admitted 2% of harmful transfers on that target in the CPU tier, but missed many transfers useful to the starting model. Two short propositions explain these distinct failures. We also evaluate a held out audit that leaves gate verdicts intact. After training, it decides whether each final model is deployed or the site falls back to local training, and reports admissions that have gone stale. With independent audit data its guarantees hold after any training history. At 600 rounds the audit deployed 65 to 72% of models, none with negative benefit on its target, and kept 98% of the gain. No stale report had the wrong sign in our runs. With 20,000 audit observations, a variance-adaptive bound found 99% of stale transfers that cost at least 0.01 in log loss. A common-target Qwen3 comparison on 60 selected transfers shows the same tension over 100 rounds. With 38% adversarial sites, FedFomo and CS-Gate were the only evaluated methods with positive mean gain in every honest group.
[LG-121] Stress-Testing the Robustness of LLM -Based Causal Inference
链接: https://arxiv.org/abs/2611.03656
作者: Ankitkumar Patel,Jigarkumar Patel,Amit Kumar,Supreetha Sreeram,Venkatesh Kulkarni
类目: Machine Learning (cs.LG)
*备注:
Abstract:Why do some of the world’s most powerful Artificial Intelligence (AI) systems still struggle to reason about cause and effect? Causal discovery, the ability to discern the underlying drivers of a system, is fundamental to scientific inquiry and has traditionally relied on structured data and statistical methods. Recently, Large Language Models (LLMs) have been explored as an alternative paradigm for causal inference, leveraging their broad knowledge and reasoning capabilities. Yet, despite their linguistic fluency, LLMs often rely on surface-level pattern recognition rather than genuine causal logic. While it is known that LLMs stumble in this domain, we lack a precise map of where, why, and how significantly these failures occur. In this paper, we propose a systematic stress-test framework across six critical dimensions: model scale, causal graph complexity, prompting strategy, metadata quality, data quality, and sensitivity to uncertainty. Our findings reveal a significant “optimism bias” in LLMs; models consistently overestimate causal links, leading to low precision across the board. Furthermore, we demonstrate that traditional evaluation metrics can be deceptive: while reasoning-heavy models lead in performance, the highest scores often occur when uncertainty in inference is overlooked. Once uncertainty is integrated into the evaluation, performance drops, providing a more honest reflection of causal reliability. We also find that, while scaling model size improves outcomes, these gains are fragile; performance degrades sharply as the quality of metadata decreases. This work serves as a practical guide to the blind spots of LLMs, offering a clear-eyed assessment of when these tools can be integrated into a causal discovery pipeline and when they are likely to lead researchers astray.
[LG-122] owards Automatic Heart Murmur Characterization from Multi-spot Phonocardiograms
链接: https://arxiv.org/abs/2611.03654
作者: Marcelo Nogueira,Jorge H. Oliveira,Carlos G. Ferreira,Miguel T. Coimbra,Alípio M. Jorge
类目: Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:Cardiac auscultation traditionally detects the presence of a heart murmur, but clinicians further characterize it by timing, shape, pitch, quality, and grade. Most automatic phonocardiogram analysis systems stop at binary detection and do not estimate this richer description. In this work, we formulate automatic murmur characterization as five patient-level classification tasks and compare three multi-spot strategies on recordings from the aortic, pulmonary, mitral, and tricuspid auscultation locations. First, classical and deep-learning classifiers are trained independently at each location, and predictions are combined via a maximum-severity late-fusion rule. Second, a synchronous multi-input CNN jointly processes one representative segment per location for a single task. Third, a unified multi-input, multi-output CNN predicts all five attributes from one shared multi-spot representation. Using 735 patients from the CirCor DigiScope dataset with complete four-spot recordings, the best late-fusion configurations reach an unweighted mean sensitivity between 85.56% and 93.81% across the five tasks, with class-specific recalls between 80.00% and 100.00% for every relevant category. The multi-input CNN trails late fusion by 7-16 points depending on the task, while the multi-output model reaches values between 72.59% and 79.40%, estimating all five attributes from one shared representation. We further show that naively balancing training data by adding all minority-class segments degrades patient-level performance under the maximum-severity rule, since it introduces non-representative, noisy segments not filtered by the clustering-based segment selection used for evaluation. These results indicate multi-spot murmur characterization is feasible with both fusion-based and jointly learned architectures, outlining a path toward automatic, structured murmur reports complementing binary screening.
[LG-123] Four Extensions of the Universal Law of Robustness: Log-Concave Covariates Heterogeneous Mixtures Vector-Valued Predictors and Markovian Data
链接: https://arxiv.org/abs/2611.03653
作者: Tobias J. Peter(ZHAW Zurich University of Applied Sciences)
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注: 62 pages
Abstract:Bubeck and Sellke’s universal law of robustness says that fitting n noisy samples below the noise level with a class of p real parameters forces a Lipschitz constant of order \sqrtnd/p , when the covariate distribution on \mathbbR^d is isoperimetric: smooth interpolation costs a factor d of overparametrization. Its hypotheses are Gaussian-type concentration, a homogeneous covariate law, scalar outputs, and independent samples; we relax each one. Exponential concentration suffices, at the unchanged rate and absolute constant and at the price of a capacity burn-in, some form of which we show is necessary; this gives the law for normalized log-concave covariates, with a \log d loss from the current KLS bound. For heterogeneous mixtures the worst component constant is replaced by the weighted average of the component constants, under a typicality condition on the mixture composition. For vector-valued predictors the bound at the Frobenius Lipschitz scale is free of the output dimension K , and the Euclidean bound loses only K^-1/2 , a loss that covariate-independent noise removes. Stationary Markov covariates retain the i.i.d. rate, the pseudo-spectral gap entering only the sample-size conditions. One master theorem carries the shared argument, and constants are explicit throughout.
[LG-124] Latent Cluster Separation as a Regularizer for Lifelong Anomaly Detection
链接: https://arxiv.org/abs/2611.03652
作者: Sabarish Radhakrishnan,Nathalie Japkowicz
类目: Machine Learning (cs.LG)
*备注: 10 pages, 1 figure
Abstract:Lifelong (continual) anomaly detection systems must learn a sequence of tasks without forgetting how to recognize anomaly types seen earlier – a property known as backward transfer. A common way to try to improve backward transfer is to reshape a model’s internal representation around learnable concept centroids, typically coupled with a new way of scoring anomalies based on distance to those centroids. Because these two design choices are usually introduced together, it is unclear which one is actually responsible for any performance gain. In this paper, we unbundle the two mechanisms within a VAE-based lifelong anomaly detector and study them separately. We find that used purely as a training-time regularizer – while leaving anomaly scoring unchanged – latent cluster separation can improve detection quality and backward transfer, though whether it outperforms the alternative scoring approach depends on the dataset’s underlying representation. Switching to distance-based scoring is instead where most of the instability commonly associated with this class of method actually originates; we identify and explain the underlying mechanism. This benefit depends on the dataset’s underlying representation, and we report this as practical guidance for choosing between the two design choices, rather than as a list of caveats.
[LG-125] Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
链接: https://arxiv.org/abs/2508.05941
作者: Zhanyi Sun,Shuran Song
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound into failure. Common strategies to mitigate this issue involve expanding the training distribution through human-in-the-loop corrections or synthetic data augmentation. However, these approaches are often labor-intensive, rely on strong task assumptions, or compromise the quality of imitation. We introduce Latent Policy Barrier, a framework for robust visuomotor policy learning. Inspired by Control Barrier Functions, LPB treats the latent embeddings of expert demonstrations as an implicit barrier separating safe, in-distribution states from unsafe, out-of-distribution (OOD) ones. Our approach decouples the role of precise expert imitation and OOD recovery into two separate modules: a base diffusion policy solely on expert data, and a dynamics model trained on both expert and suboptimal policy rollout data. At inference time, the dynamics model predicts future latent states and optimizes them to stay within the expert distribution. Both simulated and real-world experiments show that LPB improves both policy robustness and data efficiency, enabling reliable manipulation from limited expert data and without additional human correction or annotation.
[LG-126] Conformal Prediction at Performative Equilibrium: The Price of Performativity
链接: https://arxiv.org/abs/2611.05277
作者: Ahmed Mahrous,Roberto Di Pietro
类目: atistics Theory (math.ST); Machine Learning (cs.LG)
*备注: 45 pages, including supplementary material
Abstract:Conformal prediction gives finite-sample coverage when calibration and test points are exchangeable, but exchangeability can fail when deployment changes the data distribution. We study conformal prediction in performative settings, where the deployed predictor affects the data distribution, and seek prediction sets valid at performative equilibrium: coverage must hold under D(f^\star) for a performatively stable predictor f^\star . Under total-variation L -sensitivity of the distribution map, we prove a phase transition at L=1 . If L \ge 1 , no finite-sample procedure can uniformly guarantee both equilibrium-valid coverage and nontrivial prediction sets. If L1 , the minimax excess miscoverage over informative procedures is \widetilde\Omega(1/((1-L)\sqrtn)) , implying a sample-complexity inflation of order 1/(1-L)^2 for fixed excess miscoverage. Our main technical tool is a transfer theorem: any procedure returning a predictor within \varepsilon of equilibrium yields equilibrium-valid coverage up to an additive L\varepsilon term after split-conformal calibration. We instantiate the theorem with two retraining algorithms: a model-free method for discretizable classes and a parametric tail-averaged method that matches the n - and L -dependence of this rate up to logarithmic factors. Experiments, including semi-synthetic credit scoring, demonstrate the predicted threshold behavior and scaling laws.
[LG-127] Post-stratified importance sampling
链接: https://arxiv.org/abs/2611.05238
作者: Yuling Yao,Sergei Tikhonov
类目: Methodology (stat.ME); Machine Learning (cs.LG); Computation (stat.CO)
*备注:
Abstract:Importance sampling is a building block of modern statistical computing, but the weighted estimate is prone to high or infinite variance. Moreover, its empirical standard error is often unreliable, leaving importance sampling without practical uncertainty quantification. Motivated by multilevel regression and poststratification (MRP) in survey sampling, we propose post-stratified importance sampling (PIS) for estimating target expectations from proposal draws. PIS reformulates arbitrarily high-dimensional weighting into two one-dimensional probabilistic modeling problems: regression for the conditional mean given the importance ratio and marginal density estimation for the importance ratio. Different modeling choices yield a family of PIS estimators, encompassing ordinary importance sampling as a special case. We develop a model-based implementation that combines one-dimensional Gaussian process regression for the conditional mean and score-informed heavy-tailed density estimation for the marginal law, using both the observed weights and the available density evaluations. We study the oracle gains of PIS and establish consistency under general and model-specific conditions. Simulations and applications to cross-validation demonstrate improvements in estimation accuracy and uncertainty quantification.
[LG-128] Regret-based Characterization Information Limits and the Sample Complexity of Auditing Adaptive Policies
链接: https://arxiv.org/abs/2611.05227
作者: Irene Aldridge
类目: Econometrics (econ.EM); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 13 pages
Abstract:A policy can earn its performance by where it locates on average or by reacting to information. For any Markov policy \pi in a finite-horizon MDP, let \nu be its open-loop marginal, which plays \pi 's average action distribution at each step. We show that J(\pi)-J(\nu) equals \sum_t tr Cov(Q^\nu_t,\pi_t) under endogenous dynamics. Audits based on immediate costs alone can have the wrong sign. Writing the covariance as a joint-versus-product gap gives the bound |J(\pi)-J(\nu)|\le\sum_t span_t\sqrtI(s_t;a_t)/2 , with a sharp constant. Hence, an MDP’s adaptivity gap is bounded by the information that its optimal policy uses. Estimating J(\pi)-J(\nu) from \pi 's own bandit-feedback logs takes \Theta(\bar\chi^2 \log(1/\eta)/\delta^2) rounds, where \bar\chi^2 is an average \chi^2 -divergence. At the same time, deterministic non-constant policies cannot be audited at all. We also provide an importance-sampling auditor for episodic MDPs.
[LG-129] A Random Matrix Analysis of Symmetric Tensor Unfolding
链接: https://arxiv.org/abs/2611.05137
作者: Tom Szwagier,Xiaoyi Mai,José Henrique de Morais Goulart
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:We study a fundamental problem in machine learning: partial recovery of a planted signal \boldsymbolv \in \mathbbS^n-1 from a spiked tensor \boldsymbol\mathsfX = \lambda , \boldsymbolv^\otimes p + \boldsymbol\mathsfZ , where \boldsymbol\mathsfZ has symmetric Gaussian entries of variance \asymp n^-1 . While previous works replace \boldsymbol\mathsfZ by an i.i.d. standard Gaussian tensor to circumvent technical difficulties caused by dependencies, this paper directly analyzes the symmetric model via random matrix theory. We show that the square unfolding algorithm is subject to a sharp phase transition for partial recovery at \lambda \asymp n^(p-2) / 4 , validating an open conjecture from Montanari and Richard (2014). We get precise expressions for the limiting spectral distribution, deterministic equivalent, phase transition threshold and overlap. The main message is that unfolded symmetric tensors spectrally behave like standard Wigner matrices, up to simple transformations accounting for high-order symmetries. We consider extensions to multiple orthogonal spikes, recursive unfolding and non-square unfolding via disproportionate random matrix theory, leading to an interesting conjectured statistical-computational tradeoff regarding the choice of unfolding: square versus long.
[LG-130] Solving the Nonlinear Westervelt Equation in Ultrasound Propagation Using Physics-Informed Neural Networks
链接: https://arxiv.org/abs/2611.05130
作者: Luke Oluwaseye Joel
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 17 pages, 21 figures
Abstract:Modeling the nonlinear acoustic wave propagation in biological tissues needs accuracy, especially in ultrasound imaging and therapeutic applications. The 1D Westervelt equation, ( p_xx - p_tt + \gamma, p_ttt + \eta,(p^2)_tt=0, ) could be used to describe a finite-amplitude ultrasound propagation in these tissues. This equation accounts for nonlinearity, (\eta), and attenuation, (\gamma). To solve this equation, we observe that traditional solvers may struggle to do so correctly. This is due to the difficulties arising from the nonlinear nature of the equation. We solve the equation by defining three synthetic cases - linear ((\eta=0.001), (\gamma=10^-6)), shock-dominated ((\eta=0.02), (\gamma=10^-6)), and dissipation-dominated ((\eta=0.01), (\gamma=5\times10^-4)) - and using Physics-Informed Neural Networks (PINNs) to showcase the solution. A sinusoidal activation function is used to capture the oscillatory behavior of the problem. The results of our study, with PDE residual converging to at least (10^-6), boundary condition loss to (10^-5), and the PINN loss converging to (10^-5) for each case at (30,000) epochs, show that (\gamma) scales the rate of amplitude decay, while (\eta) governs frequency-dependent phase speeds.
[LG-131] Data informed Ansatz Selection for Variational Quantum Circuits
链接: https://arxiv.org/abs/2611.05079
作者: Manav Seksaria,Advith Desu,Anil Prabhakar
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:Choosing a variational quantum circuit (VQC) usually means searching over ansatze and depths, and training each candidate. We give three rules that fix these choices from the data and the gate set alone, before any training. First, a circuit without entangling gates has a rank-one Fourier coefficient tensor on every subset of qubits. A singular value decomposition of a dataset’s Fourier coefficients therefore indicates whether entanglement is needed, which qubits it connects, and a lower bound on the error of omitting it. Second, the rank of the quantum Fisher information matrix grows by p_0 per layer, after a first-layer deficit d_0 , until it reaches a ceiling set by the dynamical Lie algebra. This law fixes the depth. Third, reading out all 2^N - 1 Pauli-Z products from one batch of shots resolves half of the state’s directions, the most any single-basis readout can. A single-qubit readout resolves an exponentially small fraction. For the Madelon dataset, the rules prescribe 7 blocks of 3 qubits at depth 3: a 239-parameter classifier with 78.5% test accuracy, found without a grid search. Ablations confirm three predicted effects: shallower circuits, smaller blocks and dropping reuploading all lower accuracy.
[LG-132] Ablating Design Choices for Protein Ensemble Generators Across Structural Regimes
链接: https://arxiv.org/abs/2611.05055
作者: Eoin Quinn,Oliver Bent
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注: 13 pages, 2 figures
Abstract:Generative models of protein conformational ensembles inherit much of their design from structure prediction for folded proteins. Ordered and disordered proteins, however, occupy distinct structural regimes, and we investigate how design choices hold up across them. Using a minimal C \alpha flow-matching model, we ablate choices of architecture, sequence conditioning, rigid-body alignment and training source, and evaluate each variant against experimental SAXS and NMR chemical-shift data partitioned into ordered, disordered, multi-domain and transitional proteins. Mixture-of-experts routing, a static pair representation and a disorder probe have no detectable effect, a smaller protein language model performs worse on ordered proteins but not on disordered ones, and alignment in the loss degrades agreement with SAXS. Replacing coarse-grained simulations of disordered proteins with fragment-assembled ensembles improves agreement with chemical shifts at a cost to SAXS, while an equal mixture of the two keeps most of the gain at a smaller cost. This points to the training data, rather than the architecture, as the main constraint.
[LG-133] One-shot Distributed Learning via Weighted Averag ing with Server-side Information
链接: https://arxiv.org/abs/2611.04700
作者: Pavle Tepavčević,Dušan Jakovetić,Miloš Savić
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 16 pages, 3 figures, 3 tables. Includes appendices with proofs and numerical experiment details
Abstract:Distributed learning has drawn considerable attention due to its capability of learning from distributed data without aggregating raw observations. However, frequent exchange of model updates can become the main bottleneck of communication. Therefore, one-shot approaches where each worker fits a local model and sends it to the server to aggregate emerge. It is known that averaging can achieve the so-called pooled-data mean squared error (MSE) order of a hypothetical node that has access to data from the server and all the workers, and performs empirical risk minimization (ERM) on it. However, when the number of workers increases too fast compared to the local sample size, nonzero local estimation bias prevents averaging from achieving this order. In this paper, we study whether independent server data can overcome this limitation. We consider m workers with n observations each and a server with p observations, all i.i.d., giving N=mn+p observations in total. Under certain regularity conditions, a pooled-data ERM achieves MSE of order \mathcal O(N^-1) . We analyze deterministic nonnegative averaging of convex-loss M-estimators, with equal worker weights and the server’s own estimator included. Under our assumptions, including nonzero local bias of order n^-\alpha , \alpha\geq1 , sample-size weighted averaging achieves the pooled-data MSE order whenever m=\mathcal O(n^2\alpha-1) . Above this worker scale, it does so if and only if p=\Omega(nm\cdot m/n^2\alpha-1) . Hence, server-only estimation, whose MSE is of order \mathcalO(p^-1) , can achieve pooled-data order while sample-size weighting fails. Analytical examples and synthetic experiments illustrate when averaging matches the pooled-data MSE order, complemented by CIFAR-10 experiments comparing aggregation strategies with linear and nonlinear classifiers. Comments: 16 pages, 3 figures, 3 tables. Includes appendices with proofs and numerical experiment details Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2611.04700 [stat.ML] (or arXiv:2611.04700v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2611.04700 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-134] Estimating “Realized” Skewness using Convolutional Neural Network
链接: https://arxiv.org/abs/2611.04598
作者: Haozhe Jiang,Ostap Okhrin,Michael Rockinger
类目: atistics Theory (math.ST); Machine Learning (cs.LG)
*备注: This paper first proves that bias exist given a fixed-horizon of high-frequency data to estimate the low-frequency skewness by using the plug-in method. Then it provides an innovation machine learning method to approximate the optimal estimator by fixing a temporal aggregation architecture for neural network before learning
Abstract:We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks followed by Multi Layer Perceptrons. Monte Carlo experiments involving stochastic volatility models, with and without jumps, show that the estimator reduces finite-sample bias relative to existing realized-skewness estimators and remains accurate under model misspecification. Empirically, our estimator exhibits temporal stability and delivers cross-sectional performance in actual skewness-sorted portfolios. We characterize the finite-sample bias of conventional plug-in skewness estimators and establish risk consistency properties of the proposed NN estimator under standard regularity conditions.
[LG-135] Conditional independence and graphical models for rankings
链接: https://arxiv.org/abs/2611.04586
作者: Aritz Pérez
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Ranking models define probability distributions over rankings and are fundamental in applications such as preference learning and decision-making. Without structural assumptions, these models are intractable due to their factorial complexity. Although independence assumptions are key to reducing complexity and enabling interpretability, standard notions of conditional independence must account for the mutual exclusivity constraints inherent to rankings. We introduce relative rank models, a probabilistic framework based on structured coarsenings of the ranking space that relax these constraints while preserving relative order information. This representation enables a well-defined notion of conditional independence for rankings and allows rankings to be modeled using Bayesian networks. As a result, standard methods for learning and probabilistic inference can be applied to rankings, with interpretable representations based on conditional independence. We further show that two prominent ranking models based on independence assumptions tailored to rankings arise as special cases, providing a unifying framework for independence in ranking models. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2611.04586 [stat.ML] (or arXiv:2611.04586v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2611.04586 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-136] Nearly Linear-Time Follow-the-Perturbed-Leader for Weakly Observable Graph Bandits
链接: https://arxiv.org/abs/2611.04307
作者: Jongyeong Lee,Hyundam Yoo
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: preprint
Abstract:We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Leader (FTPL) policy that achieves best-of-both-worlds guarantee with nearly linear expected time complexity. To preserve the simple structure of FTPL, we develop a novel graph-based loss estimator that combines forced exploration, conditional geometric resampling and adaptive learning rates with surrogate probabilities. Our policy achieves \tilde\mathcalO(\delta_^1/3 T^2/3) regret for adversarial regimes as well as \tilde\mathcalO(\delta_ \log T / \Delta_\min^2) regret for stochastic regimes, where \delta_* and \Delta , respectively, denote the fractional weak domination number and the suboptimality gap. These results show that refined guarantees for problems with minimax regret of \Theta(T^2/3) can be obtained through appropriately designed adaptive learning rates and loss estimators without sacrificing the simplicity of FTPL.
[LG-137] How Risky Is Your Data-Driven Policy?
链接: https://arxiv.org/abs/2611.04292
作者: Omar Besbes,Alon Rieger
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Data-driven algorithms are ubiquitous across industries, and there is an extensive literature on analyzing their \emphaverage performance. The risk profile of such algorithms, while critical, is much less understood. This paper quantifies the risk of data-driven policies and isolates its sources. We evaluate a policy through the conditional value-at-risk (CVaR) of its loss over the training sample, any policy randomization, and the future outcome, at a fixed unknown distribution. Regret against an oracle that knows the distribution decomposes into expected post-sample regret (PS), the average outcome-only CVaR gap of the selected action, and a sample-risk premium (SR), the additional tail risk omitted by averaging those evaluations. The analogous premium vanishes under expected loss. We characterize when the premium vanishes and how large it is relative to post-sample regret. For losses with a continuous density near the tail cutoff, we identify conditions under which both components decay at the same rate, yet the limiting ratio of the premium to post-sample regret can reach the thousands. When a probability mass at the cutoff moves with the action, the premium can decay more slowly and dominate post-sample regret. We establish conditions under which sample average approximation learns the oracle action at rate n^-1/2 in both settings, but its combined-risk regret is of order n^-1 in the first and n^-1/2 in the second. In finite-sample numerical examples of SAA in both settings, the premium accounts for approximately 74% – 97% of regret. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2611.04292 [stat.ML] (or arXiv:2611.04292v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2611.04292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-138] Integrating Machine Learning and Uncertainty Quantification for Real-Time Decision-Making in Digital Twins: A System-Centric Review and Perspective
链接: https://arxiv.org/abs/2611.04269
作者: Yi-Ping Chen,Ying-Kuan Tsai,Seul Lee,Vispi Karkaria,Dex Doksoo Lee,Wei Chen
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注:
Abstract:Digital Twins are evolving from passive virtual representations into closed-loop systems that continuously synchronize with physical assets and drive real-time decisions. Yet predictive modeling, machine learning (ML), uncertainty quantification (UQ), and decision-making have been studied as separate capabilities, leaving limited understanding of their integration and maintenance during online operation. This narrative review, scoped by the functional requirements of real-time decision-making rather than by Digital Twin terminology alone, examines how such decisions are generated and sustained in engineering Digital Twins. We first examine foundational decision-making architectures and representative engineering applications, followed by methods that address their limitations across the decision loop. We identify optimization-based, policy-based, and knowledge-based approaches as the major pathways from virtual representation to action. Across them, ML plays three roles in decision-making: enabling efficient surrogate modeling, transforming heterogeneous physical feedback into decision-relevant information, and accelerating or directly generating decisions. UQ supports uncertainty-aware decisions and model adaptation, while continual validation, drift detection, and adaptation help maintain reliable operation as the physical system evolves. Together, these findings underscore the need for a shift from a model-centric to a system-centric perspective on Digital Twins, where its performance depends on the interactions with sensing, state estimation, computation, decision-making, and adaptation rather than on their standalone performance. Therefore, future research should shift from isolated algorithm development toward system-level co-design, scalable decision-making, and approaches to design, and data-efficient continual adaptation and validation for AI-enabled autonomous systems.
[LG-139] wo-Layer Random ReLU Networks Can Separate Disjoint Classes
链接: https://arxiv.org/abs/2611.04211
作者: Yichen Liu,Jianbin Yang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 46 pages,7 figures
Abstract:Set separation by continuous linear functionals is fundamental in functional analysis and convex optimization and underlies binary classification in machine learning. Support vector machines provide maximum-margin affine separators for linearly separable data, while kernel methods represent nonlinear decision rules through linear separation in a reproducing-kernel Hilbert space. We study when two classes, not necessarily convex or linearly separable in the input space, can be separated with positive margin by finite ReLU networks and by random ReLU features. At the deterministic level, we prove that positive-margin separation by a finite ReLU network is equivalent to the existence of finite closed convex covers whose opposite-label members have positive pairwise distances, and exactly two hidden layers suffice. For random separation, suppose each class is represented as a finite union of nonempty closed convex cells, possibly unbounded. If every opposite-label cell pair has positive distance and its closed difference cone is pointed, then two independent random ReLU layers yield a common positive-margin affine separator with high probability. The first layer separates the feature image of each negative cell from the feature image of the positive class by a hyperplane through the origin. It also controls all cross-class distances uniformly. The second layer combines cellwise scores through Chebyshev polynomials and realizes the resulting polynomial using Gaussian Hermite features, followed by a uniform finite-width approximation. With the remaining geometric, bias, and confidence parameters fixed, each sufficient width is quadratic in the largest Gaussian width of a spherical difference-cone section, hence at most linear in its span dimension. An example shows that this squared Gaussian-width dependence is sharp for both layers. Comments: 46 pages,7 figures Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2611.04211 [stat.ML] (or arXiv:2611.04211v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2611.04211 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-140] Point interpolation and distributional universality in flow-based generative models
链接: https://arxiv.org/abs/2611.04207
作者: Antonio Álvarez-López,Jingpu Cheng,Liyan Zhang,Qianxiao Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 45 pages, 3 figures
Abstract:Motivated by the expressive power of neural ordinary differential equations in flow-based generative modeling, we study the approximation of probability measures by pushforwards generated by parameterized families of Lipschitz vector fields. For Wasserstein distances, we establish a quantitative relationship between finite-sample point interpolation and distributional approximation, and derive universality criteria based on L^p -approximation of transport maps. In particular, affine-invariant families containing a non-affine vector field are universal from compactly supported absolutely continuous base measures. For total variation, we show that coordinate-wise dynamics are insufficient in dimensions d\geq 2 and establish sufficient conditions based on one-dimensional Sobolev approximation and shear-type mixing. We further examine the role of point interpolation, establishing when different regimes relate to one another and when approximate interpolation can be made exact.
[LG-141] Descending Everest Part II: Oracle Complexity of Minimizing a Sum of Convex (L_0L_1)-Smooth Functions
链接: https://arxiv.org/abs/2611.04171
作者: Mikhail Rudomanov,Ivan Fomin,Alexander V. Gasnikov,Fedor S. Stonyakin
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 57 pages, 4 figures, 5 tables, 5 algorithms. Second paper in the Descending Everest series; Part I: arXiv:2610.21461
Abstract:We study the minimization of the average of n convex (L_0,L_1) -smooth functions: the Hessian norm of each function is bounded by an affine function of its gradient norm. We assume known upper bounds R on the distance from the starting point to a minimizer and \delta on the difference between the minimum of the average and the average of the infima of the components. We derive upper and lower bounds on the oracle complexity of reaching accuracy \varepsilon in function value in expectation. Up to logarithmic factors, both bounds are of order n(1+(L_1R)^2/3) +\sqrt\fracn(L_0+L_1^2\delta)R^2\varepsilon+n(L_1R)^2 . The upper bound is attained by the proposed method YETI. The method combines ideas of accelerated proximal methods and variance reduction methods with a heuristic for choosing additional points. The number of oracle calls made by the heuristic is bounded, which preserves the upper bound on the total number of queries needed to reach the target accuracy. In the experiments, YETI is compared with first-order methods on synthetic problems and Poisson regression problems. We also examine the stopping criterion and the effect of the heuristic separately. The lower bound is obtained by an explicit construction of families of instances that are hard for any first-order method.
[LG-142] Nonparametric GARCH: An Iterated Neural Estimator
链接: https://arxiv.org/abs/2611.04157
作者: Ruizhi Deng,Guohao Shen,Ngai Hang Chan
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:
Abstract:We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved conditional variances. Under mild conditions, we establish finite-sample bounds for the time-averaged in-sample variance-path error and show that the estimator is minimax rate-optimal up to logarithmic factors. Under stationarity and mixing conditions, we establish the same rate for recursion-map estimation and derive finite-sample bounds for out-of-sample variance forecasts and expected excess Gaussian quasi-likelihood (QLIKE) loss. The convergence rates depend on the true lag orders and remain unchanged when the fitted orders are overspecified. Simulation studies illustrate the effectiveness of our estimators under nonlinear and time-varying volatility dynamics. Real-data applications suggest that our neural methods perform competitively and can incorporate option-price and high-frequency information.
[LG-143] Hardware-Aware Design and Layout Selection in Quantum Reservoir Computing for Insurance Claims Forecasting
链接: https://arxiv.org/abs/2611.03891
作者: Renáta Rusnáková,Eugen Šlapak,Gaurav K. Pandey,Juraj Gazda
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Statistical Finance (q-fin.ST)
*备注:
Abstract:Quantum reservoir computing (QRC) processes temporal information through quantum dynamics, but it remains unclear whether small QRC models can forecast real data competitively with classical models and whether their predictions survive the transfer from ideal simulation to noisy hardware. We address both questions for a six-qubit gate-based reservoir that forecasts next-day motor-insurance claim counts. Five reservoir candidates were selected and frozen using hardware-native interaction graphs, Trotterized Ising dynamics, memory and measurement-robustness criteria, circuit cost, and calibrated-noise screening. On 232 held-out test days, the ideal candidates reached root-mean-square errors (RMSEs) of 5.66-6.12 claims/day, compared with 5.36 for ridge regression. For four of the five, the difference from ridge was not significant under a 95% block bootstrap, and their accuracy was comparable with classical recurrent and reservoir baselines. On the 156-qubit IBM Kingston processor, we ran 63 evaluations in 21 paired rounds, comparing hardware-aware, random-native, and Qiskit optimization level 1 layouts. Hardware-aware placement gave lower RMSE than Qiskit in every round, with a maximum transfer distortion of 7.59 claims/day (11.84 for Qiskit), and avoided the severe transfer failures seen for some random layouts. Most of the hardware distortion appeared as a systematic forecast shift, which a short hardware probe on training inputs detected before the test runs. Longer replay windows raised circuit cost without a consistent gain in forecasting accuracy or hardware transfer. Small QRC models can thus reach competitive accuracy on this task, and hardware-aware layout selection makes their transfer to IBM Kingston more consistent.
[LG-144] Learning Almost Complex and Complex Structures
链接: https://arxiv.org/abs/2611.03872
作者: Edward Hirst,Tancredi Schettini Gherardini,Tomás S. R. Silva,Jakob Stein
类目: Complex Variables (math.CV); Machine Learning (cs.LG)
*备注: v1. 23 pages, 5 figures, 7 tables. Code and data available at this https URL
Abstract:We study the numerical constructions of almost complex and complex structures on spheres and products of spheres up to real dimension six. Our general set-up parametrises almost complex structures via a global matrix field in an ambient Euclidean space, compressed by the tangent projector. The projector’s algebraic residual measures the almost complex condition, while its Nijenhuis tensor is evaluated in the intrinsic norm of the round product metric. Polynomial features and neural networks are trained without prescribed targets to perform general searches for these structures. The almost complex search passes numerical admissibility checks on all nine examples that admit such structures and rejects S^4 , where a topological obstruction is known. A matrix correction then enforces the almost complex identity, subject to a novel uniform admissibility condition. For integrability, a free polynomial representation with Levenberg-Marquardt optimisation learns the complex structures on S^2\times S^2 , S^3\times S^1 , S^3\times S^3 and S^5\times S^1 , with the largest Nijenhuis norms found by adversarial search all below 10^-9 . These general searches were unsuccessful on S^6 , S^4\times S^2 and S^4\times T^2 . Therefore, for these three cases direct numerical reconstructions were built in torus-fibred coordinates. They are integrable by construction, satisfy their compatibility conditions to better than 10^-11 , and corroborate the recent constructions on S^6 and S^4\times S^2 .
[LG-145] BEND 2.0: Benchmarking the Capabilities Needed to Design DNA
链接: https://arxiv.org/abs/2611.03806
作者: Andreas Bjerregaard,Rachael DeVries,Dionysia Danai Brilli,Panagiotis Antoniadis,Yan Li,Anders Krogh,Wouter Boomsma,Ole Winther,Frederikke Isa Marin
类目: Genomics (q-bio.GN); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: Leaderboard on this https URL and code on this https URL
Abstract:Genomic foundation models are increasingly used to propose DNA sequences and edits. But successful design requires capabilities that current benchmarks do not measure. Here we introduce BEND 2.0 (BENchmark for DNA language models), which tests how well models predict the effects of DNA changes and whether these predictions can guide sequence design. Across eight tasks built on five experimental datasets, we find that model choice and how representations are accessed both affect performance. Linear readouts fitted to frozen representations can match or exceed models’ native outputs, and functional post-training substantially improves the models’ capabilities. Linear readouts transfer well across experimental assays and genes, suggesting that models capture functional patterns beyond the data used to fit individual predictors. At the same time, strong prediction of average effects can conceal weaker prediction of context-specific effects, and accurate prediction of combined mutation effects does not imply accurate prediction of their interactions. In design tasks, model predictions can reduce the number of candidates needed to find a desired response, but still leave substantial room for better design choices, particularly for context-specific and multi-cell objectives. Our benchmark reveals that genomic models now display signs of general utility but have yet to saturate on performance in even simple tasks.
[LG-146] 3D dark matter map of the JWST COSMOS-Web field using diffusion-based generative priors
链接: https://arxiv.org/abs/2611.03786
作者: Diana Scognamiglio,Brandon Zhao,Katherine L. Bouman,Olivier Doré,Caitlin M. Casey,Maximilien Franco,Ghassem Gozaliasl,David Harvey,Olivier Ilbert,Jeffrey B. Jewell,Jeyhan S. Kartaltepe,Anton M. Koekemoer,Gavin Leroy,Richard Massey,Jason Rhodes,Brant E. Robertson,Marko Shuntov,Sune Toft,Greta Toni,Michael A. Troxel
类目: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Machine Learning (cs.LG)
*备注: 24 pages, 5 main figures, 6 extended data figures
Abstract:Mapping the three-dimensional distribution of dark matter is key to tracing the growth of cosmic structure. Two-dimensional weak lensing mass maps recover its projected distribution, but they blend structures at different distances along the line of sight. We reconstruct the three-dimensional matter distribution in the 0.54 deg ^2 COSMOS-Web field using deep James Webb Space Telescope imaging, with an effective source density of 121 galaxies arcmin ^-2 We combine a physical weak lensing forward model with a diffusion-based Bayesian generative prior learned from cosmological simulations, yielding a posterior distribution of the matter field across 20 lens planes to z \simeq 2 , doubling the redshift reach of previous Hubble Space Telescope-based 3D reconstructions and approaching cosmic noon, an epoch of rapid galaxy growth. The reconstruction separates structures that overlap in projection and recovers non-Gaussian matter structure across a range of spatial scales, including over-densities associated with independently identified galaxy clusters that were not detected in earlier weak lensing maps. Posterior sampling quantifies the range of three-dimensional matter distributions allowed by the data, enabling a probabilistic visualization of the cosmic web while propagating uncertainties in its inferred structure. Null and simulation-based tests show that the recovered structures are supported by the lensing signal and are consistent with expectations from the fiducial \Lambda CDM cosmology. These results extend three-dimensional weak lensing mapping into the high-redshift, non-Gaussian regime and establish a route towards probabilistic mapping of the evolving cosmic web with current wide-field lensing surveys.
[LG-147] Complete Donor-Set Guidance for Frozen Diffusion Docking Models
链接: https://arxiv.org/abs/2611.03731
作者: Hyunwoo Kim,Insoo Park,Ziseok Lee,Wooyeol Lee,Yoorim Gang,Kyungsu Kim
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注: 38 pages including appendices, 4 figures. Hyunwoo Kim and Insoo Park contributed equally
Abstract:Metalloprotein docking requires a ligand pose to satisfy both global pocket fit and the identity and geometry of metal-coordinating atoms. General-purpose diffusion docking models do not explicitly condition generation on externally specified complete ligand donor sets, and their predicted poses can exhibit incorrect metal-donor relationships. Metal-aware models address coordination during training and generation; here we ask whether external donor information can correct already-trained general-purpose models without retraining. We propose Complete-Identity Contract Guidance (CICG), which applies a supplied complete donor set through translation, rotation, and torsion updates, and Historical Contract Transfer (HCT), which supplies donor identities from earlier complexes at compatible receptor sites. We compare HCT with trajectory-local controls retaining the historical donor count with or without elemental composition. On 86 assessable HCT-covered queries, HCT-guided CICG increases frozen-confidence-selected joint success from 21.5% to 36.6% on SurfDock and from 25.0% to 34.9% on DiffDock-L. The joint criterion requires correct global ligand placement, complete native donor recovery, coordination geometry, preserved stereochemistry, and absence of fatal receptor clashes. Composition-matched guidance reaches 33.14% and 34.30%, leaving the additional benefit of fixed historical membership unresolved. Our results therefore support donor-set guidance as a conditional correction for frozen docking models, while identifying donor-source reliability and physical-quality preservation as the main barriers to broader use.
[LG-148] All Paths All at Once: A Quantum Monte Carlo and Neural Committor Approach to Reaction Pathway Search
链接: https://arxiv.org/abs/2611.03699
作者: Chien-Chai Chang
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注:
Abstract:Locating competing reaction pathways is difficult when potential-energy gradients are unavailable. Here we present tReax_expedition, an energy-only approach combining coupled Monte Carlo exploration, stochastic quench labelling and restrained continuation. Exploration identifies connected states, while continuation resolves candidate channels and records sampled chain maxima. Single-search benchmarks on three two-dimensional potentials recovered an intermediate and competing routes without transition-state guesses. For eight of nine gas-phase reactions on GFN2-xTB, sampled chain maxima differed from independently refined saddle energies by less than 0.76~kJ,mol ^-1 . Alanine dipeptide instead revealed incomplete conformational relaxation, showing that route connectivity can persist despite structural and energetic errors. Neural committor learning and local straddling-pair sampling connect the resulting paths to transition-region sampling, although molecular shooting diagnostics indicate limited committor calibration. These results show how energy-only exploration and continuation recover candidate pathways, while distinguishing route connectivity from geometric localization, barrier resolution and finite-temperature transition statistics.
[LG-149] A local orchestration harness for molecular design with foundation models
链接: https://arxiv.org/abs/2611.03676
作者: Sakib Mostafa,Md Atik Ahamed,James Zou,Lei Xing,Md Tauhidul Islam
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注:
Abstract:Molecular design requires coordinated generation, property prediction and iterative editing, with each decision linked to the correct chemical structure and supporting evidence. Integrating these capabilities in practice presents three major challenges: retaining local control of molecular data, connecting specialized models across tasks and verifying that proposed changes satisfy chemical constraints. Here we present MolCrew, a locally deployable system that coordinates four separately trained molecular specialists with a language-model orchestrator and RDKit calculations. The specialists generate structures from descriptions, describe molecular structures, predict biological endpoints and propose edits towards specified goals. Separate model weights allow each capability to be evaluated and updated independently within the workflow. RDKit calculations check structural validity, quantify descriptor changes and assess specified structural constraints, while returned observations guide further tool calls. We evaluated low-rank adaptation of the orchestrator for tool-use decisions and failure recovery while leaving specialist weights unchanged. On 3,300 ChEBI-20 test examples, MolCrew achieved 96.4% exact structure recovery, compared with 38.7% for published Atomas-large results. Across eight MoleculeNet datasets evaluated using scaffold splits, mean area under the receiver operating characteristic curve was 0.851, compared with 0.770 for published Atomas results. In a recorded evaluation using the unadapted orchestrator, 1,554 of 1,699 requests (91.5%) passed every predefined workflow check. By preserving inspectable links between molecular proposals, model outputs and chemical calculations, MolCrew provides a modular framework for computational candidate assessment and refinement under local data control.
[LG-150] A Simulation-Driven Digital Workflow Integrating Bayesian Optimization for Early-Stage Process Design: A Methyl Acetate Production Case Study
链接: https://arxiv.org/abs/2611.03673
作者: Trung Ngoc Trinh,Trung Thanh Nguyen,Nikolaos Tsakoumis,Roman Tschentscher
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG)
*备注: 53 pages (including appendices), 12 figures, 4 tables
Abstract:Methyl acetate production via direct carbonylation of dimethyl ether and carbon monoxide involves strong nonlinear interactions among reactor operation, recycle structure, and energy consumption, making conventional simulation-based optimization computationally demanding. In this work, a data-driven framework integrating steady-state process simulation, surrogate modeling, and Bayesian optimization was developed for process intensification. A dataset of 2730 steady-state simulations was generated using a Python-based automated process simulation workflow and a structured design of experiments across the operating domain. Surrogate models were trained to predict carbon conversion yield (C-yield) and cost of specific energy consumption (C-SEC), with neural network-based models showing the best predictive performance and robustness. These models were integrated into a multi-objective Bayesian optimization framework to identify optimal operating regions. Results indicate that high C-yield (up to ~95%) can be achieved with reduced energy cost through lower recycle flow rates and compression demand. Pareto analysis revealed a nonlinear trade-off between C-yield and C-SEC, where further improvements near the conversion limit require disproportionately higher energy input. Validation against rigorous simulations showed deviations below 3% for C-yield and 10% for C-SEC.
[LG-151] Predicting Factor Decay: ML Models for Cross-Factor Predictability Erosion
链接: https://arxiv.org/abs/2611.03668
作者: Chorok Lee
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注: 10 pages, 6 figures
Abstract:We develop ML models that predict when cross-factor predictability will erode, enabling prospective risk management. A decay prediction model achieves C-index = 0.93 on held-out factor pairs, providing early warning of signal erosion—the key predictor is initial signal strength: stronger signals decay faster (``regression to the mean’'). An online regime detector enables real-time classification with 6.6-day average detection delay. Prior work explains why factors decay (arbitrage, publication effects); we predict when decay will occur. Limitation: The primary HML \to SMB signal does not replicate in post-break OOS (2013–2024); this null result is consistent with genuine decay but limits confirmatory claims. Motivation: Among 30 directed Fama-French factor pairs (1990–2024), predictability targeting the Size factor (SMB) decays fastest. Of 7 pairs showing exponential decay ( R^2 0.3 ), 4 target SMB (Mkt-RF \to SMB, CMA \to SMB, HML \to SMB, RMW \to SMB; half-lives 2.4–4.1 years). This SMB-concentration pattern motivates the predictive models. Methodology: Using a regime-conditional Granger framework with Student- t HMMs, we document structural breaks clustering around June 1998 ( p 10^-10 ). A pre-break OOS test (HMM trained 1990–1995, tested 1996–2007) confirms signals existed: HML \to SMB shows p = 0.013 (Elevated) and p 0.001 (Crisis). The primary HML \to SMB signal does not replicate in post-break OOS (2013–2024), consistent with genuine decay rather than overfitting—after \sim6 half-lives, the expected signal is 1% of its 1990s peak. International analysis finds 2/4 non-US markets with Bonferroni-surviving OOS effects; correction for full specification search was not applied, so results remain exploratory.
[LG-152] ail reliability in probabilistic electricity price forecasts: comparing model combination and conformal prediction in the GB day-ahead market
链接: https://arxiv.org/abs/2611.03661
作者: Anirudh Agrawal,Aidan O’Sullivan
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注:
Abstract:Probabilistic forecasts of day-ahead electricity prices are increasingly used to manage price risk, yet competing models are usually ranked on aggregate scores that average over the price regimes in which uncertainty matters most. This study evaluates probabilistic forecasts of hourly EPEX SPOT GB day-ahead prices over a full held-out calendar year, applying a common pre-auction information boundary and rolling-origin design to linear quantile regression, a quantile regression forest (QRF), a LEAR-QRA benchmark and an encoder-decoder quantile long short-term memory (LSTM) network. Two routes to more reliable intervals are then compared: conformal recalibration and Quantile Regression Averaging (QRA). Aggregate scores separate the LSTM and QRF only weakly, with a non-significant 4.4% difference in continuous ranked probability score (CRPS), but their conditional behaviour differs sharply. Above 200 GBP/MWh the LSTM covered 0.12 of outcomes and QRF 0.71, consistent with the forest retaining realised spikes in its leaf distributions while the network learned a smooth upper tail. Conformal recalibration corrected marginal coverage but did not improve the interval score or materially repair spike coverage. QRA combining the LSTM, QRF and LEAR-QRA forecasts reduced CRPS to 5.14 GBP/MWh and raised coverage above 200 GBP/MWh to 0.65 at unchanged interval width. Recalibration and combination therefore address different failures and are not substitutes.
[LG-153] Mapping urban heat from citizen-collected data: a scalable Gaussian process approach to correcting for preferential sampling
链接: https://arxiv.org/abs/2611.03659
作者: Ellie K. Kim,Zachary D. Calhoun,Michael H. Bergin,David E. Carlson
类目: Applications (stat.AP); Machine Learning (cs.LG)
*备注:
Abstract:The urban heat island (UHI) effect amplifies heat stress in cities, increasing heat-related illness, mortality, sleep disruption, and energy demand. Because UHI intensity varies at neighborhood scales, intra-city temperature mapping is essential for assessing public health impacts, yet existing monitoring networks are sparse. Crowdsourced weather stations provide dense, hourly observations but suffer from noise and systematic biases, including preferential sampling driven by where residents install sensors. We introduce two scalable Gaussian process frameworks which adjust for preferential sampling and enable statewide, high-resolution temperature prediction. The first integrates sampling correction directly within a sparse variational Gaussian process (SVGPR+PS). The second uses a two-stage estimator involving a log-Gaussian Cox process (LGCP+GP) that estimates the sampling intensity field and incorporates it into temperature prediction. Both methods rely on inducing-point approximations and variational inference to scale beyond traditional Gaussian processes. Applied to approximately 10,000 citizen stations across North Carolina, the models generate hourly temperature fields within minutes. Validation against independent urban heat campaign data shows that the preferential sampling correction improves predictive performance, increasing average afternoon correlations across four cities from 0.26 to 0.43. This scalable framework enables neighborhood-level UHI assessment and supports studies of heat exposure disparities statewide.
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