本篇博文主要内容为 2026-11-11 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 (2026-11-11)
今日共更新831篇论文,其中:
- 自然语言处理共113篇(Computation and Language (cs.CL))
- 人工智能共248篇(Artificial Intelligence (cs.AI))
- 计算机视觉共151篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共238篇(Machine Learning (cs.LG))
- 多智能体系统共10篇(Multiagent Systems (cs.MA))
- 信息检索共20篇(Information Retrieval (cs.IR))
- 人机交互共34篇(Human-Computer Interaction (cs.HC))
多智能体系统
[MA-0] ReTrace: Distilling Tool-Using Agents into Planner-Free Workflows
【速读】：该论文旨在解决生成式 AI（Generative AI）在业务流程自动化中因频繁调用大语言模型（LLM）而带来的高延迟与高成本问题。传统LLM代理在执行标准操作程序（SOP）时，每一步决策均依赖于LLM重新生成，导致效率低下且难以规模化。其解决方案的关键在于提出ReTrace框架，通过分析少量已记录的代理运行轨迹，自动提取每个步骤背后的逻辑条件，将这些条件合并为紧凑的规则文档，并基于规则生成可执行的工作流。该工作流以代码形式实现步骤选择，不再依赖LLM进行每步决策，从而显著降低计算开销。此外，该方法具备平台无关性，规则文档可编译适配不同编排平台；通过引入对训练失败案例的修复机制，进一步提升了任务成功率与稳定性，在多个基准测试中表现出优于或接近原生代理的性能，同时大幅降低单任务成本与执行时间。
链接: https://arxiv.org/abs/2611.14492
作者: Kaiyuan Hou,Yang Ping Kuo,Indranil Bhattacharya,Rohith Nama,Arushi Gupta,Carlos D. Castillo,Prathap Ramachandra,Xiaofan Jiang,Francesco Carbone
机构: Columbia University (哥伦比亚大学); Amazon(亚马逊)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Business process automation runs the same Standard Operating Procedure (SOP) again and again on new inputs. Large Language Model (LLM) agents can follow an SOP written in natural language, but they call an LLM to choose every step of every run, which adds latency and cost at scale. We present ReTrace, which turns an SOP and a few logged agent runs into a reusable workflow in which code, not an LLM, selects the next step. The agent’s reasoning, the explanation it writes with each tool call, often states why it took an action; the tool calls alone do not. ReTrace extracts the condition behind each step, merges these conditions across runs into a compact rule document, and generates an executable workflow from it. Only this last stage depends on the platform, so rule documents can be compiled for different orchestration platforms. On 79 procedures from our production platform, ReTrace workflows reach 93.8% completion, against 96.8% for the agent, at 24% lower cost per task. On \tau^2 -Bench, they reach 75.8% task success (agent: 80.6% ) and run in 1.3 s instead of 170 s; three rounds of repair on training failures lift k=2 workflows from 66.7% to 81.2% . On eight SOP-Bench procedures, ReTrace without repair trails few-shot generation ( 80.6% vs. 83.6% ), but repair from labeled training failures lifts it to 87.8% at 0.40 s per task, compared to 85.5% in 46.6 s for the agent.
[MA-1] Dont Just Trust Your Memory: Evidence-Linked Memory Reduces Memory-Induced Degradation in LLM Agents ACL
【速读】：该论文旨在解决大语言模型（LLM）智能体在使用外部记忆进行经验学习时可能出现的“记忆诱发退化”（memory-induced degradation）问题。具体而言，当智能体依赖从历史任务轨迹中提炼出的简短经验教训（lesson）时，若这些教训本身存在错误、被误用或误解，反而会导致其在原本能够解决的任务上失败。根本原因在于，这些教训仅基于任务成功或失败的标签生成，且在后续使用中通常不附带原始轨迹信息，使智能体无法验证或修正教训的可靠性。为应对这一挑战，论文提出WikiTrace——一种基于证据链接的外部记忆系统，其核心创新在于将每个教训与原始轨迹的摘要及原始数据本身进行绑定，从而实现可追溯性与可验证性。在新任务执行过程中，智能体可从所有已存储的见解出发，当某一见解缺乏行动所需细节、与其他见解冲突，或源自失败任务时，通过链接调用终端工具回溯原始轨迹，以补充信息或修正见解。实验在五个代理型基准测试中验证了WikiTrace的有效性，结果表明其不仅显著优于现有记忆方法和仅依赖相同见解的基线，还能有效缓解记忆诱发退化现象。
链接: https://arxiv.org/abs/2611.14320
作者: Yukyeong Lee,Yumin Choi,Jinheon Baek,Sung Ju Hwang
机构: KAIST(韩国科学技术院); DeepAuto.ai
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Submitted to ACL Rolling Review (October 2026 cycle); under review
Abstract:External memory enables LLM agents to improve from experience by distilling trajectories of past tasks into concise lessons for new tasks. However, memory can also lead to memory-induced degradation, where an agent fails on tasks it would solve without memory, often because it follows a lesson that is incorrect, or misapplies or misreads a valid one. This is because lessons are inferred from trajectories labeled only with task success or failure, yet are typically provided without the trajectories they came from, leaving the agent no way to check them. To address this, we introduce WikiTrace, an evidence-linked memory system designed to let agents check each lesson against its original trajectory. Specifically, motivated by the finding that a trajectory often still holds the evidence needed to catch an erroneous lesson, WikiTrace stores each lesson as a concise insight linked to a summary of why its trajectory succeeded or failed and to the raw trajectory itself. Then, on a new task, the agent starts from all insights and, whenever one lacks details needed to act, conflicts with another, or comes from a failed task, descends through its links with terminal tools to fill in missing details or correct the insight before acting. We validate WikiTrace on five agentic benchmarks, showing that it consistently outperforms existing memory approaches and a baseline given the same insights alone, while also reducing memory-induced degradation. Our code is available at this https URL
[MA-2] Personalized Affective Perception via Continual Learning in Human-Agent Negotiation
【速读】：该论文旨在解决智能代理在与人类协商过程中，因通用面部情绪模型在个体间和情境间泛化能力差而导致的情绪感知偏差问题，即“泛化差距”（generalization gap）。其核心解决方案是提出一种情感自适应的协商策略，关键在于引入基于想象的持续学习面部表情识别（Continual Learning with Imagination for Facial Expression Recognition, CLIFER），实现对个体效用价值（valence-arousal）的实时个性化感知。通过差异反馈机制，将情绪维度的变化方向与强度转化为代理目标效用的动态调整；同时采用异步架构，在实时交互中后台持续更新CLIFER模型，避免干扰交互流程。实验结果表明，相较于基线方法FaceChannel，该方案显著提升了个体效用和参与者让步率，且在平均唤醒度估计和输出范围上表现更优，验证了持续情感个性化与差异反馈耦合在实时协商中的实际价值。
链接: https://arxiv.org/abs/2611.14247
作者: Mehmet Onur Keskin,Berk Buzcu,Nikhil Churamani,Reyhan Aydogan,Hatice Gunes,Catholijn Jonker
机构: Özyeğin University (Özyeğin大学); University of Cambridge (剑桥大学); TU Delft (代尔夫特理工大学); HES-SO Valais-Wallis (洛桑联邦理工学院瓦莱-瓦利分校)
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Multiagent Systems (cs.MA); Robotics (cs.RO)
备注: 17 pages, 11 figures. Submitted to IEEE Transactions on Affective Computing
Abstract:Self-interested intelligent agents that negotiate with humans should interpret nonverbal expressions and adapt their behavior accordingly. However, generic facial-affect models degrade across individuals and contexts, creating a generalization gap. We introduce an affect-adaptive negotiation strategy that incorporates Continual Learning with Imagination for Facial Expression Recognition (CLIFER) to personalize valence-arousal perception in real time. Differential feedback converts changes in valence-arousal direction and intensity into adjustments of the agent’s target utility. An asynchronous architecture updates CLIFER in the background during live interaction. In a counterbalanced within-subject study in which 66 participants negotiated with a humanoid robot, the CL-enabled agent achieved significantly higher individual utility and elicited higher participant concession rates than the FaceChannel baseline. The CL condition yielded significantly higher mean arousal estimates, alongside a broader output range. Participant utility and Nash distance did not change significantly between conditions. These outcomes support the practical value of coupling continual affective personalization with differential feedback for real-time negotiation.
[MA-3] PARLEY: Evaluating LLM Collaboration Through Cooperative Asymmetric Puzzles
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在作为自主智能体（autonomous agents）协作时，其协作机制与失败模式缺乏系统性理解的问题。为应对这一挑战，研究提出PARLEY平台，通过四个基于认知心理学的非对称谜题任务（参照性沟通、联合协调、观点采择和空间推理）来考察LLM智能体间的协作表现。其解决方案的关键在于：从实际协作对话中提炼出数据驱动的失败类型分类体系——PAC分类法（Perception, Adaptation, Coordination），将六类失败模式归纳为感知、适应与协调三大维度。研究发现，未能有效整合外部反馈是导致协作失败的核心因素，表现为智能体持续重复无效策略。进一步通过基于该分类法的干预措施（N=200），任务成功率由60.5%提升至70.5%，验证了该分类体系在指导智能体协作优化方面的有效性。因此，PARLEY平台不仅提供了评估协作能力的基准框架，更实现了从行为观察到可操作改进路径的数据驱动闭环。
链接: https://arxiv.org/abs/2611.14242
作者: Javier Hernandez,Andrew D. Wilson
机构: Microsoft Research(微软研究院); Microsoft Research(微软研究院)
类目: Multiagent Systems (cs.MA)
备注: 12 pages, 8 figures, 2 tables
Abstract:As large language models power autonomous agents, understanding how they collaborate and where they fail becomes critical. We introduce PARLEY, an extensible platform for studying LLM agent collaboration through four asymmetric puzzle tasks grounded in cognitive psychology (referential communication, joint coordination, perspective-taking, and spatial reasoning). GPT-4o agent pairs succeeded in 60.5% of conversations (N=400, 100 per task), with task-level variation (34-80%) reflecting distinct collaborative demands. From these conversations, we derived the data-driven PAC taxonomy: six failure types organized into Perception (visual interpretation and spatial reasoning), Adaptation (progress tracking and feedback integration), and Coordination (verification and grounding). Failure to incorporate external feedback was a critical indicator of unsuccessful collaboration, with agents repeatedly applying unsuccessful strategies. We also show that a taxonomy-guided intervention (N=200) improved average task success to 70.5%. These results demonstrate how PARLEY can be used to derive meaningful, data-driven insights that can in turn lead to improved agentic collaboration.
[MA-4] Safety and Connectivity Certificates for Finite-Time Formation Transitions
【速读】：该论文旨在解决多智能体系统在有限时间形成转移过程中，如何在保证精确有限时间收敛的同时，确保过渡阶段的碰撞规避与通信连通性的问题。现有方法虽能实现有限时间协调与安全约束控制，但缺乏对过渡阶段分歧（disagreement）与形成几何结构、耦合增益选择之间显式关联的理论分析，难以对安全机动进行形式化验证。本文提出一种基于分歧的框架，用于认证任意预设形成轨迹在有限时间跟踪过程中的瞬态安全性和连通性。其核心解决方案包括：局部反馈设计可实现无图依赖的有限时间精确跟踪，并提供统一的收敛时间上界；同时引入图依赖的分歧上界，推导出保证碰撞避免与连通性维持的充分几何条件，进而建立基于可用几何裕度的离线耦合增益选取规则。数值仿真验证了耦合增益增大时收敛速率与控制努力之间的权衡关系，同时展示了所提证书的有效性与保守性。
链接: https://arxiv.org/abs/2611.13927
作者: Yash Purswani,Shivansh Agarwal,Anuj Tiwari
机构: Indian Institute of Technology Madras (印度理工学院马德拉斯分校)
类目: ystems and Control (eess.SY); Multiagent Systems (cs.MA)
备注: 7 Pages, 2 Figures, under review
Abstract:Finite-time formation transitions are useful for coordinating multi-agent systems in time-critical missions, but convergence to a prescribed formation in finite time does not guarantee collision avoidance or communication connectivity during the transition. Existing approaches address finite-time coordination and safety-constrained formation control, yet explicit connections between disagreement during transitions, formation geometry, and coupling-gain selection need to be developed for certifying safe maneuvers. This paper develops a disagreement-based framework for certifying transient safety and connectivity during finite-time tracking of arbitrary prescribed formation trajectories. Local feedback guarantees exact finite-time tracking with a graph-independent settling-time bound, while a graph-dependent disagreement bound is used to derive sufficient geometric conditions for collision avoidance and connectivity preservation. These conditions further yield an offline coupling-gain selection rule based on the available geometric clearance. Numerical simulations illustrate the trade-off between convergence rate and control effort as the coupling gain increases. They also demonstrate the effectiveness and conservatism of the proposed certificates.
[MA-5] DrugCoding: A Scientific Programming Agent for Drug Discovery
【速读】：该论文旨在解决生成式AI在复杂药物发现流程中面临的自主任务规划能力不足、专用模型动态选择困难以及多阶段科学计算可靠执行难题。其核心解决方案在于提出DrugCoding——一个集科学任务规划、自适应模型路由、多智能体协作与程序化工具执行于一体的科学编程代理系统。该系统通过动态生成和执行代码，实现对专业化科学模型的灵活调用，并借助多智能体协同机制保障复杂科研任务的高效执行与可信验证。实验表明，DrugCoding在跨语言模型与科学工具接口的多轮工具调用任务中，整体会话级Pass@1达到97.35%，显著优于最强基线Codex（提升9.24%），在挑战性子集上更达97.92%，验证了其在复杂药物发现工作流自动化中的优越性能。
链接: https://arxiv.org/abs/2611.13651
作者: Junwei Hu,Zhuohao Lin,Kun Li,Hengnan Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Drug discovery involves multiple interconnected research stages, including molecular design, property prediction, and virtual screening. These processes are characterized by complex workflows, interdependent computational tasks, and diverse specialized tools, placing substantial demands on the coordination of scientific models and computational resources. However, existing large language model (LLM)-based scientific agents still face challenges in autonomous planning of complex tasks, adaptive selection of specialized models, and reliable execution of multi-stage scientific computations, limiting their effectiveness in automating complex drug discovery workflows. To address these challenges, we propose DrugCoding, a scientific programming agent system that integrates scientific task planning, adaptive model routing, multi-agent collaboration, and programmatic tool execution. DrugCoding enables autonomous task planning and dynamic model adaptation for complex research objectives. It can flexibly invoke specialized scientific models, autonomously generate and execute code according to task requirements, and leverage multi-agent collaboration to support efficient execution and reliable validation of complex scientific tasks. To evaluate DrugCoding’s performance in scientific tool invocation and multi-stage task execution, we conduct multi-turn tool-calling experiments across diverse language models and scientific tool interfaces. Experimental results demonstrate that DrugCoding achieves an average session-level Pass@1 of 97.35% on the full benchmark, outperforming the strongest baseline, Codex, by 9.24%. On the challenging subset, DrugCoding achieves 97.92%, exceeding the strongest baseline.
[MA-6] Do AI Agents need Preventive Care? An Epidemiologic Cohort Design for Agent ic AI
【速读】：该论文旨在解决当前生成式 AI（Generative AI）在自主执行任务过程中，其行为演化机制及其对长期结果影响缺乏系统性评估的问题。现有评估方法主要聚焦于模型在预设任务上的性能表现，忽视了对AI代理（AI agent）在真实工作环境中行为动态演变的追踪与因果分析。为此，论文提出一种基于流行病学队列研究设计的框架，将单个AI代理视为队列中的个体，其底层模型类比为“遗传背景”，累积的工作量作为时间尺度，自然产生的行为模式作为潜在暴露因素，进而探究这些行为对后续不良事件的影响。该方案的关键在于引入纵向追踪视角，结合因果推断方法，量化可预防负担，并刻画代理在其“工作生命周期”中行为特征的变化规律。通过两个前瞻性观察队列研究，分别探讨好奇心与试探性行为的安全性问题，以及数据核查与分析准确性所反映的可靠性问题，验证了该框架的可操作性与普适性，为构建可推广的AI代理流行病学研究体系提供了理论基础与实践蓝图。
链接: https://arxiv.org/abs/2611.13333
作者: Shu Jiang,Graham A. Colditz
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:As AI agents increasingly perform tasks autonomously across sectors, including healthcare and biomedical research, understanding how their behaviors develop and influence outcomes over time becomes increasingly important. Existing evaluations largely focus on model capabilities and performance on predefined tasks. Here, we propose an epidemiologic cohort design for studying agentic AI, drawing on principles established in human population studies. The framework treats individual agents as members of a cohort, their underlying model as genetic makeup, accumulated work as their time scale, and naturally occurring behaviors as exposures that may influence subsequent outcomes. By following agents longitudinally, this approach enables investigation of behavioral determinants of adverse events, estimation of their potential preventable burden under causal assumptions, and characterization of behavioral changes over an agent’s work life. We illustrate the design through two prospective observational cohorts examining curiosity and probing as a safety question, and data checking and analytical accuracy as a reliability question. Together, the framework and illustrative studies provide a generalizable blueprint for designing epidemiologic cohorts of AI agents.
[MA-7] From Process Models to Composable Agent ic Workflows: Combining BPMN Agents and Enterprise Knowledge
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）驱动的智能体（agent）在构建复杂协同应用时面临的可理解性、可监控性和可优化性难题。随着智能体任务逻辑分散于提示词（prompt）、工具调用及应用代码中，工作流呈现隐式结构，导致系统难以调试与维护。为此，论文提出**递归能力组合（Recursive Capability Composition）**作为核心解决方案，通过将业务流程建模为基于BPMN（Business Process Model and Notation）的显式工作流，实现对多智能体系统执行过程的可视化与可控化。其关键在于：利用LangGraph/Pregel运行时将语义丰富的SemTalk BPMN模型转化为可执行的智能体工作流，其中智能体任务（Agentic Tasks）在特定节点引入大语言模型推理能力，而传统BPMN元素则负责处理确定性流程控制、决策判断、系统交互和人工审批等环节；同时，通过将智能体行为或可执行流程封装为可复用的智能体工具（Agentic Tools），支持跨任务的能力复用与组合。此外，论文融合组织特有的流程模型与知识图谱（Knowledge Graph），为智能体提供结构化的企业上下文知识，从而增强其决策合理性。整体贡献体现在三个方面：模型驱动的执行、递归能力组合机制以及企业级上下文支持，显著提升了智能体系统的透明度、可管理性与可扩展性。
链接: https://arxiv.org/abs/2611.13196
作者: Christian Fillies,Jan Fillies,Frauke Weichhardt,Gay Anne Wood-Albrecht
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 14 pages, 3 figures
Abstract:LLM-based agents can reason, use tools and collaborate. However, coordinating increasingly complex agentic applications becomes difficult to understand, monitor and improve. Decisions distributed across prompts, tool calls and application logic make workflows implicit. We introduce Recursive Capability Composition, enabling agents to execute business processes and expose them as reusable Agentic Tools. This paper presents a BPMN-based approach to Agentic Workflows, combining explicit orchestration with autonomous reasoning. SemTalk BPMN models are transformed into executable workflows using a LangGraph/Pregel runtime. Agentic Tasks introduce LLM reasoning at selected activities, while conventional BPMN controls process flows, deterministic decisions, system interactions and human approvals. Agentic Participants provide reusable interfaces for agent behavior or other executable processes. BPMN orchestration makes multi-agent systems more understandable and manageable. Explicit process models provide developers and operators with a shared view of workflow structures and execution progress, facilitating debugging, monitoring and optimization. Enterprise agents also need organization-specific context. Process Models and Knowledge Graphs describe activities, participants, information objects, decisions and relationships between processes. They provide structured knowledge of how an organization operates, supporting informed agent decisions and actions. Our contributions are threefold: 1. Model-Driven Execution: Transforming BPMN models into executable Agentic Workflows. 2. Recursive Capability Composition: Exposing agents and executable business processes as reusable tools for other agents. 3. Enterprise Context: Using Process Models and Ontologies to provide organization-specific knowledge for agent reasoning. Comments: 14 pages, 3 figures Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2611.13196 [cs.AI] (or arXiv:2611.13196v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.13196 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Christian Fillies [view email] [v1] Mon, 9 Nov 2026 20:18:32 UTC (247 KB) Full-text links: Access Paper: View a PDF of the paper titled From Process Models to Composable Agentic Workflows: Combining BPMN, Agents and Enterprise Knowledge, by Christian Fillies and 3 other authorsView PDF view license Additional Features Audio Summary Current browse context: cs.AI prev | next new | recent | 2026-11 Change to browse by: cs cs.MA 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)
[MA-8] Auditable Human-Agent Teams for Autonomous X-ray Experimentation
【速读】：该论文旨在解决闭环自主实验系统在物理仪器上长期稳定运行的难题，尤其是如何降低其实施门槛以提升科学发现效率。其核心解决方案在于构建基于可验证科学方法的编码代理（coding-agent）基础设施，实现对纳米粒子超晶格在同步辐射X射线实验中的自主控制与动态目标调整。关键创新点在于通过完整的人-代理及代理-代理交互记录，实现了决策过程的可追溯性（provenance），并量化分析了认知劳动分工：在三轮实验中，46%至67%的通信内容涉及解释或判断，且方法论与科学贡献比例从首轮的46%逐步上升至56%-61%，表明系统在迭代过程中逐渐增强自主科学决策能力；同时，59%-68%的错误修正涉及多方协作，约一半决策采纳了至少一名其他参与者的输入，凸显了多代理协同机制在保障可靠性与创新能力方面的价值。该研究为多代理科学系统的设计、代理模型训练策略及可访问的自主实验平台发展提供了实证依据与设计原则。
链接: https://arxiv.org/abs/2611.12933
作者: Gunik Luthra,Ronald J. Pandolfi,Benjamin T. Diroll,Ricky Ronquillo,Ruipeng Li,Christopher B. Murray,Marcus M. Noack,Kevin G. Yager,Esther H. R. Tsai
机构: Brookhaven National Laboratory (布鲁克海文国家实验室); Lawrence Berkeley National Laboratory (劳伦斯伯克利国家实验室); Argonne National Laboratory (阿贡国家实验室); University of Pennsylvania (宾夕法尼亚大学)
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Closed-loop autonomous experimentation can accelerate scientific discovery, but implementation effort limits accessibility. Agentic systems may lower this barrier, although sustained operation on physical instruments remains largely unexplored. Here, using coding-agent infrastructure grounded in validated scientific methods, we demonstrate autonomous synchrotron X-ray experiments on nanoparticle superlattices under evolving, human-defined objectives. Complete human-agent and agent-agent transcripts enable an audit that reconstructs decision provenance and characterizes the division of cognitive labor. Across three experiments, 61-67% of messages involved interpretation or judgment, while the share of methodological and scientific contributions increased from 46% in the setup-focused first experiment to 56-61% in later experiments. Provenance showed that 59-68% of error corrections involved multiple participants and that about half of all decisions incorporated the input of another participant. These results may guide design principles for multi-agent scientific systems and inform future agent harnesses, model training, and accessible autonomous experimentation.
[MA-9] Spatial patterns of transportation choice in Niterói
【速读】：该论文旨在解决城市交通流动态特征难以通过高时空分辨率数据获取的问题，其核心挑战在于如何在缺乏个体层面精细轨迹数据的情况下，构建具有微观动态特性的城市出行模拟模型。解决方案的关键在于提出一种计算框架，该框架利用宏观、聚合且静态的城市数据（如建成环境、交通网络与合成人口），结合现有活动模拟器，生成具有空间细节和时间动态性的个体出行行为。通过将合成人口与实际城市结构融合，模型能够模拟居民每日通勤路径，并合理分配步行、驾车或公共交通等出行方式。应用该框架对巴西尼泰罗伊市约47.5万名合成居民的模拟结果显示，其分区域的交通模式与实证活动指标高度相关（皮尔逊相关系数r=0.885），揭示了城市中心区步行比例更高、外围地区私家车使用增加、公交走廊仍保持较高公交分担率的空间规律。此外，通过缩短至公交站点的步行时间30%，模型显示公交/私家车出行比提升11.3%，尤其在边缘区域效应更显著，表明改善公交可达性可有效促进公共交通使用。该方法为在数据受限条件下实现精细化城市交通模拟提供了可行路径，并识别出优化交通政策的关键空间节点。
链接: https://arxiv.org/abs/2611.14499
作者: Sandro Martinelli Reia,Taylor Anderson,Dieter Pfoser
机构: George Mason University (乔治梅森大学); Center for Geospatial Intelligence (地理空间智能中心)
类目: Physics and Society (physics.soc-ph); Multiagent Systems (cs.MA); Other Statistics (stat.OT)
备注:
Abstract:Characterizing how people move through a city requires spatially and temporally fine-grained data that are often costly and difficult to obtain. Here, we introduce a computational framework that uses macroscopic, aggregated, and static urban data to construct a microscopic, dynamic, and spatially detailed representation of urban mobility. Building on an existing activity simulator, the framework combines a synthetic population, the built environment, and transport networks to generate daily trips and assign walking, car, or public transit. Applied to approximately 475,000 synthetic residents of Niterói, Brazil, simulated neighborhood-level transit patterns show a strong association with an empirical activity proxy (Pearson r=0.885). Walking is more prevalent in compact central areas, car use increases toward the periphery, and public-transit use remains comparatively high along bus corridors. Reducing walking times to and from bus stops by 30% increases the modeled public-transit-to-car ratio by 11.3%, with larger transit-share gains in several peripheral areas. These results reveal spatial differences in transportation choice and identify where improved transit access may have the greatest modeled effect.
自然语言处理
[NLP-0] AutoScaffold: AI-Guided Scaffolding for Reinforcement Learning of Language Models
【速读】： 该论文旨在解决强化学习中因仅使用结果奖励（outcome rewards）而导致训练过程中大量中间信息被浪费的问题。传统方法将每一轮尝试简化为单一数值奖励，忽略了丰富的过程性经验，从而限制了模型的学习效率与泛化能力。为此，本文提出AutoScaffold——一种面向AI的统一自适应框架，其核心在于通过一个冻结的教师模型（frozen teacher）自动分析学生模型在训练过程中的经验，识别瓶颈并动态生成两类可适应的辅助结构（scaffolds）：(i) 适用于任务类别的通用策略；(ii) 针对个别困难任务的提示（hints），且根据学生的学习进展自动调整支持的内容与强度。这些辅助结构在评估阶段被移除，确保最终性能不受干扰。关键创新点在于无需人工干预或领域特定知识，即可实现从训练经验到智能引导机制的自动化转化。实验表明，在ALFWorld、基于搜索的问答、数学推理和GPU内核生成等多个任务上，采用AutoScaffold训练的学生模型均显著优于基准方法，尤其在成功率与准确率方面取得明显提升，且分析揭示教师能够随学习进程动态调整策略，有效促进学生更高效地掌握任务本质。
链接: https://arxiv.org/abs/2611.14515
作者: Zijian Zhang,Rizhen Hu,Jiangweizhi Peng,Shiyang Li,Sujay Bhatt,Sihan Zeng,Mingyi Hong
机构: University of Minnesota, Twin Cities (明尼苏达大学双城分校); JPMorgan AI Research (摩根大通人工智能研究)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Reinforcement learning with verifiable rewards (RLVR) produces rich training experience, yet outcome rewards reduce each attempt to a single number and leave most of this information unused. Training-time scaffolds, such as strategies or hints added to the prompt during training, offer a natural way to turn this information into guidance, but their design and adaptation are largely left to humans and tailored to specific domains. We introduce AutoScaffold, a unified AI-for-AI framework that turns a student’s training experience into adaptive scaffolds, with minimal domain-specific information and no human intervention during training. A frozen teacher periodically examines this experience, diagnoses bottlenecks, and designs scaffolds in two forms: (i) strategies shared across a class of tasks and (ii) hints for individual hard tasks, adjusting the content and amount of support as the student learns. The scaffolds are removed at evaluation. We apply the same framework to ALFWorld, search-based QA, mathematical reasoning, and GPU kernel generation. When evaluated without scaffolds, students trained with AutoScaffold outperform those trained with the corresponding RL baselines in all four domains, with gains of 7.2 points in ALFWorld success and 4.0 points in average QA exact match. On GPU kernel generation, AutoScaffold increases the fraction of tasks with a correct kernel that runs faster than the PyTorch reference by up to 5.0 points. On math, our student surpasses QuestA’s reported accuracy using approximately one-third as many student training steps. Analyses show that the teacher identifies a different failure in each task class and shifts its scaffolds from step-by-step workflows to targeted fixes as the student improves, and that its revised scaffolds help the student learn more than keeping the previous scaffold or using no scaffold.
[NLP-1] Contrastive Discovery: Open-Ended Scientific Discovery over Competing Explanations NEURIPS2026
【速读】： 该论文旨在解决大语言模型（LLM）代理在开放式、数据驱动的科学发现中因采用点对点（pointwise）假设检验方式而产生的认知局限性：即当某个假设 $ h $ 被证据否定时，代理仅能得知该解释不成立，却无法明确识别出被证据所支持的替代解释。其核心解决方案是提出“对比发现”（Contrastive Discovery），通过将单一假设替换为一个对比集（contrast set），即一个科学问题及其互斥且穷尽的多个竞争性解释构成的集合。每个实验的证据通过其在对比集中重新分配信念的程度进行评分，当信念集中于某一解释时，问题即被解决。该方法仅改变代理的命题与评估单位，无需修改现有框架，因而可无缝应用于已有证据的再评估或指导新实验搜索。在六个领域中的实验证明，89% 的点对点候选者在最大信念转移后被拒绝，而使用对比集可在相同证据下将其中73% 的问题明确解析至具体替代解释；在搜索引导场景中，对比集使代理在相同实验预算下比基于点对点意外性（surprisal）的方法多解决67%的问题。盲测研究显示，38名人类专家在78%的已解决问题及82%的点对点代理拒绝假设后停止的问题中，均选择了相同的解释。这些结果表明，显式引入竞争性解释作为对比集，显著提升了发现代理从相同证据和验证预算中解析更多科学问题的能力。
链接: https://arxiv.org/abs/2611.14514
作者: Ziang Liu,James J. Kim,Yijia Dai,Jennifer J. Sun
机构: Cornell University (康奈尔大学); codiscovery.ai/contrastive-discovery
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at NeurIPS 2026. 55 pages. Project page: this https URL
Abstract:LLM agents are increasingly used for open-ended, data-driven scientific discovery, yet most of them evaluate hypotheses pointwise, testing an empirical claim h only against its complement \neg h . When evidence rejects h , the agent learns that one explanation is unlikely but not which alternative the evidence supports. To recover that alternative, we introduce Contrastive Discovery, which replaces the single hypothesis with a contrast set, a scientific question paired with mutually exclusive and collectively exhaustive competing explanations. Each experiment is scored by how its evidence redistributes belief across the set, and the question is resolved when belief concentrates on one explanation. Because contrast sets change only the unit an agent proposes and scores, an existing agent can use them to re-evaluate evidence it has already collected or to guide its search. Across six domains, 89% of the pointwise candidates with the largest belief shifts are rejections. On the same evidence, contrast sets resolve 73% of these candidates to a specific alternative. When contrast sets guide search, the agent resolves 67% more questions than pointwise surprisal under the same experiment budget. In a blinded study, 38 human experts chose the same explanation on 78% of resolved questions and on 82% of questions where the pointwise agent rejected its hypothesis and stopped. Together, these results support explicit competing explanations as a way for discovery agents to resolve more scientific questions from the same evidence and validation budget.
[NLP-2] Comparative Analysis of Agent Evaluation Frameworks: Stability Detection and Discovery in Enterprise Analytics Agents KDD2026
【速读】： 该论文旨在解决企业在部署基于大语言模型（Large Language Model, LLM）的分析代理时，如何在复杂的多工具工作流中可靠检测潜在故障的问题。现有评估框架在面对复杂系统时存在稳定性不足、误报率高及对隐蔽错误识别能力弱等缺陷。其解决方案的关键在于提出并对比三种不同评估范式：基于执行轨迹的LLM判断、仅依赖文本输入的LLM判断结合红队测试（red-teaming），以及确定性启发式规则。研究通过三阶段实验发现，基于轨迹的评估框架在功能错误检测中表现出最佳的精确率-召回率平衡（F1=0.90），而纯文本评估虽具备较高召回率但产生五倍于前者的误报；更重要的是，在未知风险发现方面，功能性测试与安全红队测试揭示了24个无重叠的新故障，证明二者具有互补性。研究结果表明，评估质量取决于评估框架可获取的信息类型，且基于LLM的评估能捕捉人类审查者难以察觉的细微缺陷。因此，推荐采用“双框架策略”：以基于轨迹的评估进行功能验证，以对抗性红队测试保障安全性。
链接: https://arxiv.org/abs/2611.14493
作者: Howard Zhang,Chuck Arvin,Logan Brown,Mihir Bhatia,Neelu Choudhary,Lily Miao
机构: Amazon(亚马逊)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Accepted to KDD 2026, Jeju, Korea. 9 pages, 3 figures
Abstract:Deploying LLM-based analytics agents in enterprise settings requires evaluation frameworks that can reliably detect failures across complex, multi-tool workflows. We present a three-phase comparative study of three evaluation frameworks, each representing a distinct evaluation paradigm (trace-based LLM judging, text-only LLM judging with red-teaming, and deterministic heuristics), applied to two analytics agents in a controlled research setting using frozen execution traces. Phase 1 quantifies evaluation harness stability through repeated measurements, finding that 97% of configurations meet the stability threshold and that stability patterns are agent-independent. Phase 2 measures known risk detection on 72 ground-truth test cases, where the trace-based framework achieves the best precision-recall balance (F1=0.90) while text-only evaluation matches on recall but generates 5x more false alarms. Phase 3 evaluates unknown risk discovery, uncovering 24 novel failures with zero overlap between functional testing and security red-teaming. Our results demonstrate that (1) the information available to the evaluation harness determines evaluation quality, (2) functional and security evaluation are complementary, and (3) LLM-based evaluation harnesses surface subtle errors that human reviewers miss. We recommend a two-framework strategy: trace-based evaluation for functional assessment and adversarial red-teaming for security validation.
[NLP-3] Mechanistic Interpretability Needs a Logic of Inference NEURIPS2026
【速读】： 该论文旨在解决生成式人工智能（Generative AI）模型内部复杂计算过程的可解释性问题，即如何从神经网络的内部表征中推断出能够解释模型行为的机制性原理。其核心挑战在于，尽管机械可解释性（Mechanistic Interpretability, MI）技术已能识别出与模型行为相关的神经元、特征、电路结构及动态模式，但如何基于这些观测结果构建可靠且可推广的机制性解释仍存在显著瓶颈。解决方案的关键在于提出一套五项原则，并将其整合为一种递归逻辑框架，以实现更严谨的机制推断流程。该框架不仅有助于将局部的可解释性发现转化为模型内部机制的累积性知识体系，还能使所推导出的机制具备足够精确性，从而生成可验证的、超越模型本身的外部域假设，最终推动科学发现。
链接: https://arxiv.org/abs/2611.14483
作者: Zhimin Hu,Chen Jiang,Hanbo Xie,N.Apurva Ratan Murty,Sashank Varma
机构: Georgia Tech(佐治亚理工学院); McGill University(麦吉尔大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: NeurIPS 2026 Interp4Discovery (Oral)
Abstract:Neural networks are increasingly studied not merely as predictive systems, but as complex systems whose internal computations are themselves objects of scientific inquiry. Recent progress in mechanistic interpretability (MI) has made it possible to identify neurons, features, circuits, population structures, and dynamical patterns associated with model behavior. As these tools improve, attention is shifting from what can be observed inside a model to how those observations support mechanistic explanations. We examine the parallel efforts of neuroscience and MI to address this question and identify recurring inferential bottlenecks when inferring mechanisms from complex neural systems. We propose five principles and organize them into a recursive logic for a more rigorous procedure of mechanistic inference. These principles provide a framework that supports scientific discovery in two senses: it helps turn local interpretability findings into cumulative knowledge of mechanisms within models, and it makes those mechanisms sufficiently precise to generate testable hypotheses about domains beyond the models. Evidence beyond MI is ultimately required to establish what model mechanisms reveal about the world.
[NLP-4] Pterodactyl: Pushing the Limits of Efficient Automated Translation Evaluation
【速读】： 该论文旨在解决机器翻译自动质量评估中的两个关键问题：段落级质量评分预测（segment-level quality score prediction）以及无错误段落的检测（error-free segment detection）。针对这些问题，其解决方案的核心在于提出一种名为Pterodactyl的可高度定制、超低成本的系统，该系统专为翻译筛选（translation triage）与数据清洗（data curation）中的二分类任务优化。其关键技术路径是基于大语言模型作为评判者（LLM-as-a-judge）的范式，通过受ROC分析指导的提示工程（prompt engineering）与模型选择策略，显著提升分类性能，同时保持极低的计算成本。此外，研究还开发了多个针对子任务3的Pterodactyl变体，以进一步增强特定任务的适应性与准确性。
链接: https://arxiv.org/abs/2611.14480
作者: Evelyn Y. Garland,Carola F. Berger
机构: 1020 Group; Acta Language Services, LLC; CFB Scientific Translations LLC
类目: Computation and Language (cs.CL)
备注: 6 pages, 4 figures
Abstract:In this paper, we present 1020 Group’s submission to the WMT26 shared task on automated quality evaluation. Our system, Pterodactyl, was engineered specifically for subtasks 2 and 3, that is, segment-level quality score prediction and the detection of error-free segments. Pterodactyl is a highly customizable, ultra-low cost system optimized for binary classification in translation triage and data curation. It is based on an LLM-as-a-judge approach, with prompt engineering and model selection guided by ROC analysis. We also developed several variants of Pterodactyl that were specifically adapted for subtasks 3.
[NLP-5] When does text encoding matter for language modeling?
【速读】： 该论文旨在解决因字节级语言模型普遍采用可变长度编码（如UTF-8）而导致的跨语言不平等性问题，探究文本编码策略对模型性能的影响。其关键解决方案在于提出Aether——一种混合编码方案，结合特定语系的优化编码与UTF-8的全Unicode覆盖回退机制，在保持兼容性的同时实现对四种书写系统差异显著语言的建模。研究发现，字符信息密度高的语言（如中文、韩文）从压缩型编码中获益显著，而单个字符信息量较低的语言（如格鲁吉亚语、泰米尔语）则对编码方式不敏感。因此，论文主张文本编码应作为语言特异性设计决策，而非默认的统一标准。
链接: https://arxiv.org/abs/2611.14459
作者: Eugene Jang,Catherine Arnett,Terra Blevins
机构: Khoury College of Computer Sciences, Northeastern University (东北大学计算机科学学院); EleutherAI
类目: Computation and Language (cs.CL)
备注:
Abstract:UTF-8’s role as the de facto encoding standard in language models introduces cross-lingual inequities due to its variable-length design. However, the effect that this encoding choice has on model performance remains largely unexamined. In this work, we investigate how text encoding impacts the performance of byte-level language models, comparing UTF-8 with a wide range of other encoding strategies, including other Unicode-based approaches, language-specific encodings, and our own customized encodings. To facilitate our analysis, we propose Aether: a hybrid encoding scheme that uses script-specific encodings for our target setting while maintaining full Unicode coverage through UTF-8 fallbacks, using this framework to model four orthographically diverse languages. Surprisingly, we find that while languages with information-dense characters (Chinese, Korean) significantly benefit from compressive encodings, languages whose characters carry less information individually (Georgian, Tamil) are largely insensitive to encoding choice. In light of these findings, we argue that text encoding should be a language-specific design decision, rather than a universal default.
[NLP-6] Earlybird: Predictive Streaming Speech Recognition in 29M Parameters
【速读】： 该论文旨在解决流式语音识别（streaming speech recognition）中响应延迟过高的问题，即传统模型通常在说话人完成一个词后90–400毫秒才输出对应词汇，导致语音助手无法及时启动搜索或生成回复。其核心解决方案是提出Earlybird——一个仅29M参数、4比特量化后仅16.8 MB的轻量级流式识别器，通过在训练时使用随机截断的音频（包括单词进行中的片段），并要求模型预测截至当前时刻应输出的所有完整词汇及接下来两个词汇的预测，从而实现对未完成词汇的提前识别。该方法利用原始令牌概率直接生成每个词（包括正在发音的词）的置信度，使模型具备对未结束词的实时置信度评估能力。实验结果表明，在干净有声书语料（LibriSpeech test-clean）上，Earlybird可将词汇的中位预测时间提前90毫秒，且69%的词汇在发音结束前被正确预测，显著优于现有流式模型（3–25%）。此外，其置信度校准良好，预期校准误差（ECE）为3.7至5.5，在八个英语开放语音识别排行榜数据集上表现优于同规模的Moonshine Tiny，并与Whistle模型相当。
链接: https://arxiv.org/abs/2611.14421
作者: Sahil Mahendrakar
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 9 pages, 3 figures, 6 tables. Model: this https URL
Abstract:Streaming speech recognizers output a word only after the speaker has finished it, typically 90-400 ms later. For a voice agent every millisecond counts: the sooner the words are outputted, the sooner the agent can start a search or plan its reply. We present Earlybird, a 29M-parameter streaming recognizer (16.8 MB at 4 bits). Earlybird learns from audio cut off at random points, including partway through a word, and is asked to predict every word that is due by then in full, plus a guess of the next two words. Its raw token probabilities give a confidence for every word including the one still being spoken. On clean audiobook speech (LibriSpeech test-clean), the median word is predicted correctly 90 ms before the speaker finishes it, while released streaming models get it 90 to 400 ms after it ends. Earlybird is the earliest on all six test sets we time, covering audiobooks, TED talks, public speech, earnings calls, and synthetic speech. On the same audiobooks, 69% of words are predicted correctly before they end, against 3-25% for the other models. For words still being spoken, confidence is calibrated, with an expected calibration error (ECE) of 3.7 to 5.5. On the eight English Open ASR Leaderboard sets, it is more accurate than Moonshine Tiny on every set and roughly ties Whistle, a model of the same size.
[NLP-7] Bits or permutations? Spending a fixed inference budget on weight precision or on option-order averag ing in small local language models
【速读】： 该论文旨在解决在低精度量化（low-bit quantization）下，生成式模型因权重压缩导致的选项位置偏差（answer-slot skew）加剧问题，同时探究在固定计算预算内，采用更精细的权重位数还是通过多轮选项重排（cyclic order averaging）来提升模型鲁棒性与性能的最优策略。其核心问题是：低比特量化是否恶化了对答案选项顺序敏感的问题？在资源受限条件下，哪种方式更高效？解决方案的关键在于通过模拟量化（round-to-nearest）与多轮选项重排相结合的方法，在保持模型精度的同时实现成本最优。研究发现，6比特量化可维持与fp16相当的性能，而4比特时出现显著的选项位置偏差，但通过平均四个循环重排的输出，可在0.6B模型上恢复高达+12.6分的性能增益。进一步地，作者以“读取权重字节数×推理遍次数”为统一成本单位进行权衡分析，结果表明：当权重精度达到6比特及以上时，将剩余预算用于增加最多四次选项重排（每次一通）的收益优于继续增加位数；最终决策表建议：优先使用6比特权重，并将余下预算分配给最多四次选项重排。该研究首次系统探讨了量化与选项顺序处理的协同效应，且实验基于两个小型模型和最简量化器，未见此前相关工作。
链接: https://arxiv.org/abs/2611.14413
作者: Mohammed Nayeem Khan,Logesh L,Aliyas Ahmad Misri
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 25 pages, 15 figures, 9 tables
Abstract:Local language models can save memory by storing weights in fewer bits, and they can reduce sensitivity to the order of answer options by averaging a few reorderings. We ask whether the first makes the second worse, and which is the cheaper way to spend a fixed budget. On Qwen3-0.6B and Qwen3-1.7B with simulated round-to-nearest weight quantization (8, 6, 4 and 3 bits, fp16 reference), read from the logits of the letters A to D on ARC-Easy, ARC-Challenge, MMLU and HellaSwag (300 items each), 6 bits lose no accuracy against fp16 (they score +1.8 (+1.0 to +2.5) and +0.9 (+0.3 to +1.5) points higher, which we do not explain), and 8 bits change it by -0.7 (-1.1 to -0.3) and -0.4 (-0.7 to -0.2) points. At 4 bits the answer-slot skew rises from 0.15 to 0.53 (0.6B) and from 0.07 to 0.12 (1.7B); Gaussian noise at the same weight SNR gives 0.15 and 0.37 (two draws) and 0.19 (one draw), so the control is inconclusive on whether rounding adds slot skew beyond equal-power noise. Flip rates rise but stay below an accuracy-matched baseline of independent uniform errors. Averaging four cyclic orders recovers +12.6 points at 4 bits for 0.6B (+5.3 at fp16). We price bits and passes in one machine-independent unit, weight bytes read times passes. In cost-matched pairs, extra orders beat extra bits only when the weights have 6 or more bits, and the decision table reduces to: use 6-bit weights and spend the rest of the budget on up to four orders, one pass each. Everything is simulated on two small models with the simplest quantizer; a title-level search found no earlier study of quantization together with option order.
[NLP-8] REAL Loss: Optimizing Real Expected Acceptance Length for Speculative Decoding
【速读】： 该论文旨在解决生成式 AI（Generative AI）中大语言模型（LLM）推理加速时，基于推测解码（speculative decoding）的预期接受长度（Expected Acceptance Length, EAL）优化不足的问题。现有方法虽引入块级训练目标以直接优化EAL，但其本质仍为EAL的近似代理，存在协方差偏差（covariance gap），导致梯度方向可能被错误引导。本文提出REAL（Real EAL loss），其关键在于通过路径权重（path weights）实现无偏估计，并结合Rao-Blackwellization进行方差缩减，从而精确建模真实EAL。实验表明，在9个任务子集与5个评估种子下，REAL显著优于WTV、VSD、DBLast及静态LK基线，在概率服务接受长度上取得一致提升；且仅需修改训练损失函数，无需调整推测模型架构或推理流程，验证了真实EAL训练在实际应用中的可行性与有效性。
链接: https://arxiv.org/abs/2611.14399
作者: Yu Tian
机构: The University of Hong Kong (香港大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 19 pages, 10 figures
Abstract:Speculative decoding accelerates LLM inference by having a draft model propose multiple tokens for the target model to verify in one pass. The speedup is governed by the expected acceptance length (EAL), yet standard training objectives such as cross-entropy, KL divergence, and total variation focus only on distribution matching or single-step overlap. Recent studies instead propose block-level training objectives, aiming to optimize EAL directly. However, these objectives remain surrogates rather than exact formulations of EAL. For example, some average later acceptance over target-distributed continuations rather than the verification branches selected by accepted tokens, introducing a covariance gap that can reverse the gradient direction. We analyze this gap and derive REAL, a real EAL training loss that uses path weights for unbiased estimation and Rao-Blackwellization for variance reduction. In a controlled loss-level comparison with a Qwen3.6-35B-A3B target and a Domino drafter, REAL outperforms WTV, VSD, DBLast, and the static LK baseline on probabilistic serving acceptance length across 9 task subsets and 5 evaluation seeds. These gains require only a change to the training loss, with no modification to the drafter architecture or inference procedure, demonstrating the practicality of real EAL training. A reference implementation of the REAL loss is available at this https URL.
[NLP-9] Blurring the Block Boundaries: Overlapping Block Denoising for Efficient Diffusion Language Models
【速读】： 该论文旨在解决生成式扩散语言模型（Diffusion Language Models, DLMs）在延迟敏感任务中推理速度受限的问题。尽管现有方法采用具有自编辑能力的分块扩散机制，通过逐块迭代优化提升生成质量，但其固有串行性导致每个块必须等待前一个块中最慢的令牌完成处理后才能启动，严重制约了整体效率。为此，论文提出一种名为“模糊块边界”（Blurring the Block Boundaries, B3）的新方法，其核心创新在于通过共享前向传播过程，实现两个相邻块的并行精细化处理，同时动态校准进度——仅在模型准备好生成下一区块时才开启。B3无需训练且无超参数调优，可无缝集成至任意自编辑分块扩散模型中。在10个涵盖多种任务与难度级别的基准数据集上的实验表明，B3在保持与基线相当生成质量的前提下，实现了11.5%至14.5%的推理加速，显著优化了生成延迟与质量之间的帕累托前沿。
链接: https://arxiv.org/abs/2611.14387
作者: Amir Mann,Jonathan Fhima,Shaked Brody,Or Avitan,Asif Zamir,Shahar Tsiper,Niv Nayman
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion Language Models (DLMs) have emerged as effective alternatives to autoregressive models for latency-critical tasks, enabling faster token generation. Recent advances employ block diffusion with self-editing capabilities, where each block refines tokens iteratively before advancing to the next one. Yet each block is only as fast as its slowest token, and the next block cannot start before it. We suggest Blurring the Block Boundaries (B3), a novel method with which two blocks are refined in parallel within a shared forward pass. B3 calibrates itself on the fly, opening the next block only once the model is ready to start generating it. It is a training-free and hyperparameter-free acceleration method which seamlessly integrates within any self-editing block diffusion model. Evaluated on state-of-the-art DLMs across 10 diverse datasets spanning varied tasks and difficulty levels, B3 results in on par generation quality and 11.5-14.5% speedup compared to the base model, pushing the Pareto frontier for latency-quality trade-offs in DLM inference.
[NLP-10] ARE-bench: Benchmarking Agents Research Ability in Real-World Researcher Sessions and Workspaces
【速读】： 该论文旨在解决当前研究型智能体（research agent）在基准测试中表现优异，但实际应用于持续性科研项目时可靠性不足的问题。现有基准普遍聚焦于独立、封闭的任务与整洁的工作环境，忽视了真实科研场景中任务依赖历史决策、工作空间包含未完成文件、过时版本及失败尝试等复杂上下文的现实挑战。为此，论文提出ARE-bench，一个基于真实研究人员会话和完整工作区的评估基准，用于检验智能体在持续项目中推进任务的能力，同时尊重先前决策和现有工作区状态。其核心创新在于：构建了75个源自真实科研项目的工作流任务，覆盖七个研究阶段、33项核心需求与150个子需求，共含1,173项二元检查清单；每个任务均锚定于真实用户请求，保留原始需求，并以背景形式总结前序有效指令；完整工作区被回放至任务边界，不进行清理，确保历史痕迹与物质性要求（historical and material requirements）得以保留。检查清单基于意图对齐的评分标准设计，只要求有效解决方案即可得分。实验表明，七种前沿模型在仅考虑交付契约与通用规范时平均通过率约69%，但一旦引入历史与工作区约束，通过率骤降至28.4%–40.0%，且“设计”与“执行”阶段成为所有模型中最难通过的环节。这表明，忽略上下文约束的评估会显著高估智能体能力，掩盖其真实性能差异。因此，该研究的关键解决方案在于建立一种嵌入真实科研情境、保留历史状态与物质痕迹、并严格验证多层级上下文依赖要求的评估范式，从而更真实地衡量研究型智能体在持续项目中的实际交付能力。
链接: https://arxiv.org/abs/2611.14364
作者: Jiahang Lin,Wanying Ren,Wanghan Xu,Huayu Sha,Yang Shu,Zhonghang Lu,Chengjun Pan,Yi Dong,Shichun Liu,Yuyang Hu,Shihan Dou,Honglin Guo,Huijie Lv,Pujun Zheng,Zhe Sun,Yuankai Ying,Yiding Wang,Jiahao Chen,Zhixiong Yang,Dingbo Yuan,Yicheng Zou,Zhecan Wang,Guoxiu He,Xiaoyu Xing,Qi Zhang,Tao Gui,Xuanjing Huang
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Research agents achieve increasingly strong benchmark scores, yet these scores do not necessarily translate into reliable delivery in ongoing research projects. Many research-agent benchmarks emphasize standalone tasks and relatively self-contained workflows in clean or curated workspaces. However, researchers ask agents to complete one step within an ongoing project, where requests depend on earlier session decisions and workspaces contain unfinished files, outdated versions, and failed attempts. We introduce ARE-bench to evaluate whether research agents advance ongoing projects while respecting prior decisions and existing workspace state, grounded in real session context and in-progress workspaces. ARE-bench includes 75 tasks built on agent sessions and full workspaces from recruited researchers’ own projects, spanning seven research stages, 33 requirements, and 150 sub-requirements, with 1,173 binary checklist items in total. Each task is anchored at a real user request, preserves its requirements, and summarizes the earlier requests still in effect as background. The complete workspace is replayed to that boundary without cleanup, preserving artifacts from earlier turns. Each checklist is compiled from an intent-aligned rubric and credits any valid solution. Across seven frontier models, the strongest passes every checklist item on only 66.2% of tasks, and Design and Execution rank among the hardest stages for every model. Three models pass about 69% of tasks when only delivery contracts and general norms are checked, but just 28.4% to 40.0% once historical and material or workspace requirements are also checked. Evaluating delivery without these contextual requirements can therefore overestimate research agents and mask differences between them.
[NLP-11] EngineAgent : Agent izing On-Device LLM Inference Engines with Inference Stack Hierarchy Awareness and Hardware Abilities
【速读】： 该论文旨在解决在异构移动硬件上支持新型大语言模型（LLM）进行本地推理时，因模型架构快速演进与硬件多样性导致的系统性挑战。核心问题在于，新模型的部署需在模型转换、图构建、算子实现及硬件后端等多个层级间进行协调更新，而现有编码代理往往忽视跨层依赖关系，难以有效诊断主机-设备执行路径中的故障。其解决方案的关键是提出EngineAgent这一智能体系统，通过将可复用的引擎与硬件知识与特定模型实现相分离，构建分层感知的引擎索引以捕捉抽象层级与跨层依赖，并基于已支持模型预先验证后端的编译、执行与调试流程。在新模型启用过程中，构建器利用该知识协调跨层变更，审计器则通过设备端验证确保实现完整性并驱动迭代调试。实验表明，在涵盖三个移动LLM与四种引擎-后端组合（CPU/GPU/NPU）的12项任务中，EngineAgent实现了75%的端到端成功率，显著优于最强基线（33.3%），且在编译、设备执行及输出logit一致性方面均满足要求；同时相较基线显著降低单任务耗时与模型API成本。
链接: https://arxiv.org/abs/2611.14356
作者: Zhengxiang Huang,Yujie Sun,Shengheng Chen,Zhaode Wang,Chaoyue Niu,Zeyu Zhao,Chengfei Lv,Fan Wu,Guihai Chen
机构: Shanghai Jiao Tong University (上海交通大学); Alibaba Group (阿里巴巴集团)
类目: Operating Systems (cs.OS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE)
备注:
Abstract:Rapidly evolving LLM architectures and heterogeneous mobile hardware make supporting new models in on-device inference engines a recurring and costly system challenge. Each model may require coordinated changes across conversion, graph construction, operators, and hardware backends. Existing coding agents often miss these cross-layer dependencies and struggle to diagnose failures across host–device execution paths. We present EngineAgent, an agentic system for automating on-device LLM model enablement. Its key insight is to separate reusable engine and hardware knowledge from model-specific implementation. Before enablement, EngineAgent constructs a hierarchy-aware engine index that captures engine abstraction layers and cross-layer dependencies, and pre-validates backend-specific build, execution, and debugging workflows using already supported models. For each new model, a builder uses this knowledge to coordinate cross-layer changes, while an auditor checks implementation completeness and drives iterative debugging through device-side validation. Across 12 tasks spanning three mobile LLMs and four engine–backend pairs on CPUs, GPUs, and NPUs, EngineAgent achieves 75% end-to-end success with either of two underlying LLMs, versus 33.3% for the strongest coding-agent baseline. Success requires compilation, on-device execution, and output-logit agreement with reference implementations. EngineAgent also reduces average per-task time and model-API cost relative to baselines using the same underlying LLMs.
[NLP-12] MMDM: Measuring Massive Multitask Decision Making MDM
【速读】： 该论文旨在解决现有生成式决策模型在复杂、真实场景下的泛化能力评估不足的问题，尤其针对长上下文、大规模选项空间及复杂逻辑依赖等挑战。现有基准测试常因测试材料的预先暴露而难以准确衡量模型的真正泛化性能，且未能充分整合多类现实决策任务。为此，本文提出MMDM（Massive Multitask Decision Making）基准，包含5,448个高质量问题，覆盖16类任务，涵盖单选、多选和软标签决策，融合新构建任务与基于源文本重构的任务，以实现对真实情境下系统性决策能力的全面评估。其关键解决方案在于构建一个高保真、多样化且具有挑战性的评估框架，涵盖从代理状态追踪、工具与技能选择到偏好判断与评分规则应用等复杂场景，支持高达1,024个选项和64,817词元的输入长度。通过在该基准上对28个决策模型的系统评估发现，尽管多数模型在常识任务上表现良好，但在涉及选项间逻辑依赖、约束规划和多选任务时显著退化，凸显出联合推理、约束感知选择和完整多选恢复作为未来决策模型发展的核心方向。MMDM因此成为推动该领域进步的重要测试平台。
链接: https://arxiv.org/abs/2611.14309
作者: Junhao Shen,Qing Zong,Yue Guo,Zhanqiu Zhang,Yiwen Guo,Hong Cheng
机构: The Chinese University of Hong Kong; Hong Kong University of Science and Technology; LIGHTSPEED
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Project page: this https URL
Abstract:System-1 Decision Models directly make structured probabilistic decisions over query-specified options given a state in a single forward pass, without autoregressively generating reasoning trajectories or textual responses. Recent evaluations cover diverse tasks but often rely on existing benchmarks, where prior exposure to test material can obscure generalization. Moreover, real-world decision making often involves long contexts, large option spaces, and complex logical dependencies, challenges that existing benchmarks do not adequately capture together. Thus, we introduce MMDM (Massive Multitask Decision Making), a benchmark of 5,448 high-quality questions across 16 task categories, combining newly constructed tasks with source-grounded recompositions and rewrites for single-select, multi-select, and soft-label decision making in fresh contexts. It covers realistic scenarios such as agent state tracking, tool and skill selection, preference judgments, and rubric scoring, with option spaces ranging from 2 to 1,024 and inputs extending to 64,817 tokens. With MMDM, we conduct a comprehensive quantitative evaluation of 28 decision models, showing that many perform well on common knowledge yet struggle with interdependent options, constrained decisions, and multi-select tasks. For example, Perplexity Decider-V1.2 reaches 78.75% Hard Single-select Accuracy on world knowledge, but only 53.95% on option logic and 47.95% on constraint planning, while the best Hard Multi-select Accuracy across all models is 36.81%. These results highlight joint reasoning, constraint-aware selection, and complete multi-select recovery as key priorities for decision model development, with MMDM providing a testbed for measuring this progress.
[NLP-13] Semantic Chains: From Reachable Facts to Justified Answers
【速读】： 该论文旨在解决如何从问题中识别出支持答案的逻辑事实，并将其组织为一系列相互关联的陈述，以形成连贯的推理链条。其核心问题是：在给定问题与答案之间，如何通过可追溯的逻辑路径，将支撑性事实从根节点逐步传递至与问题和答案共享的对象，从而实现对答案的合理解释。解决方案的关键在于构建一个基于逻辑事实的图结构（graph of logical facts），其中每个父事实通过子事实之间的关系表达更复杂的语义，而“保持边”（preserving edge）则确保子事实的内容能够在父事实的完整表示中被准确恢复。在此模型下，若存在一条由保持边构成的路径，从能够完全支持答案的根事实通向一个同时出现在问题和答案中的共享对象，则可通过从问题中的实体出发进行搜索，逆向追踪至根事实，并覆盖所选推理扩展中的所有事实。最终，根事实可生成一段文本作为单一连接链，将问题与完整答案关联起来并提供正当性依据；对于多链接推理链，论文进一步给出了候选顺序的完整枚举方法，并明确了何种条件下各链接文本能共同维持论证的有效性并支持每一步推理过渡。
链接: https://arxiv.org/abs/2611.14252
作者: Misha Andreev
机构: 未知
类目: Computation and Language (cs.CL)
备注: 31 pages, 10 figures
Abstract:How can the facts that justify an answer be found from a question and presented as a sequence of connected statements? This paper models a context as a graph of logical facts and takes as given which expressions represent each fact. A parent fact can express a relation among its subfacts, and a preserving edge allows the content of a subfact to be recovered within a full representation of its parent. Suppose a root fact justifies the full answer and a path of preserving edges leads from that root to a fact that designates an object shared by the question and the answer. Under the conditions of the model, a search starting from the objects in the question reaches the root and covers every fact in the selected expansion. The root then admits a text that forms a single link connecting the question to the full answer and justifying it. For chains with several links, the paper gives a complete enumeration of candidate orders within the stated class and specifies when the link texts jointly preserve the justification and support every transition.
[NLP-14] An AI Assistant for Automating Anti-Corruption Expertise of Draft Legislation Based on the TRACE Framework
【速读】： 该论文旨在解决立法草案中腐败风险识别自动化不足的问题，核心挑战在于如何精准定位可能引发腐败的法律条文条款，并准确识别其对应的监管缺陷。解决方案的关键在于基于TRACE架构构建的多阶段处理流水线：该流水线融合了确定性解析核心、基于大模型（LLM）引导的单条款风险判定、基于规则的因子赋值机制以及外部人工监督，实现了从文本解析到风险标注的系统化流程。实验采用乌克兰反腐败国家局（NAZK）公开结论构建开放基准，涵盖132部草案及474个模型提取的发现。评估结果显示，在100部草案上，使用不同模型的管道系统在严格标准下最高可匹配0.46的发现（非盲测试），显著优于冻结的基线模型（0.21）。主要遗漏原因集中在系统虽定位了条款但将其归类为不同风险类型，且所有32个法律冲突型发现均未被成功匹配。此外，流水线在独立测试集上优于单一提示模板方法（严格标准下），而Opus 5.5并未带来性能提升。最终评分基于大模型判断的发现一致性，其两种访问路径在74个标签中达成73个一致，但尚未经过人类专家验证。
链接: https://arxiv.org/abs/2611.14244
作者: Serhii Zabolotnii(Cherkasy State Business College, State Scientific Research Institute of Armament and Military Equipment Testing and Certification, Uzhhorod University),Alina Tarasova(PJSC “Finport Technologies Inc.”)
机构: Cherkasy State Business College (切尔卡西州商业学院); State Scientific Research Institute of Armament and Military Equipment Testing and Certification (乌克兰武装装备与军事设备检测认证国家科研所); Uzhhorod University (乌日霍罗德大学); PJSC “Finport Technologies Inc.” (股份有限公司“Finport Technologies Inc.”)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages, 3 figures, 8 tables
Abstract:We present an AI assistant for automating anti-corruption expertise of draft legislation based on the TRACE architecture, designed to screen bills for corruption risks: pinpoint provisions an expert would criticise and name the regulatory defect in each. The pipeline combines a deterministic parsing core, model-guided single-provision qualification, rule-based factor assignment, and external human oversight. To evaluate the assistant, we constructed an open benchmark from the published conclusions of Ukraine’s National Agency on Corruption Prevention (NAZK), comprising 132 draft laws with 474 model-extracted findings. A system flag agrees with a finding when it targets a compatible provision and an LLM judge rates the risk as the same (strict) or partly the same (lenient). On 100 draft laws, the pipeline matches 0.28 of the findings within its top ten flags using the local Gemma 4 31B model, 0.30 with Sonnet 5.5, and 0.43-0.46 across all flags (non-blind; the frozen blind baseline achieves 0.21, 95% CI 0.16-0.26). Across all runs, most misses occur on provisions that the system located but qualified as a different risk, and none of the 32 legal-collision findings was matched. On 21 acts held out from tuning, the pipeline outperforms a monolithic prompt containing the full methodology under the strict measure, though not under the lenient one, and Opus 5.5 adds nothing over Sonnet. The reported scores reflect agreement with model-extracted findings rated by an LLM judge whose two access routes agree on 73 of 74 labels, without human expert validation.
[NLP-15] Dyship: Leverag ing Acoustic Features For Speaker Relationship Classification From Dyadic Conversations
【速读】： 该论文旨在解决从双人对话中自动识别说话者熟悉度（speaker familiarity）与亲属关系（kinship）的问题，尤其关注不同语音表征在该任务中的有效性。其核心挑战在于如何从自然对话中提取能够有效反映人际亲疏与血缘关系的声学线索。解决方案的关键在于系统比较五种不同的语音表征：频谱图（spectrograms）、梅尔频率倒谱系数（MFCCs）、自监督预训练模型WavLM表示、x-vectors以及估计的发音运动特征（estimated articulatory movements）。研究发现，频谱特征在熟悉度分类任务中表现最优，准确率达87.81%；而WavLM在亲属关系分类中表现最佳，准确率达到85.17%。值得注意的是，尽管x-vectors和发音运动特征在熟悉度分类中表现较弱，但在亲属关系分类中展现出显著优势，其中x-vectors的AUC达到0.909，远超此前人类专家及音频语言模型的表现。这些结果表明，不同语音表征所捕捉的对话线索存在差异，且传统声学特征在人际关系推断中仍具强大潜力，为后续探索多模态与深度表征在社交关系识别中的应用提供了重要依据。
链接: https://arxiv.org/abs/2611.14241
作者: Kieran Sturges,Thomas Thebaud,Yaohan Guan,Najim Dehak
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 5 pages, under review
Abstract:We introduce DyShip, a framework for classifying speaker familiarity and kinship from dyadic conversations in the Seamless Interaction corpus. We compare five speech representations: spectrograms, MFCCs, self-supervised WavLM representations, x-vectors, and estimated articulatory movements. Spectral features perform best for familiarity classification, reaching an accuracy of 87.81%, while WavLM achieves the highest kinship accuracy of 85.17%. X-vectors and articulatory features yield weaker results on familiarity classifiaction, but shine for kinship classification, with an AUC of 0.909 for x-vectors. Most classifiers exceed previously reported human and audio language model scores on the same dataset. These findings highlight the potential of conventional acoustic features for relationship inference and motivate further investigation of the conversational cues captured by different representations.
[NLP-16] Stagewise and interpretable emergence of inside-outside computation in Transformer-based language models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理自然语言中句法结构的可变性与解析歧义时，其内部学习机制缺乏理论解释的问题。尽管已有“事后”研究发现，经过充分训练的Transformer模型能够近似经典的解析算法（如inside-outside算法），但其学习过程如何实现这一能力仍不明确。本文的关键解决方案在于：通过构建具有可变解析树拓扑结构的合成上下文无关文法（Context-Free Grammar, CFG），系统地分析模型在训练过程中的学习动态。该方法的核心优势在于其学习曲线具有完全可解释性——损失的各个阶段不仅可解析计算，且能明确对应到对隐藏潜在树变量（latent tree variable）的表征。通过对模型表示的探针分析，研究揭示了inside-outside计算机制在深度和训练过程中逐步涌现的现象：早期阶段仅依赖局部（二元组/三元组）信息并局限于初始几层，而随着训练推进，Transformer逐渐形成编码-解码结构，中间层负责编码最抽象的潜在信息，后半部分网络则用于逐步优化最终的词元预测。这种计算组织方式与真实世界LLMs中的观察结果相似，且由于实验基于可精确控制数据统计特性的合成模型，为深入理解模型学习机制提供了理论分析的基础。
链接: https://arxiv.org/abs/2611.14221
作者: Jack T. Parley
机构: École Polytechnique Fédérale de Lausanne (EPFL)(洛桑联邦理工学院); Lausanne, Switzerland(洛桑, 瑞士)
类目: Computation and Language (cs.CL); Disordered Systems and Neural Networks (cond-mat.dis-nn)
备注: 20 pages, 9 figures
Abstract:Variability in syntactic structure and parsing ambiguity are fundamental aspects of natural language which modern LLMs have to learn to deal with proficiently. Previous “post-mortem” studies have shown that fully trained Transformers indeed approximate canonical parsing algorithms such as inside-outside, but little is established theoretically about how this is actually learned. Here, we make a first step towards addressing this by studying the learning dynamics in synthetic context-free grammars with variable parse tree topology. The stagewise learning curves are fully interpretable, in the sense that all of the loss stages a) can be computed analytically, and b) can be clearly associated to the representation of a hidden latent tree variable, which allows to lift ambiguity and utilize information from wider contexts. By probing representations, we visualize the emergence of inside-outside computation across depth and during training: while during early stages local (bigram/trigram) information is used and involves only a few initial layers, gradually the Transformer develops an encoder-decoder structure where the most abstract latent information is encoded in the middle, and the latter half of the network is used to progressively refine the final token prediction. This organization of computation is reminiscent of findings in real-world LLMs, and importantly applies here to a synthetic model which allows full analytical control of the data statistics, thus opening the door to theoretical progress.
[NLP-17] Breakdown of Autoregressive Multi-Step Reasoning Under Concentrated vs. Diffuse Attention Head Dropout
【速读】： 该论文旨在探究在长时间生成过程中，类似于哺乳动物新皮层网络中局部短暂神经沉默（off-states）的时空集中型结构扰动，如何影响自回归大语言模型（LLM）的序列推理能力。其核心问题是：当注意力头的掩码操作以集中爆发式（burst）而非均匀分布（diffuse i.i.d. dropout）的方式施加时，模型推理性能是否会显著下降？解决方案的关键在于通过对比不同扰动模式——即在相同总扰动预算下，集中式的注意力头掩码爆发与分散式的独立同分布随机丢弃——揭示出模型对结构扰动的空间聚集性具有高度敏感性。实验结果表明，集中式扰动导致显著推理退化（如Qwen3-8B准确率从91.2%降至78.8%，Δ = -12.4 pp），而同等预算下的分散扰动则几乎不造成性能损失，凸显了自回归推理链对局部结构破坏的脆弱性，为变压器架构的容错能力提供了关键实证基准。
链接: https://arxiv.org/abs/2611.14209
作者: Karol Struniawski
机构: Warsaw University of Life Sciences – SGGW (华沙生命科学大学 – SGGW); Warsaw, Poland
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:During extended wakefulness, mammalian neocortical networks experience localized periods of transient neuronal silence termed off-states. Here, we examine how analogous spatiotemporally concentrated disruptions affect sequential reasoning in autoregressive Large Language Models (LLMs). Evaluating 250 paired derivations on the GSM8K benchmark across two 8B models (Qwen3-8B and LLaMA-3.1-8B-Instruct), we contrast clustered bursts of attention head masking against budget-matched diffuse i.i.d. dropout. Concentrated bursts induce substantial reasoning degradation: at onset parameter \mathcalB=0.10 (scheduling \sim 5.7% of vulnerable head-token volume), Qwen3-8B accuracy drops from 91.2% under diffuse noise to 78.8% under burst dropout ( \Delta = -12.4 percentage points [pp], exact McNemar p = 5.5 \times 10^-6 ), while LLaMA-3.1-8B-Instruct drops from 84.0% to 76.8% ( \Delta = -7.2 pp, p = 0.0198 ). Conversely, dispersing the identical cumulative budget uniformly across generation steps via stratified quintile matching yields substantially milder degradation (retaining 91.2% in Qwen and 84.0% in LLaMA). In an exploratory factorial dissociation ( N=100 ), single-step dropout density is associated with an acute drop in a single schedule draw ( -10.0 pp on Qwen, p_\textholm = 0.0425 ), while multi-token continuity exhibits an inconclusive compounding trend ( -7.0 pp, p_\textholm = 0.1892 ). Error analysis reveals that Qwen failures reflect derivation errors within formatted responses (conditional accuracy drop of -11.4 pp), whereas in LLaMA 22.2% of discordant losses lack a final answer format line (conditional gap narrows to -5.0 pp). These findings demonstrate that autoregressive reasoning chains are particularly vulnerable to concentrated structural disruptions, providing empirical benchmarks for fault tolerance in transformer architectures.
[NLP-18] Representational Atoms in Neural Networks
【速读】： 该论文旨在解决神经网络内部表示的组织机制及其与人类概念结构之间关系的刻画问题，核心挑战在于如何形式化定义并有效识别神经网络中具有语义一致性的基本表征单元（Fundamental Representational Units, FRUs）。其解决方案的关键在于提出两个评估标准——保真度（fidelity）与稳定性（stability），并通过理论证明，在适当条件下，阈值激活的稀疏自编码器（Threshold-activated Sparse Autoencoders, TSAEs）能够以高保真度和高稳定性识别出FRUs。大规模实验验证了该方法的有效性，并进一步发现，稳定性更高的FRUs表现出更强的单义性（monosemanticity），且可在不同网络层间进行组合，重构出与人类概念结构相对应的语义组织模式。这一工作构建了一个统一的框架，用于定义、评估与识别神经网络中的基本表征单元，为揭示神经网络表征与人类概念结构之间的关联提供了理论基础与实证支持。
链接: https://arxiv.org/abs/2611.14168
作者: Chenhui Hu,Pengfei Cao,Kang Liu,Jun Zhao
机构: 中国科学院自动化研究所认知与决策智能复杂系统重点实验室; 中国科学院大学人工智能学院
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Understanding the internal representations of neural networks and characterizing their relationship with human conceptual structure are two critical questions for elucidating model mechanisms. The former reveals how learned knowledge is organized in internal representations, whereas the latter provides a basis for interpreting these representations through human conceptual structure. Here, we formally define fundamental representational units (FRUs) and establish two evaluation criteria: fidelity and stability. We further prove that threshold-activated sparse autoencoders (TSAEs) can identify FRUs with high fidelity and stability under appropriate conditions, and validate this result through large-scale experiments. Further analyses reveal that FRUs with higher stability exhibit greater monosemanticity and can be composed across layers to recover semantic organization corresponding to human conceptual structure. These results provide a unified framework for defining, evaluating and identifying FRUs in neural networks, and establish a foundation for characterizing the relationship between neural network representations and human conceptual structure.
[NLP-19] Visual Similarity in Adult Word Extension and Artificial Word Learning
【速读】： 该论文旨在解决在命名扩展任务中，当两个物体共享同一名称时，视觉相似性在多大程度上解释了概念泛化行为，以及教学示例的组成如何补充视觉距离所无法捕捉的信息。其核心问题是：在人类和人工学习者中，视觉相似性（以冻结的自监督视觉编码器DINOv2提取的特征空间距离衡量）是否足以解释词义泛化模式，以及教学示例的类别层级结构（如上位类与下位类）在其中扮演何种作用。解决方案的关键在于通过量化测试图像与教学示例之间的视觉距离，并结合示例的类别层级构成进行分析。研究发现，视觉距离显著预测了人类和人工学习者的泛化倾向（log odds = -3.23/标准差），且在控制分类层级与教学条件后仍具统计效力；更重要的是，示例的类别层级结构对泛化范围具有决定性影响——当教学示例为下位类时，泛化仅覆盖1.1%的上位类对象，而当示例为上位类时，泛化率高达77.5%。此外，尽管视觉编码器的距离与人类感知相似性和混淆度高度相关（rho = .501–.795），但这些距离并不能充分预测学习难度，尤其在个体差异层面，表明人类认知机制中存在超越纯视觉相似性的结构性因素。因此，该研究揭示了视觉相似性与示例组成共同塑造概念泛化的双重机制，其中示例的层级结构提供了视觉距离之外的关键信息。
链接: https://arxiv.org/abs/2611.14116
作者: Patricio M. Vera
机构: Neurocreaciones
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 5 figures. Code: this https URL . Data: this https URL and this https URL
Abstract:When two objects share a name, how much does their visual similarity explain, and what do the teaching examples add? First, we reanalyzed trial-level data from a large adult word-extension study (Lewis Frank, 2018), computing each test image’s distance to the examples shown in the space of a frozen self-supervised visual encoder (DINOv2). Distance was associated with extension (log odds -3.23 per standard deviation; 520 people), and an exploratory post hoc analysis suggested that this association persisted within the taught domain after controlling for taxonomic level and teaching condition. Example composition also mattered: after three examples, adults extended a word to 1.1% of superordinate items when the examples were subordinate and to 77.5% when they were superordinate. Second, in an artificial learner using the same encoder, words joining two categories were learned less accurately the farther apart the categories were (84.7%, 65.4%, and 53.0% naming accuracy for near, mid, and far pairs). The encoder’s distances correlated with human similarity and confusability measures from three datasets (rho = .501-.795), but this representational alignment did not translate into conclusive evidence that human-derived distances predict learner difficulty, and a second encoder trained on one child’s experience reproduced the tier ordering but not the pair-level effect. The secondary human data were analyzed under an internal plan fixed before the confirmatory analyses (not externally preregistered). Visual similarity accounts for part of how generalization is organized in both systems, while the composition of examples contributes what the distance we used does not capture.
[NLP-20] Sckwoky at SHROOM-Visions: Character-Level Hallucination-Span Detection in Image-Conditioned VLMs via Dual-Prompted LoRA Decoders
【速读】： 该论文旨在解决图像条件下的视觉-语言模型（Vision-Language Model, VLM）响应中字符级幻觉片段（character-level hallucination span）的检测问题，尤其关注多语言环境（英语、法语、意大利语和中文）下的泛化能力。其核心挑战在于：由于采用因果解码器结构的主干模型（如Gemma-4-12B-it）在生成过程中仅能单向建模上下文，导致对答案中每个位置的隐藏状态缺乏双向上下文感知，从而影响幻觉定位精度。解决方案的关键创新在于——在提示（prompt）中重复候选答案，并从其第二次出现的位置提取隐藏状态，以此在非自回归的一次前向传播中近似实现双向上下文建模。在此基础上，设计了一个字符级定位器（character-level locator），通过融合中间隐藏状态，联合预测幻觉概率、BIO边界以及响应级别的拒答分数；同时引入语言特定分类器，对每个幻觉片段进行五类幻觉类型的判别。实验表明，该方法在闭测试集上实现了0.4404的宏平均字符级交并比（IoU），显著优于官方基线（0.1854），并在四语言任务中位列第5–6名。消融实验进一步验证了重复答案策略与LoRA适配器均对性能提升具有相当贡献，且系统高度依赖图像与答案的配对关系，随机打乱图像会导致性能降至“不预测”基准甚至更低。此外，该框架可复用生成模型的原始权重，无需额外独立验证器，在本地问答系统部署中具备实用性。
链接: https://arxiv.org/abs/2611.14100
作者: Mikhail Kulakov(Novosibirsk State University),Ruslan Solikhov(Novosibirsk State University),Ivan Bondarenko(Novosibirsk State University)
机构: Novosibirsk State University (Новосибирский государственный университет)
类目: Computation and Language (cs.CL)
备注: 7 pages, 3 figures. Published in the Proceedings of the Third Workshop on Uncertainty-Aware NLP (UncertaiNLP 2026), Budapest, Hungary. Presented as a poster at the workshop
Abstract:We present our system for the SHROOM-visions 2026 shared task on character-level hallucination span detection in image-conditioned vision-language model (VLM) responses, evaluated across English, French, Italian, and Chinese. The detector is built on a frozen Gemma-4-12B-it backbone with trainable LoRA adapters (approximately 10.7M parameters). Because the backbone is a causal decoder, hidden states read from a single copy of an answer cannot attend to subsequent tokens; we therefore repeat the candidate answer within one prompt and extract features from its second occurrence, giving every position approximate bidirectional context in a single non-autoregressive pass. A character-level locator combines intermediate hidden states to predict hallucination probabilities, BIO boundaries, and a response-level abstention score, while separate language-specific classifiers assign one of five hallucination types to each predicted span. On the closed test set, our system reaches a macro-averaged character IoU of 0.4404, more than doubling the official baseline (0.1854), and ranks 5th-6th on IoU across the four languages. On the English tuning split, controlled ablations attribute a gain to the repeated answer that is comparable to that of LoRA adaptation (its held-out gain is smaller and concentrated on hallucinated answers), and they show that the detector relies on the image-answer pairing: shuffling images across examples drives IoU down to the level of the predict-nothing floor or below it in every language. In deployments where the same Gemma-4-12B-it model also generates answers, its base weights can be reused, making the approach practical for local question-answering systems that cannot run a separate verifier.
[NLP-21] HandsOnAid: Building and Evaluating an Offline First-Aid Assistant for Emergency Response Scenarios
【速读】： 该论文旨在解决在无网络信号且缺乏急救人员的紧急情况下，如何利用手机端小型语言模型（Small Language Model, SLMM）提供安全、准确的急救指导这一关键问题。其核心挑战在于，在资源受限环境下，如何确保生成式AI（Generative AI）在无互联网支持时仍能输出符合医学规范、具备高安全性的可靠建议。解决方案的关键在于构建并优化一个轻量化、可离线运行的4-bit量化微调模型——HandsOnAid，基于Gemma 2B架构，采用QLoRA方法在包含5,550个问答对的FirstAidQA数据集上进行微调，并引入检索增强机制以弥补训练数据中对ANZCOR指南关键细节覆盖不足的问题。实验表明，最佳配置（4-bit微调+检索）在严格评估体系下达到2.51/5的平均得分，显著优于基础模型（2.34），且在实际设备（Pixel 7 Pro）上实现了6.86秒内首屏响应、14.88秒内完成完整回答的低延迟表现，验证了其在真实应急场景中的可行性与实用性。
链接: https://arxiv.org/abs/2611.14059
作者: Rezwan Islam Salvi,Saiyma Sittul Muna,Mushfiqur Rahman Mushfique
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 2 figures, 10 tables
Abstract:Emergencies can occur where there is no phone signal and no ambulance nearby. A small language model on a phone can be helpful in such situations, but its advice needs to be safe and accurate. We built HandsOnAid, a 4-bit Gemma 2B model fine-tuned with QLoRA on FirstAidQA dataset, a corpus of 5,550 first-aid question-answer pairs. We compared seven configurations, including the base, 4-bit and 8-bit fine-tuned models, and versions with a length floor, safety gate, or retrieval. We used a 41-pair question-answer bank with medically reviewed references that followed ANZCOR guidelines, and a strict 0-5 score rubric, with twelve safety overrides. Four frontier LLM judges (GPT-5.6 Sol, Claude Opus 5, DeepSeek V4 Pro, GLM-5.3) blindly scored every answer, with 45 hidden controls used to check judge reliability. Fine-tuning improved the mean score by +1.08 over the base model to 2.34. The best configuration scored 2.51 out of 5 which was the fine-tuned 4-bit model with retrieval. A medical professional also conducted a blind evaluation of 40 answers across the base, the 4-bit fine-tuned, and the retrieval configurations, and the scores were similar to the LLM judges’ average scores. A keyword audit found limited coverage of several details of ANZCOR guidelines in the training corpus, that are required by the rubric, which suggests that missing information in the training data could be a cause of the low scores. On a smartphone device (Pixel 7 Pro) with six CPU threads and no network connection, the 4-bit fine-tuned model produced its first displayable text in 6.86 seconds and a complete answer in 14.88 seconds on average over ten questions.
[NLP-22] MISO-BR: An Open Corpus for Misogyny Detection in Brazilian Portuguese
【速读】： 该论文旨在解决巴西葡萄牙语中针对女性的网络仇恨言论（misogyny）缺乏公开、可审计资源的问题。其核心解决方案是构建并发布MISO-BR，一个包含896条平衡标注的社会媒体文本语料库，涵盖二元分类的性别歧视标签。关键创新在于采用可复现的构建流程：结合基于词典和目标导向的X平台爬取、来自已有的大型语言模型预筛选阶段（基于释放的提示与解码参数）的数据，以及三位标注员依据书面指南进行的人工标注，确保了整个数据生产管道的透明性与可审计性。通过多模型对比实验发现，传统分类器在宏平均F1分数上表现介于0.651至0.676之间，偏向精确率但召回率较低；而微调后的BERTimbau Base与Large模型分别达到0.833的宏平均F1与准确率（95%置信区间[0.77, 0.88]），性能差距约16个百分点，且二者在测试集上无显著差异，尽管训练成本相差2.6倍。研究强调，该资源的核心价值不在于指标本身，而在于其完整可追溯的构建流程。同时指出三项重要局限：人为的类别平衡对真实场景下精确率的影响、来源与标签之间的关联性可通过释放的源字段进行测量，以及基于词典引导采样和自动筛选带来的选择偏差。所有语料、标注指南、模型及实验脚本均已开源。
链接: https://arxiv.org/abs/2611.14036
作者: Fábio A.S. Passos,Péricles B.C. Miranda,Ana Paula Cavalcanti
机构: CESAR School; UFRPE(联邦伯南布哥州大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Misogyny is among the most prevalent forms of online hate speech directed at Brazilian users, yet open and auditable resources dedicated to it in Brazilian Portuguese remain scarce. We introduce MISO-BR, an open, documented and versioned corpus of 896 balanced social media texts annotated for binary misogyny, released with its construction protocol and reproducible baselines. The corpus combines lexicon- and target-guided scraping from X with items imported from OLID-BR, a large language model triage stage whose prompt and decoding parameters are released, and manual annotation by three annotators following a written guide. Inter-annotator reliability reaches a Fleiss’ kappa of 0.788 over 816 items. Under a single fixed protocol we compare four classical classifiers (Multinomial and Bernoulli Naive Bayes, a linear SVM and a random forest) with fine-tuned BERTimbau Base and Large. The classical models cluster between 0.651 and 0.676 macro-F1 and are precision-oriented but recall-poor. The transformers reach 0.833 macro-F1 and accuracy (Wilson 95% CI for accuracy [0.77, 0.88]), a gap of roughly 16 points, while Base and Large are indistinguishable on the test set despite a 2.6x difference in training cost. We argue that the value of the resource lies less in its headline scores than in the auditability of its pipeline, and we state three limits that users must reckon with: the artificial class balance, whose effect on precision under realistic prevalence we quantify; the association between provenance and label, which the released source field makes measurable; and the selection bias induced by lexicon-guided sampling and automatic triage. The corpus, annotation guide, models and experimental scripts are publicly available.
[NLP-23] Program-Aided Patchable Skill Evolution
【速读】： 该论文旨在解决当前大语言模型（LLM）智能体在执行复杂交互任务时，其技能（Agent Skills）难以高效维护与演进的问题。现有方法虽通过从智能体执行轨迹中提炼可复用经验并基于下游任务反馈进行技能优化，但普遍将技能表示为自然语言文档，导致过程逻辑与描述性知识混杂，降低了技能的可编辑性和可演化性。为此，本文提出程序增强型可修补技能演化框架（PASE, Program-Aided Patchable Skill Evolution），其核心在于将可复用的程序化逻辑从自然语言描述中分离，并以可执行程序的形式进行封装。这一设计使得技能的更新聚焦于可修改的程序组件，显著减少对自然语言技能文本的重复修订需求，从而提升技能演化的效率与稳定性。实验结果表明，PASE在多种模型与基准测试中均显著优于现有基线方法，性能提升超过2.8%，且展现出更强的演化稳定性和泛化能力，验证了程序化知识在技能演化中的关键作用。
链接: https://arxiv.org/abs/2611.14019
作者: Jiahao Wang,Yuqi Xiong,Qing Yang,Zhipeng Xu,Zhenghao Liu,Zulong Chen,Yubin Bao
机构: Northeastern University(东北大学); Alibaba Group(阿里巴巴集团)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures, 5 tables
Abstract:Agent skills have emerged as an effective means of enabling LLM agents to solve complex, interactive tasks by encoding reusable knowledge without modifying model parameters. Recent approaches further improve skill adaptation by distilling reusable experience from agent execution trajectories and evolving skills based on downstream task feedback. However, existing methods primarily represent skills as natural language documents, intertwining procedural logic with descriptive knowledge and making skills difficult to maintain and evolve. To address this limitation, we propose PASE (Program-Aided Patchable Skill Evolution), a program-aided framework that iteratively evolves skills by associating them with executable programs. Unlike prior approaches that encode trajectory-derived experience primarily in natural language, PASE separates reusable procedural logic into editable programs, enabling targeted updates to executable components while reducing the need to repeatedly revise natural language skills. Extensive experiments across diverse models and benchmarks demonstrate that PASE consistently outperforms all baselines, by achieving over 2.8% improvements. Further analyses show that PASE exhibits greater evolutionary stability and generalisation capability, highlighting the critical role of programmatic knowledge in skill evolution. We will release all data and code via GitHub.
[NLP-24] One Sequence Is Enough: Fingerprinting Language Models from Fixed-Length Integer Sequences
【速读】： 该论文旨在解决模型身份伪造问题，即用户通过API或第三方平台请求服务时，所接收到的实际模型可能与其声明的模型不一致，导致信任危机。为应对这一问题，论文提出基于可观察输出进行模型身份验证的方法，核心在于识别生成式AI（Generative AI）在特定任务中隐含的、可区分的行为模式。其解决方案的关键是构建一个名为NumPrint的基准数据集，包含51个模型在中、日、英三种语言提示下生成的共计5,036条固定长度整数序列（每条序列包含294个1至355之间的整数），用于捕捉模型在数值选择任务中的细微偏好差异。研究进一步提出浅层多层感知机与因果变换器（Shallow MLP and Causal Transformer, SMCT）联合架构，以有效建模生成序列中的聚合数值偏好与行为模式。实验结果表明，在51类闭集分类任务中，仅使用单个整数序列，SMCT即可达到92.72%的识别准确率，显著优于现有方法；当使用两个和四个响应序列时，准确率分别提升至96.06%和97.12%，证明单一整数序列已蕴含丰富的模型指纹信息，且多轮输出可进一步增强识别性能。
链接: https://arxiv.org/abs/2611.14010
作者: Fantong Meng,Chengjie Sun,Shuangjia Yang,Zhiyuan Zhao,Yansheng Pei,Xinyang Li
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computation and Language (cs.CL)
备注: 17 pages including appendices, 6 figures
Abstract:Users of model APIs and third-party platforms often rely on the declared model name to identify the model responding to their requests. However, the actual model may differ from the declared one, motivating identity verification based on observable outputs. Previous studies have shown that preferences in simple numerical-choice tasks can serve as model fingerprints. We investigate whether a single fixed-length integer sequence can combine multiple numerical observations to identify its source model. For this task, we introduce NumPrint, a benchmark of 5,036 answers from 51 models, collected using Chinese, Japanese, and English prompts that request 294 integers between 1 and 355 in one response. We propose the Shallow MLP and Causal Transformer (SMCT) to capture aggregate numerical preferences and behavioral patterns in generated sequences. In 51-class closed-set evaluation, SMCT achieves 92.72% accuracy from a single sequence, outperforming all methods compared in this study. Using two and four answers increases accuracy to 96.06% and 97.12%, respectively. These results show that a single integer sequence contains useful model fingerprints and that additional answers further improve identification.
[NLP-25] Coding Agent Memory Post-Training II: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via On-Policy Distillation
【速读】： 该论文旨在解决语言模型代理在处理长时程任务时面临的记忆管理难题，即当交互历史超过模型的上下文窗口限制时，如何有效决定哪些信息应被记录、何时读取以及如何利用外部存储进行记忆维护。现有方法通过在策略中引入基于策略的蒸馏（On-Policy Distillation, OPD），将记忆控制纳入策略学习框架，但依赖于预训练阶段未涵盖的记忆工具与压缩协议，导致教师模型无法提供有效指导，学生模型也难以充分学习有意义的记忆操作行为。其解决方案的关键在于提出Coding Agent Memory Gym II（CAMG-II）这一多场景长时程智能体强化学习环境套件，涵盖购物、编码、自动研究、通用任务及视觉任务，并为每个环境提供标准Bash访问和跨回合持久化工作区，使智能体能够以文件形式创建、修改、搜索和复用记忆；同时引入Coding Agent Memory Post-Training II（CAMP-II），采用完全异步的OPD机制，在冻结教师模型的先验指导下，联合训练单一策略以直接学习基于文件的记忆行为。实验表明，CAMP-II在CAMG-II上相较最强基准提升2.97倍，且所学记忆行为可泛化至Terminal-Bench 2.1和SWE-Bench Pro V2两个外部基准，显著提高成功率。
链接: https://arxiv.org/abs/2611.14009
作者: Lirui Luo,Kelong Mao,Xinwei Yang,Rongqing Li,Luyu Chen,Kieran Wong,Yudong Guo,Xinrui Wang,Jiayin Zhu,Gu Simiu,Sulong Xu,Cong Fang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 40 pages, 6 figures, 8 tables
Abstract:Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model’s active context. They must therefore decide what to write down and what to read back, and when to do either. Recent work has begun to use on-policy distillation (OPD) to make memory control part of the policy, relying on memory tools and compaction protocols that lie outside either model’s pre-training. However, this weakens the distillation on both sides: the teacher cannot give useful guidance on an interface it has never used, and the student samples too few meaningful memory actions for that guidance to take effect. To study this problem, we introduce Coding Agent Memory Gym II (CAMG-II), a suite of long-horizon agentic-RL environments spanning Shop, Coding, AutoResearch, General, and Visual. Alongside each environment’s native task interface, CAMG-II provides ordinary Bash access and an episode-persistent workspace, enabling the policy to create, revise, search, and reuse files as memory across context replacements. We also introduce Coding Agent Memory Post-Training II (CAMP-II), which trains a single policy jointly across all five environments with fully asynchronous OPD to learn this file-based memory behavior directly from a frozen teacher with a prior for file operations. On CAMG-II, CAMP-II obtains 2.97 times the gain of the strongest OPD baseline. The memory behavior CAMP-II learns generalizes to two external benchmarks, Terminal-Bench 2.1 and SWE-Bench Pro V2, with improved success rates.
[NLP-26] Specify Privacy Yourself: Assessing Inference-Time Personalized Privacy Preservation Ability of Large Vision-Language Models
【速读】： 该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在图像中高精度推断敏感个人信息所带来的隐私泄露问题，尤其针对现有模型在通用隐私保护（如信用卡信息）方面虽表现良好，但对个性化、上下文依赖的隐私需求缺乏有效应对能力。其核心挑战在于如何实现推理时的个性化隐私保护（Inference-Time Personalized Privacy Protection, ITP³），即允许用户通过自然语言指令动态定义隐私边界。解决方案的关键在于提出首个系统性评估框架SPY-Bench，包含32,700个涵盖67类隐私场景与24种真实情境的图像-问题对及个性化隐私指令，并设计基于用户意图与上下文感知的新颖评估指标。实验表明，当前21个主流LVLM在ITP³任务上的平均合规准确率仅为约24%，且普遍缺乏上下文隐私理解能力。为此，研究进一步引入一种改进的噪声对比对齐（Noise Contrastive Alignment）方法，在保持合理泛化性能的同时，将合规准确率提升至96.88%，标志着迈向可控制、伦理化部署更智能视觉语言模型的重要进展。
链接: https://arxiv.org/abs/2611.13997
作者: Xingqi Wang,Xiaoyuan Yi,Xing Xie,Jia Jia
机构: Tsinghua University (清华大学); Microsoft Research Asia (微软亚洲研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: Accepted by ACM Multimedia 2025 (Brave New Ideas track)
Abstract:Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities but raise significant privacy concerns due to their abilities to infer sensitive personal information from images with high precision. While current LVLMs are relatively well aligned to protect universal privacy, e.g., credit card data, we argue that privacy is inherently personalized and context-dependent. This work pivots towards a novel task: can LVLMs achieve Inference-Time Personalized Privacy Protection (ITP ^3 ), allowing users to dynamically specify privacy boundaries through language specifications? To this end, we present SPY-Bench, the first systematic assessment of ITP ^3 ability, which comprises (1) 32,700 unique samples with image-question pairs and personalized privacy instructions across 67 categories and 24 real-world scenarios, and (2) novel metrics grounded in user specifications and context awareness. Benchmarking the ITP ^3 ability of 21 SOTA LVLMs, we reveal that: (i) most models, even the top-performing o4-mini, perform poorly, with only \sim 24% compliance accuracy; (ii) they show quite limited contextual privacy understanding capability. Therefore, we implemented initial ITP ^3 alignment methods, including a novel Noise Contrastive Alignment variant which achieves 96.88% accuracy while maintaining reasonable general performance. These results mark an initial step towards the ethical deployment of more controllable LVLMs. Code and data are available at this https URL
[NLP-27] On the information rate of phones syllables and words
【速读】： 该论文旨在解决语音内容编码所需比特率最小化的语言单元选择问题，即在音素（phone）、音节（syllable）、词（word）、多词组合及更大或中间尺度的语义单元中，哪类单位能实现最优的信息压缩效率。这一问题对语音与语言系统的设计以及人类语言处理机制的理论构建具有重要意义。研究基于960小时英语有声书的转写语料库，对比了音素、音节、词、多词短语及中间粒度分词方案下的信息速率（information rate）。尽管在理想语言模型下所有单位表现等价，但在实际建模能力受限的情况下，需权衡不同单位的表现；因此，研究采用n-gram与Transformer语言模型进行评估。结果表明，在建模资源有限时，以词为单位的编码可实现最低比特率，而略大于词的单位（如短语）有时表现更优。核心结论是：当建模容量受限时，比音节大但远小于长距离多词短语的中等粒度单元（intermediate units），在信息速率方面提供了最高效的表示方式。
链接: https://arxiv.org/abs/2611.13990
作者: Herman Kamper
机构: Stellenbosch University (斯蒂伦博斯大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: 5 pages, 1 table, 2 figures
Abstract:Which linguistic units minimise the bitrate required to encode speech content: phones, syllables, words, maybe larger units, or something in between? This question has implications for the development of speech and language systems and theories of human language processing. Using a transcribed corpus of 960 hours of English audiobooks, I compare the information rates of phones, syllables, words, multi-word combinations and intermediate tokenisations. While all units are equivalent under a perfect language model, it makes sense to compare these under limited modelling budgets; I use n -gram and transformer language models. The lowest bitrates are achieved using words, with slightly longer units sometimes performing best. The results show that, when modelling capacity is limited, units larger than syllables but smaller than long multi-word phrases provide the most efficient representation in terms of information rate.
[NLP-28] Understanding Mixture-of-Experts Language Models Beyond Expert Boundaries
【速读】： 该论文旨在解决混合专家模型（Mixture-of-Experts, MoE）中专家专业化本质的争议问题，即领域特异性专家偏好与基于标记身份或句法模式的路由机制之间的矛盾。其核心挑战在于：现有研究在专家层级上难以统一解释路由行为的驱动因素，而本文通过突破传统专家层面的分析范式，转而从神经元层级揭示领域专用计算的组织结构。解决方案的关键在于提出一种名为“路由感知幅度剖析”（Routing-Aware Magnitude Profiling, RAMP）的新方法，用于识别具有领域特异性的神经元集合，并追踪其在预训练与后训练阶段的重要性演化。研究发现，尽管专家层级的重要性排名相对稳定，但神经元层级的重要性仍持续重组，表明领域专业化在神经元层面具有动态演化特性。基于此，进一步提出基于RAMP的剪枝方法（RAMPr），通过选择跨专家的代表性神经元，在保持多领域能力均衡的前提下实现高效压缩。实验表明，在保留75%神经元时，模型平均性能损失仅为2%-9%；在50%保留率下，可降低36%-43%的驻留GPU内存占用，并提升批处理预填充吞吐量17%-39%，显著优化了MoE模型的效率与可扩展性。
链接: https://arxiv.org/abs/2611.13975
作者: Zhenhong Zhou,Mao Zheng,Mingyang Song,Qiankun Li,Kun Wang,Junfeng Fang
机构: Nanyang Technological University (南洋理工大学); Tencent(腾讯); National University of Singapore (新加坡国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 55 pages
Abstract:In Mixture-of-Experts (MoE) models, the nature of expert specialization remains controversial. Reports of domain-associated expert preferences contrast with analyses attributing routing primarily to token identity or syntactic patterns. To clarify these differing accounts, we look beyond expert-level routing to investigate the neuron-level organization of domain-specialized computation using Routing-Aware Magnitude Profiling (RAMP), a measure developed for this analysis. Using mixed-domain calibration data, we identify domain-specialized neuron sets with 58-74% overlap across multi-source samples of the same domain, compared with 15-24% across domains. To understand how this organization evolves during training, we trace neuron importance throughout pre-training and post-training across three fully open MoE families. Neuron importance continues to reorganize between pre-training checkpoints despite comparatively stable expert-level importance rankings, while post-training largely preserves neuron rankings. Building on these neuron-level perspectives, we further introduce RAMP-based Pruning (RAMPr) to select neurons across experts for balanced multi-domain capability preservation. At 75% neuron retention, RAMPr preserves average 91-98% of original full performance across six models. At 50% retention, our pruning method can reduce resident GPU memory by 36-43% and improves batched prefill throughput by 17-39% effectively. Our code is available at this https URL
[NLP-29] Beyond Fluent Responses: Finding User Dissatisfaction in Human-AI Interactions
【速读】： 该论文旨在解决传统大语言模型响应评估中过度依赖响应本身质量（如正确性与连贯性）而忽视用户中心评价指标的问题，即如何有效捕捉真实对话中用户的主观满意度与感知有用性。其核心挑战在于，用户实际的满意程度往往不完全反映在生成响应的质量上，而是隐含于对话文本中的细微语用线索。论文的关键解决方案是通过分析带有自我报告不满标签的用户话语，探索从对话文本中检测隐性不满信号的可能性。研究发现，在英文对话场景下，仅基于用户自身话语构建的模型即可在AUPRC指标上超出17.8%的基准不满率达15个百分点，表明用户话语中蕴含可被识别的不满线索；进一步研究表明，结合前一回合用户话语的变化特征，尤其是采用异常检测方法，能显著提升不满识别效果，凸显了动态语境变化在用户情绪感知中的关键作用。
链接: https://arxiv.org/abs/2611.13894
作者: Yuka Saito,Ikumi Numaya,Jun Suzuki,Reina Akama
机构: Tohoku University (东北大学); RIKEN (理化学研究所); NII LLMC (国立情報学研究所自然言語処理研究センター); NINJAL (日本語音声・言語情報処理研究センター)
类目: Computation and Language (cs.CL)
备注: Accepted to LUHME 2026
Abstract:As large language models generate increasingly high-quality responses, recent work on response evaluation has moved beyond response-centered criteria, such as correctness and coherence, toward user-centered criteria, including user preferences and perceived usefulness. This shift is fundamentally important for developing technologies that are truly useful to real-world users. While conventional response evaluation quantifies the quality of the response itself, users’ satisfaction in real conversations also depends on their subjective preferences and perceptions of usefulness, which do not necessarily align with response quality. This study investigates whether such user-centered signals can be detected from conversational text. Specifically, we analyze user utterances with self-reported dissatisfaction labels to examine whether user dissatisfaction in text can be detected. In English interaction, models using user utterances exceeded the 17.8% dissatisfaction label prevalence in AUPRC by up to 15 percentage points. These results suggest that user utterances reveal cues to implicit dissatisfaction. Furthermore, changes from the immediately preceding user utterance may improve dissatisfaction detection, with anomaly detection methods appearing particularly effective.
[NLP-30] Are Causal Neuron Circuits Unique and Transferable?
【速读】： 该论文旨在解决生成式模型中神经元群干预实验的可解释性局限问题，即现有方法仅能证明某组神经元对模型输出具有影响，却无法确定其必要性、可替代性及泛化能力。核心问题是：当干预一组神经元会改变模型答案时，究竟需要多少该组神经元才是关键？其解决方案的关键在于系统性地评估神经元组内所有可能的子集（共4,096个12神经元子集）在不同任务中的因果效应。研究发现，在双选项首都识别任务中，尽管零消融特定12个神经元会使答案置信度下降27%，但这些神经元的激活值在不同问题间近乎恒定，缺乏问题特异性内容；而在两跳关系链任务中，用第一跳问题的激活值“替换”12个神经元即可将答案导向桥接实体（如从“Scar”变为“Simba”），且该效果在多数链路中成立（9/11、9/11、8/10），而随机神经元则无此效应。进一步分析表明，完整神经元组通常非必需，少数神经元即可引发答案切换，且多个不相交子集独立具备翻转答案的能力，尤其在模型置信度较低时更常见。此外，这种干预效应可迁移至同一链路的重述版本，但在其他链路上显著减弱。研究强调了神经元层面因果干预与子集探索的重要性，但未重建完整神经电路的连接结构。
链接: https://arxiv.org/abs/2611.13885
作者: Ömer Tekin Yavuz(İletişim Yazılım)
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 12 pages, 5 figures, 4 tables. Code, data and results: this https URL
Abstract:Interpretability demonstrations often show that intervening on a group of neurons changes a model’s answer. A recent example removes an 18-neuron group in Qwen3-32B and the model answers “Simba” instead of “Scar” to a two-hop Lion King riddle. Such a demonstration establishes that the group matters, but not how much of it is necessary, whether other groups could do the same, or whether the effect carries over to other inputs. We ask: when intervening on a group of neurons changes a model’s answer, how much of that group is actually necessary? For every selected group we measure all 4,096 subsets of 12 MLP neurons. On a two-alternative capital task (Qwen3-0.6B), zero-ablating 12 selected neurons lowers the answer margin by 27% while random neurons barely change it, yet replacing the same neurons with their values from other questions also barely changes it: their activations are nearly constant across questions, and we find no evidence that they carry question-specific content. On two-hop relation chains, patching 12 neurons with activations from the first-hop question shifts the answer to the bridge entity (from Scar to Simba): the greedy answer starts with the bridge in 9/11 chains for Qwen3-1.7B, 9/11 for Llama-3.2-1B and 8/10 for Gemma-3-1B; random neurons never do. The full group is rarely necessary: a few neurons often suffice, and in many chains disjoint subsets each flip the answer on their own, in an exploratory analysis most often when the model is less confident. Group effects transfer to rephrasings of the same chain but are much weaker on other tested chains. We study neuron-level causal interventions and alternative subsets; we do not recover the full connectivity of circuits.
[NLP-31] Beyond Passive Imitation: On-Policy Mid-Training Distillation for Language Models
【速读】： 该论文旨在解决持续预训练（Continual Pre-Training, CPT）中标准的下一个词预测（Next-Token Prediction, NTP）目标所导致的训练-生成不匹配问题。传统NTP方法将所有语料库中的词元视为固定目标，忽视了其对当前模型的实际价值，导致相同训练序列在不同基础模型上产生截然不同的效果——例如，在相同10亿词的数学语料上，NTP能提升Llama-3.2-3B的表现，却显著损害Qwen3-1.7B-Base的性能，暴露出模型依赖性与训练目标僵化的问题。为此，论文提出基于策略的中期蒸馏（On-Policy Mid-Training Distillation, OPMD），其核心创新在于将中段训练从静态的离线文本模仿转变为动态、查询驱动的知识获取过程。OPMD在选定锚点位置采样自学生策略的轨迹，并利用冻结教师模型提供的密集词元级反KL奖励进行训练，使模型学习其实际访问的状态，而非仅基于真实前缀的监督信号。这一在策略（on-policy）目标有效缓解了教师强制式NTP带来的训练-生成偏差。实验表明，OPMD在MATH-500和Minerva基准上均优于原始基线，尤其显著提升了Qwen模型的表现，且无需额外推理开销，为持续预训练提供了一种具备模型感知能力的主动领域适应方案。
链接: https://arxiv.org/abs/2611.13855
作者: Ruize Zheng,Xuyang Liu,Siyu Ren,Xiaobing Tu,Yinggui Wang,Jinkui Ren,Xiantao Zhang,Linfeng Zhang
机构: Fudan University (复旦大学); The Hong Kong Polytechnic University (香港理工大学); Sichuan University (四川大学); Alibaba Group (阿里巴巴集团); Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 17 pages, 4 figures, 4 tables
Abstract:Continual pre-training (CPT) adapts large language models (LLMs) to specialized domains, yet its standard next-token prediction (NTP) objective treats every corpus token as a fixed target, regardless of its utility to the current model. In a controlled study on the same 1B-token mathematical corpus, we observe sharply different outcomes across base models: NTP improves Llama-3.2-3B’s performance on mathematical benchmarks but substantially degrades Qwen3-1.7B-Base’s performance, indicating that, under a fixed training recipe, the same training sequences can have model-dependent effects. To address this problem, we introduce On-Policy Mid-Training Distillation (OPMD), which reformulates mid-training from offline text imitation into active, query-driven knowledge acquisition. At selected anchor positions, OPMD samples trajectories from the evolving student policy and applies dense token-level reverse-KL rewards from a frozen teacher, training the model on states it actually visits rather than only on ground-truth prefixes. This on-policy objective reduces the training-generation mismatch of teacher-forced NTP. On MATH-500 and Minerva, OPMD improves the Qwen student over its base performance and outperforms the evaluated NTP baseline, which degrades on both benchmarks. OPMD integrates into CPT pipelines without added inference-time overhead and offers a model-aware alternative to passive domain adaptation.
[NLP-32] Alania-2: Turkish text-to-speech with instant and fine-tuned voice cloning
【速读】： 该论文旨在解决土耳其语文本转语音（TTS）系统在语音自然度、可懂度及个性化语音克隆方面的关键挑战，尤其针对合成语音中数字、缩写和外来词等复杂文本元素处理不当导致的语义丢失问题。其核心解决方案是提出Alania-2——一个基于VoxCPM架构的488M参数模型，采用语言模型骨干网络结合扩散头生成连续音频潜在表示，并通过18,195小时土耳其语语音数据进行端到端训练，其中90%为由更大教师模型生成并自动筛选的合成语音。关键创新包括：引入文本规范化模块，将数字、缩写和外文名称转换为符合土耳其语发音习惯的词语表达，显著提升合成语音的可懂性（无此模块时96个测试句中仅86%完整保留关键信息）；通过高质量训练数据实现小模型高可懂度，表明数据质量对语音清晰度的影响大于模型规模；在语音克隆方面，仅以参考音频作为条件而非延续其内容，可将漏词率从21.9%降至1.7%，且20–30秒参考音频效果最佳，配合低秩适配器可在约两分钟内实现优于即时克隆的说话人相似度；此外，优化推理引擎使首个音频输出延迟降低至32毫秒，显著提升实时性。研究还公开了评估工具链与三个土耳其语语音与文本数据集，推动领域发展。
链接: https://arxiv.org/abs/2611.13845
作者: Sezgin Saygili,Emre Kaplaner,Oncel Ozgul,Fikri San Koktas
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 21 pages, 11 figures, 10 tables. Evaluation harness and results: this https URL
Abstract:We present Alania-2, a Turkish text-to-speech (TTS) system with two built-in voices, voice design from text descriptions, and two kinds of voice cloning. Alania-2 is a 488M-parameter model with the VoxCPM architecture, which generates continuous audio latents with a diffusion head on top of a language-model backbone. We train it from scratch on 18,195 hours of Turkish speech, 90% of which is synthetic speech produced by a larger teacher model and filtered automatically. A text normalizer rewrites numbers, abbreviations and foreign names into the words a Turkish speaker would say; without it, none of 96 test sentences with digits kept all its critical parts, such as an amount or a date, through synthesis and speech recognition, against 86 with it. On Freya-TR-Eval, a public benchmark of 495 Turkish sentences scored with automatic speech recognition, Alania-2 reaches a word error rate of 1.44%, compared with 5.99% for our previous system. On its built-in voices, it comes within half a point of character error rate of a 2.3B-parameter model at under a quarter of the size, and a 7.6M-parameter single-voice model trained with the same recipe matches the most intelligible systems we measured, which suggests that training data matters more for intelligibility than model size. We also report practical findings on voice cloning: conditioning on the reference audio alone, rather than continuing it, cuts skipped words from 21.9% to 1.7%; 20-30 seconds of reference audio works best; and a low-rank adapter trained in about two minutes raises speaker similarity well above instant cloning. Finally, we describe inference-engine changes that bring first audio down to 32 ms for a single stream. We release our evaluation harness and three Turkish speech and text datasets.
[NLP-33] PolyRoute: A Twelve-Language and Code-Mixed Benchmark for Typed-Decision Routing with Baselines and a Leaderboard
【速读】： 该论文旨在解决多语言环境下生成式决策编码器（Typed-decision encoders）在非英语场景下的行为评估缺乏统一基准的问题。现有方法通常仅在英语上进行验证，导致跨语言性能难以客观衡量。为此，作者发布了PolyRoute数据集，包含322条涵盖12种语言和9种文字系统的原生语言消息、156条混合语或罗马化文本（如Hinglish、Spanglish、Arabizi），以及967条可选的本地化训练样本，并设计了一个三向部门路由决策任务的标准化评估协议。所有标签均由Qwen3.8-27B模型生成，并通过盲测第二轮验证（保留91%候选项），独立模型gemma4-31b在测试集上达成97.2%的一致性，且任意测试文本与训练文本之间的最大字符5-gram相似度仅为0.33，表明数据具有足够的多样性与挑战性。实验结果显示，基于该基准的13个系统中，以英语为根的检查点得分为61.2%，多语言预训练模型达到77.3%，最优微调模型则高达96.6%；而TF-IDF结合逻辑回归方法亦达91.9%。在四个保留语言上的表现分别为77%、90%和93%，验证了模型的泛化能力。此外，在混合语文本上，英语根模型（85%）虽略优于多语言预训练模型（79%；p=0.134），但两者差异仅在性能提升超过约4个百分点时才显著。该研究的关键在于构建了一个高质量、多样化且严格控制的多语言评估基准，推动了生成式决策编码器在真实世界复杂语言环境中的可比性与可复现性研究。
链接: https://arxiv.org/abs/2611.13843
作者: Mohammed Nayeem Khan,Sarun Kumar K S
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 25 pages, 18 figures, 26 tables
Abstract:Typed-decision encoders answer a question about a text in one forward pass, but their behaviour outside English is rarely measured on a fixed yardstick. We release PolyRoute: 322 native-language support messages in 12 languages and 9 writing systems, 156 code-mixed or romanised messages (Hinglish, Spanglish, Arabizi), 967 optional native training rows, and a protocol for a three-way department-routing decision. Labels come from Qwen3.8-27B with a blind second pass (91% of candidates kept); an independent model, gemma4-31b, agrees on 97.2% of test items, and the largest character 5-gram similarity between any test text and any training text is 0.33. A leaderboard of 13 systems gives 61.2% for the English-root checkpoint, 77.3% for the shipped multilingual one and 96.6% for the best fine-tune; TF-IDF with logistic regression reaches 91.9%. On four withheld languages the three multilingual systems score 77%, 90% and 93%. On code-mixed text the English root (85%) is nominally ahead of the shipped multilingual checkpoint (79%; p=0.134). The split resolves differences only above about 4 points. All texts and labels are LLM-generated.
[NLP-34] Antalia-2 Mini: A 7.6-million-parameter Turkish text-to-speech model trained entirely on synthetic speech
【速读】： 该论文旨在解决低资源语言（土耳其语）下高质量文本到语音（TTS）合成的挑战，特别是如何在有限训练数据和计算资源条件下实现高保真、低错误率的语音生成。其核心问题在于：如何利用大规模预训练教师模型（VoxCPM2）的知识进行高效迁移，以训练出参数量仅为760万的轻量化学生模型（Antalia-2 Mini），同时保持接近工业级的语音质量与自动语音识别（ASR）准确率。解决方案的关键在于采用“知识蒸馏”策略，即通过教师模型生成合成音频并进行自动质量筛选，仅保留约1,790小时高质量语音用于学生模型训练；此外，通过对声码器（vocoder）在学生模型自身生成的梅尔频谱图上进行微调，显著降低了Whisper语音识别系统的词错误率（WER）——从3.89%降至1.75%。值得注意的是，尽管学生模型在设计上模仿了教师模型的缺陷（如以“ö”开头的音节起始失真），但通过输出前缀截断的方式有效规避了该问题。在公开的Freya-TR-Eval基准测试中，版本1.0.1达到WER 1.09%与字符错误率0.22%，性能可与参数量更大的860万参数模型EMA Lightning相媲美，且在全带宽UTMOS（一种基于英语训练的质量评估代理指标）上表现更优。研究强调了模型轻量化与高质量之间的平衡，其权重、推理代码及评测框架已开源（Apache-2.0），但训练代码与数据未公开。
链接: https://arxiv.org/abs/2611.13811
作者: Sezgin Saygili,Emre Kaplaner,Oncel Ozgul,Fikri San Koktas
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 38 pages, 22 figures, 9 tables. Weights, inference code and benchmark harness: this https URL
Abstract:Antalia-2 Mini is a single-voice Turkish text-to-speech model with 7,620,931 parameters. It reads characters and produces 48 kHz audio at 41 times real time on an Apple M5 laptop CPU (2 threads). Its training audio came only from VoxCPM2 (the teacher), about 300 times larger, cloning one synthetic clip; automatic checks kept about 1,790 h. Fine-tuning the vocoder on the acoustic model’s own mel spectrograms cut the word error rate (WER) of Whisper’s transcripts from a mean of 3.89% to 1.75% at adjacent pre-release checkpoints. The teacher corrupts the start of a clip when the first word begins with ö, the student learned this, and a spoken prefix we cut from the output hides it. On the 495-sentence Freya-TR-Eval benchmark, where we tuned settings, version 1.0.1 has WER 1.09% and character error rate 0.22% on our public harness. A paired bootstrap cannot distinguish these from EMA Lightning, an 8.6M Turkish model, which scores higher on full-band UTMOS, an English-trained quality proxy. No human listening test exists. Weights, inference code and the benchmark harness are public under Apache-2.0; training code and data are not.
[NLP-35] LLM Validation Makes Minimal-Pair Benchmarks Easier for Language Models
【速读】： 该论文旨在解决最小对（minimal-pair）基准测试中标签质量依赖人工标注成本过高的问题，提出以大语言模型（LLM）替代高成本的人工验证。其解决方案的关键在于利用多类LLM（强制选择、评分及集体判断）进行自动化验证，并通过与不同规模人类评审团（1、3、5名裁判）的对比评估其有效性。研究发现，在BLiMP和QFrBLiMP两个数据集上，尽管强制选择型LLM验证者的共识率高于五人评委组，但其识别出的人类拒绝标签的能力远低于单个裁判；从点估计看，任何单一验证者的价值不超过一名人类裁判。更重要的是，错误并非随机分布：在所有70种模型-验证组合中，经预注册的LLM过滤器所标记为“人类支持”的样本比接受样本在难度上平均高出18.9分，且显著高于单个裁判所标记的样本，表明该过滤机制有效剔除了模型表现不佳但人类仍能正确判断的项目，从而提升整体准确性，优于随机删除。此外，冻结协议前的验证流程在荷兰语版BLiMP-NL上复现了相同结果，进一步验证了方法的稳健性。研究揭示，使用LLM进行验证反而使基准测试对目标模型而言更简单，为此作者公开代码、验证输出及项目级得分。
链接: https://arxiv.org/abs/2611.13788
作者: Guofang Tang
机构: Ghostcloud
类目: Computation and Language (cs.CL)
备注: 11 pages, 1 figure
Abstract:Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation. We test it on BLiMP (English) and QFrBLiMP (Quebec French), the two public minimal-pair datasets releasing every forced-choice human judgment. Three LLMs validate by forced choice, by rating, and as a panel, scored against human panels of one, three and five judges in two pre-registered phases. On BLiMP, forced-choice validators agree with held-out human majorities more often than five-judge panels, yet catch far fewer pairs whose label humans reject than one judge does. By point estimate, no validator is worth more than one judge. The errors are not random. On both datasets, across 10 open LMs, human-supported pairs that the pre-registered LLM filter flags are 18.9 points harder than those it accepts, in all 70 LM-validator combinations, and harder than pairs a single human judge flags. Filtering thus removes items on which LMs fail but humans do not, raising accuracy more than random removal. A protocol frozen before any output replicates the gap and the lift on Dutch BLiMP-NL. LLM validation makes minimal-pair benchmarks easier for the models they are meant to test; we release code, validator outputs and item-level scores.
[NLP-36] Word-Level Alignment of OCR and Ground Truth under Zoning and Reading-Order Errors ICDAR2027
【速读】： 该论文旨在解决光学字符识别（OCR）后处理中词级对齐的“分区问题”（Zoning Problem），即在存在版面错位、文本行顺序错乱等情况下，如何实现OCR输出与真实文本（ground truth）之间准确的词级对齐。传统单调对齐方法在读序被置换时容易丢失匹配位置，导致对齐失败。其解决方案的关键在于引入一种基于构造阶段词级真值（gold）的读序随机置换测试协议，通过在OCR端施加读序变换（如块移动、段落互换、列交错等），评估不同对齐方法在读序扰动下的鲁棒性。核心创新包括：采用唯一词或唯一n-gram作为锚点的多区域对齐策略，结合块到窗口的分配机制，以及对2019年提出的位置感知对齐器进行重锚定扩展。实验结果表明，尽管局部追踪在大位移下仍脆弱，但基于锚点的方法在多数读序变化场景中表现更优，然而当唯一锚点稀缺时仍会失效。此外，研究揭示了现有数据集在真值标注精度、文章排序惯例、残差标注缺失及现代多栏版面词级真值缺乏等方面的局限性，制约了对齐性能的进一步提升。
链接: https://arxiv.org/abs/2611.13752
作者: Fonseca Cacho,Jorge Ramón
机构: University of Nevada, Las Vegas (内华达大学拉斯维加斯分校)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages. Submitted to IJDAR (ICDAR 2027 Journal Track). Code: this https URL
Abstract:OCR post-processing needs OCR text aligned word by word with its ground truth. Data sets rarely provide that mapping, and a monotone aligner can lose its place when zoning errors permute the reading order. We revisit this problem, the Zoning Problem of our 2019 paper, with a protocol that tests correctness under such permutations. We apply OCR-side reading-order permutations with construction-time word-level gold and score correct-match F1, cascade length and recovery. We compare the 2019 location-aware aligner, a re-anchoring extension of it, a RETAS-style baseline (RETAS-lite), a multi-region aligner that combines unique-word and unique n-gram anchors with chunk-to-window assignment, genalog’s RETAS and the original Java RETAS. Experiments use synthetic character noise with block moves, paragraph swaps and column interleaving, historical newspaper reading-order permutations, geometry-derived gold for Tesseract OCR, and a small word-box set. Synthetic tests show where local tracking fails after large displacements and where unique-anchor methods collapse when unique anchors are scarce. On our synthetic boilerplate page the 2019 method had the highest F1 among those tested. Historical newspaper and geometry-derived evaluations carry the limitations of approximate gold and the article-order convention. The first multi-region version has a quadratic scaling cost. Local tracking remains fragile under large reading-order changes; the tested anchor-based methods handle many of those cases but fail when unique anchors are scarce. Approximate gold, the article-order convention, missing residual annotation, and the lack of modern multi-column word-level ground truth remain limitations.
[NLP-37] Do Language Models Feel the Sentiment? Investigating Emotion Vectors
【速读】： 该论文旨在解决生成式语言模型中情感向量（emotion vectors）在情感分类任务中的作用机制不明确的问题，尤其关注其在预训练模型内部表征中的实际贡献与可操作性。研究聚焦于Qwen2.5-0.5B-Instruct与Qwen2.5-3B-Instruct两个模型，通过情感探针（sentiment probing）、激活干预（activation interventions）及监督修正（supervised correction）等方法，系统评估了从GoEmotions数据集中提取的8个情感方向对情感分类性能的影响。关键发现在于：仅使用情感空间的探针即可在二元TweetEval任务上达到88.66%和90.70%的准确率，表明模型内部已编码显著的情感信息；匹配消融实验显示，移除原始情感空间导致的准确率下降幅度显著高于随机子空间移除（分别多出2.39和1.10个百分点），说明情感方向具有非随机且特异性的语义影响；此外，在多个基准测试中，原始与去噪后的情感影响均在经过Holm校正后表现显著，但情感修正分类器在亚马逊极性评论任务上未能持续超越仅依赖边际信息（margin-only）的基线，提示情感向量虽具可识别性，但其直接用于分类优化的效果有限。因此，该研究的关键在于区分了模型表征中蕴含的情感信息、改变这些表征所引发的行为变化及其在实际分类任务中的价值，揭示了情感向量并非唯一决定性机制，而是一种可被利用但需谨慎解释的中间表征成分。
链接: https://arxiv.org/abs/2611.13744
作者: Ali Hassan Khan,Saad Bin Ather,Muhammad Saif,Maryam Bashir,Hajra Waheed
机构: 未知
类目: Computation and Language (cs.CL)
备注: 16 pages, 3 figures, 11 tables. Accepted at ExplAI-2026: 2nd Workshop on Advanced AI in Explainability and Ethics for the Sustainable Development Goals
Abstract:Emotion vectors are directions in a language model’s internal representations that are associated with emotional concepts. Prior work shows that these vectors can influence model behavior, but their role in sentiment classification remains unclear. This study investigates emotion directions in Qwen2.5-0.5B-Instruct and Qwen2.5-3B-Instruct. Eight directions extracted from GoEmotions are evaluated through sentiment probing, activation interventions, and supervised correction. Emotion-only probes achieve 88.66% and 90.70% accuracy on binary TweetEval. In exploratory matched-ablation experiments, raw emotion-space removal causes 2.39 and 1.10 percentage points more accuracy loss than matched isotropic random-subspace removal for 0.5B and 3B, respectively. All four TweetEval raw and denoised effects are significant after Holm adjustment across eight comparisons. An exploratory SST-2 extension shows smaller raw excess losses of 0.66 and 0.57 points, with no effects surviving Holm adjustment. Frozen sentiment-correction classifiers are also tested on 20,000 Amazon Polarity reviews. Emotion-based correction improves accuracy over uncorrected Qwen, but does not consistently outperform margin-only baselines. These results distinguish sentiment information available in model representations, the effects of changing those representations, and their practical value for classification. They do not establish a uniquely emotional mechanism.
[NLP-38] You Are What You Read: Indian Political News Media Bias Dataset for Hindi Language
【速读】： 该论文旨在解决印度语境下，尤其是印地语（Hindi）等印地语族语言中媒体偏见（media bias）的理解与检测这一研究不足的问题。由于印度语言多样性显著且人口庞大，同一事件常由多家新闻机构以不同语言报道，从而引入潜在的偏见风险。为此，本文构建了一个全面、多标注者参与且具备可解释性的数据集，包含来自四家主流印地语新闻机构的448篇独特政治新闻文章。数据集由14名专家进行标注，将文章分类为四种偏见类型：选择性偏见（selection bias）、框架偏见（framing bias）、标题党偏见（clickbait bias）和遗漏偏见（omission bias），并针对每篇被判定为有偏的文章提供文本解释，以支持未来基于语言模型的半自动化方法实现可解释性推理。实验结果显示，简单二分类任务的Cohen’s Kappa值为0.81，多标签任务各偏见类别的Kappa值分别为0.89（标题党）、0.75（选择性）、0.81（框架）和0.79（遗漏），平均Kappa达0.80，表明标注者间具有高度一致性。该研究的关键在于通过高质量、可解释的多专家标注数据集，为印地语媒体偏见检测提供可靠基准，并推动生成式AI在该领域中的可解释性应用。
链接: https://arxiv.org/abs/2611.13686
作者: Kushal Trivedi,Mayank Mahor,Dhruv Sanghvi,Roshni Chakraborty
机构: 未知
类目: Computation and Language (cs.CL)
备注: 17 pages, 3 figures, 13 tables
Abstract:Media bias understanding and detection remains an under-researched problem for Indian context, specifically for Indic languages, such as, Hindi, which is widely spoken in India. In this paper, we introduce a comprehensive, multi-annotator, explainable dataset consisting of 448 unique articles from four leading Indian news agencies publishing in Hindi. We focus on Hindi, the most spoken and an official language of India. The study of media bias is particularly relevant in the Indian context due to the country’s linguistic diversity and large population, where the same event is often reported by multiple news agencies in different languages, thereby introducing scope for bias. The dataset comprises Hindi political news articles annotated by 14 experts and classified into four bias categories - selection bias, framing bias, clickbait bias and omission bias - when an article is identified as biased or unbiased. For each biased article, annotators also provide a textual explanation, enabling future semi-automated approaches to incorporate explainability when language models reason over the dataset. Cohen’s Kappa scores were 0.81 for simple bias/no-bias classification reflecting strong inter-annotator agreement. For the multi-label task, the Cohen’s Kappa values are 0.89, 0.75, 0.81, and 0.79 for clickbait, selection, framing, and omission biases, respectively, resulting in an average Kappa of 0.80, which also reflects strong agreement.
[NLP-39] OpticalRAG : Pixel-Space Compression for Token-Efficient Retrieval-Augmented Generation NEURIPS2026
【速读】： 该论文旨在解决生成式 AI（Generative AI）中检索增强生成（Retrieval-Augmented Generation, RAG）因大语言模型（LLM）上下文窗口令牌预算限制所导致的效率与信息保真度之间的矛盾问题。传统文本压缩方法在降低输入长度时会不可逆地丢失细粒度语义信息，而单纯增加上下文长度又带来计算成本激增。其核心解决方案在于改变信息压缩的模态：提出 OpticalRAG 框架，将文档渲染为页面图像，并利用冻结的视觉编码器以约百个视觉令牌高效编码整页内容，显著提升信息密度且保留丰富语义。该框架的关键创新在于两个无需跨模态训练的组件：(i) 编码器桥接检索（Encoder-Bridged Retrieval），通过共享投影层将文本查询映射至视觉特征空间，实现基于文本查询的视觉令牌检索；(ii) 查询驱动蒸馏（Query-Driven Distillation），采用轻量级交叉注意力机制将检索到的视觉令牌压缩为仅16个与查询相关的前缀令牌。实验表明，OpticalRAG 在 LongBench-v2、BAMBOO 与 LooGLE-v2 基准上均实现了最优的压缩感知准确率（Compression-Aware Accuracy, CAP），同时仅注入16个令牌，显著提升了闭卷版 Qwen2.5-7B-Instruct 的性能（提升5.17点）。
链接: https://arxiv.org/abs/2611.13675
作者: Senhao Liu,Yuheng Zhang,Chunyu Wei,Yueguo Chen,Xinran Zhang
机构: Renmin University of China (中国人民大学); ComindX; Claremont McKenna College (克莱蒙特·麦肯纳学院)
类目: Computation and Language (cs.CL)
备注: Accepted at NeurIPS 2026
Abstract:Retrieval-augmented generation (RAG) is bottlenecked by the token budget of large language models: feeding long documents into the context window is expensive, while aggressive textual compression discards fine-grained information that is irreversibly lost in a one-dimensional token sequence. We argue that this bottleneck is not fundamental to information density but to the choice of compression \emphmodality. Modern visual encoders can pack the contents of a rendered text page into roughly a hundred visual tokens with little semantic loss, opening a new compression-fidelity tradeoff that purely textual methods cannot reach. We present \textbfOpticalRAG, a framework that renders documents as page images, encodes them with a frozen vision encoder, and distills the retrieved visual tokens into as few as 16 injected tokens before passing them to the LLM. Two ingredients make this work without expensive cross-modal training: (i) \emphEncoder-Bridged Retrieval, which projects text-query states into the visual feature space of the frozen encoder, through a projection shared with the compressor, to retrieve visual tokens from a text query, with no contrastive training; and (ii) \emphQuery-Driven Distillation, a lightweight cross-attention compressor that condenses the retrieved visual tokens into a small set of query-conditioned prefix tokens. On LongBench-v2, BAMBOO, and LooGLE-v2, OpticalRAG achieves the best compression-aware accuracy (CAP) on all three benchmarks while injecting only 16 tokens, and improves the closed-book Qwen2.5-7B-Instruct backbone by 5.17 points on this http URL is available at this https URL.
[NLP-40] An Interpretable Stylometric Framework for Early Chinese Buddhist Translator Styles: A Case Study on the Attribution of Disputed Works of An Shigao
【速读】： 该论文旨在解决早期汉译佛典的作者归属问题，即在缺乏明确历史记载的情况下，如何通过文本风格特征准确判定译者身份。其解决方案的关键在于构建一个可解释的风格计量（stylometric）分析框架，该框架整合了佛教文献学专家定义或验证的语言学特征，包括虚词使用、句式结构、句末字类型、句子长度以及词性n元组等。研究基于东汉至西晋七位译者的31部确证译作建立风格谱系模型，并将8篇存在争议的文本与安世高（An Shigao）的风格谱系进行比对。结果显示，T1557和T0101两篇文本与安世高已确认译作的风格相似度最高（分别为0.783和0.687），其归属判断与基于BERT的已有研究结果一致。该方法无需特定任务的训练过程，且具备高度可解释性，使学者能够追溯支持每项归因的具体语言特征，从而提升文本归属判断的科学性与可信度。
链接: https://arxiv.org/abs/2611.13636
作者: Yating Pan,Xinyue Zhang,Jun Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注: Presented as a long paper at Digital Humanities 2026 (DH2026) in Daejeon, Republic of Korea. The abstract appears DH2026 Book of Abstracts, pp. 545-549, available at this https URL
Abstract:Attributing early Chinese Buddhist translations requires evidence that scholars can trace back to the texts. We develop an interpretable stylometric framework that uses features defined or reviewed by Buddhist philologists, including function characters, sentence formulas, sentence-final characters, sentence length, and part-of-speech n-grams. We construct stylistic profiles from 31 authenticated translations by seven translators from the Eastern Han through the Western Jin, then compare eight disputed texts with An Shigao’s profile. Two texts, T1557 and T0101, show the strongest stylistic similarity to the authenticated An Shigao corpus, with scores of 0.783 and 0.687, respectively. The attributions agree with those reported by a published BERT-based approach on the same set of disputed texts. The framework requires no task-specific training and allows scholars to inspect which linguistic features support each proposed attribution.
[NLP-41] Synapse-Control: Making Agent Experience Actionable with Lightweight Runtime Control
【速读】： 该论文旨在解决大语言模型代理在执行复杂工具依赖任务时，因重复调用生成式诊断（Generative Diagnosis）导致的推理开销过高与效率低下问题。现有系统如Sentry需频繁调用大模型生成故障诊断以复用经验，显著增加了计算成本。其核心解决方案是提出Synapse-Control代理运行时架构，其关键在于设计轻量级控制平面：通过规则筛选相关建议，利用一个仅1.7B参数、基于教师标注的状态-建议对进行微调的分类器对候选建议进行评分，而非生成诊断；同时引入微合约（Micro-contracts）编码可复用的接口校验逻辑（如必填参数或有效选项），避免对完整任务程序的硬性规定；并借助提示价值表（Prompt Value Table）记录历史指导与反馈，通过冷却机制和令牌预算检查抑制重复建议。实验表明，在AppWorld和WebShop上的多轮测试中，Synapse-Control相比基线模型提升成功率29.8%，减少4,987次生成式诊断调用，并分别降低50.2%和51.0%的辅助控制时间，显著提升了经验复用效率与系统性能。
链接: https://arxiv.org/abs/2611.13615
作者: Zhiyuan Huang,Shengan Zheng,Chenghu Yang,Song Zhang,Lingyun Li,Chen Yu,Weiquan Huang,Linpeng Huang
机构: Shanghai Jiao Tong University (上海交通大学); Ant Group (蚂蚁集团)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages, 8 figures, 18 tables, including appendices
Abstract:Large language model agents reuse execution experience to improve reliability on complex tool-based tasks. Systems such as Sentry call large models to generate failure diagnoses before retrieving advice, adding inference cost to experience reuse. We present Synapse-Control, an agent runtime whose lightweight control plane selects advice and determines when to send it to the task-executing model. Rules shortlist relevant advice, and a lightweight 1.7B classifier fine-tuned on teacher-labeled state-suggestion pairs scores those candidates instead of generating a diagnosis. Micro-contracts encode reusable interface checks, such as required arguments or valid options, without prescribing complete task programs. A Prompt Value Table records earlier guidance and feedback to limit repeated suggestions through cooldown and token-budget checks. Across 1,308 attempts per method in AppWorld and WebShop, Synapse-Control achieves 270 successes (29.8% more than Base), replaces 4,987 generative diagnosis calls, and reduces auxiliary control time relative to Sentry by 50.2% and 51.0%, respectively.
[NLP-42] Post-OCR Correction of Mixed-Script Uzbek Text: Byte-Level ByT5 with an Empirical Homoglyph Noise Matrix STOC
【速读】： 该论文旨在解决乌兹别克斯坦官方与技术文档中由于拉丁字母与西里尔字母间存在大量同形异义字符（homoglyphs）而导致的光学字符识别（OCR）错误问题。在文字书写系统过渡阶段，拉丁文与西里尔文常在同一页面甚至同一句中混用，导致同形异义错误密度较单一文字系统文本高出近五倍。针对此问题，研究提出一种在本地硬件上部署的解决方案，其核心在于结合字节级ByT5模型与基于359个手动标注错误事件估计采样权重的噪声生成器，并引入基于莱文施泰因距离（Levenshtein distance）约束的保守解码规则。该方法在包含5,204行文本对的黄金测试集上，将字符错误率从0.0578降低至0.0369，成功恢复91.3%的同形异义字符，同时仅引入84个新错误，显著提升了多文种混合文本的OCR准确性。
链接: https://arxiv.org/abs/2611.13609
作者: Mannon Ochilov,Oybek Narzullayev,Bahodir Zaripov
机构: Tashkent University of Information Technologies named after Muhammad al-Khwarizmi (塔什干信息科技大学，以穆罕默德·花剌子密命名); Tashkent State University of Economics (塔什干经济大学)
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 7 pages, 2 figures, 7 tables. Accepted at the 10th International Conference on Future Networks and Distributed Systems (ICFNDS 2026), ACM ICPS. Code: this https URL
Abstract:In Uzbek official and technical documents, a large share of OCR errors is caused not by poor image quality but by characters that are visually identical across the two alphabets in use. Because the script transition is still under way, Cyrillic and Latin appear on the same page and often within a single sentence, and in our measurements the density of homoglyph errors on such pages is close to five times higher than in single-script text. We study this problem in a setting where the corpus of engineering-geological and ICT infrastructure reports may not leave the organisation, so every stage was executed on local hardware. The proposed system combines a byte-level ByT5 model with a noise generator whose sampling weights are estimated from 359 manually classified error events, together with a Levenshtein-bounded conservative decoding rule. On a Gold set of 5,204 line pairs it lowers the character error rate from 0.0578 to 0.0369, restores 91.3% of the homoglyphs and introduces only 84 new errors.
[NLP-43] One Byte One Token: Byteifying a 27B Hybrid-Attention LLM with Self-Generated Data
【速读】： 该论文旨在解决将已训练的270亿参数子词模型（subword model）直接转换为纯字节级模型（byte-level model，即一个字节对应一个标记）的问题，同时在不引入补丁结构（patching structure）或局部编码器（local encoder）的前提下，尽可能保留原模型的大部分能力。其核心挑战在于：字节级表示会隐藏中文的拼写信息（如拼音、形声结构），可能削弱模型对汉字语义和结构的理解能力。解决方案的关键在于通过大幅缩减词汇表至384个条目（包含256个标准字节符号及特殊标识符），并保持其余所有权重不变，仅对模型进行两阶段课程学习（curriculum learning）微调——第一阶段专注于字节编码，第二阶段聚焦于知识读取。监督信号完全由原始模型自生成（on-policy），涵盖对话、行为、编码任务等，其中54%的数据来自百科全书文本。实验表明，该方法在多个基准测试中（如MATH-500、HumanEval+、MMLU-Pro、非思考模式GSM8K）与原模型性能差距处于可接受范围内，但在涉及深度汉字认知的任务（如发音、笔画数）上出现显著退化，且思考模式无法弥补这一缺陷。此外，尽管字符级任务表现优异（如CUTE、EXECUTE-zho），但因每步输出量减少3–5倍，导致吞吐率下降约四分之一，预填充延迟增加6.5倍。研究还系统总结了四种字节级模型的失效模式及其修复策略。
链接: https://arxiv.org/abs/2611.13607
作者: Liwei Zhang,ZhiMing Tong
机构: Landi Lab(澜砥实验室)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Full text in English followed by the full text in Chinese. 39 pages, 2 figures, 7 tables. Code and task suite: this https URL
Abstract:Byte-level BPE fragments Chinese text (for Qwen3.8 on Chinese Wikipedia, 3.84 bytes per token on average; 64% of tokens are single characters) and hides spelling from the model. We ask whether an already-trained 27B subword model can be converted directly into a pure byte-level model (one byte, one token), without a patching structure or a local encoder, while keeping most of its ability. Starting from Qwen3.8-27B (48 Gated DeltaNet layers + 16 full-attention layers), we shrink the vocabulary from 248,320 to 384 entries (the 256 byte symbols already in the BPE vocabulary plus special ids), leave every other weight untouched, and continue training in a two-stage curriculum (encoding first, then reading) on 4.77 GB of supervised bytes: all dialogue/behavioral supervision is generated on-policy by the original model itself (character dictionary, word dictionary, general dialogue, coding sessions), and knowledge reading uses encyclopedia text (54% of supervised bytes). Training takes 178.6 h on 8xH200. On the same GPU and serving stack, the byte model’s gap to the original falls within the paired bootstrap interval on MATH-500, HumanEval+, MMLU-Pro and non-thinking GSM8K, and it loses 4-6 points on C-Eval, thinking-mode GSM8K and non-thinking IFEval. Without thinking it leads on every character-level task (CUTE 75.2 - 84.0, EXECUTE-zho 81.6 - 93.9, our Chinese character suite 54.4 - 64.0), but lookup knowledge about Chinese characters such as pronunciation and stroke count drops, and thinking does not recover it. The cost is throughput: per-step speed is identical, but each step yields 3-5x less output; Chinese characters per second is about 1/4 of the original, and prefill at 64K bytes is 6.5x slower. We also document four byte-model failure modes (greedy runaway, sampling breaking UTF-8, ineffective penalty terms on a small vocabulary, repetition) and their fixes.
[NLP-44] Compute the Interactions Not the Attributes: What a Fact Layer Must Supply in the Chinese Sexagenary System
【速读】： 该论文旨在解决在规则驱动领域中，语言模型系统与确定性事实层结合时，事实层应提供何种事实这一关键问题缺乏量化评估的困境。其核心挑战在于：尽管生成式AI（Generative AI）在开放域任务中表现优异，但在需要精确逻辑推理与多属性组合计算的封闭规则系统中，直接依赖静态事实或简单属性查询难以实现准确推断。研究以中国传统命理学中的干支系统（BaZi）为实验场景，构建了包含682个可验证事实项及160个流转-出生冲突项的基准测试集，并在不同推理模式下进行评估。结果显示，仅依赖静态属性的事实层无法有效提升模型性能，而通过查询过滤机制构建的动态事实层（query-filtered layer）显著提升了多个大模型的表现，使Qwen3-8B和Qwen3-32B的准确率提升至78%–92%，其他模型达到96%–100%。此外，三款专有推理模型在无外部辅助下即可完成68%–95%的冲突计算。在国际象棋位置预测任务中也验证了相同结论：属性无法替代关系信息，且错误常源于干扰项的误归因。因此，解决方案的关键在于：事实层必须主动提供经过计算的交互关系（computed interactions），其呈现形式需根据模型架构与具体领域需求进行适配。
链接: https://arxiv.org/abs/2611.13597
作者: Huixue Han,Jiaying Liu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Language-model systems in rule-governed domains often pair the model with a deterministic fact layer, but which facts that layer should supply is rarely measured. We measure it in the Chinese sexagenary system underlying BaZi chart analysis, where every fact has one table-checkable value: 682 items over twelve fact categories and 160 transit–natal collision items, posed closed-book, with the reference table, and with computed facts at increasing levels. In eleven open-weight models answering directly, errors concentrate where attributes must be combined: none gets more than 4% of sexagenary days or 1% of collisions right. Static attributes have no consistent effect, and the complete relation list leaves most direct-answering models near zero although it contains the answer. A query-filtered layer (exploratory) raises Qwen3-8B and Qwen3-32B to 78–92% and four further models to 96–100%. Three proprietary reasoning models compute 68–95% of collisions unaided. On chess positions, under predictions locked in advance, attributes again do not substitute for relations and listed distractors are misattributed. The fact layer must supply computed interactions; how they are presented depends on the model and the domain.
[NLP-45] owards Reliable VLM Judges: State-Conditional Invariance and Presentation-Aware Diagnostics NEURIPS2026
【速读】： 该论文旨在解决当前视觉-语言模型（Vision-Language Models, VLMs）作为多模态与具身任务自动化评判者时，其判断结果的可靠性问题。现有评估方法仅关注整体准确率或一致性，无法揭示模型是否因正确原因做出决策。为此，论文提出状态条件不变性（State-Conditional Invariance, SCI）这一可靠性原则，要求评判模型在保持语义不变的前提下对呈现方式的变化保持稳定，同时对影响任务结果的因果干预作出合理响应。关键解决方案在于构建GridWM-Judge——一个基于确定性MiniGrid轨迹的仿真基准测试平台，包含完整、证据移除和反事实三种变体，并集成呈现方式与表征探针，用于诊断模型在局部感知、动作条件转移、呈现稳定性、表征可交换性以及全量-反事实因果区分等方面的性能。实验表明，模型存在异质性失败模式，如对证据呈现与编码敏感、对成功轨迹激活不足、反事实判别能力弱等，且视觉锚定与目标恢复成为显著的、依赖模型的瓶颈。因此，论文主张应联合报告正确性、呈现稳定性与因果敏感性，而非依赖单一指标。
链接: https://arxiv.org/abs/2611.13596
作者: Qinan Zhang,Xinyu Wang,Qihang Jin,Tianyuan Wang,Hongbing Luo,Pengfei Li,Wenjun Lv,Yu Kang,Jian Di
机构: University of Science and Technology of China(中国科学技术大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to NeurIPS 2026 (Track on Evaluations and Datasets). 47 pages, 16 figures. Project page: this https URL ; Code: this https URL ; Data: this https URL
Abstract:Vision-language models (VLMs) are increasingly used as automated judges for multimodal and embodied tasks, yet aggregate accuracy or consistency alone does not reveal whether a judge changes its verdict for the right reason. We propose State-Conditional Invariance (SCI), a reliability principle requiring a judge to remain stable under semantics-preserving changes in presentation while responding to causal interventions that change the task outcome. We instantiate SCI in GridWM-Judge, a simulator-grounded diagnostic benchmark built from deterministic MiniGrid trajectories with aligned Full, evidence-removal, and counterfactual variants, plus presentation and representation probes. Across five diagnostic tasks, GridWM-Judge tests local perception, action-conditioned transitions, presentation stability, representation exchangeability, and Full-CF causal discrimination. Our diagnostic evaluations reveal heterogeneous failure modes, including sensitivity to evidence presentation and encoding, low activation on successful trajectories, and weak paired counterfactual discrimination. Perception-controlled analyses further indicate that visual grounding and objective recovery are substantial, model-dependent bottlenecks. These results motivate jointly reporting correctness, presentation stability, and causal sensitivity instead of relying on a single score.
[NLP-46] POEM: Output-Relative Block-Sparse Attention for Long-Context Prefilling
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署中面临的双重挑战：一方面需确保生成内容的安全性以抵御越狱攻击（jailbreak attacks），另一方面需保持模型对良性请求的响应能力，避免过度拒绝（over-refusal）。现有基于表示空间的防御方法通常依赖预定义的引导方向或学习到的安全区域来调节内部激活，但在不依赖预设目标的情况下动态适应输入并控制干预强度仍存在困难。为此，本文提出一种无需目标（target-free）的熵引导残差调制（Target-free Entropy-guided Residual Modulation, TERM）机制，其核心在于利用提示校准的注意力熵偏差作为自适应干预信号，在推理阶段动态调节模型自身的残差更新。通过将注意力熵相对于各输入的预填充（prefill）阶段基线进行校准，构建输入特异性参考，从而减少对正常安全敏感请求的误判；结合自适应尺度归一化与时间累积策略，有效处理熵偏差的尺度差异与持续性，生成可控的干预强度信号。该信号以有界方式缩放模型自身残差更新，无需依赖预设的安全方向，实现了对越狱攻击的有效防御，同时在多种主流模型（Llama、Qwen、Gemma，含Qwen3 0.6B至32B）上展现出优异的鲁棒性与低过拒率，且推理开销较小。
链接: https://arxiv.org/abs/2611.13578
作者: Haoyi Wu,Yunkai Dang,Wanyu Zhang,Lei Wang,Wenbin Li
机构: Nanjing University (南京大学); University of Wollongong (卧龙岗大学); Shenzhen Research Institute of Nanjing University (南京大学深圳研究院)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have shown strong capabilities across diverse applications, making both safety and utility essential for reliable deployment. Jailbreak attacks use diverse forms of adversarial prompting to bypass alignment safeguards and induce LLMs to generate harmful responses. Some representation-based defenses regulate internal activations using predefined steering directions or learned safety regions. However, adapting intervention to each input without a predefined representational target while limiting over-refusal remains a challenge. In this paper, we propose Target-free Entropy-guided Residual Modulation (TERM), an inference-time defense that uses prompt-calibrated attention entropy deviations to guide adaptive modulation of the model’s own residual updates. Attention patterns vary across prompts and decoding steps, making raw entropy values difficult to interpret as a consistent signal for intervention. We therefore calibrate attention entropy against each prompt’s own prefill baseline to establish an input-specific reference for reducing unnecessary intervention on benign requests. Adaptive scale normalization and temporal accumulation then account for differences in deviation scale and persistence to determine intervention strength. The resulting signal controls bounded scaling of the model’s own residual updates, without relying on a predefined safety direction. We compare our method with seven defenses on seven jailbreak attacks and two over-refusal benchmarks across Llama, Qwen, and Gemma models, including Qwen3 models from 0.6B to 32B. Results show our method improves jailbreak robustness while maintaining low over-refusal on benign safety-sensitive requests with modest inference overhead.
[NLP-47] ERM: Target-Free Jailbreak Defense via Entropy-Guided Residual Modulation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际部署中面临的双重挑战：一方面需抵御“越狱攻击”（jailbreak attacks），即攻击者通过对抗性提示绕过对齐安全机制，诱导模型生成有害内容；另一方面需避免过度拒绝（over-refusal），即对本应合法的安全敏感请求误判为危险而拒绝响应。现有基于表示空间的防御方法通常依赖预设的引导方向或学习到的安全区域进行干预，但在缺乏明确目标表示的情况下难以自适应地调整干预强度，易导致保护不足或误伤正常请求。本文提出一种无需目标（target-free）的熵引导残差调制（Target-free Entropy-guided Residual Modulation, TERM）方法，其核心在于利用提示校准后的注意力熵偏差（prompt-calibrated attention entropy deviation）作为动态信号，在推理阶段自适应地调节模型自身的残差更新。通过将注意力熵与每个输入的预填充（prefill）阶段基线进行对比，建立输入特异性的参考标准，从而减少对良性请求的误干预；同时引入自适应尺度归一化与时间累积机制，以处理偏差幅度和持续性的差异，最终生成可控的、有界的比例缩放因子，作用于模型内部残差路径，实现不依赖预定义安全方向的动态防御。实验表明，TERM在多种主流模型（Llama、Qwen、Gemma，涵盖0.6B至32B参数量级）上均显著提升对七类越狱攻击的鲁棒性，同时保持较低的过度拒绝率，且仅带来适度的推理开销。
链接: https://arxiv.org/abs/2611.13577
作者: Yunkai Dang,Wanyu Zhang,Meiyi Zhu,Qi Fan,Tianyu Ding,Wenbin Li
机构: Nanjing University (南京大学); Microsoft (微软); Shenzhen Research Institute of Nanjing University (南京大学深圳研究院)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have shown strong capabilities across diverse applications, making both safety and utility essential for reliable deployment. Jailbreak attacks use diverse forms of adversarial prompting to bypass alignment safeguards and induce LLMs to generate harmful responses. Some representation-based defenses regulate internal activations using predefined steering directions or learned safety regions. However, adapting intervention to each input without a predefined representational target while limiting over-refusal remains a challenge. In this paper, we propose Target-free Entropy-guided Residual Modulation (TERM), an inference-time defense that uses prompt-calibrated attention entropy deviations to guide adaptive modulation of the model’s own residual updates. Attention patterns vary across prompts and decoding steps, making raw entropy values difficult to interpret as a consistent signal for intervention. We therefore calibrate attention entropy against each prompt’s own prefill baseline to establish an input-specific reference for reducing unnecessary intervention on benign requests. Adaptive scale normalization and temporal accumulation then account for differences in deviation scale and persistence to determine intervention strength. The resulting signal controls bounded scaling of the model’s own residual updates, without relying on a predefined safety direction. We compare our method with seven defenses on seven jailbreak attacks and two over-refusal benchmarks across Llama, Qwen, and Gemma models, including Qwen3 models from 0.6B to 32B. Results show our method improves jailbreak robustness while maintaining low over-refusal on benign safety-sensitive requests with modest inference overhead.
[NLP-48] Pair-Merge Tokenisation Is Hard to Approximate on Binary Strings and Strongly NP-Hard on Unary Datasets
【速读】： 该论文旨在解决生成式 AI 中文本分词（tokenisation）效率优化的核心问题：如何在多项式时间内选择 $ k $ 次合并操作，使长度为 $ n $ 的字符串尽可能缩短，从而减少后续计算开销。现有方法如字节对编码（Byte-Pair Encoding, BPE）通过重复合并相邻符号对来压缩文本，但其最优性缺乏理论保障。论文证明，在除非 P = NP 的前提下，任何多项式时间算法都无法达到优于 $ c \cdot n / \log n （ c > 0 $ 为常数）的近似比，即使对于单一二进制字符串和全局替换型 BPE 式合并规则亦然。其关键创新在于将 $ k $ 次合并操作建模为具有 $ k $ 条规则的上下文无关语法（grammar），并利用顶点覆盖（vertex cover）的近似难解性转化为语法规模（grammar size）的下界，再通过重标记副本的二进制编码与显式合并构造实现该下界。针对可跳过出现位置的合并情形，论文提出一个简单算法可实现 $ O(n / \log\log n) $ 的近似比，并指出除非 coNP ⊆ NP/poly，否则无法通过 Karp 多项式归约证明更严格的下界。此外，研究还揭示了在单字母字母表下，由 Kastreva 等人遗留的两个分词问题在存在重复字符串的数据集上为强 NP 完全问题。
链接: https://arxiv.org/abs/2611.13568
作者: Kundan Baliga,Aiden Xie,Aditya Byju
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄本那-香槟分校); Georgia Institute of Technology (佐治亚理工学院)
类目: Data Structures and Algorithms (cs.DS); Computational Complexity (cs.CC); Computation and Language (cs.CL)
备注: 34 pages, 11 figures. Code and certificates: this https URL
Abstract:Tokenisers such as Byte-Pair Encoding (BPE) shorten text by repeatedly merging an adjacent pair of symbols into a new one; fewer tokens mean less computation. We ask whether an efficient algorithm can choose k merges that leave a string of length n almost as short as possible; doing nothing is already within a factor n . We prove that, unless P = NP, no polynomial-time algorithm comes within a factor c,n/\log n , for some constant c0 , already for one binary string and for BPE-style merges that replace every occurrence. The key idea is that k merges form a grammar with k rules: a vertex-cover gap becomes a grammar-size gap, which we amplify with relabelled copies, encode in binary and realise with explicit merges. For merges that may skip occurrences, a simple algorithm achieves O(n/\log\log n) , and no Karp reduction can prove hardness beyond order n/\log n unless coNP \subseteq NP/poly. Over a one-letter alphabet, both tokenisation problems left open by Kastreva et al. are strongly NP-complete on datasets with repeated strings.
[NLP-49] A Concept-Based Interpretable Framework for Clinical Text-based Prediction via Token-Concept Attention without Concept Supervision
【速读】： 该论文旨在解决临床文本预测任务中，生成式解释方法依赖人工标注概念标签所带来的高成本问题。现有基于概念的解释方法通常需要大量带标注的概念标签来训练模型识别输入中的医学概念，而临床文本的标注过程复杂且昂贵。为克服这一限制，论文提出一种无需概念监督的新型框架，其核心创新在于设计了一种定制化的交叉注意力瓶颈（cross-attention bottleneck），通过预训练语言模型对临床文本及医学概念描述进行嵌入，并利用交叉注意力机制自动推断每个概念在文本中的相关性，生成概念重要性得分。这些得分经池化后用于线性预测层，实现对临床结局的可解释建模。实验结果表明，该框架在胸外伤严重程度分层与院内死亡率预测两个任务上，性能接近黑箱模型（AUROC差距小于0.02），同时支持队列级和患者级的解释能力，有效揭示了概念在整个队列中的总体重要性及个体患者的特定贡献。因此，该方案的关键在于通过无监督的交叉注意力机制实现无需标注的概念感知，从而在保持强预测性能的同时显著提升临床文本模型的透明度。
链接: https://arxiv.org/abs/2611.13564
作者: Jifan Gao,Junjie Hu,John Caskey,Madeline Oguss,Simona Cristea,Anoop Mayampurath,Matthew Churpek,Majid Afshar,Guanhua Chen
机构: Dana-Farber Cancer Institute (丹娜-法伯癌症研究所); University of Wisconsin-Madison (威斯康星大学麦迪逊分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Objective: Concept-based interpretation has emerged as a promising approach for explaining blackbox models using human-understandable, high-level concepts. However, most existing methods require annotated concept labels to train the model to identify concepts in the input, and obtaining such annotations in clinical text is often expensive. This study aims to enable concept-based interpretation for language models without concept supervision on clinical prediction tasks. Methods: We propose a novel framework that enables concept-based interpretation without concept supervision through a tailored cross-attention bottleneck. Clinical text and the text descriptions of medical concepts are first tokenized and embedded by a pretrained language model. A cross-attention module then estimates the presence of each concept for each text token, producing concept relevance scores that capture how concepts are involved throughout the note. These scores are pooled and used in a linear prediction layer to model clinical outcomes in an interpretable manner. Results: The performance of the proposed framework was compared against blackbox models across two tasks: chest trauma severity stratification using a private dataset and an in-hospital mortality prediction task using the publicly available MIMIC-III dataset. The proposed framework achieved AUROC values within 0.02 of the corresponding blackbox models. The framework also enabled cohort- and patient-level interpretation, capturing the overall concept importance across the cohort and patient-specific concept contributions. Conclusion: Our proposed framework realizes concept-based interpretation without requiring ground-truth concept presence labels. It improves the transparency of clinical text predictions while maintaining strong performance in clinical prediction tasks.
[NLP-50] An evidence-traceable benchmark of clinical reasoning in large language models
【速读】： 该论文旨在解决医学大语言模型（Medical Large Language Models, LLMs）基准测试得分难以解释的问题，即评分标准缺乏与临床证据的可追溯性。其解决方案的关键在于构建了“医学证据基准”（Med Evidence Bench, MEB），包含1,536个由医师构建的评测项，涵盖三个层级：指南知识、标准化临床情景和真实世界实验室情景。每个医疗主张（medical claim）均关联至版本化的原始文献片段，实现了证据链的可追溯性。通过引入证据溯源评分（Evidence Trace Score, ETS），研究发现不同模型在各层级的表现差异显著：指南知识层面的ETS为0.975–0.997，而标准化情景和真实世界情景分别为0.292–0.658和0.182–0.447；尤其在真实世界情景中，模型识别临床信息的能力（平均0.770）远高于匹配证据（0.165）或提出建议（0.160）。自动化评分与三位医师多数投票结果具有较高一致性（宏观F1=0.930），但证据应用环节一致性较低。研究结果表明，模型对指南内容的记忆并不等同于个体化临床证据的应用能力，也无法证明其临床有效性。
链接: https://arxiv.org/abs/2611.13562
作者: Zhiqun Zhao(1),Xiaodong Yang(2),Bruce Gu(3 and 4),Ming Li(5),Yi Fu(6),Song Li(7),Qi Mei(8),Zhi Liu(9),Kai Zhang(10),Kexue Fu(3 and 4),Jing Wu(11),Xiaofei Lu(12),Yule Li(1),Zheng Wang(2),Haiquan Lu(1),Xiaoming Wu(3 and 4) ((1) Advanced Medical Research Institute and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, China, (2) Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, (3) Shandong Computer Science Center, National Supercomputer Center in Jinan, Jinan, China, (4) Qilu University of Technology, Shandong Academy of Sciences, Jinan, China, (5) Department of Rheumatology and Immunology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, (6) Information Network Management Office, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, (7) Department of Medical Oncology, Qilu Hospital, Shandong University, Jinan, China, (8) Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, (9) Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, (10) Department of Breast Surgery, General Surgery, Qilu Hospital, Shandong University, Jinan, China, (11) Department of Central Laboratory, Public Health Clinical Center Affiliated to Shandong University, Shandong University, Jinan, China, (12) Department of Breast Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, China)
机构: Shandong University (山东大学); Shandong First Medical University (山东第一医科大学); Shandong Provincial Hospital Affiliated to Shandong First Medical University (山东省立医院附属山东第一医科大学); Qilu University of Technology (Shandong Academy of Sciences) (齐鲁工业大学（山东省科学院）); Shandong Computer Science Center (National Supercomputer Center in Jinan) (济南市计算机中心（国家超级计算济南中心）); Huazhong University of Science and Technology (华中科技大学)
类目: Computation and Language (cs.CL)
备注: 25 pages, 8 figures, 4 tables. Zhiqun Zhao, Xiaodong Yang and Bruce Gu contributed equally. Corresponding authors: Zheng Wang, Haiquan Lu and Xiaoming Wu
Abstract:Benchmark scores for medical large language models (LLMs) are difficult to interpret when scoring criteria cannot be traced to clinical evidence. Here we developed Med Evidence Bench (MEB; this https URL), comprising 1,536 physician-constructed items across three levels: guideline knowledge, standardized clinical scenarios and real-world laboratory scenarios. Each criterion stating a medical claim is linked to a versioned source-text span. Across ten LLMs, the Evidence Trace Score (ETS) was 0.975-0.997 for guideline knowledge, 0.292-0.658 for standardized scenarios and 0.182-0.447 for real-world scenarios, and rankings varied across levels. For real-world scenarios, scores for identifying clinical information (mean, 0.770) exceeded those for matching evidence (0.165) or formulating recommendations (0.160). On 1,296 criterion-level judgments, automated grading agreed with the majority vote of three physicians (macro-F1, 0.930; 95% confidence interval, 0.915-0.945), although agreement was lower for evidence application. These findings from Chinese-language materials indicate that guideline recall does not ensure patient-specific evidence use and do not establish clinical effectiveness.
[NLP-51] Are Dense Rewards Worth Their Tokens in GRPO?
【速读】： 该论文旨在解决生成式AI（Generative AI）在训练语言模型进行推理时，评估与优化策略的偏差问题。现有方法如组相对策略优化（GRPO）对解题过程中的每个词元赋予相同奖励，而密集过程奖励（dense process rewards）通过蒙特卡洛采样评估中间步骤以提升反馈精度，但其实际代价被低估——仅连续生成部分就占用了91%的生成词元。为此，作者提出“匹配计算协议”（matched-compute protocol），确保所有训练干预在生成词元数和训练GPU小时数上具有相同的计算预算，并在预注册研究中引入对照机制控制副作用。实验覆盖6个模型共1,668次运行发现，当计算量相等时，匹配计算使蒙特卡洛奖励的性能排名反转：在Qwen2.5-1.5B上，尽管其单次更新优于标准GRPO，但在相同词元数量下准确率低14.3个百分点，相同训练时间下低7.8个百分点，且四种密集奖励变体均未超越GRPO。此外，基于梯度测试的每词元评估虽能正确排序奖励方式，但普遍高估各方法效果1至2个百分点，接近其宣称的增益幅度。相反，在预测成功概率最接近0.5的提示样本上进行训练，可在匹配计算下实现显著提升：在Qwen3-0.6B上提高1.7个百分点，在Qwen2.5-1.5B上提高0.8个百分点，且结果在新种子和未见问题上可复现；这一策略还提前预测了五项预注册测试中的四个。这些收益在训练早期即显现，而使用两倍初始学习率的标准GRPO即可逼近其性能。因此，论文的核心结论是：训练干预必须在匹配计算条件下验证，这不仅揭示了部分看似有效的改进实为计算资源不均衡所致的假象，也证实了特定提示选择策略的有效性。
链接: https://arxiv.org/abs/2611.13560
作者: Vikash Singh,Weicong Chen,Debargha Ganguly,Yanyan Zhang,Mohsen Hariri,Sreehari Sankar,Wang Yang,Chaoda Song,Alexander Nemecek,Osama Zafar,Yuting Shao,Kai Ye,Biyao Zhang,Xinpeng Li,Shouren Wang,Erman Ayday,Jing Ma,Sanmukh Rao Kuppannagari,Vipin Chaudhary
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 59 pages (10 pages of main text), 18 figures, 27 tables. Code: this https URL
Abstract:RLVR trains language models to reason by rewarding correct final answers. A widely used algorithm, group-relative policy optimization (GRPO), gives every token of a solution the same credit, so dense process rewards have been proposed to credit intermediate steps, for example with a Monte-Carlo reward that scores each step by sampling continuations from it. Comparing such a reward with GRPO per training update, or vetting it with a gradient test on a single batch, leaves out what it costs: in our setting, the continuations alone take 91% of all generated tokens. We introduce a matched-compute protocol that gives every training intervention the same budget, measured in generated tokens and training GPU-hours, and tests it in preregistered studies with controls for side effects. Across 1,668 runs on six models, matching compute reverses the ranking. On Qwen2.5-1.5B, the Monte-Carlo reward leads standard GRPO per update, yet ends 14.3 percentage points lower at equal tokens and 7.8 points lower at equal training time, and none of four dense-reward variants outperforms GRPO even with as much training time. A per-token gradient test orders these rewards as training does but overestimates each by one to two points, as much as the gains it is meant to detect. In contrast, training on the prompts whose predicted success is nearest 1/2 improves accuracy at matched compute, by 1.7 points on Qwen3-0.6B and 0.8 on Qwen2.5-1.5B. These gains replicate on fresh seeds and unseen problems, and a simple rule, that the candidate prompts are predicted to fail more often than not, predicted where they appear in five of six preregistered tests. Both gains come early in training, and standard GRPO with twice the initial learning rate comes within half a point of them. We argue that training interventions should be validated at matched compute, which overturns some apparent gains and confirms others.
[NLP-52] Darmok: Championship Cryptic Crossword Solving via Constraint Tightening and Resilient Orchestration
【速读】： 该论文旨在解决在英国式字谜填字游戏（cryptic crossword）中实现超人类表现的挑战，此类谜题具有表面语义误导性强、定义与词法构造混合隐藏、网格稀疏导致大量字母未被交叉验证等特点，且比赛规则对任何错误或空格均采取“零容忍”策略。为突破现有生成式AI模型在实际应用中因云接口延迟、超时、安全拒绝或陷入推理死循环而引发的可靠性瓶颈，论文提出Darmok系统，其核心解决方案在于构建多阶段协同验证机制：通过三个独立模型对线索进行交叉验证；利用已确认的交叉字母解锁高难度线索；结合全谜题级模型与词典查表完成剩余空白，同时避免覆盖已确认词汇。该设计实现了五个月每日评估中的近零误差表现，尽管在常规基准下冗余组件影响微乎其微，但在极端情况（如主模型严重延迟）下，备用路径仍确保了系统的完整求解能力，揭示出当基础模型趋于掌握平均性能时，复杂人工智能系统的核心挑战已从准确性转向最坏情况下的鲁棒性。
链接: https://arxiv.org/abs/2611.13557
作者: Siddhartha Chatterjee(The University of Texas at Austin)
机构: The University of Texas at Austin(德克萨斯大学奥斯汀分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 60 pages, 10 figures, 15 tables
Abstract:We present Darmok, the first automated system to reach superhuman performance on British cryptic crosswords. Unlike American-style crosswords, cryptic clues disguise an unmarked definition and compositional wordplay beneath a misleading surface reading, sparse grids leave nearly half of each word’s letters unchecked, and tournament rules rank a grid with even a single wrong or blank square behind all error-free solutions regardless of speed. Competing alongside an elite human field at the 55th Times Crossword Championship in London, Darmok solved all seven championship puzzles error-free and finished faster than every human competitor in every round. It solved the Final puzzle in 6m 52s, a full 3m 13s ahead of the human champion. Today’s frontier AI models can on average solve a whole puzzle in a single try with comparable accuracy and speed. However, cloud API calls still stall, time out, trigger safety refusals, or get stuck in reasoning loops. To simultaneously achieve tournament-grade accuracy, speed, and reliability, Darmok cross-checks clues across three independent models in rounds; uses verified crossing letters to unlock harder clues; and uses a whole-puzzle model and dictionary lookups to fill any remaining gaps without overwriting confirmed words. This design maintained near-zero-error performance across a five-month daily evaluation period. Under normal benchmark conditions, removing backup models, fallback steps, or elaborate prompt instructions barely changes accuracy, making the architecture appear over-engineered. Yet during both the worst days of daily operation and a severe slowdown of our primary model in the live Championship Final, the backup paths carried the solve, showing that as foundation models master the average case, the central challenge in building complex AI systems shifts to resilience in the worst case. Comments: 60 pages, 10 figures, 15 tables Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2611.13557 [cs.AI] (or arXiv:2611.13557v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.13557 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[NLP-53] How Post-Training Influences Verbalized Algorithmic Computation in Language Models
【速读】： 该论文旨在解决后训练（post-training）阶段如何影响语言模型内部信息利用方式及显式链式思维（Chain of Thought, CoT）表达有效性的问题。其核心挑战在于：尽管后训练可提升模型推理能力，但其对内部状态（如进位值）的表征变化以及对书面推理过程的依赖程度尚不清晰。研究通过分析Olmo-3-7B-Instruct模型在多数字加法任务中经历的持续监督微调（SFT）、直接偏好优化（DPO）和强化学习视觉反馈（RLVR）等不同阶段的检查点，发现虽然内部激活中进位信息在SFT与RLVR阶段的可恢复性相似，但仅在RLVR阶段生成的CoT表现出显著更高的推理准确性，并且与进位相关的语句在模型决策中的重要性评分也明显提升。这表明，后训练不仅改变了模型对书面推理内容的敏感度，更关键的是提升了其对推理内容的实际利用效率——即增强对相关语义线索的响应能力并转化为更高性能，而单纯的敏感度提升（如DPO阶段）并不必然带来性能改善。因此，解决方案的关键在于：通过强化学习类方法（如RLVR）促进模型将内部状态与显式推理过程更有效地对齐，从而实现从“能写”到“写得有用”的质变。
链接: https://arxiv.org/abs/2611.13552
作者: Niall Dalton
机构: University College London (伦敦大学学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Post-training improves language-model reasoning, but how it changes the use of internal information and verbalized chain of thought (CoT) remains unclear. We study successive SFT, DPO, and RLVR checkpoints of Olmo-3-7B-Instruct on multi-digit addition, using carry values as known intermediate state. Probes recover carry information from internal activations with similar accuracy in SFT and RLVR, although manipulating this information does not reliably control model outputs. In contrast, the usefulness of verbalized reasoning differs: CoTs produced by RLVR yield higher accuracy across receiving checkpoints than those produced by SFT or DPO. We also show that carry-related statements receive higher importance scores after RLVR than matched random controls. DPO shows similar sensitivity to carry-related statements but without comparable accuracy gains. These results suggest that post-training changes the usefulness of written reasoning and sensitivity to its content in meaningful ways, although greater sensitivity does not necessarily coincide with better problem performance.
[NLP-54] Sanity Checks for Attention Surgery: Null Models and Matched Interventions for Representation-Motivated Design
【速读】： 该论文旨在解决生成式 AI 模型中自注意力机制（self-attention）组件是否具有冗余性的问题，尤其关注特定注意力头输出中与自身值向量对齐的分量是否可被移除而不影响性能。其核心挑战在于：现有方法常基于内部表示的统计量（如注意力头输出与自身值向量的余弦相似度）判断冗余性，并直接移除相应成分，但缺乏严谨的因果验证，易误判统计显著性、几何不变性及方向特异性。为此，作者提出三个可复用的检验框架：1）评估统计量是否显著高于噪声基线；2）检验该统计量在几何零假设下是否仍存在；3）验证训练收益是否依赖于被移除方向的特定性。通过在九个多头预训练模型（5,408 个注意力头，参数量 124M 至 6.9B）上应用这些检验发现，早期位置的因果零假设解释了 48%–63% 的头-值余弦对齐，且无单调尺度趋势；在分组查询注意力（grouped-query attention）中，该对齐仅存在于同一键值组内，跨组则反向（组内 +0.24 至 +0.27，跨组 -0.11 至 -0.19）。分裂半可靠性分析显示，每头干预效应在 GPT-2 中可分辨（Δr = 0.80），但在 Pythia-160M（0.42）和 Pythia-410M（0.53）中减弱，而结构自余弦可靠性在三者中均接近完美（r_stat ≈ 0.99），表明预测能力具有模型依赖性。在一项注册的 3臂×8种子配对因子实验中（每轮 4.0×10⁸ 标记，共 3.999 亿标记），固定任意秩一方向与基线无差异（+0.0011 nats，p = 0.197），而自指向移除显著降低验证损失（-0.0029 nats，Holm 校正后 p = 0.0023），并优于固定控制（-0.0040 nats，p = 0.0018）。结果表明，在约 5090 万规模下，训练增益确实依赖于自身值方向而非任意方向，但大余弦对齐本身不足以证明冻结模型中的冗余性。因此，解决方案的关键在于引入系统性因果检验框架，以避免对注意力冗余性的误判，强调必须同时满足统计显著性、几何鲁棒性与方向特异性才能确认可移除性。
链接: https://arxiv.org/abs/2611.13543
作者: Basavaraj R M,Aryan Bharat Kumar
机构: Independent AI ML Systems Researcher(独立人工智能与机器学习系统研究员); Independent ML Researcher(独立机器学习研究员)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 13 pages, 7 figures, 4 tables. Code: this https URL (archived at this https URL )
Abstract:Transformer architectural modifications frequently measure a statistic on internal representations, observe that it is large, remove it architecturally, and report an improvement. Interpreting that sequence requires evaluating whether the statistic exceeds its noise floor, whether it survives a geometric null, and whether the training gain requires the specific direction removed. We formulate these requirements as three reusable checks and apply them to self-attention removal, which deletes the component of an attention head’s output aligned with its own value vector. Across nine multi-head pretrained models (5,408 heads, 124M to 6.9B parameters), an earlier-position causal null accounts for 48% to 63% of the head-value cosine alignment, with no monotone scale trend. Under grouped-query attention, this alignment is specific to each head’s own key-value group and reverses across groups (+0.24 to +0.27 within-group, -0.11 to -0.19 across-group). Split-half reliability demonstrates that the per-head intervention effect is resolvable in GPT-2 ( r_\Delta = 0.80 ) but attenuated in Pythia-160M (0.42) and Pythia-410M (0.53), whereas structural self-cosine reliability is near-perfect in all three ( r_\textstat 0.99 ); which statistic predicts its own intervention is model-dependent. In a pre-registered 3-arm by 8-seed paired factorial at 4.0 \times 10^8 tokens per run (399,900,672 tokens), a fixed arbitrary rank-one direction does not differ from baseline (+0.0011 nats, p = 0.197 ), while self-directed removal lowers validation loss (-0.0029 nats, Holm-corrected p = 0.0023 ) and outperforms the fixed arbitrary control directly (-0.0040 nats, p = 0.0018 ). At 50.9M scale the training benefit requires the own-value direction rather than a fixed arbitrary one, but a large cosine alignment alone does not establish redundancy in frozen models.
[NLP-55] Framing-Induced Vulnerability in Large Language Models : Evidence from Chinese News Headlines
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在新闻内容评估中是否存在对信息框架（framing）的敏感性，即模型对新闻标题的判断是基于事实本身，还是受标题表述方式的影响。其核心问题是：当仅改变标题的表达框架时，大语言模型是否会因此产生对事件严重性或误导性的误判，从而影响新闻筛选与分发的公正性。研究的关键解决方案在于通过结合大规模生态审计（63,926条中文新闻标题）与受控实验，系统检验四款中国主流大语言模型（DeepSeek、MiMo、Dots、LongCat）在不同标题框架下的表现。结果表明，仅改变标题的语义框架即可显著影响模型对事件严重性和误导性的判断，且效应方向一致，说明模型存在显著的“框架偏差”；而提供正文内容可使这种偏差降低54%至80%，且所有减少均在统计上显著（p < 0.01）。这揭示出当前仅依赖标题进行评估的生成式AI（Generative AI）系统存在可量化的鲁棒性缺陷，提示在实际部署与治理中，应将“框架敏感性”作为关键可靠性指标加以考量。
链接: https://arxiv.org/abs/2611.13531
作者: Mo Zhou,Yiwei Zhang
机构: Nanjing Normal University (南京师范大学); Communication University of China (中国传媒大学)
类目: Computation and Language (cs.CL)
备注: 7 pages, 5 figures
Abstract:Large language models increasingly evaluate, route, and moderate news, so a model’s judgment of a headline has become a gatekeeping decision. We ask whether that judgment follows the fact being reported or the way the fact is framed. We combine an ecological audit of 63,926 Chinese news headlines with a controlled experiment. The experiment covers 30 real events, 30 synthetic events, and 29 natural headline pairs, tested on four Chinese models. Changing only the headline frame shifted both perceived misleadingness and event severity, and the direction and ordering of the shifts were the same for DeepSeek, MiMo, Dots, and LongCat. Emotionally intensified headlines raised severity ratings by 0.65 to 0.88 points, whereas positively reframed headlines lowered them by 0.34 to 0.52 points. Supplying the article body reduced this severity bias by 54 to 80 percent, and all four reductions were significant at p .01. A small human reference study showed the same direction of effect. Headline-only LLM evaluation therefore carries a measurable robustness gap, and framing sensitivity belongs among the reliability properties that deployment and governance check.
[NLP-56] Differentiable Filtering for Inertial Odometry
【速读】： 该论文旨在解决惯性里程计（Inertial Odometry）在长期运行中因传感器噪声与偏差积分导致的累积漂移（unbounded drift）问题。传统方法如TLIO虽采用神经伪测量（neural pseudo-measurements），但其训练过程与下游滤波器解耦，难以实现端到端优化。本文提出可微分滤波（Differentiable Filtering for Inertial Odometry, DFIO），其核心创新在于将误差状态卡尔曼滤波器（error-state Kalman filter）的更新步骤直接嵌入计算图中，实现可微分的滤波更新。在独立预训练后，DFIO通过可微测量更新引入后验位置监督，联合优化位移输出头与协方差输出头，从而提升轨迹估计的准确性与不确定性建模能力。实验结果表明，相较于TLIO基线，DFIO在行人数据集上分别降低了13.2%的平均绝对位移误差和39.6%的一秒相对位移误差；在未校准智能手机惯性数据上的评估也验证了其在高噪声条件下有效抑制平移漂移并保持轨迹对齐的能力。
链接: https://arxiv.org/abs/2611.13517
作者: Yaru Gu,Ting Zou
机构: Memorial University of Newfoundland (纽芬兰纪念大学)
类目: Robotics (cs.RO); Computation and Language (cs.CL)
备注: 19 pages, 4 figures
Abstract:Accurate inertial odometry is critical for autonomous systems but suffers from unbounded drift caused by the integration of sensor noise and biases. In approaches such as TLIO, neural pseudo-measurements are trained independently of their downstream filter. This paper introduces Differentiable Filtering for Inertial Odometry (DFIO), a learning framework that embeds the update stages of an error-state Kalman filter directly into a computational graph. Following standalone pre-training, DFIO jointly optimizes the displacement and covariance output heads using posterior-position supervision through differentiable measurement updates. The proposed system is validated on public pedestrian motion datasets featuring both calibrated and uncalibrated inertial measurements. Experimental results on a pedestrian dataset demonstrate that DFIO reduces mean absolute and one-second relative translation errors by 13.2% and 39.6% relative to the TLIO baseline, and by 7.7% and 17.4% relative to its pre-trained counterpart. Furthermore, evaluations on uncalibrated smartphone inertial data indicate that the proposed method effectively mitigates translational drift and maintains trajectory alignment under highly noisy sensor conditions. Source code will be made publicly available.
[NLP-57] Mobile AI Factory: Building Specialized Language Models for Your Mobile Device
【速读】： 该论文旨在解决在移动设备上部署小型语言模型（Small Language Models, SLMs）时，如何高效、自动化地实现针对特定任务与目标设备的模型微调与量化，以兼顾性能、隐私保护和离线可用性的问题。当前方法依赖专家反复手动调参，效率低下且难以泛化。其解决方案的关键在于提出一个名为“Mobile AI Factory”的智能体系统，该系统通过一个编排型大语言模型（Orchestrator LLM）动态决策是否进行超参数调优、真实数据挖掘或合成样本生成，并结合固定回滚规则确保模型精度不下降；系统从小型模型开始逐步搜索，跨基础模型与量化方案进行迭代，动态调整精度目标，并利用目标设备的真实推理引擎与后端测量数据进行优化。实验表明，该方法在十项任务与四款安卓手机上的表现优于两个五次提示前沿模型，且在实体抽取任务中仅需1/19的GPU时间即可逼近全量训练27种模型变体的最优结果。此外，实测设备性能对最佳配置具有显著影响，若缺乏真实设备测量，即使提供完整硬件与模型规格，仍会在25%的中等难度及60%的高难度案例中未能满足应用需求，凸显了真实设备测量在模型部署中的关键作用。
链接: https://arxiv.org/abs/2611.13509
作者: Evan Li,Krishna Deshpande,Wen Cheng,Shyamnath Gollakota
机构: University of Washington (华盛顿大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Small language models (SLMs) running on mobile devices can keep user data private and work without connectivity. But they perform well only when fine-tuned for the target task and sized for the target device, a process that today requires repeated expert tuning. We present \it Mobile AI Factory, an agentic system that builds a fine-tuned, quantized SLM for a given task and phone. Given a task specification and a target phone, an orchestrator LLM chooses whether to tune hyperparameters, mine real data, or generate synthetic examples, while fixed rules roll back changes that hurt accuracy. The factory searches from small to large models across base models and quantizations, adapts its accuracy goal as it searches, and uses real measurements of the target phone across inference engines and backends. We evaluate Mobile AI Factory on ten tasks and four Android phones. On an unseen testset, its on-device models outperform two five-shot frontier models on five tasks, by up to 0.34, and match them on three more. Its search comes within 0.03 of exhaustively training all 27 model variants on an entity extraction task, while using 19 \times less GPU time. Phone measurements also matter: the best configuration changes across phones. Without phone measurements, Claude Opus 5.5, given the full hardware and model specifications, misses the app’s requirements in 25% of easy and moderate cases and in 60% of hard ones. Code is available at: this https URL.
[NLP-58] Evaluating Compositional Generalization in Transformers: The Role of Composition Equivalence and Module Coverag e NEURIPS2026
【速读】： 该论文旨在解决生成式模型在组合泛化（compositional generalization）任务中表现不一致的问题，即模型在训练时仅接触部分模块组合，却需对未见过的组合进行有效推理。其核心挑战在于现有评估方法因依赖有限且随意定义的训练-测试划分，导致结果不可靠。研究通过系统性分析发现，影响组合泛化性能的两个关键因素是组合等价性（composition equivalence）和模块覆盖度差异（module coverage shift）。其中，组合等价性指不同模块序列虽路径不同但最终输出函数相同，而直接模型（direct models）的高表现主要源于对这类等价性的投机性利用；当基准测试消除组合等价性后，模型性能显著下降，表明其无法真正泛化到产生新端到端函数的已知模块组合。此外，随着模块在训练与测试中出现上下文差异（即模块覆盖度差异）增大，无论是直接模型还是逐步推理模型（step-by-step models）的性能均单调下降。这一发现对基准设计具有重要启示：包含组合等价性的基准会严重夸大直接模型的泛化能力，而同时存在组合等价性和覆盖度偏差的基准则会加剧捷径学习（shortcut learning）。作为案例研究，作者验证了组合等价性在预训练模型Llama3-8B于LiveCodeBench代码输出预测任务中的显著影响，揭示其实际泛化能力受限于对表面模式的依赖而非真正的组合理解。
链接: https://arxiv.org/abs/2611.13498
作者: Purva Pruthi,Andrew Yuan,Alexander D’Amour,David Jensen
机构: University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校); Google DeepMind(谷歌深度思维)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Track on Evaluations and Datasets
Abstract:Compositional generalization is an important form of out-of-distribution generalization, defined as the ability to train on some combinations of modules (e.g., functions such as sort, filter) and then generalize to unseen combinations. Despite substantial empirical work evaluating compositional generalization in transformers, findings have been inconsistent because evaluations were often conducted on a limited set of train-test splits, defined in an ad hoc manner. Through a systematic evaluation, we identify two key aspects of the train-test distribution that strongly affect performance: composition equivalence and module coverage. We show that the apparent performance of direct models (trained only on final outputs) results primarily from exploiting composition equivalences, meaning different sequences of modules that reduce to identical end-to-end functions. When benchmarks eliminate these equivalences, performance drops significantly, showing their inability to generalize to compositions of known modules that produce novel end-to-end functions. We further demonstrate that performance degrades monotonically as module coverage shifts increase (when modules appear in different contexts between train and test), affecting both direct and step-by-step models (trained on intermediate and final outputs). These findings have concrete implications for benchmark design: benchmarks that include compositional equivalences can lead to significant overestimation of the generalization abilities of direct models, and benchmarks that include both module coverage violations and compositional equivalences can exacerbate shortcut learning. As a case study, we demonstrate the role of compositional equivalence on the generalization performance of the pre-trained model Llama3-8B on the code-output prediction task in the coding benchmark LiveCodeBench.
[NLP-59] Inherited Weights: Measuring the Tokenization Premium and the Sovereignty Cost of Language Models for the Global South
【速读】： 该论文旨在解决当前全球范围内非中美两国部署的语言模型在多语言适配性与文化主权方面的根本性缺陷，其核心问题在于：大多数海外部署的语言模型均基于少数预训练模型家族进行微调（fine-tuning），而微调无法改变模型底层继承的关键属性，包括词汇表、知识结构、文化对齐性、记忆数据及许可证限制。这些问题导致低资源语言尤其是非洲和南美原住民语言在分词时出现严重的“分词溢价”（tokenization premium）——例如阿姆哈拉语在旧版分词器中溢价高达10倍，即使在最新商业分词器中仍达6.8倍；南美原住民语言的溢价介于1.6至2.7倍之间。解决方案的关键在于三个方面：第一，提出可复现的跨31种语言、7种2023–2024年发布的分词器的分词溢价测量方法，并通过明确修正未知词符（unknown-token collapse）实现更准确评估；第二，设计了一套无需访问模型权重的八项黑盒主权审计测试（black-box sovereignty audit），使购买方可在不依赖内部参数的情况下验证模型的自主可控性；第三，提出“制度性论证”（regime argument），主张在能源与法律责任约束下，应以每焦耳能量的准确性、校准度及选择性风险控制作为模型设计的核心目标，而非单纯追求原始准确率，且相关指标需具备抗“回避策略”（abstention）膨胀的能力。研究最后报告了基于上述理念构建的私有架构的初步内部测试结果，同时公开数据与测量代码以支持可复现性。
链接: https://arxiv.org/abs/2611.13441
作者: Paulo Camara(LUA Vision)
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 6 pages, 2 tables. Data and measurement code: this https URL
Abstract:Most language models deployed outside the United States and China are fine-tuned descendants of a small number of pretrained families. We argue that fine-tuning leaves the decisive properties of a model inherited from its base: vocabulary, knowledge, cultural alignment, memorized data and license. We contribute (i) an updated, reproducible measurement of the tokenization premium across 31 languages and 7 tokenizers released in 2023-2024, on 27 articles of the Universal Declaration of Human Rights, with explicit correction for unknown-token collapse. Premiums reach 10x for Amharic in older tokenizers and remain at 6.8x in the newest commercial one; Indigenous South American languages pay between 1.6x and 2.7x under global tokenizers. A vocabulary built from Portuguese lowers the premium for 11 of 13 Latin-script Global South languages relative to every global tokenizer tested, by about 24% for Guarani and Quechua. (ii) An eight-test black-box sovereignty audit that a buyer can run without access to weights. (iii) A regime argument: under energy and liability constraints, accuracy per joule, calibration and selective risk should replace raw accuracy as design objectives, with metrics that abstention cannot inflate. We close with preliminary internal measurements from a proprietary architecture built around these objectives, reported with their limitations. Data and measurement code are included.
[NLP-60] BioMem: Agent ic Memory for Biomedical LLM Agents
【速读】： 该论文旨在解决生成式 AI 在生物医学领域中，代理记忆系统（agentic memory system）在信息保留、检索、更新与利用方面的有效性与可靠性问题。核心挑战在于如何在跨任务和多轮交互中维持信息的准确性与上下文相关性，尤其在生物医学知识推理、临床预测及长期对话记忆等场景下，面临证据相关性弱、信息失真以及推理利用率低等关键障碍。其解决方案的关键是提出一种无需训练的混合记忆检索与利用框架（Hybrid Memory Retrieval and Utilization, HMRU），通过联合优化证据的可访问性与任务感知的利用能力，显著提升生物医学推理与长期对话记忆性能。实验表明，HMRU 在生物医学问答与对话记忆基准上平均相对提升8.6%，在特定临床预测任务中表现更优，并将对话回答错误率降低24.9%，同时缩小了48.0%与全上下文推理之间的性能差距，为构建更可靠的记忆增强型大语言模型提供了系统性认知与实用路径。
链接: https://arxiv.org/abs/2611.13426
作者: Mingchen Li,Yongkang Xiao,Rumeng Li,Rui Zhang,Hong Yu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have demonstrated considerable potential in biomedical applications, while agentic memory systems have emerged as a promising approach to preserving and utilizing information across tasks and interactions. However, a fundamental question remains: how effectively and reliably do these systems retain, retrieve, update, and utilize biomedical information? In this study, we systematically investigate agentic memory in the biomedical domain, focusing on task effectiveness, retrieval quality, information fidelity, and long-term robustness. We evaluate five memory approaches across four LLMs in three representative settings: biomedical knowledge reasoning, clinical prediction, and long-term conversational memory. Our analyses reveal how memory construction, evidence relevance, and conversational history shape downstream biomedical performance, highlighting the distinct challenges of retrieving relevant information, preserving its fidelity, and effectively utilizing it for reasoning. Building on these findings, we introduce Hybrid Memory Retrieval and Utilization (HMRU), a training-free framework that jointly enhances evidence accessibility and task-aware utilization to improve biomedical reasoning and long-term conversational memory. HMRU achieves an average relative improvement of 8.6% across biomedical question-answering and conversational memory benchmarks, with additional gains in selected clinical prediction tasks. It further reduces conversational answer errors by 24.9% and closes 48.0% of the performance gap to full-context inference. Our findings provide a systematic characterization of biomedical agentic memory and practical insights into developing more effective and reliable memory-augmented LLM systems.
[NLP-61] Free Lunch Uncertainty: Sequence-Level Rényi Entropy from Particle Decoding for Adaptive LLM Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）生成过程中对完整生成序列的不确定性量化问题，这对于实现选择性预测（selective prediction）和自适应计算资源分配至关重要。现有方法如基于局部熵或对数概率的零成本信号仅反映单个词元层面的信息，而自一致性（self-consistency）虽能提供更全面的估计但需大量额外生成，计算开销高。本文提出的关键解决方案是利用序列蒙特卡洛（Sequential Monte Carlo, SMC）解码进行幂采样时，天然可估计幂分布的归一化常数，从而在零边际成本下获得序列级瑞尼熵（Sequence-level Rényi Entropy, SRE），且在任意粒子预算下均具备理论保证。进一步地，通过引入瑞尼链式法则，可扩展得到任意阶次的分步熵度量。研究证明，高于1阶的熵值与每一步最可能词元相关，因此与对数概率高度一致；而1/2阶成员则累积了每步搜索努力的对数，成为13个测试场景中10个的最佳零成本选择性预测信号。在MATH500数据集上，该指标相较对数概率的AUROC提升0.022至0.044，且在不同长度四分位区间内表现稳定，同时在各设置留出验证中仍保持一致最优选择。此外，8粒子探测器可识别出需更多粒子的复杂问题，两阶段策略则在三组随机种子下以34.2%更少的粒子数达到64粒子的精度水平，显著提升了效率。
链接: https://arxiv.org/abs/2611.13414
作者: Erfan Baghaei Potraghloo,Milad Heidari Sorkhehdizaj,Shahab Rahimirad,Aliasghar Pourghani,Seyedarmin Azizi,Souvik Kundu,Abolfazl Hashemi,Joseph Campbell,Vijay Gupta
机构: University of Southern California(南加州大学); Purdue University(普渡大学); Intel(英特尔)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Quantifying uncertainty over an entire LLM completion is critical for selective prediction and adaptive compute. Zero-cost signals such as mean token entropy and log-probability are local to tokens or to one completion, and self-consistency needs many extra completions. We show that Sequential Monte Carlo (SMC) decoding for power sampling already estimates the normalizing constant of the power distribution, which gives the sequence-level R’enyi entropy (SRE) of the model’s completion distribution at zero marginal cost, with a guarantee at every particle budget. A R’enyi chain rule extends this readout to a family of stepwise entropies of any order. We prove that orders above one are tied to the most likely token of each step, which explains why they track log-probability, while the order-1/2 member accumulates the log of per-step search effort. This member is the best zero-cost selective-prediction signal in 10 of 13 settings. On MATH500 its AUROC is 0.022 to 0.044 above log-probability with all four models and within length quartiles, and the same order is selected when each setting is held out. An 8-particle probe identifies the problems that benefit from more particles, and a two-pass policy matches 64-particle accuracy on MATH500 with 34.2% fewer particles over three seeds.
[NLP-62] Mapping Instructors Attitudes Towards Cheating with AI: An Exploratory Analysis Using Forum Analysis
【速读】： 该论文旨在解决生成式 AI（Generative AI）在教学与学习环境中引发的学术诚信问题，尤其关注其导致的作弊行为对教育公平、知识获取及学术伦理带来的挑战。研究聚焦于大学教师群体在面对生成式 AI 技术普及后，如何认知和应对学生利用 AI 完成学术任务的现象。其解决方案的关键在于通过对 r/Professors 子版块中超过 201 万条关于作弊讨论的文本数据进行大规模语料分析，系统提炼教师群体在维护学术诚信方面的核心动机与具体策略，进而构建“为何”（why）与“如何”（how）应对作弊的综合框架，揭示未来人工智能与教育深度融合过程中可能面临的潜在风险与治理困境。
链接: https://arxiv.org/abs/2611.13366
作者: Iman Mohammadi,Ali Keramati,Sina Rismanchian
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 5 pages, 1 table, 0 figures. Short paper published in ICLS 2026
Abstract:In teaching and learning environments, cheating and the violation of academic integrity have always been one of the challenges, involving different levels of concerns such as administrative, personal knowledge, learning, and equity. The introduction of large language models has brought cheating in dialogue to the forefront, given the apparent capabilities of these models to complete students’ academic assignments. This study aims to leverage recent dialogues among university professors about cheating with Generative AI to better understand their beliefs about academic honesty. In doing so, we analyze a corpus of 2.01 million posts (threads and comments) in the r/Professors subreddit discussing cheating after the release of ChatGPT, providing a better understanding of teachers’ motivations to discourage cheating and the strategies they use to do so. We synthesize the why and how of addressing cheating to identify caveats about the future of AI and academic integration.
[NLP-63] Understanding Students Rationales for AI-Mediated Cheating: An Instructor Perspective from Online Forums Analysis
【速读】： 该论文旨在解决生成式 AI 在学术环境中被滥用所引发的学术诚信危机，尤其聚焦于当前研究中对教师群体如何看待学生在禁止场景下使用 AI 的动机缺乏深入理解的问题。其解决方案的关键在于通过对 201 万条 r/Professors 社区帖子进行质性分析，系统揭示了教师群体普遍认为学生使用 AI 进行作弊的四大核心动因：（1）学术体系博弈（Academic System Gaming），即学生将 AI 视为绕过僵化评价体系的工具；（2）数字-学术素养鸿沟（Digital-Academic Literacy Gap），反映学生在数字技术应用与学术规范认知之间的不匹配；（3）绩效焦虑（Performance Anxiety），体现学生在高学业压力下对成绩表现的过度担忧；（4）不诚实行为的常态化（Normalization of Dishonesty），指学生对学术不端行为的接受度逐渐提升，形成不良风气。这一分析框架为构建更具针对性的学术治理策略提供了实证依据。
链接: https://arxiv.org/abs/2611.13358
作者: Iman Mohammadi,Sina Rismanchian,Ali Keramati,Shayan Salehi
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 3 pages, 1 table, 0 figures. Poster paper published in ICLS 2026
Abstract:The extensive adoption of AI has precipitated a crisis in academic integrity. While studies attempt to quantify cheating, less is known about the motivations instructors believe students have for using AI in prohibited ways. We analyze 2.01 million posts on r/Professors to understand how instructors perceive students’ justifications for AI-mediated cheating. Our qualitative analyses reveal four primary themes: (1) Academic System Gaming, (2) Digital-Academic Literacy Gap, (3) Performance Anxiety, and (4) Normalization of Dishonesty.
[NLP-64] Measuring Definition Sensitivity in LLM Annotation of Social Science Concepts NEURIPS2026
【速读】： 该论文旨在解决生成式 AI 在社会科学文本标注任务中因概念定义模糊而导致的标注结果不可靠问题。具体而言，现有研究依赖大语言模型（LLM）通过提示词（prompt）中的概念定义进行标注，但诸如仇恨言论、虚假新闻和恐怖主义等概念往往存在多个理论上合理且相互竞争的定义，而模型实际是依据提示中的定义进行判断，还是基于其预训练过程中形成的固有认知作出标签决策，尚不明确。为此，作者提出通过双重标注实验来检验模型对定义的敏感性：即对同一组文本分别使用不同但具有同等合理性的定义进行标注，并比较两次标注结果的差异，该差异被定义为“定义敏感性”（definition sensitivity）。同时，作为对照，采用语义不变但表述不同的改写版本定义进行标注，以计算“释义基线”（paraphrase floor），从而区分真实定义依赖与表面表述差异的影响。实验结果表明，在六个概念和三种主流大语言模型上，更换竞争性定义所导致的标注变化显著高于释义基线，证明标注结果高度依赖于所输入的定义。因此，研究的关键在于揭示并量化大语言模型在文本标注中的定义敏感性，强调任何基于大语言模型的标注工作必须同时报告其使用的定义及对应的定义敏感性和释义基线，以确保结果可解释、可复现，并避免误导性结论。
链接: https://arxiv.org/abs/2611.13339
作者: Bogdan Mamaev,Qizhou Wang,Christopher Leckie,Matteo Vergani
机构: Deakin University (迪肯大学); University of Melbourne (墨尔本大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Accepted at the NeurIPS 2026 Workshop TAE: Can We Trust AI Evaluation? Robustness, Causality, and Risk in Modern AI Assessment
Abstract:In social science research, text annotation is a fundamental task that is traditionally a costly, hard-to-scale manual process. Researchers have used large language models (LLMs) to perform annotation by writing a definition of a concept into a prompt - a rule that determines whether a label should be attributed to a text depending on its content. However, concepts such as hate speech, fake news, and terrorism typically have several theoretically defensible competing definitions. It is also unknown whether the model actually applied the definition to provide the label or the label was based on the model’s pretrained notion of the concept. To test this, we have the model annotate the same texts twice, each time with a different definition. We call the disagreement between the two sets of annotations definition sensitivity. As a control, we paraphrase one definition but leave its requirements unchanged - the disagreement between the annotations with the two wordings becomes the paraphrase floor. Across six concepts and three LLMs, a swap of competing definitions always changes more annotations, on average, than a paraphrase. This shows that the annotations depend on the prompted definition, so any set of annotations should be interpreted together with the definition that produced it. The LLM annotator’s definition sensitivity and its paraphrase floor should both be reported in such work.
[NLP-65] Decode Token Parallelism: Memory-Efficient Distributed Attention Parallelism for Scalable LLM Decoding
【速读】： 该论文旨在解决大语言模型在高并发长上下文场景下，由于键值缓存（KV-cache）容量与注意力带宽随解码请求池规模线性增长所带来的扩展瓶颈问题。现有并行化策略存在根本性权衡：数据并行导致模型权重复制，限制了KV-cache容量；张量并行在模型的KV头数量上达到注意力扩展极限，并引入跨节点密集通信；而解码上下文并行（DCP）虽通过分片序列恢复容量，却需在节点间聚合部分注意力结果，带来额外开销。本文的关键创新在于提出解码令牌并行（Decode Token Parallelism, TKNP），其核心思想是将权重放置与注意力所有权解耦：将单个模型副本均衡分布于各计算节点，每个活跃节点在其本地执行完整批次的投影与前馈网络（MLP），利用节点内张量并行高效处理；同时，各节点仅需基于本地驻留的KV-cache计算自身请求的注意力。该设计实现了请求局部注意力在节点间的无缝扩展，无需复制权重、仅传输当前令牌激活值，且密集型全归约操作保留在NVLink域内，避免跨节点注意力结果合并。实验表明，在64块H100 GPU上对Llama 3.1 405B和Nemotron-4 340B进行评估，TKNP相较DCP最高提升1.7倍吞吐，相较数据并行最高提升2.06倍，显著优化了长上下文、高并发场景下的生成效率。
链接: https://arxiv.org/abs/2611.13293
作者: Susav Shrestha,Brad Settlemyer,Narasimha Reddy
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 14 pages, 12 figures
Abstract:Serving large language models at high concurrency with long contexts requires KV-cache capacity and attention bandwidth that grow with the decode pool. Existing parallelization strategies trade one against the other: data parallelism replicates model weights, limiting KV capacity; tensor parallelism stops scaling attention at the model’s KV-head count and pushes dense collectives across nodes; and decode context parallelism (DCP) recovers capacity by sharding the sequence but adds cross-node aggregation of partial attention results. We observe that during decode, dense layers and attention scale differently: dense layers amortize weight reads across the batch and run efficiently within a single NVLink domain, whereas attention cost grows with every active request’s KV cache. We present Decode Token Parallelism (TKNP), a decode-pool architecture that decouples weight placement from attention ownership. TKNP distributes a single model copy across nodes in balanced layer blocks. The active node holding a block executes its projections and MLP for the full batch using intra-node tensor parallelism, while every node computes attention for its own requests against locally resident KV cache. This extends request-local attention across nodes without replicating weights: only current-token activations cross the network, dense all-reduces remain within NVLink, and attention requires no cross-node merging of partial results. Implemented in vLLM and evaluated on Llama 3.1 405B and Nemotron-4 340B on up to 64 H100 GPUs, TKNP improves completion throughput for long-context, high-concurrency workloads by up to 1.7 \times over DCP and by up to 2.06 \times over data parallelism at matched GPU count.
[NLP-66] What drives the emergence of symbolic mechanisms in large language models ? NEURIPS2026
【速读】： 该论文旨在解决大语言模型中涌现的抽象推理能力背后的驱动因素问题，具体聚焦于生成式符号机制（symbolic mechanisms）的形成原因。研究发现，尽管现有工作已识别出此类机制的存在，但其背后的关键驱动力尚不明确。为探究这一问题，作者在抽象序列任务上从零开始训练多种神经网络模型，系统地改变模型架构与数据分布特征，并通过表示分析、注意力分析及因果中介分析等方法评估各因素对符号机制涌现的影响。结果表明，尽管基于Transformer的模型在任务中成功发展出符号机制，但更关键的是数据多样性——在多个数据多样性维度上，更高的数据多样性显著增强了符号特征的表达强度。更为重要的是，即使在经过修改的Transformer架构甚至多层感知机（Multilayer Perceptron, MLP）中也观察到相同的符号机制，说明模型架构中的归纳偏置（inductive biases）并非必要条件。此外，在实体-属性绑定任务中亦呈现出一致规律。综上，研究揭示了数据多样性是推动神经网络中符号计算能力涌现的主要因素，而非模型架构本身。
链接: https://arxiv.org/abs/2611.13270
作者: Melody Zixuan Li,Taylor Webb
机构: Princeton Neuroscience Institute(普林斯顿神经科学研究所); Princeton University(普林斯顿大学); Department of Psychology(心理学系)
类目: Computation and Language (cs.CL)
备注: 25 pages, 15 figures, 5 appendices. To appear at NeurIPS 2026
Abstract:Recent work has identified a set of emergent symbolic mechanisms that support abstract reasoning in large language models, but it remains unclear what factors drive the emergence of these mechanisms. To address this question, we trained neural networks from scratch on abstract sequence tasks, varying both architectural and data distributional factors, and investigated the effects of these factors on the emergence of symbolic mechanisms. Using representational, attentional, and causal mediation analyses, we first confirmed that transformer-based language models trained from scratch on our task developed symbolic mechanisms. We then found that, across multiple axes of data diversity, greater diversity produced stronger symbolic signatures. Surprisingly, the same signatures emerged in modified transformer architectures and even multilayer perceptrons, indicating that architectural inductive biases are not necessary for the development of symbolic mechanisms. We also observed consistent patterns in an entity-attribute binding task. Together, these findings identify data diversity, not architecture, as the primary driver of emergent symbolic computation in neural networks.
[NLP-67] Domain-Routed Delta Optimization for Multi-Teacher On-Policy Distillation
【速读】： 该论文旨在解决多教师在线蒸馏（Multi-teacher on-policy distillation, MOPD）中因共享学生参数更新而导致的领域特异性知识保留不足的问题。传统方法为每个样本分配一个领域教师，但所有样本共同更新同一组共享的学生参数，导致不同领域间的优化方向相互干扰，损害了原始专家模型（expert model）的几何结构保持能力。本文提出的领域路由增量优化（Domain-Routed Delta Optimization, DRD-MOPD） 的关键在于：明确指定每个领域样本所更新的学生参数组件，通过将学生模型分解为一个冻结的基础部分（frozen base）和一组从“专家减去基础”权重初始化的可训练组件，使各组件在前向传播中均参与计算，并支持四种独立的更新模式，每种模式具有独立的优化器状态。该设计确保了各领域知识在参数空间中的解耦更新，从而有效保留原始专家方向的几何特性。实验表明，与此前工作Open-MOPD相比，本方法在20次更新后仍能保持沿各组件原始方向的投影系数接近1，成对角度维持在88.04至92.41度之间，且增量能量中仅有4.90%位于原始专家方向之外（相较其他方法显著降低），同时在六项基准测试上实现32.72%的宏平均得分，优于初始任务算术融合结果（32.44%），充分验证了其在几何保真度与模型性能上的双重优势。
链接: https://arxiv.org/abs/2611.13249
作者: Lin Yao
机构: Shanghai Jiao Tong University (上海交通大学); Zhongguancun Academy (中关村学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Multi-teacher on-policy distillation assigns each sample a domain teacher but typically updates shared student parameters. We introduce Domain-Routed Delta Optimization (DRD-MOPD), which specifies which parameter components each domain’s samples update. The student is the sum of a frozen base and trainable components initialized from expert-minus-base weights. All components participate in every forward pass; four update modes define the correspondence between sample domains and components, each with independent optimizer states. We analyze these update rules and measure retention of the original expert geometry through angles, projection coefficients, and energy. Prior work such as Open-MOPD distills directly from the base model; 97.02% of its released final checkpoint’s increment energy lies outside the span of the original expert directions. In comparison, our Own checkpoint after 20 updates retains coefficients near one along each component’s original direction and pairwise angles ranging from 88.04 to 92.41 degrees; its exported BF16 student’s increment has 4.90% outside energy, versus 10.52% for the sum of saved FP32 components. The same checkpoint achieves a 32.72% macro score across six benchmarks, compared with 32.44% for its initial task-arithmetic merge. These measurements describe geometry and capability at the evaluated training stages and settings.
[NLP-68] Deletion Sensitivity Is a Property of the Model: A Three-Condition Measurement across Seven Open Reasoning Models
【速读】： 该论文旨在解决多轮工具调用（multi-turn tool calling）场景中推理历史（reasoning history）被删除所带来的不可见成本问题。在实际应用中，73%的用户交互跨越多个回合，而上游服务提供方会将每条推理轨迹与发起者绑定，导致请求跨服务商传递时历史信息被清除，但这一清除操作所带来的性能影响尚未被量化测量。本文首次通过盲测、多种子、三类渲染条件、七种开源权重推理模型、三个种子、794个任务及官方验证器，实现了对完整推理历史删除行为的直接测量。结果表明，删除操作对任务表现的影响范围为-4.4至+51.3分，且两端均不包含零，说明其影响显著且方向不确定；对于同一模型在0%到100%删除率之间重新配置的情况，性能变化不超过1.5分，表明删除敏感性（deletion sensitivity）是模型层面的固有属性。该属性具有可重复性：模型在不同部署环境下表现一致，仅需两次运行（一次保留、一次删除）即可完成评估，因此应作为路由和聚合阶段的先验信息进行显式建模，而非作为隐含默认成本忽略。解决方案的关键在于识别并量化“删除敏感性”这一模型级特性，并将其纳入系统设计的显式考量，从而实现对推理链中断影响的精准评估与优化。
链接: https://arxiv.org/abs/2611.13223
作者: Hanzhang Jia,Liheng Zeng,Zhiyu Yang,Yi Gao,Bo Ma
机构: University of Sussex (英国萨塞克斯大学); Zhejiang Gongshang University (浙江工商大学); Chang’an University (长安大学); Shanghai Shuyuan Information Technology Co., Ltd. (上海书源信息技术有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 10 pages, 5 figures, 8 tables
Abstract:Reasoning models write a reasoning trace at every turn of a multi-turn tool session, and later turns read this history. Across 94k ShareGPT conversations, 73 percent of user interactions span multiple turns. Upstream providers bind each trace to its issuer, so a request forwarded across issuers arrives with its history deleted, and the task cost of this stripping remains unmeasured. To the best of our knowledge, this paper presents the first direct, blind, multi-seed measurement of full reasoning-history deletion in multi-turn tool calling, through three rendering conditions, seven open-weight reasoning models, three seeds, 794 tasks, and the official checker. The same deletion spans -4.4 to +51.3 points with both ends excluding zero, and for the one model re-dosed from 0 to 100 percent deletion, the reading varies by at most 1.5 points. Deletion sensitivity is a model-level property. The reading follows the model itself and stays constant across deployments, and a retrained or re-released model is a new model that a two-cell screen, one retention run and one deletion run, re-reads for the cost of two runs. Deletion sensitivity is thus a measurable property of the model, and the reading must enter routing and aggregation as prior information rather than persist as an invisible default cost.
[NLP-69] Latin American Studies since the 1980s: From Apocalyptic Dreams to Assimilationist Consolidation
【速读】： 该论文旨在探讨拉丁美洲研究（Latin American Studies, LAS）自1982年以来在三大主流期刊中的演变路径，重点分析其学科定位、作者构成及引用模式的变迁。研究发现，拉斯经历了同化式整合（assimilationist consolidation），即该领域已形成相对稳定的学术制度化结构，核心期刊在学科网络中占据枢纽地位，并成为跨学科交流的桥梁。然而，这种整合强化了以英语学术规范为核心的等级体系，导致原属核心的拉美本土期刊被边缘化，尽管拉美作者占比有所上升，其学术表达与研究范式却日益趋近北方（盎格鲁-撒克逊）学术标准，反映出知识生产中的权力不平等格局。
链接: https://arxiv.org/abs/2611.13216
作者: Thiago Dumont Oliveira,Juan Grigera,Pedro Mendes Loureiro
机构: 未知
类目: Digital Libraries (cs.DL); Computation and Language (cs.CL)
备注:
Abstract:We investigate how Latin American Studies (LAS) has evolved since 1982 in three leading LAS journals. Using network analysis, text mining, and bibliometrics, we trace transformations in disciplinary positioning, authorship, and citation patterns. Our main claim is that LAS has undergone an assimilationist consolidation. The field is now institutionally recognisable, with core LAS journals occupying central positions in disciplinary networks and acting as bridges across fields. Yet this consolidation has reinforced hierarchies privileging Anglophone academic norms. Journals based in Latin America, once central, have been marginalised. The share of Latin American authors has grown, but their profiles converge towards Northern standards.
[NLP-70] HiCAP: Hierarchical Cultural Adaptation via Personality for Dialogue Generation
【速读】： 该论文旨在解决对话系统在跨文化语境下生成符合特定文化语言规范与社会期待回应时，忽视个体人格特质对文化表达和沟通风格影响的问题。现有方法通常仅关注文化层面的适配，未能充分建模人格因素如何塑造交流行为。为此，论文提出一种名为HiCAP（Hierarchical Cultural and Personality-aware Dialogue Framework）的新框架，其核心在于将人格特质融入文化自适应对话生成过程。关键创新在于采用分层式人格条件控制机制：首先根据目标文化的语调基调对响应风格进行对齐，再基于个体人格属性进一步精细化语言表达与内容生成。为实现这一目标，模型结合监督微调与基于指令的调优策略，并引入动态指令模板及多智能体示范，以捕捉文化与人格之间的多样化组合。在CulturalPersonas与CPED数据集上的实验表明，HiCAP在多项对话生成指标上均取得最优表现，验证了其在文化适应性与人格建模方面的有效性。
链接: https://arxiv.org/abs/2611.13207
作者: Lingzhi Shen,Xiaohao Cai,Yunfei Long,Imran Razzak,Shoaib Jameel
机构: University of Southampton (南安普顿大学); Queen Mary University of London (伦敦玛丽女王大学); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Cultural adaptation in dialogue systems refers to the ability to generate responses that conform to linguistic norms and social expectations across different cultures. However, existing dialogue generation approaches pay limited attention to the role of personality in shaping cultural expression and communication style. We therefore propose HiCAP, a novel framework that integrates personality traits into culturally adaptive dialogue generation. HiCAP employs a personality-conditioned control mechanism that hierarchically modulates response style by first aligning it with the cultural tone and then refining linguistic expression and content according to individual personality attributes. To embed these factors, the model combines supervised fine-tuning and instruction-based tuning, supported by dynamic instruction templates and multi-agent demonstrations to capture diverse combinations of culture and personality. Experiments on CulturalPersonas and CPED show that HiCAP achieves the strongest overall performance across various dialogue generation metrics, demonstrating effective cultural adaptability and personality modelling.
[NLP-71] One-Sided Expert-Parallel All-to-All for Mixture-of-Experts Inference over NVLink
【速读】： 该论文旨在解决大规模模型中混合专家（Mixture-of-Experts, MoE）层在推理阶段因专家并行（Expert Parallelism, EP）导致的通信开销问题。在推理场景下，每个GPU处理的令牌数量较少，且批量大小较小，因此固定开销（如通信初始化、元数据交换等）不再随令牌数量线性增长，反而成为延迟的主要瓶颈。现有方案如DeepEP依赖于双向的all-to-all通信，需进行频繁的元数据交换和数据复制，造成显著性能损耗。为此，论文提出NVLinkOneSided——一种基于单边通信（one-sided communication）的EP all-to-all机制，其核心创新在于：源端秩（source rank）直接将令牌推送至目标GPU的秩主导（rank-major）缓冲区，无需预先交换元数据；同时，分组矩阵乘法（grouped GEMMs）可直接从这些缓冲区读取数据，避免了为每个专家单独复制数据的开销。为满足不同场景需求，论文设计了两种同步协议：适用于高吞吐场景的Fence协议和适用于低延迟场景的Sentinel协议。实验结果表明，在Blackwell GPU上，NVLinkOneSided在小批量情况下比DeepEP快1.2–3.0倍，整体MoE层性能最高提升2.4倍，甚至在每GPU最多128个令牌时超越融合式DeepGEMM MegaMoE，展现出对解码任务的竞争力。此外，该方案通过模块化接口实现与激活量化格式解耦，兼容多种计算后端，易于集成到主流推理框架中。
链接: https://arxiv.org/abs/2611.13188
作者: Bo Li,Chulian Zhang,Or Biri,Anthony Chang,Anton Korzh,Noam Duvdevani,XingFei Xi,Daniel Stokes,Zhenhuan Chen,Ludwig Schneider,Artem Y. Polyakov,Julien Demouth,Chandler Zhou
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Computation and Language (cs.CL); Machine Learning (cs.LG); Performance (cs.PF)
备注: 19 pages, 8 figures, 14 tables
Abstract:Expert parallelism (EP) shards the experts of a Mixture-of-Experts (MoE) layer across GPUs, so every MoE layer requires two all-to-all communications, namely dispatch and combine. Compared with training, inference processes relatively few tokens per GPU at a time, and at small batch sizes fixed costs that do not grow with the number of tokens, rather than data movement, dominate the latency. We present NVLinkOneSided, a one-sided EP all-to-all for GPUs within a single NVLink domain. Each source rank pushes its tokens, without first exchanging metadata, into rank-major buffers on the target GPUs, and the grouped GEMMs read them in place instead of from a per-expert copy. We provide two protocols for the synchronization that the communication requires, namely Fence and Sentinel, for high-throughput and low-latency scenarios respectively. On Blackwell GPUs, NVLinkOneSided completes dispatch and combine 1.2-3.0 \times faster than DeepEP, with the largest speedups at small batch sizes. The resulting MoE layer is up to 2.4 \times faster than DeepEP with DeepGEMM. It even outperforms the fused DeepGEMM MegaMoE at up to 128 tokens per GPU, making it still competitive for decoding. We expose NVLinkOneSided through a modular interface, so that it is agnostic to the activation quantization format, decoupled from the MoE compute backend and easy to integrate into inference frameworks.
[NLP-72] Agreeing on the Answer but Not the Reason : Measurement Limits of LLM Reasoning Failure Taxonomies
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理过程中错误分类的可靠性问题，特别是针对“组合性坍缩”（Compositional Collapse）这一错误类别。研究发现，当前主流的失败模式分类方法存在严重依赖测量过程的问题：同一错误在不同分类顺序下可能被归入不同类别，导致报告的错误分布具有高度主观性和不稳定性。其解决方案的关键在于揭示并量化这种测量偏差——通过在多语言数学基准上对12个模型进行系统测试，发现50.1%的错误同时符合多个类别，且分类结果随判断顺序显著变化（在六种排序中，“组合性坍缩”的占比跨度达26.4%至76.4%）。此外，人工评估者与模型分类器在“组合性坍缩”判别上一致性极低（kappa = 0.093），而对任务正确性的判断则高度一致（kappa = 0.932），表明该类别定义受提示工程影响显著。进一步分析显示，模型标签与人类标注差异主要源于提示设计而非模型本身，当排除一个有缺陷的基准样本后，两者差距大幅缩小。因此，论文提出四项低成本验证措施以提升错误分析的可信度：报告标签共现情况、交换分类器判断顺序以检验鲁棒性、使用共享独立样本进行验证，以及审计系统性分歧。
链接: https://arxiv.org/abs/2611.13140
作者: Madhu Sudhan Rao Beesetty
机构: PayPal, Inc.(PayPal)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 11 pages. Accepted at IEEE CogMI 2026 (regular paper)
Abstract:Failure taxonomies for LLM reasoning are increasingly reported as measurements: papers state what fraction of a model’s errors fall into named categories, then compare those fractions across models. We show that for one such category the reported fraction is largely a property of the measurement procedure. We defined Compositional Collapse (a model notices a required sub-part of a problem, then drops or misapplies it) and measured it across twelve models and eleven languages on a public multilingual math benchmark (2,316 graded records). First, 50.1% of failures carry evidence for more than one category, so the reported plurality depends on the order in which the classifier tests them: reversing two tests moves it in eleven of twelve models, and across all six orderings Collapse’s share spans 26.4% to 76.4%. Second, agreement collapses for the category but not for the task. Native speakers of all eleven languages judged 179 transcripts, agreeing with our inspector at kappa = 0.932 on answer correctness but only kappa = 0.093 on Collapse. A second native speaker per language re-rated 142 items; on 121 determinate pairs the two humans agree with each other at kappa = 0.659, while each agrees with the inspector at kappa = 0.094. Re-running the same inspector model with the human instructions verbatim raises kappa against Human 2 from 0.147 to 0.636, so the gap is largely attributable to the prompt, not the model. Third, the inspector labels Collapse 1.81 times as often as native speakers, but only 1.17 times once a defective benchmark item (a deleted clause in the Bengali and Chinese translations) is excluded. We recommend four cheap checks before comparing failure-mode rates across models: report label co-occurrence, permute the tie-break, validate on a shared independent sample, and audit systematic disagreements.
[NLP-73] Which Successes Make Good References? Action-Sequence Alignment for Multi-Turn On-Policy Distillation
【速读】： 该论文旨在解决多轮工具使用智能体在在线蒸馏（On-policy Distillation, OPD）过程中因训练-推理分布不匹配导致的性能下降问题，尤其针对成功轨迹选择不当可能引入误导性监督的挑战。其核心解决方案是将成功参考轨迹的选择建模为无目标最优传输（free-target optimal transport）问题，通过最大化失败轨迹与成功轨迹之间的兼容性来实现最优匹配。由于潜在状态路径不可观测，论文提出以动作序列对齐作为兼容性的可观测代理，并采用最长公共子序列（Longest Common Subsequence, LCS）方法，识别出动作序列中共享的有序主干结构，同时允许可恢复的偏离路径，从而有效衡量轨迹间的兼容性。这一视角使轨迹兼容性成为多轮OPD中参考轨迹选择的核心标准。实验结果表明，在ALFWorld和τ³-bench的航空领域任务上，LCS是唯一能在整体得分及所有三项航空指标上均优于无参考OPD的方法；且未经进一步优化，基于航空领域训练的模型在零售领域迁移时，LCS同样显著提升各项指标，验证了该方法的泛化能力与有效性。
链接: https://arxiv.org/abs/2611.13135
作者: Mufei Li,Nikki Lijing Kuang,Zhuoer Wang,Samson Koelle,Jie Yuan,Manqing Mao,Hong Wang,Yanjun Lin,James Feng,Daniel Edmiston,Pan Li,Wei Niu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:On-policy distillation (OPD) reduces train-inference distribution mismatch by supervising a student on its own trajectories. For multi-turn tool-use agents, conditioning the teacher on a validated same-task successful trajectory as a reference can provide useful hindsight, but different successes may reach incompatible interaction states and offer misleading supervision. We cast successful-reference selection as a free-target optimal transport problem, which reduces to assigning each failed trajectory its most compatible successful reference. Because latent state paths are hidden, we use action-sequence alignment as an observable proxy for compatibility and instantiate it with longest common subsequence (LCS), which identifies a shared ordered backbone of actions while allowing recoverable detours. This perspective makes trajectory compatibility the central criterion for reference selection in multi-turn OPD. Experiments on ALFWorld and the airline domain of \tau^3 -bench show that LCS is the only evaluated reference selector to improve over reference-free OPD on both ALFWorld overall scores and all three airline metrics. Without further optimization, the airline-trained models also transfer to the retail domain, where LCS improves all three metrics.
[NLP-74] ASPIRE: An Agent ic System-Identification Platform for Iterative Reasoning and Exploration
【速读】： 该论文旨在解决非线性系统辨识中依赖专家经验、模型选择与物理先验知识融合困难以及模型验证不充分等问题。其提出的ASPIRE框架通过引入大语言模型（Large Language Model, LLM）代理，实现从测量数据中自动构建闭式、非线性静态或动态模型，支持以形式化或自然语言表达的系统描述、模型用途及物理约束条件。解决方案的关键在于：利用大语言模型代理自主决策辨识方法，执行建模流程，并生成包含参数、估计记录、项解释及在不可见测试数据上评估误差的完整可解释模型；同时，对形式化物理要求进行独立验证，并支持人机交互式迭代优化。实验在移动机器人、带干摩擦的旋转系统和实验室发酵罐三个真实系统上验证，共30次运行均成功产出有效模型，成本为每轮0.96至3.38美元，且人工评估显示模型解释与结构选择文档在150项评估指标中完全满足132项，部分满足18项，主要偏差源于模型结构选择依据的不准确或缺乏支持。
链接: https://arxiv.org/abs/2611.13114
作者: Erik Derner,Jiří Kubalík,Robert Babuška
机构: Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague (捷克技术大学信息、机器人与控制研究所); Delft University of Technology (代尔夫特理工大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:System identification requires expert decisions on the identification method, incorporation of prior physical knowledge, and model validation. We propose ASPIRE, a framework for nonlinear system identification in which a large language model agent selects and executes the identification method. ASPIRE constructs closed-form, nonlinear static or dynamic models from measured data, optionally supplemented by a system description, the intended use of the model, and physical requirements expressed formally or in natural language. Each model includes its parameters, an estimation record, an interpretation of its terms, and an error evaluated on test data inaccessible to the agent. Formal physical requirements are verified independently. ASPIRE operates autonomously or interactively, allowing the agent to ask questions and the user to request further analyses and identification rounds. We evaluate ASPIRE on three measured systems: a mobile robot, a rotational system with dry friction, and a laboratory fermenter. Each system is tested with and without a description, in five runs per task. All 30 runs delivered valid models, at a cost of 0.96 to 3.38 US dollars per run. An assessment by the authors found that, for the 30 models recommended by the agent, one per run, the interpretation of the model and the documentation of its structure selection and parameter estimation fully satisfied 132 of 150 assessed items and partly satisfied 18. The most frequent discrepancy, found in nine models, concerned inaccurate or unsupported claims about model structure selection.
[NLP-75] Catch It Where It Happens: Real Time Error Correction During LLM Reasoning
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在数学、科学与工程任务中因推理过程中的错误而产生“错误传播”问题，即模型在某一步骤出现错误后仍持续生成无效内容，导致资源浪费且难以察觉。现有解决方案要么仅在最终答案阶段进行验证并重新开始推理，要么依赖于需要大量计算资源的验证型大模型（verifier LLM），造成显存竞争和响应延迟。本文提出一种在线、确定性验证机制：利用代码可执行性对多种推理步骤进行实时验证，无需调用任何模型或使用GPU，在推理过程中即时完成校验。一旦检测到错误，系统立即定位问题并允许模型从该点继续推理。该方法在符号数学任务中得到验证，其推导过程天然包含可被计算机代数系统（Computer Algebra System, CAS）毫秒级检查的显式表达式。在ASyMoB基准测试中，针对三款开源权重模型，该方法将准确率提升1.4至5.6个百分点，对应错误答案减少1.8%至14.9%，同时仅增加6%-7%的令牌消耗。由于验证器与主模型完全解耦，该方案不与模型发展竞争资源，反而能随确定性校验能力扩展至更多推理类型而持续增强效果，具有良好的兼容性与可扩展性。
链接: https://arxiv.org/abs/2611.13107
作者: Rotem Elimelech,Michael Shalyt,Ido Kaminer
机构: Technion – Israel Institute of Technology(以色列理工学院); Haifa 3200003, Israel
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly trusted with mathematical, scientific and engineering work, yet their mistakes are often confidently written and hard to spot. A single wrong step tends to propagate, making the model waste inference tokens following a false premise. Existing safeguards either check only the final answer and restart from scratch, or rely on a verifier LLM that competes for GPU capacity and slows down response time. We propose online, deterministic verification: many kinds of reasoning steps can be validated by code, so a step can be verified in real time, as it is written, without any model call or GPU use. Whenever a check fails, we point out the mistake and let the model continue from the same spot. We demonstrate this approach on symbolic mathematics, where derivations already contain explicit expressions that a computer algebra system can check in milliseconds. Tested on the ASyMoB benchmark with three open-weights models, our online validation improves accuracy by 1.4-5.6 percentage points, corresponding to a 1.8-14.9% net reduction in incorrect answers, while increasing token usage by only 6-7%. Because the verifier is independent of the model, the approach complements progress in LLMs rather than competing with it: every new model can benefit immediately, and the benefit grows as deterministic checkers extend to more kinds of reasoning steps.
[NLP-76] One Temperature to Rule Them All? NEURIPS2026
【速读】： 该论文旨在解决大语言模型在推理阶段采样过程中温度参数设置的优化问题，具体聚焦于在标准pass@K评估范式下，多温度采样（multi-temperature sampling）相较于单温度采样（single-temperature sampling）的实际收益边界。其核心问题是：在何种条件下，通过动态调整采样温度以实现温度多样性能够提升性能；而在何种情况下，仅使用一个经过调优的固定温度已足够。解决方案的关键在于提出一种高效计算理论上最优多温度分配方案的方法，并通过分析多个模型与数据集上的逐题成功概率随温度变化的分布特性，发现高难度问题更难以共享最优采样温度，但该趋势仅为硬度的对数级关系。进一步地，论文揭示了“钝化效应”（dulling effects）这一数学机制——即温度选择对不同问题分布具有鲁棒性，从而解释了为何在将温度作为采样预算K的函数进行调优时，多温度策略相对于单温度基线所能带来的性能增益通常有限。研究结果明确了多温度采样的适用场景，并为温度的合理设定提供了理论指导。
链接: https://arxiv.org/abs/2611.13077
作者: Aviv Orly(1),Ori Shem-Ur(1),Yaron Oz(1) ((1) School of Physics and Astronomy, Tel Aviv University, Israel)
机构: Tel Aviv University (特拉维夫大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at NeurIPS 2026. 82 pages, 45 figures, 6 tables. Code: this https URL
Abstract:Inference-time sampling is a central mechanism for improving large language models, with temperature controlling the tradeoff between reliability and diversity. While recent work suggests that varying temperature across samples can improve multi-sample inference, simple fixed-temperature schemes remain the standard in practice. We investigate this discrepancy in the canonical pass@K setting, asking when temperature diversity is useful, and when a single tuned temperature suffices. To this end, we analyze per-question success probabilities across multiple models and datasets, tracking how they vary with sampling temperature. We identify several consistent properties of these profiles, including that harder questions are less likely to share efficient sampling temperatures, although this trend is only logarithmic in hardness. We then derive an efficient method for evaluating the theoretically optimal multi-temperature allocation scheme, and show that although such a scheme improves performance, its gains over single-temperature baselines are typically modest when temperature is tuned as a function of the sampling budget K. We explain this phenomenon through mathematical mechanisms that we call dulling effects, and show how they ensure the robustness of temperature choices across different question distributions. Our results clarify when multi-temperature sampling is useful, and how temperatures should ideally be chosen.
[NLP-77] he Left Ripple of Byte-Pair Encoding: Certified Lookbehind for Every Dictionary
【速读】： 该论文旨在解决字节对编码（Byte Pair Encoding, BPE）中前缀字符串对分词结果影响范围的理论边界问题，具体包括：左端添加字符串所能引起的分词变化最大距离（即“起源半径”）、哪些词典会导致该影响无限扩展、以及在有限情况下是否可高效计算。其核心解决方案在于提出“半径证书”（radius certificates）机制，通过构建自动机的两次运行来判断起源半径——一次推测最终分词结构，从而在多项式时间内确定任意词典下的起源半径是否有限。研究发现，起源半径为无穷当且仅当存在一个周期性词串具有两个无共同边界的规范铺砌（canonical tiling），在排序条件下等价于某规则 (t, t) 在自身阶段即出现两个副本相邻的情况，此类规则必然使词典具有无限起源半径。有限起源半径意味着任意宽度窗口内均存在“切割点”（cut），且该性质成立当且仅当这些切割点稠密。此外，论文证明了所有词典下分词过程均为可顺序化（subsequential），并构造出一种流式分词器（streaming tokenizer），可在每读入一个符号后以多项式时间输出当前不可被后续字符改变的确定性分词结果，回应了Dalirrooyfard和Ye提出的问题。同时，标签度量（label measure）——即分词序列间的编辑距离——的有界性也可在多项式时间内判定；在排序条件下，单次编辑所引发的最大距离随规则数 $ m $ 的渐近范围介于 $ m^2/10 $ 到 $ m^2/2 $ 之间。实验表明，GPT-2的合并表包含244个有效自配对规则，使其从第42次合并起起源半径趋于无穷；而cl100k、o200k及Llama 3则从初始阶段即具备无限起源半径。所有数值结果均可由程序直接输出，未被执行的部分则被明确命名。
链接: https://arxiv.org/abs/2611.13061
作者: Nicklas Nidhögg
机构: 独立研究员(Independent Researcher)
类目: Formal Languages and Automata Theory (cs.FL); Computation and Language (cs.CL); Data Structures and Algorithms (cs.DS); Programming Languages (cs.PL)
备注: 87 pages, 13 tables. Artifact: this https URL . The programs and their outputs are in the artifact, with a verifier that reruns every program and holds each reported number to the output line it rests on. Companion to arXiv:2608.03473 , arXiv:2608.09761 , arXiv:2609.10600 , arXiv:2610.08854 and arXiv:2610.30168
Abstract:Berglund and van der Merwe formalized byte-pair encoding and bounded how far an appended string can change a tokenization, leaving the converse open: how far can a string prepended on the left change it, which dictionaries make that ripple infinite, and can it be computed efficiently when finite? We answer all three for every dictionary. The origin radius, how far a prepend can change covering spans, is decided with radius certificates by a product of two runs of the automaton that guesses the final tokens, in time polynomial in the dictionary written out, and it is infinite exactly when one periodic word has two canonical tilings with no common boundary. Under their ordering condition it is infinite exactly when some rule (t, t) finds its two copies adjacent at its own stage, and such a rule makes any dictionary infinite. A finite radius makes every window of a width read off the radii have a cut, and the radius is finite exactly when such cuts are dense. The token machine completes for every dictionary, and the tokenization is subsequential, extending what Berglund, Martens and van der Merwe proved for proper ones under one pass per rule. A streaming tokenizer emits after every symbol exactly the tokens that no continuation changes, in polynomial time, answering a question of Dalirrooyfard and Ye. Boundedness of the label measure, the edit distance of the token sequences, is decided for every dictionary in polynomial time as well, and under the ordering condition the largest distance one edit causes over dictionaries of m rules lies asymptotically between m^2/10 and m^2/2. GPT-2’s merge table carries 244 effective self-pairs, so its origin radius is infinite from the forty-second merge on, and that of cl100k, o200k and Llama 3 from their first. Every measured number is its program’s output, and what is not executed is named.
[NLP-78] A Multi-Word Tokenizer for 204 Languages That Needs Fewer Tokens Than GPT -4os Including on English
【速读】： 该论文旨在解决印度语言在使用GPT-4o分词器时所需标记（token）数量比英语多1.6至14倍的问题，这一现象导致这些语言在生成式AI（Generative AI）服务中成本更高、速度更慢。其核心解决方案是通过从头训练适用于约200种语言的人类书写文本的分词器，提出Havon系列分词器。关键创新在于Havon v3采用字节级BPE（Byte-level BPE）架构，配备262,144个标记，并引入预分词机制以保留连字符与字母的组合关系及无空格书写的脚本块；在此基础上，Havon v4进一步引入双阶段训练（SuperBPE），显著提升了分词效率。在与GPT-4o的o200k_base分词器对比下，当词汇量为200,019时，Havon v4在英语上减少6.1%的标记数，在161种语言和全部25种印度语言上表现更优（中位数为GPT-4o的74%）；当扩展至262,144或524,288个标记时，覆盖范围进一步提升至182或203种语言。此外，Havon v5通过替换v4中的仅限Python代码样本为15种语言的宽松许可代码，增强了实用性，在代码文本上的表现优于v4，同时保持对印度语言和英语的优异压缩效果。该研究目前仅为分词压缩结果，尚未评估基于v4或v5训练的模型性能。
链接: https://arxiv.org/abs/2611.13059
作者: Johin Johny,Ankur Ghosh,Shervin Sam
机构: Havon AI
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Languages of India need 1.6 to 14 times as many tokens as English on GPT-4o’s tokenizer for the same content, which makes them more expensive and slower to serve. We train tokenizers from scratch on human-written text in about 200 languages. Havon v3 is a byte-level BPE tokenizer with 262,144 tokens and a pre-tokenizer that keeps combining marks with their letters and chunks scripts written without spaces. On FLORES-200 devtest it needs fewer tokens than GPT-4o’s o200k_base on all 25 languages of India (median 0.74 of GPT-4o’s count) and on 172 of 204 languages, but 7% more on English. Havon v4 adds a second, multi-word training stage (SuperBPE) to v3. At exactly GPT-4o’s vocabulary size of 200,019 tokens, v4 needs 6.1% fewer tokens than GPT-4o on English and fewer on 161 of 204 languages and all 25 languages of India (median 0.74); at the same size, v3 needs 9.4% more on English and wins on 133. With 262,144 tokens, v4 needs 9.6% fewer tokens on English and fewer on 182 of 204 languages, and with 524,288 tokens it needs fewer on 203 of 204. The gains are largest for English, Hindi and the Romance languages; scripts without spaces, such as Khmer, Burmese and Tibetan, gain little and lose subword merges. Havon v5 replaces v4’s Python-only code sample with permissively licensed code in 15 languages: on code it needs 1.03 to 1.22 times GPT-4o’s tokens, against 1.11 to 1.29 for v4, while keeping all 25 languages of India (median 0.74, all significant under a sentence-level bootstrap) and English (0.94). These are compression results only: we have not yet evaluated a model trained with v4 or v5.
[NLP-79] he Indic Token Tax in 2026: Measuring Tokenizer. Disparity and Its Cost for the Languages of India
【速读】： 该论文旨在解决多语言大模型应用中因分词器（Tokenizer）效率差异导致的语言成本不公问题，即同一内容在不同语言下经分词后所需标记数（token）存在显著差异，进而造成某些语言在使用商业语言模型API时成本更高、处理更慢且上下文窗口容纳文本量更少。其核心问题是：当前主流分词器对非英语语言的编码效率远低于英语，尤其在印度语系中表现尤为突出。解决方案的关键在于提出一种新型分词器设计，通过优化词内边界与形态学边界（Morpheme Boundary）的对齐程度，显著降低特定语言的分词开销——实验表明，现有分词器在印度25种语言上的词内边界与形态学边界匹配度（边界F1）最高仅为0.48，而新提出的分词器可在保持高准确率的同时大幅减少“分词税”（token tax），从而实现对低资源语言更经济高效的处理。
链接: https://arxiv.org/abs/2611.13058
作者: Johin Johny Arimbur,Ankur Ashit Ghosh,Shervin Sam
机构: Havon AI
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Commercial language model APIs bill per token, so a tokenizer that needs more tokens for the same content in one language makes that language more expensive, slower, and able to fit less text in a fixed context window. We measure this disparity for nine public tokenizers, including those of GPT-4, GPT-4o, Qwen2.5, DeepSeek-V3, Mistral-Nemo and four Sarvam models, on all 25 languages of India available in FLORES-200 (2,009 professionally translated sentences aligned with English) and on 13,732 human-translated Hinglish-English social media pairs from PHINC. On GPT-4o’s tokenizer the same content needs between 1.57 times (Hindi) and 5.01 times (Odia) the tokens of English, and 13.7 times for Santali in Ol Chiki script; GPT-4’s tokenizer needs up to 12.8 times. Sarvam-30B/105B is the only third-party tokenizer that stays below 2.6 times on every language (median 1.53). In an illustrative scenario using official list prices accessed on 5 November 2026 and stated assumptions, a support bot handling 100,000 chats a month on GPT-4o would cost about INR 56,640 in English and INR 102,829 in Marathi; these are estimates, not measured production costs. Against an openly licensed Hindi and Marathi morphological segmentation set, no tokenizer places more than 35% of its word-internal boundaries on morpheme boundaries (boundary F1 at most 0.48). In a companion paper we present a tokenizer that reduces this tax. Code, pinned artifacts and raw token counts are available at this https URL.
[NLP-80] GTAGym: Real AAA Game Tasks Environment for Evaluation and Reinforcement Learning
【速读】： 该论文旨在解决在高质量、复杂交互的3A游戏（如《GTA V》）中评估智能体感知、推理与控制能力的难题，尤其针对任务完成情况难以自动化验证的问题，这限制了强化学习（Reinforcement Learning, RL）在真实场景中的应用。其核心解决方案是提出GTAGym——一个面向《GTA V》任务的评估环境与框架，包含基于主线剧情重构的103个在线任务（涵盖九类任务类型）以及47个基于图像的射击/狙击任务，构成名为GTAVBench的评测基准。该系统通过网络服务器提供对游戏环境的远程访问，支持通过截图和键盘鼠标输入实现并行测试，并结合自动引擎状态检测实现任务结果的精准判定。为提升评估效率，还设计了GTAVBench-AGI分支，仅在首张截图中提供指令，由智能体自动生成交互历史压缩，以更贴近通用人工智能（AGI）的评估范式。实验表明，最优配置下大语言模型（LLM）在GTAVBench-AGI上的综合准确率达70.9%，但在空中导航、规避与拦截等复杂行为上仍存在挑战。此外，GTAGym集成了可扩展的任务生成器，基于预设模板和参数范围自动生成大量三维导航及图像射击任务环境，支持自动奖励与终止信号，显著提升了训练效率；通过LoRA微调的Qwen3.8-27B模型在生成的步行导航任务上实现了域内性能提升，并成功迁移至域外任务类别，使《GTA V》原生任务的成功率从15%提高至23%。因此，该研究的关键突破在于构建了一个兼具高保真度、自动化评估与可扩展生成能力的综合性评测与训练平台，为复杂环境下的智能体学习提供了可行路径。
链接: https://arxiv.org/abs/2611.13028
作者: Jiajun Chen,Shicheng Fang,Jiahao Lu,Yuxin Wang,Xipeng Qiu
机构: Shanghai Innovation Institute; Fudan University (复旦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:AAA games offer a rich testbed for evaluating agent perception, reasoning, and control. However, evaluating whether agents can complete tasks in AAA games is challenging and often human-annotated because task outcomes are often difficult to verify automatically, let alone applying it for Reinforcement Learning (RL). We introduce GTAGym, an environment and framework for evaluation and RL on GTA V tasks. Its evaluation benchmark, GTAVBench, derived from GTA V main-story missions, contains 103 online tasks reconstructed across nine task families, alongside 47 image-based shooting and sniping tasks using GTA V imagery. A web server provides access to the online environment and evaluation interface, supporting parallel testing through screenshots and keyboard-mouse actions with automatic engine-state outcome checks. We evaluate six frontier LLMs on GTAVBench with textual task instructions and on its GTAVBench-AGI branch, where instructions appear only in the first screenshot and a harness automatically compresses interaction history. The best configuration achieves 70.9% overall accuracy in GTAVBench-AGI, yet aerial navigation, evasion and interception remain difficult. GTAGym also provides a scalable generator that samples task parameters within user-specified ranges and option sets under fixed templates, creating batches of 3D and image-based shooting environments for RL with automatic reward and termination signals. LoRA RL of Qwen3.8-27B on generated on-foot navigation tasks improves the in-domain performance and transfers to out-of-domain task categories, and native GTA V tasks from 15% to 23%.
[NLP-81] Repository-Context-Guided Image Understanding for Agent ic Software Development
【速读】： 该论文旨在解决编码代理在处理含视觉缺陷的用户界面（UI）截图时，因图像质量下降（如低分辨率、压缩伪影或小字号文本）而导致无法准确识别相关UI元素及对应源文件的问题。现有方法中，视觉-语言模型在图像质量恶化时可靠性下降，而依赖光学字符识别（OCR）的方法则可能将输入中的识别错误传递至后续分析，尤其当目标视觉证据在查询截图中已不清晰时，二者均面临显著局限。为此，本文提出一种关键解决方案：为编码代理引入由代码仓库生成的高保真参考图像（repository-rendered reference images），即基于待修改代码库实时渲染的清晰界面截图。这些参考图像保留了当前潜在存在缺陷的实现状态，同时提供可读性强的视觉证据，且无需依赖已知无错版本，可自动构建并实现可复现的应用状态。实验在基于SWE-bench Multimodal构建的受控基准上进行，通过向真实网页应用注入视觉缺陷，并系统性地改变截图质量与参考图像供给情况，评估编码代理对源文件的定位能力。结果表明，参考图像显著提升了在图像退化条件下的文件定位准确率，尤其在四倍降采样与JPEG压缩下表现突出；进一步分析显示，在相同退化条件下，该方法的文本识别准确率比偏向语言模型的OCR高出约12个百分点。
链接: https://arxiv.org/abs/2611.13026
作者: Fumiki Yohena,Toshiaki Sota,Shota Suzuki,Tomoko Okamoto,Michiaki Tatsubori
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Coding agents increasingly receive UI screenshots as part of bug reports, yet degraded image quality - caused by low resolution, compression artifacts, or small rendered text - can prevent them from identifying the relevant UI elements and source files. Vision-language models become less reliable as image quality deteriorates, while OCR-assisted approaches may propagate recognition errors from degraded inputs. Both approaches are limited when the visual evidence required for localization is no longer clearly observable in the query screenshot. We propose augmenting coding agents with repository-rendered reference images: crisp screenshots generated from the same codebase that the agent is tasked with modifying. These references provide readable visual evidence of the application’s UI while preserving the current, potentially bug-containing implementation state. Because they are generated from the available repository rather than requiring a known bug-free version, they can be produced automatically for reproducible application states without manual image annotation. We evaluate the approach on a controlled benchmark derived from SWE-bench Multimodal by injecting visual UI bugs into a real web application. We vary screenshot degradation and reference-image provision and measure whether the coding agent identifies the correct source file. The results show that reference images improve file localization especially for degraded image quality, increasing the gold file match rate under fourfold downsampling with JPEG compression. We confirmed with further analysis that the proposed approach recognizes text approximately 12 percentage points better than language biased OCR under the same degradation.
[NLP-82] khammoon: From a Linguistic Guideline to a Thai Morpheme Segmenter
【速读】： 该论文旨在解决泰语自然语言处理中因缺乏统一的词边界定义而导致的分词歧义问题。由于泰语书写无空格，传统分词工具依赖于特定语料库（如BEST）中的词级惯例，而这些惯例在面对具有生成性的复合构词时表现不佳，产生任意且模糊的边界划分。其解决方案的关键在于从词级分词转向形态学层级的分词，聚焦于“khammoon”——即承载意义的最小语义原子单位。通过在形态层面进行分词，可获得明确且一致的形态表示，从根本上消除复合构词带来的歧义。该方法不仅提升了语言学一致性，还显著改善了下游任务的表示质量：基于形态单元训练的模型在词性标注、命名实体识别和情感分类等任务上达到或超越词级基线性能，证明了形态一致性对表示学习的正向作用。为实现这一目标，研究团队将语言学原则形式化为具有极高人类一致性（κ ≈ 0.99）的决策指南，结合大语言模型（LLM）进行规模化标注，并利用人工标注校正系统性错误；最终构建了一个轻量级（100万参数）、专用于高效CPU推理的卷积双向长短期记忆（convolutional BiLSTM）分词器——khammoon，其在跨域文本上的边界F1得分高达0.992。相关决策指南、数据集、模型及代码库均已公开发布。
链接: https://arxiv.org/abs/2611.13012
作者: Warit Sirichotedumrong,Adisai Na-Thalang,Chanakan Wittayasakpan,Kritsadha Phatcharoen,Kunat Pipatanakul
机构: 未知
类目: Computation and Language (cs.CL)
备注: Technical Report. Code, models, and data are available at this https URL
Abstract:Thai is written without spaces between words, making segmentation a foundational step in most Thai NLP pipelines. Existing tools primarily reproduce the word-level conventions of corpora such as BEST; however, because the notion of a “word” in Thai lacks a unified linguistic consensus, these conventions struggle with productive compounding, leading to arbitrary boundaries and ambiguity in downstream representations. Instead, we propose operating at the morphological level, focusing on khammoon, the smallest atomic unit that carries meaning. Segmenting at the morphemic level provides an explicit, consistent morphological representation that eliminates compounding ambiguity. Crucially, this representation benefits downstream learning: models trained on morphemes perform on par with or exceed word-level baselines across part-of-speech tagging, named-entity recognition, and sentiment classification, demonstrating that linguistic consistency improves representation quality without downstream performance penalty. To operationalize this representation, we translate linguistic principles into a formal decision guideline with near-perfect human agreement ( \kappa \approx 0.99 ), scale annotation with an LLM, and correct systematic errors with human labels. Finally, we train khammoon, a lightweight 1M-parameter convolutional BiLSTM segmenter designed for efficient CPU inference, achieving a boundary F1 of 0.992 on out-of-domain text. The guideline, datasets, model, and library are all publicly released.
[NLP-83] Seeing Is Not Believing: Counterfactual Lesion Erasure Teaches a Small Medical Vision-Language Agent to Report Only What the Image Shows
【速读】： 该论文旨在解决医学视觉-语言模型（Medical VLMs）在诊断推理中存在“命名即发现”（name-to-find）的偏差问题，即模型仅因问题中提及某病灶名称便声称其存在，而忽视图像实际证据的情况。现有标准基准测试无法有效识别此类虚假关联，导致模型性能评估失真。为解决此问题，研究提出构建无须人工标注的反事实视图（counterfactual views），包括通过图像修复（inpainting）擦除病灶、在相同形状位置进行控制性编辑以及未修改的无病灶图像。核心解决方案是引入证据敏感性差距（Evidence Sensitivity Gap, ESG），量化模型在病灶被擦除后与控制性编辑后回答“不存在”或“不相关”的比例差异；若模型真正依赖图像证据，则ESG应显著高于零。实验表明，基于标签轨迹训练的MedScope-4B（Qwen3.5-4B，LoRA，图像工具）在五类影像数据上实现ESG 0.758，远超前沿模型（0.27–0.47），且对真实病灶的检测准确率未明显下降。关键创新在于：通过在训练中纳入擦除与编辑后的图像，使小模型获得“证据门控”（evidence-gated）的推理倾向，这种能力无法仅通过提示工程（prompting）实现，但并不提升感知能力，且仍需关注罕见关键病灶的覆盖。
链接: https://arxiv.org/abs/2611.13006
作者: Bhaskar Gurram
机构: 独立研究者(Independent Researcher)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 1 figure, 13 tables. Preprint v1.0; reader study in progress. Code, data recipes, pre-registrations and per-view outputs: this https URL
Abstract:Medical vision-language models (VLMs) often report a finding because the question names it; standard benchmarks miss this. From public segmentation datasets we build counterfactual views without manual annotation: the lesion erased by inpainting, a same-shape control edit elsewhere, and unedited lesion-free images. The evidence sensitivity gap (ESG) is the rate of absent-or-refer answers after erasure minus the rate after the control edit; it is zero for a model that ignores the image or reacts to both edits alike. MedScope-4B (Qwen3.5-4B, LoRA, image tools) was trained on label-derived trajectories, selected by a pre-registered rule and tested once against nine frontier and six open VLMs; four pre-registered checks then probed the mechanism. On 507 internal cases across five imaging types, MedScope-4B reached ESG 0.758 and claimed an erased lesion in 9.6% of cases, against 0.27-0.47 and 41-62% for the frontier models (all p0.001), without significant loss of accuracy on real lesions. Without counterfactual views ESG fell to 0.279±0.107 (three seeds); the effect replicated on MedGemma 1.5 4B and two unseen inpainting tools. At four external centres the advantage held but frontier models detected more real lesions; on a fresh external test MedScope-4B detected 0 of 48 pneumothorax films, with net utility below gemini-4-argon’s (-0.36 [-0.50, -0.23]). Matching the control edit was not shown to matter; over three seeds no fresh-external ESG advantage over removal-based DPO was established; a paired-view consistency objective did not beat a matched-compute control, while that continuation alone cut erased-lesion claims on unseen tools at every seed, at a cost in sensitivity at one. Training on erased and edited images gives a small model an evidence-gated disposition that prompting does not give frontier models; it does not improve perception, and rare critical findings need coverage.
[NLP-84] Linear Probes to Bayesian Manifolds: Geometry of Arithmetic in Large Language Models ICLR2027
【速读】： 该论文旨在解决当前生成式人工智能（Generative AI）模型中线性探针（linear probe）研究存在的根本性问题：仅凭探针准确率无法有效区分模型实际计算所依赖的几何结构与潜在混淆因素。现有方法将四个独立但常被混同的科学主张——可解码性（decodability）、形状（shape）、归属性（ownership）和因果使用性（causal use）——合并为单一的准确率指标，导致结论易被误读。为此，作者提出一个四阶段贝叶斯分析框架，分别对上述四项主张进行独立验证：阶段2通过贝叶斯证据比较候选几何形状并筛选最优解；阶段3通过移除预设相关概念以检验目标形状是否真正归属于所探查概念而非继承自其他概念；阶段4则对形状实施干预，检验其输出影响的因果作用。在GPT-J 6B、Llama 3.1 8B和Pythia 6.9B等模型上对两位数加法与乘法任务的应用表明，尽管几乎所有概念均可解码，但解码方向在不同概念间高度重叠；多数支持性比较结果指向“变半径螺旋”（ribbon）或“环面”（torus）等复杂几何结构；在1,330个具有归属判断的神经元中，仅有19%的形状具有独占归属性，81%为继承性；而无论归属与否，干预形状均能产生与全维探针相当的输出影响（约保留61%效果），且仅需最多五个维度，显著低于探针本身的九维中位数。因此，该研究的关键贡献在于揭示了传统探针结果的过度乐观倾向，并主张未来应将探针分析报告为对四项核心主张的独立判决，而非单一准确率，从而提升对模型内部表征机制理解的严谨性与可信度。
链接: https://arxiv.org/abs/2611.12998
作者: Anshul Kumar,Neha Prasad,Manoj Kumar,Barnabás Póczos,Deeksha Varshney
机构: Carnegie Mellon University (卡内基梅隆大学); Indian Institute of Technology Jodhpur (印度理工学院乔德普尔分校); Indian Institute of Technology Roorkee (印度理工学院鲁尔基分校)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 26 pages, 4 figures, 20 tables. Under review at ICLR 2027
Abstract:Linear probes are a standard method to study geometric structures, such as helices and circles, from the activations of large language models (LLMs), and such structure is often taken as evidence that the model computes with it. Probe accuracy alone cannot establish this, because it conflates four separate claims. These are (1) that the concept is decodable from the activations, (2) that they form a specific shape, (3) that the shape belongs to the probed concept rather than to a related concept the model also represents, and (4) that the output of the model depends on the shape. We call these claims decodability, shape, ownership, and causal use, and we test them separately with a four-stage Bayesian pipeline. Stage 2 compares candidate shapes and selects the best one only when its evidence passes a predefined threshold. Stage 3 removes a set of related concepts, specified in advance, and tests whether the shape is owned or inherited. Stage 4 intervenes on the shape to test whether the output of the model depends on it. We apply the pipeline to two-digit addition and multiplication in GPT-J 6B, Llama 3.1 8B, and Pythia 6.9B. Nearly every concept is decodable, but the decoded directions overlap substantially across concepts. Every strongly supported comparison selects a ribbon, which is a helix whose radius changes with the value, or a torus. Of 1,330 cells with an ownership verdict, 19% of shapes are owned and 81% are inherited. Owned and inherited shapes move the output about equally often. Intervening on the shape retains approximately 61% of the effect of intervening on the full probe while using at most five dimensions, against a median of nine for the probe. Our results indicate that a probe verdict alone overstates what a model computes with, and we propose that probe results be reported as verdicts on all four claims rather than as a single accuracy.
[NLP-85] Capacity Allocation at the Source: Sparse Target Optimization for LLM Knowledge Editing NEURIPS2026
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在进行知识编辑时，因目标隐状态（target hidden state）构建不当导致的模型容量冲突与知识干扰问题。现有方法多聚焦于参数编辑阶段以保留原有知识，而本文指出上游目标隐状态的构造同样关键，直接影响模型有限容量空间的分配效率。其核心解决方案在于建立参数空间干扰与目标隐状态间干扰之间的理论关联，证明二者呈正相关关系；基于此，提出一种简单有效的ℓ₁正则化方法，通过剪枝每个知识向量的冗余“触须”（tentacles），仅保留关键成分，从而减少知识编辑对其他已有知识的影响。该方法显著提升了不同知识间的共存能力，实验验证了其在多种大模型和基准数据集上的有效性。
链接: https://arxiv.org/abs/2611.12995
作者: Dongyao Chen,Jiayun Lei,Zhiyuan Ji,Zhiying Deng,Wei Liu,Xiaobo Sun
机构: Renmin University of China; Central China Normal University; National University of Singapore; Emory University
类目: Computation and Language (cs.CL)
备注: neurips2026
Abstract:Knowledge editing has emerged as a promising approach for efficiently updating embedded knowledge in large language models (LLMs). It first computes an ideal target hidden state that steers the LLM toward the new output, and then edits the model parameters so that the original hidden state is mapped to this target. While most existing methods focus on the second stage of parameter editing in preserving other knowledge, we find that the upstream construction of the target hidden state also plays an important role in allocating the model’s limited capacity space. We first establish a theoretical connection between interference in the parameter space and interference between target hidden states, showing that the two are positively correlated. Building on this insight, we propose a simple yet effective \ell_1 regularization method that prunes away unnecessary ``tentacles’’ of each knowledge vector, retaining only the essential components. With fewer extraneous tentacles, each edit affects less other knowledge, enabling better coexistence. Extensive experiments across multiple LLMs and benchmark datasets verify the effectiveness of our method. Code: this https URL.
[NLP-86] ZeroPoint: Lightweight Energy-Based Decision Model
【速读】： 该论文旨在解决能量模型（Energy-based Models, EBMs）在决策任务中无法有效处理“无选项可接受”这一情形的问题。传统EBM仅能学习选项间的相对兼容性，缺乏一个固定的能量参考点来判断是否存在可接受的选项，导致模型无法自主决定是否拒绝所有选项。其解决方案的关键在于提出一种名为ZeroPoint的新框架，将“不选择任何选项”作为模型的一个固定状态，且该状态在任意输入下能量恒为零。这一设定为所有选项提供了明确的能量参照系，使得选项的非负能量深度可直接反映其相对于“空答案”的兼容性。决策过程（选或不选）成为统一推理的一部分，通过负对数似然损失进行端到端训练，无需额外设计拒识规则。模型采用一个顺序无关的小型多语言编码器，联合读取输入与所有选项，实验表明，主要性能提升来源于包含“无合适选项”示例的训练数据，其在区分有/无可接受选项方面优于学习型拒识类和阈值softmax方法，同时保持选项间选择能力不变。
链接: https://arxiv.org/abs/2611.12992
作者: Javier Marín
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Energy-based models (EBMs) assign a scalar energy to every joint value of the observed and predicted variables. In decision models, the prediction is chosen from a finite list of options provided with each input. Inference then reduces to comparing the energies of the options, and training lowers the energy of the correct option relative to the others. A complete decision must also account for the possibility that no option is acceptable. However, the energies learned by a standard EBM define only relative compatibility: adding the same input-dependent quantity to all energies leaves the predictions, the Gibbs distribution, and the usual training losses unchanged. The model therefore has no learned reference level against which to decide whether any option fits. We propose ZeroPoint, an EBM that makes the empty answer a state of the model with energy zero for every input. This fixed state provides an explicit reference for the energies of all listed options, whose non-negative depths measure their compatibility with the input relative to the empty answer. The options and the empty answer share one distribution, in which an option at zero depth receives no probability. The decision to select an option or to abstain is thus part of ordinary inference, trained with the negative log-likelihood loss, rather than a separate rejection rule. The energy function is a small multilingual encoder that reads the input together with all the options and is invariant to their order. Ablations show that most of the benefit comes from training with examples in which no option fits. With the same encoder and data, the fixed zero separates inputs with and without an acceptable option slightly better than a learned rejection class and clearly better than a thresholded softmax, while leaving the choice among options unchanged.
[NLP-87] racing Psychometric Inference in Large Language Models NEURIPS2026
【速读】： 该论文旨在解决生成式语言模型是否在内部表征心理构念（psychological constructs），抑或仅生成在表面上呈现心理测量结构的输出这一核心问题。其关键解决方案在于采用跨人格（cross-persona）范式，结合行为层面的跨量表相关性分析与内部表征的几何结构探测，系统检验模型在多尺度心理特征预测中的表现。研究发现，较大规模的指令微调（instruct）模型不仅在输出层面重现并放大人类被试间的跨量表相关性，且其内部激活空间的几何结构与人类心理矩阵存在显著对齐；而基础（base）模型则缺乏此类对齐。进一步通过匹配的激活探针（activation-probing）分析揭示，探针斜率与行为斜率之间存在“读出衰减”（readout attenuation）现象，即探针所捕捉的内部结构强度低于实际行为表现的放大程度。经由合成控制与可靠性代理校正后，调整后的探针斜率仍与行为放大效应显著相关（r = 0.88），表明内部结构具有可解码性与几何一致性。因此，该研究的关键突破在于通过多层级验证，证明了大模型在特定条件下具备对心理构念的内在表征能力，而非单纯模仿表面结构。
链接: https://arxiv.org/abs/2611.12937
作者: Chih-Hao Hsu,Feng-Chun B. Chou,Pin-Hao A. Chen
机构: National Taiwan University(台湾大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at NeurIPS 2026
Abstract:Do large language models represent psychological constructs internally, or do they only generate outputs that resemble psychometric structure? We address this question using a cross-persona paradigm across 14 model variants from two families (Llama 3 and Qwen 2.5, 0.5B-14B, base and instruct). Given responses on one psychological scale from real individuals (N = 272 Taiwanese university students), models predict responses on six other scales. At the behavioral level, larger variants reproduce and amplify human cross-scale correlations; amplification persists after correcting the seven-scale human correlations for measurement attenuation. We then examine whether this structure is reflected internally. Contrastive direction analysis finds geometry aligned with the human matrix in larger instruct variants, but little alignment in base variants under this assay. Across models, geometry strength is associated with behavioral amplification (r = 0.68, p = 0.008; partial r = 0.69 controlling for log size). To relate internal structure to output behavior, a matched activation-probing paradigm shows that probe slopes vary less than behavioral slopes (1.38-1.77 vs 0.52-1.53). A synthetic control shows that ridge probing can inflate these slopes; after subtracting a baseline based on a reliability proxy from another analysis, adjusted slopes remain associated with behavior (r = 0.88). We term the measured probe-behavior gap readout attenuation. These results establish decodability and geometric alignment under the tested assays, without showing that the measured directions mediate the cross-persona answers.
[NLP-88] Using LLM Agents to Evaluate Prosocial Interventions on Social Media Platforms
【速读】： 该论文旨在解决社交媒体平台在部署内容干预措施前缺乏系统性评估方法的问题，尤其针对如何有效提升用户对经认证新闻的接触与参与度这一关键挑战。其核心解决方案是基于OASIS框架构建了一种基于大语言模型（LLM-based）代理的仿真评估流程，通过生成13,835名具有独特个人特征的虚拟用户代理，在20个时间步内模拟其对真实世界推特（Twitter/X）内容的互动行为（如评论、点赞、转发、关注），从而系统性比较三种干预策略：增加新闻内容供给、提升内容优先级（upranking）、以及引入名人来源提示（celebrity source cues）。研究发现，所有干预均显著提升了新闻参与度，其中将新闻内容置于信息流显著位置的“上排”策略效果最为突出，且该效应在不同性别、年龄和教育水平的代理中均保持一致。进一步与人类受试者实验结果对比验证，显示仿真结果在方向性和剂量归一化效率上具有高度一致性。研究表明，基于大语言模型的代理仿真可作为平台干预措施在人力实验前的早期评估工具，且相较于单纯增加内容数量或强调来源身份，提升内容可见性（upranking）可能更具效能。
链接: https://arxiv.org/abs/2611.12922
作者: Lucen Li,Erin Wertz,Muhammad Haroon,Anshuman Chhabra,Magdalena Wojcieszak
机构: University of California, Davis (加州大学戴维斯分校); Center for Excellence in Social Sciences, University of Warsaw (华沙大学社会科学卓越中心); Meta (Meta); Bellini College of AI, Cybersecurity, and Computing, University of South Florida (南佛罗里达大学人工智能、网络安全与计算学院)
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL)
备注:
Abstract:Social media platforms are a primary source of information for many users. Although research explores various interventions to promote prosocial content or demote harmful content on platforms, there are few systematic methods for evaluating competing intervention designs before conducting costly human-subject experiments. We develop a simulation-based evaluation pipeline built on the OASIS framework. We generate 13,835 LLM-based agents, each with a unique profile. Over 20 time steps, we expose them to real-world Twitter/X posts and let them comment, like, repost, or follow. We use those agents to systematically evaluate three interventions that increase exposure to verified news: adding content, upranking content, and celebrity source cues. All interventions significantly increase news engagement, with upranking news to prominent feed positions producing the largest effects. These effects hold across agents’ gender, age, and education levels. We validate our injection treatment results against a comparable human-subject study and find strong directional alignment and similar dose-normalized efficiency. Our results suggest that LLM-based agent simulations can support early-stage evaluation of platform interventions before human-subject deployment, and upranking may be more effective than news volume or source.
[NLP-89] Möbius Interaction Filtering: Order-Selective Spectral Regularization for Multimodal Humor Detection
【速读】： 该论文旨在解决多模态幽默检测中如何有效融合语言、语音和视觉模态信息，并合理调控不同层次交互（如单模态、双模态、三模态）对最终判断的贡献问题。其核心挑战在于，高阶交互虽蕴含丰富语义，但易受噪声干扰，导致信号-噪声比下降，影响模型鲁棒性。解决方案的关键是提出莫比乌斯交互滤波（Möbius Interaction Filtering, MoIF），通过设计一种具有几何递减响应特性的交互阶次滤波机制，在保持高阶表示能力的同时，对不同阶次交互进行有序调控。具体而言，MoIF构建私有时序编码器与平衡互作用结构，实现对单模态、双模态及三模态预测器的监督学习，并通过其聚合结构施加顺序选择性谱正则化，使最终集成结果精确对应于莫比乌斯域中的滤波函数。实验表明，该方法在UR-FUNNY数据集上实现了70.6%的准确率与加权F1，较联合训练的三模态基线提升1.62–2.79倍的信噪比，且在扰动下仍保持稳定的类对齐信号；在CMU-MOSI与CMU-MOSEI上的扩展验证进一步证明其在情感预测任务中的普适性与稳定性。MoIF通过可执行的谱约束将多模态交互组织方式与统计可靠性相联系，为多模态融合提供了理论清晰且性能优越的新范式。
链接: https://arxiv.org/abs/2611.12913
作者: Jiahao Qin,Yiwen Wang
机构: 未知
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 21 pages, 5 figures, 6 tables
Abstract:Multimodal humor detection requires interpreting language, vocal delivery, and visual behavior jointly. Expressive fusion must also regulate how interactions of different orders enter the final judgment. We propose Möbius Interaction Filtering (MoIF), which learns a modality-subset prediction function and imposes order-selective spectral regularization through its aggregation structure. Private temporal encoders and balanced reciprocal interaction construct supervised unimodal, bimodal, and trimodal predictors. Their uniform aggregate is exactly a Möbius-domain filter with a geometrically decreasing interaction-order response. This formulation retains higher-order representations while regulating their contribution to prediction. Controlled UR-FUNNY perturbations reveal a pronounced decline in label-aligned signal-to-noise ratio across interaction orders. The induced filter improves the final ratio by 1.62-2.79 times relative to the co-trained trimodal predictor while preserving its own clean class-aligned signal under perturbation. Five-seed experiments achieve 70.6% accuracy and weighted F1 on UR-FUNNY, exceeding thirteen reproduced methods on these detection metrics. CMU-MOSI and CMU-MOSEI evaluations establish applicability to sentiment prediction, and cross-seed analyses show more stable estimates on all three datasets. MoIF connects the organization of multimodal interactions to their statistical reliability through an executable spectral constraint. Code is available at this https URL.
[NLP-90] Brasilidade: Measuring Brazilian Identity for Large Language Models
【速读】： 该论文旨在解决当前大语言模型（LLM）在评估巴西葡萄牙语（Brazilian Portuguese）语言能力时存在的局限性，特别是缺乏针对该语言特有语法、语义细微差别及方言变体的系统性评估基准。其解决方案的关键在于提出名为Brasilidade的新基准评估方法，该方法由标准化任务构成，覆盖四个不同领域，首次公开了涵盖语言细微差别、语法结构及巴西葡萄牙语变体的第一领域任务。实验结果表明，模型规模本身并不能决定性能表现，多个中等规模模型在特定评测协议中超越了更大模型；尽管部分模型专为葡萄牙语训练，但整体表现并未显著优于平均水平。此外，不同任务层级揭示了显著的语言复杂性：部分任务对多数模型而言较为简单，而另一些则构成极端挑战，还存在性能波动较大的混合型任务，凸显出现有模型在处理巴西葡萄牙语时的不均衡能力。因此，该研究强调构建专门针对巴西葡萄牙语语言特性与复杂性的评估基准的重要性，以推动未来训练周期有针对性地改进模型在该语言上的表现。
链接: https://arxiv.org/abs/2611.12906
作者: José Roberto Homeli da Silva,Felipe Jorge do Nascimento,Lucas Coutinho Freitas,William Alberto Cruz Castañeda,Marcellus Amadeus
机构: SoberanIA(索贝拉尼亚); SoberanIA(索贝拉尼亚)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This work presents a new benchmark that aims to address the limitations of current methods for evaluating LLMs in Brazilian Portuguese. We introduced Brasilidade, an assessment methodology comprised of standardized tasks distributed across four different domains, and we are making available the tasks from its first domain, which address nuances of the language, grammar, and variations of Brazilian Portuguese. The results show that model size, by itself, does not determine performance; several medium-sized models outperformed larger models in the protocols evaluated. Specific models for Portuguese, in general, did not perform above average, despite their targeted training. Different task levels revealed significant nuances: while some tasks proved easy for most models, others posed extreme challenges, and some yielded mixed results, with wide variations in performance. This clearly illustrates the need for benchmarks that address the limitations of the models in Brazilian Portuguese, allowing future training cycles to seek to overcome them. Project repository this https URL
[NLP-91] Actionable Feedback: Improving Model Capabilities from User Edit Data on Clinical Documentation
【速读】： 该论文旨在解决在临床文档生成任务中，利用真实部署环境下医生对大语言模型（LLM）输出的编辑作为监督信号时所面临的“不可靠反馈”问题。具体而言，研究发现超过45%的编辑内容包含了未被原始输入支持的新增信息，若直接基于此类编辑进行微调，会加剧模型幻觉（hallucination）现象。其解决方案的关键在于：首先基于真实部署数据构建了一套医生编辑行为的分类体系（taxonomy of clinician-editing behaviors），进而基于该分类体系在MIMIC-III公开数据集上生成半合成的医生编辑数据；最后引入简单有效的过滤机制，通过专家评审验证，识别出具有可操作性的、与输入上下文一致的“有依据编辑”（grounded edits）。实证结果表明，相较于直接对原始编辑数据进行微调导致的幻觉增加，该渐进式过滤方法能在提升模型与临床医生偏好一致性的同时，有效保持生成内容对输入事实的忠实性（faithfulness）。
链接: https://arxiv.org/abs/2611.12904
作者: Daniel P. Jeong,Yewon Byun,John Giorgi,Peng Xu,Zachary C. Lipton,Michael Oberst
机构: Carnegie Mellon University (卡内基梅隆大学); Abridge AI; Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint
Abstract:Large language models (LLMs) are increasingly being deployed in clinical documentation workflows, where clinicians review and edit their outputs before use. In principle, these clinician edits represent a scalable source of supervision for model training. In practice, not all edits represent actionable feedback: for instance, on data drawn from real-world deployment of an AI clinical documentation product, our review suggests that over 45% of edited outputs contain user-added information that is not directly supported by the inputs to the model. Naively fine-tuning on such edits risks teaching the model to hallucinate. In this paper, we develop a method to address this challenge. Given that real clinician edits on deployed documentation tools are not publicly available, we first develop a taxonomy of clinician-editing behaviors, informed by data from a deployed AI clinical documentation product. Then, we develop a method for generating semi-synthetic clinician-edit data, informed by our taxonomy, on a discharge-summary generation task based on MIMIC-III, a public dataset. Finally, we introduce simple filtering methods for identifying actionable edits, with accuracy evaluated by expert review. We show empirically, on our semi-synthetic dataset, that naive fine-tuning on raw edited output increases ungrounded content, while our approach of progressively filtering toward grounded edits enables improvement on clinician-alignment metrics while retaining faithfulness to the input context.
[NLP-92] SELD: Detecting Silent Evidence Loss in Context Compression beyond the Answer
【速读】： 该论文旨在解决长输入语言模型中上下文压缩（context compression）导致的“隐性证据丢失”（silent evidence loss）问题。其核心挑战在于：现有方法依赖于比较压缩前后模型的答案或最可能输出的词，无法识别关键证据已被移除但答案保持不变的情况，从而在后续模型复用该压缩上下文时引入潜在风险。本文提出无需训练和标签的检测方法SELD（Silent Evidence Loss Detector），其关键创新在于利用读者模型在未选择答案之间的偏好变化来捕捉证据损失——即使最终答案不变，若压缩导致支持性证据缺失，模型对其他候选答案的相对偏好仍会发生显著改变。通过构建SELD-Pairs基准数据集，涵盖来自三个问答数据集的长度匹配的证据删除与控制删除样本，实验表明，SELD在多种压缩器、压缩率及读者模型上均显著优于现有基线，在答案不变的情况下成功识别83%的证据删除实例，远超最强基线的70%，证明了即使在答案未变的情况下，也能有效检测到上下文压缩中的隐性证据丢失。
链接: https://arxiv.org/abs/2611.12903
作者: Wentao Lei,Li Liu
机构: The Hong Kong University of Science and Technology (Guangzhou), China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Context compression is now routine for language models with long inputs, but a compressed context is useful only if it keeps the evidence for the answer. Existing checks compare the reader model’s answer or its most likely words, and thus miss a frequent failure, silent evidence loss: key evidence is removed while the answer stays the same, leaving the answer unsupported and risky for other models that reuse the context. To address this challenge, we propose SELD (Silent Evidence Loss Detector), which needs no training and no labels. Its principle is that when evidence is lost, a reader model’s preferences among the answers it leaves unsaid shift even if its answer does not, so comparing these preferences before and after compression reveals losses that answer checks cannot see. We also build SEL-Pairs, a benchmark of length-matched evidence and control deletions from three question-answering datasets. Quantitative and qualitative experiments with five reader models on it show that SELD consistently outperforms strong and recent baselines across compressors, compression rates and models, identifying the evidence-removing edit in 83% of matched pairs with unchanged answers, against 70% for the strongest baseline. These results show that evidence loss in compressed contexts can be detected even when the answer is unchanged.
[NLP-93] meOmni-o1: Bidirectionally Coupling Time Series Foundation Models and LLM s for Multimodal Reasoning and Forecasting
【速读】： 该论文旨在解决多模态时间序列数据中联合理解与预测的难题，即如何在同时包含时间序列数值与相关文本描述（如事件报告）的场景下，实现对序列的语义理解（如问答）与未来值预测。现有方法存在明显局限：大语言模型（LLM）虽能处理文本并生成自然语言输出，但对长序列时间数据建模能力差；时间序列基础模型（TSFM）虽具备强大的预训练预测能力，却无法读取或生成文本；而现有融合架构往往仅单向利用预训练能力，未能充分发挥两者协同优势。本文提出TimeOmni-o1，其核心创新在于构建一个双向耦合的统一框架，将预训练的TSFM与LLM深度集成：一方面，TSFM以压缩形式编码时间序列供LLM进行联合推理；另一方面，LLM生成的语义推理结果被反馈至TSFM，用于条件化生成更精准的预测。该架构实现了跨模态读写（支持时间序列与文本的输入/输出）、联合推理与预训练能力复用。为训练该模型，研究构建了MMTR——一个涵盖12个领域的多模态时间序列推理数据集，包含超过50万条样本及标注的推理轨迹。实验表明，TimeOmni-o1在多项任务上显著优于现有模型，不仅超越单独使用的LLM与TSFM，还在多个公开基准测试中展现出领先性能。
链接: https://arxiv.org/abs/2611.12902
作者: Ziyang Zhang,Shenyi Li,Wen Wu,Chao Zhang
机构: Tsinghua University (清华大学); Shanghai Artificial Intelligence Laboratory (上海人工智能实验室)
类目: Computation and Language (cs.CL)
备注: Code: this https URL Project page: this https URL
Abstract:Time series from real-world systems are often accompanied by text, such as a report on an event that affects them. Two common tasks on these series are understanding, e.g., answering questions about a series, and forecasting its future values. Both tasks require reasoning over the series and the text together. Moreover, understanding outputs text while forecasting outputs a time series, so serving both requires a model that reads and generates both modalities. Large language models (LLMs) read time series as digit tokens and handle long series poorly. Time series foundation models (TSFMs) forecast well from large-scale pretraining but typically neither read nor generate text. Time series language models add a time series modality to the LLM but forecast on the LLM side, leaving the TSFM’s pretrained forecasting ability unused. We present TimeOmni-o1, which couples a pretrained TSFM and an LLM bidirectionally. In one direction, the TSFM encodes the series in compressed form for the LLM, which reasons over it together with the text. In the other, the LLM’s reasoning is fed back into the TSFM, which produces the forecast conditioned on it. One model architecture thus reads and generates both series and text, reasons in text, and draws on the TSFM’s pretrained forecasting ability. To train it, we build MMTR, a multimodal time series reasoning dataset with annotated reasoning traces, covering both tasks across 12 domains with 500k+ samples. On MMTR, TimeOmni-o1 outperforms existing models on both understanding and forecasting across most domains, as well as the LLM and the TSFM inside it when used individually. The gains carry over to three open benchmarks, where it outperforms the dedicated time series models.
[NLP-94] Rethinking in Spikes: Mitigating Hallucinations in MDLMs with Step-Aware Decoding
【速读】： 该论文旨在解决多模态扩散语言模型（Multimodal Diffusion Language Models, MDLMs）在迭代去噪过程中出现的幻觉（hallucination）问题。研究表明，尽管整体去噪过程呈现熵值下降趋势，但部分去噪步骤中存在局部熵值突增，这些异常步骤与幻觉现象密切相关。为应对这一问题，论文提出一种名为StepRefine的即插即用解码策略，其核心在于基于熵值偏差检测异常步骤，并在此基础上实现“步感知”的离散去噪与潜在空间连续精炼之间的动态切换。具体而言，在熵值突增步骤中，模型采用概率加权嵌入以保留多样化的语义推理假设，进行多轮连续精炼；同时引入精炼控制器监控分布稳定性，避免冗余迭代并适时返回标准离散去噪流程。该方法通过保留掩码位置的分布信息，增强了候选语义与上下文间的持续交互，从而有效缓解幻觉，显著提升多模态基准上的生成可靠性。
链接: https://arxiv.org/abs/2611.12901
作者: Zhongxing Xu,Zhonghua Wang,Zhe Qian,Shiyan Su,Ming Hu,Xiaocheng Zou,Wei Feng,Mingquan Lin,Yuyin Zhou,Yifan Peng,Hamid Rezatofighi,Zongyuan Ge
机构: Monash University (莫纳什大学); Cornell University (康奈尔大学); UC Santa Cruz (加州大学圣克鲁斯分校); Northeastern University (东北大学); University of Minnesota (明尼苏达大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in multimodal diffusion language models (MDLMs) have exhibited strong global modeling capabilities through iterative refinement. We observe that mask uncertainty follows an overall downward trend during iterative denoising, while a few denoising steps exhibit localized entropy spikes closely associated with hallucinations. We argue that anomalous steps can be identified by measuring the entropy deviation of each denoising step from its neighboring temporal window. We hypothesize that discrete token commitment discards the distributional information at masked positions, limiting the continued interaction between candidate semantics and the context during entropy spikes. With this goal, we present StepRefine, a plug-and-play decoding strategy that leverages semantic context to achieve reliable denoising. The core of our method lies in a step-aware switch between discrete denoising and latent refinement. The model employs probability-weighted embeddings at entropy spike steps to preserve diverse reasoning hypotheses and performs multiple rounds of continuous refinement. Meanwhile, a refinement controller monitors distributional stability to reduce unnecessary loops and switch the model back to standard discrete denoising. Through extensive experiments, StepRefine demonstrates significant hallucination mitigation across different MDLMs on multimodal benchmarks.
[NLP-95] From Relevant Posts to Attributable Spans: Explicit Symptom Evidence for Depression Questionnaire Inference
【速读】： 该论文旨在解决基于纵向社交媒体数据推断抑郁症量表（如贝克抑郁量表，BDI）响应时，如何有效提取可归因于个体且语义上与特定症状相关的证据问题。现有方法依赖语义相似性检索帖子，易引入误导性或非个人化的信息，尤其在多症状共现或讨论他人经历的文本中表现不佳。其解决方案的关键在于提出SREE（Symptom Retrieval and Evidence-Grounded InfErence）框架：通过识别第一人称、症状特异性的文本片段，采用受设施选址（Facility Location）启发的目标选择代表性证据子集，并基于所选证据进行证据门控推理（Evidence-Gated Inference）。该方法显著提升了证据检索的精确率（相对提升70%）和F1值（相对提升59%），同时减少至少30%的冗余帖子；在多症状句子场景下，即使使用双倍检索预算仍优于密集检索模型。在eRisk 2019–2021三个数据集上的实验表明，SREE在项目级精确匹配率上达到最优，且在序数和聚合指标上保持竞争力，验证了局部化可归因证据并控制其组成对准确推断抑郁症量表响应的重要性。
链接: https://arxiv.org/abs/2611.12900
作者: Sumanth Manduru,Carlotta Domeniconi
机构: George Mason University (乔治梅森大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Longitudinal social-media histories offer an opportunity to assess depression, potentially supporting the inference of standardized depression questionnaire responses. However, this task requires evidence that is semantically relevant to a symptom and attributable to the user, while being sufficiently specific to support an individual questionnaire item. Existing approaches rely on semantic similarity to retrieve posts, which can introduce misleading or non-attributable evidence, particularly in multi-symptom posts and discussions of others’ experiences. We propose SREE (Symptom Retrieval and Evidence-Grounded InfErence), an evidence-grounded pipeline that extracts first-person and symptom-specific text spans, selects a representative subset using a Facility Location-inspired objective, and performs Evidence-Gated Inference over the selected evidence. We evaluate evidence retrieval on BDI-Sen and evidence-conditioned questionnaire inference on eRisk 2019-2021. Compared with state-of-the-art retrieval approaches, SREE achieves relative improvements of 70% in post-level precision and 59% in F1, while retaining at least 30% fewer posts. On multi-symptom sentences, it also outperforms dense retrieval in precision and F1 even with twice the retrieval budget. Across all three eRisk collections, SREE achieves the highest exact item-level agreement while remaining competitive on ordinal and aggregate metrics. These results highlight the importance of localizing attributable evidence and controlling its composition before inferring depression questionnaire responses.
[NLP-96] hermodynamics of words
【速读】： 该论文试图解决的问题是：如何从物理热化理论的角度解释文学作品中词汇频率分布的统计规律，特别是揭示罕见词大量出现且高频词集中于极低能量状态的现象。其解决方案的关键在于提出“词汇热化假说”（word thermalization hypothesis），将文本中的不同词汇类比为经典振子（或粒子），其词频（或相关标记数）对应于能量态上的占据因子。在此框架下，系统遵循总能量（即总词数）与总概率归一化（即不同词汇数量）守恒原则，从而模拟出类似于瑞利-金斯（Rayleigh-Jeans, RJ）热化与凝聚的统计行为。研究发现，实际文学文本中的有效系统能量极低，导致显著的RJ凝聚现象——即大量概率集中在最低能量态，对应于仅出现一次或两次的稀有词汇。该理论成功描述了16部文学作品（包括《圣经》、莎士比亚全集及美国国家语料库数据）中词频分布的幂律特征，并与洛伦兹曲线、帕累托曲线和齐普夫定律等经济与复杂系统中的经典模型高度吻合，揭示了语言系统与经济财富分布之间的深层相似性。
链接: https://arxiv.org/abs/2611.14039
作者: Klaus M. Frahm,Dima L. Shepelyansky
机构: Univ Toulouse (图卢兹大学); CNRS (法国国家科学研究中心); Laboratoire de Physique Théorique (理论物理实验室)
类目: atistical Mechanics (cond-mat.stat-mech); Computation and Language (cs.CL); Physics and Society (physics.soc-ph)
备注: 30 pages, 6 main figures, 14 appendix figures, journal style file and bbl file for references provided
Abstract:We introduce the word thermalization hypothesis according to which in a literary work there are effective interactions between words that drive them to thermalization where their word frequency distribution is the same as for the phenomenon of Rayleigh-Jeans (RJ) thermalization and condensation. In this concept, the different words play a role of classical oscillators (particles) and their word frequencies, or related tokens, are analogous to certain filling factors over energy state levels. The thermalization process is characterized by the conservation of total system energy, or total number of words/tokens, and total probability norm, or total number of different words. We apply this approach to analyze 16 literary works, including Bible, complete works of Shakespeare, and data from American National Corpus. We show that for these cases the effective system energy is very low and thus there is the RJ condensation phenomenon with a big fraction of total probability concentrated at the lowest energy states corresponding to a big fraction of rare words appearing only once or twice in the whole text. Such a RJ condensation is observed for light propagation in multimode optical fibers and other physical systems. We relate the thermalization of words to the recently proposed wealth thermalization for households in world countries. The economy concepts of Lorenz and Pareto curves, and also Zipf curves, are applied to the word frequency distributions of the selected 16 cases and it is shown that the thermalization theory gives a strikingly good description of real data.
[NLP-97] Walrus Technical Report
【速读】： 该论文旨在解决多任务、多语言、多说话人及混合语码（code-switching）场景下语音与音频理解与生成的统一建模难题，尤其针对现有模型在跨任务泛化能力、数据分布不均衡以及核心技能遗忘等方面存在的局限。其解决方案的关键在于提出一种以数据为中心的三阶段训练课程（data curriculum）策略，而非依赖新型网络架构；通过在标准解码器仅有的骨干网络（decoder-only backbone）上，基于Gemma 4初始化，并系统性地引入任务感知与数据集感知的加权机制，有效平衡不同任务间差异巨大的词元预算（token budget），同时防止在增量训练中对已有核心能力造成遗忘。该方法在超过6亿样本、涵盖100种语言和20余项任务的数据集上进行训练，使模型在多语言、多说话人及语码转换的自动语音识别（ASR）、语音翻译、情感识别、音频描述生成等任务上均取得领先性能，甚至在多数基准测试中超越了规模为其四倍的Qwen3-Omni模型。研究结果表明，高质量的数据工程是推动多任务语音与音频建模性能提升的核心驱动力。
链接: https://arxiv.org/abs/2611.13689
作者: Alkis Koudounas,Osamu Take,Hayato Futami,Quentin Jodelet,Emiru Tsunoo
机构: Speech Language AI Lab; Sony Group Corporation(索尼集团); Tokyo, Japan
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL)
备注: 30 pages, 3 figures, technical report
Abstract:We present Walrus, a unified 8B speech-and-audio foundation model handling speech recognition (including code-switching and multi-talker speech), speech-to-text and speech-to-speech translation, text-to-speech, emotion recognition, audio captioning, and general audio understanding. Our main contribution is not a new architecture but a data-centric training recipe; we use a standard decoder-only backbone initialized from Gemma 4, so that gains can be traced to the data. For speech generation, Walrus predicts low-bitrate discrete speech tokens, and a lightweight token-to-waveform decoder converts them into high-fidelity audio. The recipe is a three-stage data curriculum that moves from broad multilingual data to multi-talker, code-switching, and domain-specific data. At each stage, task-aware and dataset-aware weighting balances tasks with very different token budgets and protects core skills from forgetting. Trained on over 600M samples covering more than 20 tasks and 100 languages, Walrus leads on most multilingual, multi-talker, and code-switching ASR, speech translation, and audio understanding benchmarks, outperforming both open speech language models of comparable size and dedicated task-specific models. On most benchmarks, it even matches or surpasses the 4\times larger Qwen3-Omni’s performance. Results suggest that data engineering is a primary driver of quality in multi-task speech and audio modeling.
[NLP-98] Off-target posterior mass: A training-free diagnostic for fine-tuning spoken language diarization probes
【速读】： 该论文旨在解决语音语言分割（Spoken Language Diarization, SLD）模型在不同数据集上进行预训练语音模型微调时，难以预测其性能提升幅度的问题。现有研究表明，微调对SLD性能的增益具有显著的数据集依赖性，但缺乏有效的指标来提前评估微调是否带来收益。为此，论文提出了一种名为“非目标后验质量”（Off-target Posterior Mass, OTPM）的诊断指标，该指标基于语言识别的后验概率计算，用于量化模型在非目标语言上的置信度分布。OTPM与微调后的性能增益呈现正相关关系，在未见过的合成双语数据上相关系数达0.56，在真实世界语料库中为0.23，表明OTPM具备作为SLD微调有效性预测工具的潜力。其关键在于利用后验概率分布特性构建可解释、可量化的诊断指标，从而实现对微调收益的早期评估，提升模型优化效率。
链接: https://arxiv.org/abs/2611.13430
作者: Zhuoyan Tao,Aditya Kommineni,Shrikanth Narayanan
机构: University of Southern California(南加州大学); Signal and Image Processing Institute(信号与图像处理研究所); Ming Hsieh Department of Electrical and Computer Engineering(明赫工程学院电气与计算机工程系)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD)
备注: 10 pages, 2 figures, 3 tables. Submitted to JASA Express Letters
Abstract:Spoken Language Diarization (SLD) models provide frame-level language information of a speech recording. SLD modeling pipeline includes feature extraction from pre-trained speech models followed by clustering. Fine-tuning pre-trained speech models has shown performance gains for SLD with its magnitude being dataset dependent. Off-target Posterior Mass (OTPM), computed based on posterior language identification probabilities is proposed as a diagnostic metric to forecast performance gains from SLD fine-tuning. OTPM obtains positive correlation with probe gains, 0.56 on unseen synthetic bilingual data and 0.23 on real-world corpora. These results indicate promising potential of OTPM as a diagnostic metric for SLD.
[NLP-99] Orthographic depth at two scales with English also matched for lexicon size
【速读】： 该论文旨在解决读者在单一语言中阅读困难的问题，特别是探究拼写与发音映射的可预测性（predictability）对阅读能力的影响。其核心问题是：不同字母文字系统中，拼写到发音的映射不确定性（以条件熵衡量）如何影响阅读流畅性。解决方案的关键在于采用条件熵作为量化指标，从两个粒度层次（即音素对齐单元和音节）分析十二种字母正字法的拼写-发音映射不确定性。研究发现，英语在两种粒度下均表现出最高的不确定性，而芬兰语最低；葡萄牙语和丹麦语在对齐单元层面的不确定性高于英语，德语和意大利语则因粒度和转写方式不同而排序变化。此外，研究揭示了单位尺度扩大时不确定性趋势不一致——部分语言如西班牙语下降，德国语波动，芬兰语稳定，意大利语依赖转写方式，英语依赖词表，而挪威语和瑞典语则上升。尽管单词级总不确定性普遍下降，但该指标受发音边界设定和样本内记忆效应干扰，无法反映读者实际认知计算过程。通过严格匹配词典规模的20,000词样本验证，发现音节尺度的熵与初学者阅读准确率呈显著负相关（ρ = -0.74），表明音节层级的拼写-发音可预测性可能是影响阅读习得的重要因素。总体而言，该研究通过跨语言比较揭示了正字法结构中的可预测性差异，为理解阅读障碍的潜在成因提供了量化依据。
链接: https://arxiv.org/abs/2611.13400
作者: Adam Y. Shavit
机构: 未知
类目: Applications (stat.AP); Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC); Methodology (stat.ME)
备注: 38 pages, 5 figures. Spelling-to-sound entropy as a measure of orthographic depth for cross-language reading research; exploratory check against Grade 1 reading accuracy in Section 3.5. Code and data: this https URL
Abstract:A reader can struggle to read in only one of their languages, and one proposed reason is how predictably spelling maps to sound. We measure that predictability as conditional entropy, the pronunciation uncertainty left once the spelling is seen, at two grain sizes (unit sizes): the alignment unit, an aligned spelling-sound chunk, and the syllable, bounded in the pronunciation. We analyse word lists in twelve alphabetic orthographies and report five in full, with English pronunciations from a dictionary and the rest from the synthesizer eSpeak-ng, length marks removed. Among the five, English has the most uncertainty and Finnish the least at both grains. Across all twelve, Portuguese and Danish exceed English per alignment unit, and German and Italian order differently by grain and transcription. These orders hold when English is transcribed by eSpeak-ng with u allowed to spell /j/ plus a vowel. Uncertainty per unit has no shared direction as units grow: it falls in Spanish and, less securely, German, is level in Finnish, depends on transcription in Italian and on the word list in English, and rises in Norwegian and Swedish. Summed uncertainty per word falls in all twelve, but that score is handed the pronunciation’s boundaries and mixes context with in-sample memorisation, so it is no evidence of what a reader computes. An exact identity states when discarded spelling boundaries could reverse that fall. A uniform 20,000-word English sample holds 35% of the dictionary’s syllable spellings and has syllable entropy 0.098 bits lower, so lexicons must be matched in size. In an exploratory check over eleven languages, syllable-scale entropy tracked first-year reading accuracy (rho = -0.74, falling to -0.25 if English is given the lowest entropy instead), without separating the syllable scale from the alignment unit. These estimates describe sampled writing systems, not readers.
[NLP-100] Data augmentation using generative networks to identify dementia
【速读】： 该论文旨在解决医疗领域机器学习分类器训练中普遍存在的数据稀缺问题。由于伦理顾虑和数据隐私限制，医学类研究的样本量通常远低于非医疗领域的数据集规模。为应对这一挑战，论文提出利用生成式模型进行数据增强，以提升在稀疏数据场景下分类器的鲁棒性。其解决方案的关键在于采用变分自编码器（Variational Autoencoder, VAE）等生成模型，对基于语音与音频特征的交互数据进行合成，从而扩充训练样本。实验表明，该方法显著提升了深度神经网络（DNN）分类器的性能：在区分记忆门诊中四类典型患者群体的任务中，F-score由58%提升至约74%，实现了16%的性能改进。
链接: https://arxiv.org/abs/2004.05989
作者: Bahman Mirheidari,Yilin Pan,Daniel Blackburn,Ronan O’Malley,Traci Walker,Annalena Venneri,Markus Reuber,Heidi Christensen
机构: University of Oxford (牛津大学); University of Manchester (曼彻斯特大学); University of Edinburgh (爱丁堡大学); University of Glasgow (格拉斯哥大学)
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
备注:
Abstract:Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement
信息检索
[IR-0] FERN: Cross-Benchmark Table Retrieval for Text-to-SQL with Frozen Retrievers and Learned Ranking
链接: https://arxiv.org/abs/2611.14477
作者: Arjun Rao,Nishant Gopinath
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: 19 pages
Abstract:Text-to-SQL systems over large pooled catalogs must first retrieve the tables a question needs. We compare against the published table recall of RASL, which uses Claude 3.5 models to extract keywords and select tables, on pooled BIRD, Spider, and Fiben catalogs. FERN (Feature-Engineered Ranking with No generative LLM) instead fuses three frozen retrievers into 100 candidates, describes each with up to 120 features, and reorders them with LightGBM rankers fitted once on BIRD’s training questions. On Spider, where it was not fitted, its candidate pool alone reaches RASL’s published recall at fifteen tables (98.0), and the ranker raises the share of questions with every gold table in the top five (complete@5) from 83.9% to 95.6%. At five tables, FERN’s recall is within one point of RASL’s on Spider (96.7 vs 97.0) and on BIRD with its evidence hints (96.6 vs 97.5), and 8.2 points below on BIRD without them (89.3). On 121 real BEAVER questions, without BEAVER fitting, its recall at fifteen (82.1) falls between two BEAVER-trained retrievers (80.8, 84.5), but its complete@15 is below both (49.6 vs 54.5 and 62.0). On Fiben, it falls far short at fifteen tables (54.2 vs 69.2, different question sets), although its recall at fifty is numerically close to RASL’s question-only retrieval result (80.7 vs 79.4). On BIRD dev with evidence, non-retrieval features raise complete@5 by 7.7 points over retrieval signals alone. On BIRD dev, median latency is 208 ms per question on one A100, without hosted model calls.
[IR-1] Point-in-Time Encoders for Earnings Calls
链接: https://arxiv.org/abs/2611.14325
作者: Alessio Brini
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Statistical Finance (q-fin.ST)
备注: 10 pages, 3 figures, 7 tables. Accepted at the 3rd IEEE International Workshop on Large Language Models for Finance, IEEE BigData 2026. Code: this https URL
Abstract:Text encoders turn earnings calls into vectors for search, for language models that answer from retrieved passages, and for forecasting models. A live retriever can use an encoder trained once on all available text, as finance retrievers are, but a backtest needs an encoder that read no text written after the date of each vector. To address this look-ahead bias, from 412,248 calls we train vintages under four pretraining recipes, with yearly cutoffs from 2010 to 2024, and each vintage reads only text dated on or before its cutoff. We then fine-tune each vintage on the question-answer pairs of the calls themselves. The vintages we train on calls alone, with 33.8M parameters, retrieve answers on held-out calls better than the general retrievers we tested in every test quarter. Their vectors also raise by about half the rank correlation between next-day returns and forecasts built from past returns and firm size. When each vintage continues the previous one, a later vintage retrieves better on earlier quarters than the vintage available at the time, and a point-in-time language model that answers from the passages the later vintage retrieves inherits the gain. The 2024 vintage of our families also forecasts earnings surprises better, although the interval of this gain includes zero on calls that no vintage read. It forecasts returns no better, so point-in-time vintages matter most for retrieval and little for forecasts of returns.
[IR-2] StaRSE: Compact Russian Sentence Embeddings with a Sign-Coded Static Encoder SIGIR2026
链接: https://arxiv.org/abs/2611.14236
作者: Boris Malashenko,Ivan Jarsky,Egor Soldatov,Vladislav Luzhnov,Sviatoslav Stumpf,Vladislav Shmatkov,Viacheslav Shalamov,Andrey Stankevich,Valeria Efimova
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted to ReNeuIR @ SIGIR 2026. 16 pages
Abstract:Transformer sentence encoders provide strong retrieval quality, but their inference cost becomes a bottleneck when embeddings must be produced on CPUs or refreshed frequently. We introduce StaRSE, a compact Russian static sentence encoder that removes Transformer inference at deployment time: token directions are stored as sign bits, token norms are stored separately, and sentences are encoded by mean pooling over norm-scaled sign directions. StaRSE is initialized from ruBERT-base, fine-tuned on Russian adjacent-sentence and task-shaped pairs, and post-trained with a contrastive stage that directly optimizes the sign-projected token table. On RuMTEB, StaRSE reaches a task mean of 51.16 , 7.98 points above the strongest included static baseline. The packaged model occupies 11.3 MiB and has a mean CPU latency of 0.08 ms per sentence, compared with an estimated 55.7 MiB 16-bit parameter footprint and 1.43 ms mean CPU latency for the closest-quality contextual encoder. Ablations examine adjacent-sentence supervision, token norms, and direct sign-projected post-training.
[IR-3] SHARP: Semantic Hierarchical Agent ic Reasoning for Next POI Prediction with Dynamic Memory
链接: https://arxiv.org/abs/2611.14220
作者: Yuhang Zhan,Silin Zhou,Zhuo Chen,Haoxiang Lei,Yunren Feng,Lisi Chen,Shuo Shang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Next point-of-interest (POI) prediction supports personalized location services by anticipating users’ next destinations. Recent methods leverage large language models (LLMs) for semantic reasoning and graphs for collective mobility modeling, improving POI prediction. However, three challenges still remain. Bridging LLMs’ semantic reasoning and specific POI prediction remains challenging. User mobility also evolves over time, making it necessary to retain and update historical knowledge within limited LLM contexts. Meanwhile, global transition graphs smooth out time-specific category transition patterns. To address these challenges, we propose Semantic Hierarchical Agentic Reasoning for Next POI Prediction with Dynamic Memory (SHARP), an agentic hierarchical framework in which category-level reasoning guides specific POI prediction. A trajectory memory agent maintains a dynamic trajectory memory bank that preserves earlier mobility patterns and tracks behavioral changes with limited context growth. We construct multi-granularity time-aware category transition graphs with global and time-conditioned views to preserve temporal regularities obscured by global aggregation. A category predict agent combines these graphs with trajectory memory to predict likely next categories. SHARP learns semantic IDs (SIDs) through a residual-quantized variational autoencoder (RQ-VAE) to construct semantic POI representations, which are jointly optimized with a category-guided POI predictor end-to-end. On three real-world datasets, SHARP improves Acc@5 and Acc@10 by an average of 5.29 and 4.52 percentage points, respectively, over the strongest baseline.
[IR-4] From Search to Decision: How Post-Training Shapes Information-Seeking Capabilities in Large Language Models
链接: https://arxiv.org/abs/2611.14149
作者: Haohang Huang,Xuan Lu,Mingjin Kuai,Yuxuan Zhang,Wei Ji,Haiyang Zhang
类目: Information Retrieval (cs.IR)
备注:
Abstract:Information seeking in large language model (LLM) systems increasingly involves decisions about when to acquire information, where to search, and how to evaluate and use evidence. Recent post-training methods have improved several such behaviors, but their objectives and evaluations span different tasks and system settings. This paper organizes research around six interrelated information-seeking capabilities and examines the learning signals, optimization methods, update loci, and credit-assignment mechanisms used to develop them. We synthesize empirical progress in targeted behaviors, end-to-end performance, search control, and generalization. Existing studies report gains in retrieval routing, query refinement, multi-tool exploration, evidence-grounded reasoning, and resource-aware search, although the scope of behavioral evidence varies across capabilities. We identify open challenges in estimating prospective retrieval utility, coordinating information-seeking decisions, and adapting to unfamiliar retrieval environments, and discuss evaluation practices for interpreting future progress.
[IR-5] Ontology-Guided Knowledge Graph Construction and Robust Evidence Retrieval for Indian Knowledge Systems
链接: https://arxiv.org/abs/2611.14050
作者: Anand Kaniyaar Sriranga Prasad,Avinash Holenarasipur Nagendra,Mohan Raghavan
类目: Information Retrieval (cs.IR)
备注: 22 pages, 15 figures, 4 tables
Abstract:Retrieving evidence from Indian Knowledge Systems is complicated by transliteration variants, ambiguous names, and relations distributed across passages. We present an ontology-guided knowledge graph (KG) pipeline that links typed entities and relations extracted with Qwen3-32B to original textual evidence. Retrieval combines sense-aware grounding, bounded graph traversal, lexical anchors, relationship-vector search, and passage reranking. We evaluate KG alone, single-vector retrieval (SV), and KG unions with SV and Entity Notes on 320 questions from Dhara Bench, matching the per-question candidate count across internal systems under BGE and Qwen rerankers. Standalone KG has the highest Precision@5 under both rerankers and the highest MRR@20 in Counting, Composite, and Multi-hop among the evaluated internal configurations. With Qwen, KG achieves Multi-hop MRR@20 of 0.8722, compared with 0.7622 for the fully reranked KG + SV union. The KG + SV + Entity Notes union instead provides the highest overall Recall@20 (0.5134), compared with 0.4347 for KG. On the 15 Multi-hop questions, Qwen KG achieves complete-evidence MRR@20 of 0.4651 versus 0.3372 for SV, while both full unions complete ten questions within twenty results compared with nine for KG. Gemini 3.7 Flash, evaluated through manually reviewed source mappings, re mains stronger on overall MRR and nDCG, while internal systems have category-specific advantages. All comparisons are descriptive. A separate deployment timing study measures mean retrieval times of 20.374 s for KG and 0.646 s for SV. The findings support selecting KG or its unions according to the required balance of early relevance, evidence completeness, and retrieval cost.
[IR-6] From Show Programs to Linked Data
链接: https://arxiv.org/abs/2611.14037
作者: Clarisse Bardiot(APP),Bernard Jacquemin(ELICO, ENSSIB),Jacob Hart(UR2 UFRALC),George Bruseker,Antonios Lagarias(APP),Pierre-Carl Langlais,Brandon Farnsworth,Jeanne Fras
类目: Information Retrieval (cs.IR)
备注:
Abstract:Many heritage institutions hold extensive collections of show programs. Too numerous to be cataloged individually, they remain difficult to access and are still largely undigitized. This paper presents a workflow for transforming such documents into structured, interoperable data by combining vision-language models (VLMs), a custom extension of the Linked Art ontology for the performing arts, and different approaches for automatic semantic data annotation. Using the Festival d’Avignon programs (1947–present), preserved at the Bibliothèque nationale de France, as a case study, we demonstrate how VLMs achieve more than 98% text transcription accuracy from heterogeneous heritage document images and how constraining large language model generation through a knowledge graph significantly reduces hallucinations in semantic annotation. We argue that this workflow opens new possibilities for large-scale, interoperable performing arts historiography, including the creation of catalogues raisonnés for performing artists, a form of scholarship common in visual arts but absent in performing arts studies.
[IR-7] LUMIR: Decoupled Multimodal Representation Learning from Heterogeneous Data for Next-POI Recommendation RECSYS2026
链接: https://arxiv.org/abs/2611.14004
作者: Zehui Wang,Wolfram Höpken,Dietmar Jannach
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: 16 pages. Accepted at the DaQuaMRec Workshop, co-located with ACM RecSys 2026
Abstract:As multimodal tourism-related information collected from heterogeneous data sources has become increasingly available, learning effective representations has attracted attention for improving recommendation performance. Existing approaches often tightly couple multimodal fusion with the architecture and optimization objective of a specific downstream model, which limits the generality and reusability of the learned representations. To address these limitations, we propose LUMIR, a decoupled multimodal representation learning framework based on contrastive learning. Multimodal information collected from heterogeneous data sources is first semantically unified into a shared textual representation space using large language models (LLMs). An interaction-aware fusion mechanism is then designed to model cross-modal interactions, while sequential signals are incorporated as a structural constraint to guide representation learning. To evaluate the learned representations, experiments are conducted on a multimodal point-of-interest (POI) dataset under the next-POI recommendation setting across multiple sequential recommendation backbones. The results show that LUMIR consistently outperforms existing multimodal fusion baselines in both accuracy-oriented and beyond-accuracy metrics, demonstrating that decoupling representation learning from downstream optimization provides an effective paradigm for learning reusable multimodal representations. The code is publicly available at this https URL.
[IR-8] Data Quality Challenges in Multimodal Tourism Recommender Systems RECSYS2025
链接: https://arxiv.org/abs/2611.13972
作者: Zehui Wang,Wolfram Höpken,Dietmar Jannach
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: 4 pages. Presented at the DaQuaMRec Workshop, co-located with ACM RecSys 2025
Abstract:Modern recommender systems increasingly consider diverse data modalities to enhance model performance. However, the quality of the underlying multimodal data (e.g. data from social media platforms) has received limited attention so far. Focusing on tourism recommendation, we examine real-world challenges encountered when using datasets from Yelp and Instagram. Issues include noise from fake reviews, hallucinated content from LLMs, modality contradictions, and demographic bias. We outline future directions such as quality-aware fusion, structured and controlled LLM-based content generation to reduce hallucinations, and user group differentiation to improve the robustness and reliability of multimodal recommendation systems.
[IR-9] Controlled Knowledge Engineering for Taxonomy Induction with Tool-Grounded LLM Agents and Runtime-Adaptive Policies
链接: https://arxiv.org/abs/2611.13966
作者: Jonas Limniatis,Bennet Schaarmann,Georg Reuber,Robert Stahlbock
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Submitted to Knowledge-Based Systems. Code: this https URL
Abstract:Taxonomies are structured knowledge representations that support semantic search, recommendation, product catalog organization, and retrieval-augmented generation. Their automated construction from a fixed set of candidate entities requires more than plausible text generation: the resulting hierarchy must be complete, structurally valid, and semantically coherent. Large Language Models (LLMs) are promising for this task, but iterative free-form generation remains vulnerable to omitted or hallucinated entities, invalid hierarchical relations, and inconsistent organizational principles. We introduce TIGA (Taxonomy Induction with Tool-Grounded Agents), a knowledge-engineering architecture that formulates taxonomy induction as an iterative decision process over an explicit state and constrained action space. TIGA combines executable taxonomy-editing tools, an memory that records tool calls, rationales, and outcomes, and a runtime-adaptive organizational policy that the agent revises during inference. An optional global reviewer evaluates the constructed taxonomy against the candidate set and the active policy. We evaluate TIGA on four benchmark taxonomies using two LLM backbones and compare it with single-shot and iterative prompting-based baselines. TIGA achieves the strongest structural-quality results in most evaluated configurations, but does not dominate on every precision measure. The gains are accompanied by substantially higher token consumption, API-call volume, and inference time. Ablation results show consistent benefits from the policy and interaction-memory components, whereas the effect of global review depends on the dataset and the available revision budget. These findings characterize tool grounding and runtime policy adaptation as promising but costly mechanisms for improving the controlled construction of structured knowledge representations.
[IR-10] From Counterfactual Edits to Human Narratives: LLM -Driven Interpretations for Recommender System Explanations CIKM2026
链接: https://arxiv.org/abs/2611.13914
作者: Vojtech Cahlik,Amir Reza Mohammadi,Rodrigo Alves,Pavel Kordik,Eva Zangerle
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: Published at CIKM 2026 (Rome, Italy). Code: this https URL
Abstract:Recommender systems increasingly influence what users discover and consume online, yet the decision logic behind their outputs often remains opaque. Counterfactual explanation (CFX) methods provide an effective way to show which changes to a user’s interaction history would alter a recommendation. In recommendation settings, however, CFXs often require many edits, producing long, low-level lists that are hard for users to interpret. Moreover, most evaluations emphasize edit counts while largely overlooking whether the resulting CFXs are coherent and actionable for users. We argue that this gap between generating valid CFXs and communicating model behavior stems from the lack of user-centered abstraction and limited qualitative evaluation. To bridge this gap, we investigate large language models as mediators that summarize raw CFX interaction edit lists into concise natural language explanations describing higher-level preference patterns. We optimize these mediators for correctness, informativeness, and linguistic quality through preference optimization, and evaluate the resulting explanations with human-calibrated metrics derived from separate human-labeled datasets. On the MovieLens-1M dataset, across multiple counterfactual generators and language models, the resulting summaries achieve high overall linguistic quality, while the strongest semantic results are obtained with high-success CFX methods such as ACCENT and SPINRec. Direct Preference Optimization improves the semantic metrics in almost all evaluated settings.
[IR-11] Auditing LLM Pseudo-Label Distillation for Review Informativeness at E-Commerce Scale
链接: https://arxiv.org/abs/2611.13797
作者: M. Emin Dogan,Cansu Er,Aysenur Kulunk,M. Furkan Eseoglu,H. Bahadir Sahin
类目: Information Retrieval (cs.IR)
备注: 3 pages, IEEE Big Data 2026, Poster
Abstract:We study hard-label distillation from Gemini 3.1 Flash-Lite to compact encoders on 5.34 million Turkish marketplace reviews. A strict new-SKU temporal test measures teacher fidelity; a separate, teacher-label-blind audit by one company annotator measures editorial agreement. An H3 category prefix slightly improves teacher fidelity on the full new-SKU test. On the 16,000-review chronological audit subset, it instead reduces agreement with both Gemini and the annotator, with a clearer paired difference for CORN than for cross entropy. Thus the context effect depends on the evaluation population, and teacher fidelity alone does not establish editorial alignment. Under the measured offline configurations, a single-L4 student pipeline processes reviews 50.2 times faster than the teacher pipeline.
[IR-12] IFRS Compliance Verification: An Open Multi-LLM Benchmark and Content-Conditioned Ensemble Classifier
链接: https://arxiv.org/abs/2611.13595
作者: Aghoghomena Akasukpe,Li Yang,Xinyao Zhou,Chen Shen,Miguel Vargas Martin
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Verifying corporate financial disclosures against the International Financial Reporting Standards (IFRS) is a core auditing task in which experts cross-reference disclosures with hundreds of fine-grained regulatory paragraphs: a laborious, costly, and error-prone process that motivates growing interest in large language models (LLMs) as compliance assistants. Yet single-model accuracy remains insufficient for high-stakes use, and naive ensembling is vulnerable to systematic per-model biases that can drag aggregate accuracy below that of the strongest individual annotator. This gap is addressed with a multi-stage framework, supported by a new benchmark of 19,998 IFRS regulation-disclosure pairs comprising 11,161 unanimous multi-LLM labels and 8,837 human annotations on items where the LLMs disagreed. The framework couples a three-stage domain-adapted sentence embedding pipeline (BERT masked language modeling on SEC EDGAR filings, unsupervised TSDAE and SimCSE adaptation, and supervised contrastive alignment) with a learned ensemble classifier that fuses frozen embeddings and LLM probability features from GPT-4.1, Claude Sonnet 4, and Gemini 2.5 Pro via Tree-structured Parzen Estimator (TPE) optimized gradient-boosted trees. On the disagreement set, the ensemble achieves 81.7% weighted F1, exceeding the strongest single LLM by 10.4 points and majority voting by 14.6 points, and outperforms eleven unsupervised aggregation baselines; the retrieval pipeline attains Recall@15 of 45.3% on unseen regulations. The dataset, model checkpoints, annotation platform, and classifier implementation will be publicly released.
[IR-13] Exploiting Feature-invariant Scaling Laws for Efficient A/B Experimentation in Recommendation Systems
链接: https://arxiv.org/abs/2611.13539
作者: Zhiliang Chen,Sammy Nammari,Jiahao Su,Xiaoqiang Luo
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Researchers and engineers improve recommendation systems by performing A/B experiments with new model architectures, optimization methods, and data features. However, running full-scale experiments (e.g., full-model training with online data) for every feature change is prohibitively computationally intensive, while the result from a single small-scale experiment is often misleading. To address this challenge, we show that modern recommendation models exhibit feature-invariant scaling: the change of a model’s performance follows a predictable scaling-law even when different feature changes are applied to the baseline model. We introduce an efficient method that exploits scaling-laws and uses statistical inference to predict (with confidence intervals) the full-scale effect of an A/B experiment in recommendation systems. This enables practitioners to efficiently filter out neutral or regressive feature changes with only small-scale experimentation. We validate our method on three distinct recommendation model families—Wukong, HSTU, and LLM-based recommendation models—across more than 40 architectural, optimization, and data changes. Our method accurately distinguishes beneficial feature changes from neutral and regressive ones while using only a fraction of the compute required for full-scale evaluation. It also identifies several changes that improve HSTU’s state-of-the-art performance on MovieLens-20M. Our results demonstrate that scaling laws can be practically exploited to make compute-efficient decisions for improving industrial-scale recommendation systems.
[IR-14] A Technique to Model Product Information and User Reviews (MPIUR)
链接: https://arxiv.org/abs/2611.13361
作者: Emna Belguith,Fajrian Yunus,Talel Abdessalem
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Modern e-commerce platforms naturally provide three complementary modalities: textual item descriptions, item images, and user reviews. However, existing multimodal recommendation methods often omit reviews or do not integrate all three modalities within a unified framework. In this paper, we propose MPIUR, a multimodal graph-based recommendation model that encodes reviews in heterogeneous graphs, aligns image and text representations, and adapts collaborative–semantic alignment to item interaction density. Experiments on the Amazon Reviews 2023 Musical Instruments and Baby categories show that MPIUR achieves the best global nDCG@10 and Recall@10 among the evaluated methods on both datasets. It also obtains the best nDCG@10 and Recall@10 on sparse items, indicating that jointly exploiting reviews and multimodal item content is particularly useful when interaction histories are limited.
[IR-15] Activation-Guided Graph Retrieval for Cognitive Memory Reconstruction in Language Model Systems
链接: https://arxiv.org/abs/2611.13127
作者: Marvin Danig(Achiral Research)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Architecture and analysis preprint; no benchmark measurements. Companion research essay and revision notes: this https URL
Abstract:Retrieval-augmented language model systems generally select context by query-conditioned relevance. We argue that long-lived AI systems require a second operator: activation-conditioned memory availability. We specify an architecture in which request and goal cues seed an authorized memory graph, activation and fan attenuation allocate a bounded traversal frontier, retrieved entities recursively emit cues, and a typed evidence graph is reconstructed before generation. The specification yields five architectural consequences about candidate selection, traversal control, graph regularization, evidence reconstruction, and reinforcement. We derive implementation-level cost estimates and define ablations, failure conditions, and a reproducible evaluation protocol. The central claim is narrowed or falsified if a simpler graph reranker matches full traversal on causal and temporal recovery under equal budgets. This architecture-and-analysis preprint reports no benchmark measurements. If the claim holds, retrieval returns the evidence path that makes a memory available, not only a ranked list.
[IR-16] INTENT: Out-of-Sample Out-of-Time and Cold-Start Evaluation on User Event Sequences
链接: https://arxiv.org/abs/2611.13069
作者: Mattias Kvist,Erik Lidman Hillbom,Edoardo de Cal,Davis Siemens,Adam Fredriksson,Rickard Brüel-Gabrielsson
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
备注: 4 pages + references and appendix. Accepted at the MFMB workshop at ACM ICAIF '26 (non-archival). Code: this https URL
Abstract:Banks now train foundation models on ordered sequences of customer events such as transactions, app sessions, and authorizations. Deployed, such a model predicts forward in time, often for customers and merchants absent from training, yet each is evaluated on private data, so no two can be compared. Public benchmarks for such sequences rarely test a period the model has not seen; recommendation studies usually hold out each user’s last events instead. We present INTENT (INTEraction, Novelty, and Transfer evaluation for next-action prediction), a public benchmark that scores a model in seven cells defined by whether the user, the period, and the target item appeared in training. An audit of 59 public dataset families retains five datasets; the most common defect is the lack of a calendar timestamp that orders events. No date lies within the time span of all five datasets, so each is split at its own cutoff, chosen by one rule fixed before any model is evaluated. Summed over the datasets, every cell holds at least 111,769 users. In reference results the cell determines the best model: a transformer leads on seen items after the cutoff but scores zero on new items, a variant that embeds item metadata helps mainly on MicroLens, and repeating the user’s last item leads on new items in four of five datasets. This parameter-free control also shows that on three datasets most of the score lost after the cutoff reflects changed behavior rather than the model. The pipeline, the cutoff rule, and the per-cell user counts are publicly available.
[IR-17] Beyond the Document: Compiled Context and Governed Agents for Any Work Product
链接: https://arxiv.org/abs/2611.13055
作者: James Rhodes,George Kang
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 8 pages, 1 figure, 4-package held-out evaluation; 12,000 evaluated responses
Abstract:A document preserves the visible result of professional work but often discards the research, calculations, assumptions, evidence, and authorization boundaries behind it. We study a different unit of delivery: a Portable Agent Format (PAF) package that carries a work product together with compiled context, evidence, provenance, and rules for subsequent interaction. To test whether this context remains useful beyond the process that produced it, we evaluate four new packages, created after the hosted and offline readers were fixed: an investment proposal, fund research, financial-statement analysis, and an engineering incident review. Two packages were produced by specialized agents and two through a general-purpose assistant’s packaging skill. Across 400 frozen questions and 12,000 evaluated responses, a hosted governed reader succeeds on 96% of questions versus 86% with instructions plus raw-file retrieval (Holm-adjusted p 10^-7). On the 260 answerable questions per condition, success is 94% versus 79%; the gap varies by package. Source-material chat and instructions alone reach 48% and 74% overall. A separate 0.8-billion-parameter offline reader succeeds on 69% overall, but 54% on answerable questions. Its -1 response rate is 2.9%, combining materially wrong and unauthorized answers, versus 56% with the source and instructions alone. Ablations suggest advantages for the integrated selector and package-adapted writer, without isolating representation from training effects. The same run exposes a failure: offline retrieval resolves none of the 40 historical-record questions in one financial-data package. These findings support treating compiled knowledge as part of the delivered work product, while leaving independent standards interoperability and end-to-end governance conformance unproven.
[IR-18] DocFS: File Systems as Agent ic Reasoning Environments for Complex Multimodal Document Question Answering
链接: https://arxiv.org/abs/2611.12957
作者: Bingdian Zhang,Zixuan Li,Long Bai,Xiaolong Jin,Jiafeng Guo,Xueqi Cheng
类目: Information Retrieval (cs.IR)
备注:
Abstract:VLM-based agents solve complex tasks by iteratively interacting with environments, and their effectiveness depends heavily on how the environment exposes information and provides tools for acting on it. However, constructing an effective environment for complex multimodal document question answering remains challenging: documents usually span many pages, with multimodal contents distributed across hierarchical sections and linked by cross-modal associations, while existing methods organize documents into structured representations that address only the information side of the environment and lack tools aligned with the structure, limiting agents’ ability to fully exploit it. We observe that file systems naturally combine structure organization with aligned tools: directories and files encode document structure, while file operations provide rich tools to access file contents. Building on this insight, we propose DocFS, a file-system-based agentic framework that reorganizes raw documents as file systems, where directories mirror the section hierarchy and explicit links connect text with figures, tables, and equations. Based on well-established file system tools such as navigation, search, and reading, agents can iteratively explore the document structure, locate relevant evidence, and understand fine-grained local information. Paired with Qwen3.5-Plus, DocFS achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, outperforming the strongest baselines by 2.9 and 4.3 percentage points, respectively, and is up to 10.3x faster with up to 10.1x fewer tokens than the strongest baseline during reasoning.
[IR-19] IntentTwin: Deconfounding Candidate Support and the Oracle-to-Deployment Gap in Offline Web Behavior Prediction
链接: https://arxiv.org/abs/2611.12918
作者: Miaobo Hu,Shuhao Hu,Xiaobo Guo,Xin Wang,Bokun Wang,Daren Zha
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 26 pages, 10 figures
Abstract:Offline web-behavior benchmarks frequently insert the demonstrated action into a finite candidate set and then measure reranking. We show that this protocol can create a candidate-support confound: when the target is outside training support while all distractors are sampled from that support, support membership identifies the gold action without modeling behavior. We formalize this effect with support-identifiability rate (SIR), construct support-balanced candidate sets, and evaluate a target-free dual-path proposer that combines support retrieval with open-vocabulary DOM/LM proposals. On filtered OPeRA (4,282/469/992 train/dev/test steps), the original protocol has SIR 1.0000 on target-OOV rows and a support-only rule reaches 1.0000 OOV Exact@1. Support balancing reduces SIR to 0.0000; the strongest conditional reranker obtains 0.7214 overall Exact@1, 0.7988 on IV rows, and 0.5668 on OOV rows. The target-free dual-path system reaches Proposal Recall@16 0.6915 and end-to-end Exact@1 0.5212, compared with oracle conditional Exact@1 0.7450. On Mind2Web, the same audit yields SIR 0.2380 and an oracle-to-proposal gap of 0.2125. A 200-row semantic audit labels 0.7150 of exact-key OOV targets as semantically novel, 0.2450 as locator/string variation, and 0.0400 as ambiguous (Cohen’s \kappa=0.8173 ). These measurements show why support composition must be controlled alongside candidate accuracy when offline results are used to reason about deployment.
人机交互
[HC-0] Demonstration of MANA: Multi-perspective Alignment of NPC Agents
链接: https://arxiv.org/abs/2611.14431
作者: Amir Tahmasbi,Keshav Singh,Shaheer Raza,Owen Mei,Jason Wu
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:We present MANA (Multi-perspective Alignment of NPC Agents), an interactive approach for authoring NPC behavior through embodied demonstration. We show MANA in the context of Minecraft, where a developer aligns NPCs while playing the game itself. Activating MANA lets the developer dynamically switch into the point of view (POV) of any character and demonstrate, in-situ, how it would act given the surrounding context, through both movement and dialogue. MANA then distills each demonstration into a generalized policy that maps in-game context to actions. Finally, once the developer switches to another NPC or exits MANA mode, this generalized policy drives the NPC on its own. We demonstrate MANA by aligning two initially generic Minecraft NPCs: Fletcher and Farmer to act aggressively and cowardly, respectively. After roughly 2 minutes of in-game play, each character exhibits consistent and believable behaviors across new, unseen scenarios.
[HC-1] Evaluating Tactile Encoding Strategies for Communicating 3-D Shape
链接: https://arxiv.org/abs/2611.14389
作者: MacKenzie C. Harnett,Rebecca F. Friesen
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Two-dimensional haptic displays, such as programmable pin arrays and vibrotactile or friction-modulating touchscreens, enable tactile communication of complex shapes in a low-profile form factor. However, many shapes of interest, including CAD models, three-dimensional graphs, and map topographies, are inherently three-dimensional. This work investigates two methods for encoding 3-D information using a 2-D tactile display: (1) a single tactile height map providing coarse but quantifiable depth cues, and (2) a series of binary layers communicating sequential slices of a 3-D model, supporting more complex geometries such as overhangs and cavities. We further compare performance across two display types–a friction-modulating display and a pin array–to examine how the level of haptic abstraction affects users’ ability to decode discrete 3-D information. A user study demonstrates that participants were able to effectively complete a 3-D assembly task using both strategies, achieving high accuracy across all display types. While the pin array supported faster task completion and higher-rated–though not statistically significant–user experience than the friction-modulating display for the binary-layer task, these differences did not translate to significant differences in overall usability (i.e., reassembly accuracy success). Together, these results identify design trade-offs and inform development of future surface haptic displays that support the communication and interpretation of discrete 3-D information in tactile graphics.
[HC-2] AI Isnt Getting Dumber Were Getting Pickier: Rising Standards in Human Evaluation of Large Language Models
链接: https://arxiv.org/abs/2611.14333
作者: Zihan Chen,Lei Nico Zheng,Di Zhu
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 23 pages, 6 figures, 10 tables; working paper
Abstract:Users often complain that AI assistants such as ChatGPT are getting worse. Some versions do change, but human evaluation leaves a second possibility untested: the people judging may change. We separate the two on Chatbot Arena, a public platform where people compare answers from two anonymous large language models (LLMs) and pick the better one or say both are bad. We study 1,260,967 blind votes cast from May 2023 to August 2024 on pairs of models whose versions are fixed: dated vendor snapshots and models with fixed open weights. For the same pair of unchanged models, the share of votes saying “both are bad” rises by 1.71 percentage points a month on a mean of 17.1%, and by 1.91 points a month in 2024. In fourteen pairs observed often in both 2023 and 2024, it rose in every one, from 15.5% to 23.3%. The ranking does not trend: in 2024, the stronger model’s share of decisive votes is flat. The rise holds within the same voter and on the same pair, and across arrival cohorts from their first recorded vote. It holds with prompt controls, in every major language, for dated vendor snapshots, open-weight models and models with stable answer length, and between interface changes. The drift is shared across models: month effects estimated from other models’ pairs remove it from each model’s own pairs. Platform-wide, the average model would be expected to score 78% against the 2023 average, yet the share of “both are bad” votes ended near where it began. The pattern fits a standard that follows the category’s progress, so a product that stands still is judged as declining. Absolute verdicts on AI drifted while relative comparisons held steady, an evaluation drift that matters for LLM leaderboards and for the human feedback used to train models.
[HC-3] Ninety-Six Ways to Measure a Scribe: Frag mented Evaluation of Ambient Scribes in Healthcare
链接: https://arxiv.org/abs/2611.14284
作者: Amama Mahmood,Shiye Cao,Laura Fleig,Junan Jiang,Theodore Jensen,Yee-Yin Choong,Kristen Greene,Chien-Ming Huang
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Ambient artificial intelligence (AI) scribes have moved rapidly from experimental pilots to enterprise-wide deployment across health systems, promising reduced documentation burden and improved clinician experience. While research evaluating these tools has expanded quickly, there are no agreed-upon protocols or standardized metrics for assessing their performance, making it difficult to compare findings or cumulatively synthesize evidence across studies. Across 54 recent evaluation studies, we identify ten evaluation dimensions and 40 distinct metrics, operationalized in 96 different ways-35 of them (36%) used by only a single study. Assessment is dominated by documentation efficiency and time, measured in 44 of 54 studies. The other dimensions appear at broadly similar rates (16-24 studies each), and safety and clinical outcome least consistently of all. Ripple effects on the broader care system, such as impact on patients’ care outcomes, redistribution of work, changes in billing and coding intensity, and downstream use of notes by other clinicians are rarely assessed. Moreover, the metrics used rely heavily on self-report and are fragmented and inconsistently operationalized, which limits comparability and hinders synthesis across studies. As an effort to support more rigorous and cumulative evaluation, in this paper we review the study designs, data sources, and metrics commonly used to evaluate ambient AI scribes in healthcare settings, discuss the limitations of current evaluation practice, and argue for a more holistic approach grounded in a standardized, multi-dimensional set of metrics.
[HC-4] Lilo: A Multi-Agent Conversational Sandbox for Childrens Digital Communication
链接: https://arxiv.org/abs/2611.14260
作者: Ofri Elbaum,Amit Malik,Tomer Malkevich,Zohar Elyoseph,Adir Solomon,Rotem Dror,Oshrat Ayalon
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Children’s peer conflicts increasingly play out in group chats, where tone and timing are stripped away, and a hurtful message is easier to send than to say aloud. We present Lilo, a multi-agent conversational system that lets children practice such conversations, so they are better prepared when they encounter them in real digital communication. In Lilo, the child joins two AI-driven peers mid-argument, coached by a mentor character, also named Lilo, through a Stop-Think-Act traffic light. Through an exploratory study with 28 children (aged 9-11), we examine how children engage with Lilo’s simulated peer conflict and the mentor’s traffic-light interventions. The study included an intervention with Lilo, surveys before and after the intervention, and exit interviews. Participants reported significantly higher self-efficacy for coping with unpleasant messages after their interaction with Lilo. They felt safe experimenting, knowing nothing they wrote would be held against them.
[HC-5] Uneven Self-Reported AI Use: An Embedded Case Study Across Eleven Teams in a Software Organization
链接: https://arxiv.org/abs/2611.14154
作者: Daniele Veri’
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY)
备注: 11 pages, 1 figure, 4 tables
Abstract:This paper presents a descriptive, embedded single-case study examining self-reported AI use within a software organization. The study draws on 98 structured interview records collected across 11 teams, using ten standardized team-level instruments (n = 65) and one instrument with distinct response anchors (n = 33). Among respondents assessed using standardized instruments, 36 of 65 (55.4%) reported AI-use frequency scores of 4 or 5 on a five-point scale. Team-level proportions varied from 20.0% (1/5) to 71.4% (5/7). A record-level cross-tabulation revealed limited awareness or understanding of organizational AI policies across both frequency groups: 86.1% (31/36) of respondents scoring 4-5 and 96.6% (28/29) of those scoring 0-3 reported either no policy awareness or uncertainty about policy details. In the group assessed using the distinct instrument, 31 of 33 respondents (93.9%) scored 4 or 5. Although the pooled proportion reached 68.4% (67/98), differences in response anchors limit direct comparability, making the standardized-instrument results the primary analytical reference. Interviewer-recorded observations provide qualitative context on reported practices and constraints but do not support estimates of their prevalence or causal explanations for differences in AI-use frequency. The findings characterize AI-use patterns and policy awareness within the interviewed sample, without supporting inferences about organization-wide adoption, policy compliance, or the effectiveness of AI use. The study contributes an empirically grounded organizational case and an explicit account of the methodological boundaries of cross-team comparisons using heterogeneous interview instruments.
[HC-6] CUVE: Making Supervised Classification more Accessible through Visualization
链接: https://arxiv.org/abs/2611.14098
作者: Sanchitha Kuppusami,Xiyao Wang,Miguel Nacenta
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注:
Abstract:We present CUVE, an interface that enables people to build classifiers by generating visualizations that are interpretable by both humans and learning algorithms (the Visualization as Intermediate Representation approach). This UI enables users to create supervised classifiers and to visually investigate how to improve them and how they carry out their function. A preliminary study investigates the usability of our prototype and compares it with a traditional text-based programming interface.
[HC-7] BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences
链接: https://arxiv.org/abs/2611.14093
作者: Mohammad Mahdi Dehshibi,Tomás Martínez Cortés,Amar D’Adamo,Daniel de la Prida,Luis-Antonio Azpicueta-Ruíz,Laia Turmo Vidal,Marte Roel Lesur,Joaquín R. Díaz-Durán,Alexander Väljamäe,Angel Sánchez,Temitayo Olugbade,Nadia Bianchi-Berthouze,Fernando Díaz-de-María,Ana Tajadura-Jiménez
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: 18 Pages, 6 Figures, 7 Tables. Submitted to IEEE Transactions on Affective Computing
Abstract:Multimodal datasets that combine synchronised behavioural and physiological signals with subjective affective and body perception measures are still rare. This limits both the understanding of embodied experience and the development of computational models that infer subjective states from sensor data. We introduce BODYinTRANSIT-Lab, a multimodal dataset that addresses this gap by capturing affective, perceptual, and behavioural responses to the Footsteps Illusion. In this closed-loop paradigm, participants perform walking tasks while receiving real-time manipulated auditory feedback of their own footsteps intended to alter perceived body weight. The dataset comprises synchronised recordings from 101 participants recruited through systematic stratification by eating-disorder symptomatology and physical activity level. BODYinTRANSIT-Lab combines full-body kinematics with electromyography, electrocardiography, respiration, and electrodermal activity. Its annotations capture both immediate and post-trial experience, including within-trial perceived-weight ratings, post-trial assessments of body feelings and affective experience, 3D avatar body visualisations, and quantitative body maps. Psychological, behavioural, demographic, and social measures provide further information about factors that may influence individual responses. We provide a deep learning benchmark that uses walking kinematics to classify relative affective valence and perceived weight within subjects. On within-session, segment-level splits, the baseline model achieved mean balanced accuracies of 45.9% for affective valence and 44.5% for perceived weight. The BODYinTRANSIT-Lab dataset provides an experimentally controlled resource for cognitive neuroscience and affective computing research, with potential applications in affect-aware interactive systems, health and well-being technologies, and creative embodied experiences.
[HC-8] From Bias to Positionality: A critical grammar for Large Language Models
链接: https://arxiv.org/abs/2611.14079
作者: Dave Murray-Rust,Elisa Giaccardi,Federico Bomba,Maria Luce Lupetti
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Bias has become a default lens for understanding what machine learning systems do and how we should relate to them. Bias is at root a deviation from a standard; in decision making systems we can supply a standard from outside: a law, a protected attribute, a benchmark, a statistical property. However, even there, biased is often used as a shorthand, without stating what the position taken is - a silent ``relative to’‘. In a general use of language models, nothing supplies the standard: there is no decision to audit and no counterfactual output, so the shorthand fails. We argue that positionality – the located, articulated, owned and contestable position from which anything is said – is the better grammar for the question these models now force: who is speaking, from where? Against the vendors’ answer of nowhere and the seductive possibility of everywhere, we argue that when a model is taken as-if speaking, it speaks from somewhere, and this means positionality can describe models, and the way they are believed beyond their warrant, even though they are not accountable speakers. A model’s positionality is laminar – laid down across corpus, tuning, system prompt, application and the live conversation. We separate three sets of factors: the positionality of a model’s creators, the behaviour they intend for it, and the behaviour it enacts, and situate current work within this matrix. This lets us highlight what each stratum inherits from the one below, with ruptures and collapses where that inheritance is disavowed. We then return to where models speak from, and discuss the implications for how models are made, read and held accountable.
[HC-9] Where do we Jam? - The Impact of Real and Virtual XR Spaces on Musical Creativity Co-Presence Affect and Flow
链接: https://arxiv.org/abs/2611.14029
作者: Ruben Schlagowski,Jonas Bordewick,Luca Turchet,Hannes Ritschel,Elisabeth André
类目: Human-Computer Interaction (cs.HC)
备注: Accepted at IEEE International Symposium on the Internet of Sound (IS2) 2026, 3rd International Workshop on the Musical Metaverse (IWMM)
Abstract:Extended Reality (XR) technologies can place musicians in virtual, mixed, or real environments during networked music performance (NMP). To examine how NMP applications in XR should be positioned on the reality-virtuality continuum, we conducted a laboratory study (N = 28) comparing a simulated NMP experience in Virtual Reality (VR) with one in Augmented Reality (AR). In both conditions, musicians saw their remote partners in real time as 3D point clouds rendered with an optimized Gaussian Splatting shader. While VR allowed participants to select and customize fully virtual 3D environments, AR showed a pass-through view of the laboratory environment. We measured creativity support, flow, involvement, co-presence, and affect, and complemented these data with qualitative interviews on participants’ preferences and requirements for creative environments. Our results show that participants felt significantly more involved by the immersive XR experience in VR than in AR. Positive affect, flow, and creativity support were strongly correlated in both conditions, whereas involvement correlated with flow and creativity support only in VR. Qualitative findings showed substantial variation in participants’ preferences for AR or VR and also regarding the design of creative virtual environments. Together, these findings suggest that the surrounding environment is a critical design dimension in networked Musical XR, shaping collaborative musical experiences beyond merely representing remote collaborators and mitigating communication latency. Future XR-mediated NMP systems should therefore treat the surrounding space as an integral component of the collaborative musical experience, rather than as a mere visual backdrop. At the same time, they should support experiences across a broad spectrum of the reality-virtuality continuum to accommodate highly individual user requirements.
[HC-10] AVCC: Audio-Visual Conversation Corpus for Turn-Taking Prediction with a Human Benchmark
链接: https://arxiv.org/abs/2611.13957
作者: Haotian Qi,Gabriel Skantze
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Sound (cs.SD)
备注:
Abstract: Predicting who will speak next, and when, is a prerequisite for conversational agents and social robots that take turns without waiting for silence. Multiparty next-speaker prediction (NSP) has been studied on corpora with per-speaker sensors, each with its own event definitions. No public benchmark covers the single camera and microphone a robot has, and no model has been compared against how well people perform the same prediction. We turn AVCC, a 31-hour audio-visual corpus of two- and three-speaker online conversation recorded with a single static camera and microphone, into a public NSP benchmark. We add a speaker-disjoint seven-video test partition with 865 hold and 865 shift events at mutual-silence gaps, word-level forced-aligned transcripts for the whole corpus, and a human reference benchmark collected at the same events. We score an audio-only Role-Relative VAP model (RoleVAP) and the multimodal MuVAP model. Labels, transcripts, per-response human data, and the scripts that reproduce every number are released.
[HC-11] arget Selection with a Disappearing-Stimulus Mixed-Reality P300 Interface: Feasibility and Object-Distinguishable Versus Uniform Cues
链接: https://arxiv.org/abs/2611.13906
作者: Runqi Pan,Takuto Niinuma,Yunshan Huang,Masato Sugino,Kenta Shimba,Kiyoshi Kotani
类目: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC); Signal Processing (eess.SP)
备注: 5 pages, 2 figures, 1 table
Abstract:Mixed-reality (MR) P300 interfaces can register selectable destinations, such as wheelchair goals, directly in the user’s surroundings. When the cues disappear between flashes, however, the user must retain an intended target that is no longer visible. We tested whether such an interface supports target selection and whether giving each cue a stable colour-shape identity improves it compared with visually uniform spheres. Six healthy adults completed four trials in each cue condition of a 5x5 row/column task on a head-mounted MR display, with EEG from Fz, Cz, and Pz. Shrinkage linear discriminant analysis was evaluated with nested validation in which complete trials were held out from class balancing, hyperparameter selection, and fitting. Both conditions evoked a parietal target-related positivity. The decoded cell was correct in 18 of 48 held-out trials, against 1.9 expected by chance (binomial p 1e-12), and in three participants individually. Cue format made no detectable difference: complete-target accuracy was 9/24 in both conditions (paired difference 0.0 percentage points, 95% CI [-37, 37]), and held-out AUC was 0.609 for spheres and 0.585 for object cues. Disappearing-stimulus MR P300 selection is therefore feasible under strict trial-level evaluation, but the benefit of object identity, if any, is too small to resolve in a pilot of this size.
[HC-12] Seeing Without Seeing: A Methodological Review of Visual Mental Imagery Reconstruction from Brain Activity
链接: https://arxiv.org/abs/2611.13691
作者: Shiyi Zhang,Dong Liang,Yihang Zhou
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Reconstructing visual mental imagery from brain activity differs from reconstructing perceived images. Imagery is generated top-down, its signal is weak, and the imagined content is never observed, so decoders are trained and scored against a proxy such as a cue or a seen exemplar. This criterion-guided methodological review of mainly non-invasive studies (fMRI, EEG, MEG) is narrative rather than systematic. We organise the field into a two-stage pipeline, two design axes and four method families, and formalise target-free evaluation, in which the proxy gap is a systematic bias rather than noise. A two-dimensional admissibility criterion crosses trial-level separability from working-memory maintenance with the memory source of the imagined content. Among the studies reviewed, every image-scored reconstruction concerns a recalled exemplar, none builds a scorable target for concept-generated content, and all come from fMRI, consistent with missing image-level targets in electrophysiological data. Published values for five decoders, compared under a common human forced-choice framing, show seen-to-imagined drops of 5.9 to 26.1 percentage points and a change in method ordering. We treat this as a diagnostic signal, not a controlled benchmark result, and specify a within-subject test for an altered neural code. Beyond this synthesis, we make two proposals. First, because frozen generative priors can produce photorealistic images weakly tied to neural signals, we combine existing null-signal controls into a normalised neural dependence with a within-category null, an unvalidated diagnostic quantity. Second, we propose a candidate ten-item reporting checklist with illustrative pass criteria. In an exploratory application to ten studies, none reports all items in a fully checkable form; this describes reporting practice, not study quality.
[HC-13] A computational framework for understanding embodied plans
链接: https://arxiv.org/abs/2611.13669
作者: Aalap D. Shah,Basil Saeed,Joshua B. Tenenbaum,Tobias Gerstenberg,Hyowon Gweon,Ilker Yildirim
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注:
Abstract:Reasoning about people’s plans and goals based on their actions is fundamental to human social interaction and essential for cooperative, embodied artificial intelligence. Formalizing this capacity in naturalistic, embodied settings has been a longstanding challenge. Here, we present GenEA, a novel agent representation capable of reasoning over multi-step, long-horizon embodied plans. At the heart of GenEA lies a multilevel state-space that couples the coarse biomechanics of an articulated body and its interactions with nearby objects by extending the task-and-motion planning framework from robotics with simulation-based intuitive physics. By embedding this state-space in a generative model, GenEA yields a joint probability distribution over goals and embodied plans. Across several experiments spanning the qualitatively distinct domains of object manipulation and navigation, featuring diverse tasks and multiple behavioral measures, we show that GenEA, through simple transformations of this joint distribution, recapitulates how humans reason about embodied plans. Crucially, GenEA consistently outperforms strong alternative models, including plausible heuristics, standard task-and-motion planning, and a state-of-the-art vision-language model. GenEA thereby offers a new perspective on the architecture of human reasoning and social cognition, suggesting that embodied artificial intelligence systems seeking to seamlessly interact with humans should adopt GenEA-like generative models of action planning.
[HC-14] How Well Do Benchmarks Evaluate LLM s Use for Personal Health Informatics?
链接: https://arxiv.org/abs/2611.13652
作者: Fatemeh Mirhosseini,Yasaman S. Sefidgar
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Benchmarking is a valuable technique for advancing the capabilities of Large Language Models (LLMs), including in personal health informatics. There are growing efforts on benchmark creation for personal health data activities. However, it is unclear whether and how well these efforts align with needs and considerations of personal health informatics process. Addressing this need, we first analyzed 53,587 tasks across eight benchmarks related to personal health informatics and characterized the space of benchmark design. We then contrasted our observations of design choices with personal informatics theories and empirical knowledge. We find significant mismatches between existing benchmark design and domain guidelines and understanding. This lack of alignment limits the utility of the evidence current benchmarks offer about LLM capabilities. We use domain knowledge to highlight capabilities benchmarks should include and discuss opportunities our work enables for taking a human-centered and domain-driven approach to benchmark design.
[HC-15] EyeQ: Exploring Corneal Reflection for Mobile Context Sensing
链接: https://arxiv.org/abs/2611.13640
作者: Jaewook Lee,Judith Amores,Donghoon Shin,Bo-Eun Choi,Jason Kim,Davin Win Kyi,Seok-Young Kim,Jon E. Froehlich,Andrew D Wilson
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:What if a single face-facing camera could sense your surroundings—through your eyes? We introduce EyeQ, which explores corneal reflection as a modality for context-aware mobile interaction. Traditional vision-based context sensing relies on world-facing cameras that demand explicit aiming or dual-camera setups; EyeQ instead shows that an off-the-shelf VLM can read scene context from corneal reflections captured with a smartphone camera. To recover usable detail from mobile corneal crops, EyeQ pairs on-device iris-guided capture with server-side AI enhancement, then leverages VLMs to extract scene types, names, and objects from warped, unfamiliar eye-image sequences. In an N=10 study, we characterize how capture conditions shape reflection fidelity, evaluate what VLMs detect, misclassify, and hallucinate, and demonstrate interaction opportunities, including detecting a steering wheel to distinguish drivers from passengers. This work reframes the eye in HCI—from a target for gaze estimation to a passive mirror of one’s surroundings—and examines the privacy tensions it raises.
[HC-16] Navigating relational agency in AI feedback musicianship: a diffractive dialogue
链接: https://arxiv.org/abs/2611.13555
作者: Yichen Wang,Charles Patrick Martin,David Gunawan,David Cooper
类目: ound (cs.SD); Human-Computer Interaction (cs.HC)
备注:
Abstract:Musical AI systems behave in non-linear, dynamic ways that exceed the tool-oriented framings inherited from earlier instrument design. Existing frames of control and ensemble interaction do not articulate the entangled, relational agency that emerges when performers work with AI feedback instruments over time. We examine a duet practice with two AI-enabled feedback instruments, Sù and Agentier, in which feedback operates at the inner level of the AI’s recurrent processes and the outer loop of musical interaction between performer, instrument, and co-performer. Our work draws on agential realism and a diffractive dialogue methodology to read our independent reflections through one another across five performances, examining how agency is enacted between performers and AI instruments. We find that control and agency are relational and shift across performances; musical textures enact feedback moments of control with performers; reaching a productive feedback environment is itself a shared exploratory act; and performers maintain their instruments through funnelling and injection, developing the skill of navigating agency along a continuous spectrum. Feedback musicianship with AI instruments demands navigation of this relational agency, displacing the leading-versus-responding binary that initially framed our reflections. This work contributes a materially-grounded vocabulary for AI feedback musicianship and a model of human-AI creative collaboration in which mutual agency is worked for through practice rather than designed in.
[HC-17] Perspicacious: An Educational Intelligence Platform for Instructional Designers
链接: https://arxiv.org/abs/2611.13549
作者: Yumou Wei,Michael C Sutton,Paulo F. Carvalho
类目: Computers and Society (cs.CY); Emerging Technologies (cs.ET); Human-Computer Interaction (cs.HC)
备注:
Abstract:Designing effective instruction requires instructional designers to create assessment questions aligned with clearly defined knowledge components (KCs). These KCs represent specific knowledge or skills that students must acquire. This process is often time-consuming and relies heavily on expert labor. Although AI for education research has produced a growing body of automated question-generation and KC-discovery methods, few have moved beyond research prototypes into user-friendly tools that instructional designers can readily use. To bridge the research-to-practice gap, we developed and evaluated an educational intelligence platform that unifies question generation and KC discovery behind a single interface to support instructional content development. Named “Perspicacious,” our platform aims to help instructional designers “see through” complex data and algorithms to derive instructional insights. A cost model calibrated on 17 production runs revealed a cost-time trade-off: extracting KCs from a 576-question bank cost US 1.98-4.11 and took 36-158 minutes to complete, depending on the budget. A formative, partially Wizard-of-Oz study with eight instructional designers found strong perceived usability (mean SUS 78.8) and surfaced concrete requirements that we treat as prerequisites for deployment.
[HC-18] Repertoires of Contention: Building computational tools and infrastructures for community resistance to bitcoin mining
链接: https://arxiv.org/abs/2611.13527
作者: Marina Johnson-Zafiris,Owen Marshall,Jen Liu,Joseph Ferdinando,Steven J. Jackson,Chris Csíkszentmihályi
类目: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: As submitted to ACM CHI 2025
Abstract:Recent work in human-computer interaction and allied fields has sought to engage community members as co-designers, facilitators, and decision makers to challenge structural power barriers and knowledge gaps. In this paper, we apply a diverse set of computational techniques to engage and address community concerns surrounding the socio-environmental impacts of the Greenidge Bitcoin mining power plant facility in upstate New York. Through collaborative interactions with community groups, addressing problems ranging from regulatory inertia to thermal water pollution, we aim to facilitate not just the production of new and critical environmental knowledge but also new computationally-supported repertoires of contention: means of contesting the unequal relations of knowledge and power that structure and often determine the outcomes of environmental controversies. We argue such engagements can help support more politically engaged forms of HCI, oriented towards efforts to redress and repair disparities in wealth, knowledge, and influence through strategic sociotechnical interventions and alliances.
[HC-19] EJAI: Judgment-Preserving Civic AI for Data Center Environmental Justice
链接: https://arxiv.org/abs/2611.13525
作者: Evan Pačes-Wiles,Bryen Alvarez,Sara Gulabani,Akshara Kolipaka,Snehalkumar ‘Neil’ S. Gaikwad
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Communities in the United States and around the world are contesting data centers, the physical infrastructure of AI. These disputes pose environmental justice issues: residents must weigh unevenly distributed burdens using evidence scattered across metrics, corporate claims, policy documents, and public testimony. A formative study with 10 users showed that dashboards and single-agent AI can settle questions before people examine that evidence. Here we present EJAI, a source-grounded system that links an interactive map, retrieved evidence, missing-data statements, and two stakeholder personas that allow people to inspect evidence and form their own policy recommendations. In a small functional benchmark of 21 queries, retrieved evidence supported 98.4% of factual claims and the system acknowledged missing evidence in 32 of 33 cases. Persona alignment carried a grounding cost: the Corporate Executive omitted some evidence that conflicted with its assigned priorities. In a within-subjects study, 15 participants acting as residents prepared for a simulated town hall. Some checked cited AI claims while treating map metrics as official, some made conditional recommendations with safeguards such as water-use disclosure, and some found the personas simplified or biased. Our results suggest that civic AI can support preparation for public reasoning when it makes evidence, gaps, and disagreement open to inspection, and that corpus imbalance and two-sided personas remain open justice concerns.
[HC-20] Leverag ing Psychophysical Attentional Distribution for Gaze-Augmented Reward Modeling NEURIPS2026
链接: https://arxiv.org/abs/2611.13477
作者: Chaeho Lee,Junyup Kim,Wansoo Kim,Young Min Jung,Sang Ho Lee
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 15 pages, 4 figures. Accepted to the 40th Annual Conference on Neural Information Processing Systems (NeurIPS 2026 Main Track)
Abstract:Integrating human feedback into language models has gained increasing attention as a means to align model outputs with human preferences, with Reinforcement Learning from Human Feedback (RLHF) serving as a prominent framework for preference-based alignment. Recent studies have incorporated eye-tracking (ET) data as an additional supervisory signal for reward model training, grounding reward learning in human reading behavior. Building on this approach, we extend prior fixation-centric models by leveraging established findings from cognitive psychology on human letter recognition. Specifically, we model visual attention as a spatially graded distribution centered on the current gaze location and incorporate this representation into reward model training. Our results show that the reward model trained with the proposed gaze distribution achieves higher preference prediction accuracy than a baseline model. Moreover, some results from our experiments are qualitatively consistent with established findings on human attention during reading, suggesting that they potentially capture cognitively meaningful aspects of human attention allocation.
[HC-21] When Reasoning looks like Hidden Understanding: How Chain-of-Thought Fosters Anthropomorphism Perceptions of Epistemic Superiority and Cognitive Deference
链接: https://arxiv.org/abs/2611.13456
作者: Oliver Lack,Lana Tikhomirov,Humphrey Thompson,Carolyn Semmler,Melissa McCradden
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Conference submission to FAccT, 4th November 2026. No Figures
Abstract:As AI-based products permeate society, specifically systems using generative models, a belief system is emerging about what these products are and what they can and cannot do. One common belief concerns the perception of the form of language as evidence of ‘understanding’ language. As these generative models reproduce linguistic patterns rather than understand meaning, interpreting these patterns as evidence of understanding is a misattribution. This misconception is extended by the notion that generative models can ‘explain their reasoning.’ Anecdotally, these misconceptions which mistake the form of language for its meaning are driving cognitive deference to AI systems in a way that displaces human agency. Such mistakes involve attributions of cognitive properties to the system, allowing them to be treated like intentional ‘minds’. Importantly, perceived epistemic authority and superiority make that ‘mind’ worth deferring to. Anthropomorphism in tandem with epistemic authority and superiority, we argue, increases the risk that users generalise from narrow task performance to broader competencies, committing the “fallacy of composition”. This paper explores misconceptions of Chain of Thought (CoT) reasoning as sufficient transparent ‘explanation’. We explore how anthropomorphism acts as a vehicle for cognitive deference of decision-making and perceptions of epistemic superiority in AI systems. Finally, we explore CoT’s ramifications using medicine as an example. Taken together, we conclude that explainability operationalised through CoT is highly problematic and undercuts the very ethical principles it is intended to serve. While CoT may be valuable for improving output accuracy and moderation, we suggest that the practice of asking models to provide user facing reasons for their outputs should be actively discouraged, and AI literacy training should explicitly caution against its use.
[HC-22] owards Accessible Autonomous Photosynthesis Monitoring on Living Leaves
链接: https://arxiv.org/abs/2611.13387
作者: Qitong Li,Xiaoyu Zheng,Xuanang Chen,Julia Wang,Raj Nileshbhai Dave,Dayanara Chavez,Nirvik Prakash Iyer,Nivedita Arora
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Photosynthesis is a direct biomarker of plant health and functioning. However, measuring net photosynthesis on individual leaves requires expensive (USD 42,000 - USD 89,000) instruments and manual effort. This limits access to photosynthesis sensing and makes long-term monitoring across multiple leaves difficult. We present a 1000x low-cost, autonomous sensing system that enables longitudinal monitoring of photosynthesis on living leaves over multiple days. Guided by a formative user study with 13 plant biology researchers, the system integrates an automated, leaf-attached chamber with a low-cost CO2 sensor. Built using a 3D-printed clamp and thermoformed dome, the chamber periodically closes to measure CO2 drawdown and reopens to refresh the air. Each chamber costs about USD 62 and operates independently, allowing multiple leaves to be monitored simultaneously without pumps or gas tubing. We characterize each chamber and validate its measurements against a research-grade gas exchange instrument (CIRAS-4) across 29 sessions (R^2 = 0.90). We also demonstrate monitoring of C3 and CAM photosynthesis plants, adaptability to different leaf geometries, and a light-response experiment. In a nine-day deployment, six chambers captured simultaneous CO2 exchange from individual leaves of the same plant during drought and rewatering. Each leaf showed different patterns of nighttime CO2 uptake and recovery, which a whole-plant CO2 measurement instrument would obscure. To our knowledge, this is the first demonstration of such leaf-level photosynthesis dynamics using independent, low-cost autonomous chambers. Our work opens new opportunities for plant science, education, and data-driven agriculture.
[HC-23] Designing Accessible and Rigorous ML Experiments: No-Code Visual Workflows and Structured Guidance for Beginners
链接: https://arxiv.org/abs/2611.13386
作者: Henrik H.J. Detjen,David Pfrang,Max Westphal,Melissa Finster,Dörte Corr,Markus Wenzel
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:Designing machine learning (ML) experiments requires beginners to plan a methodologically sound workflow and implement it in a concrete environment. We evaluated two forms of support in two within-participant studies with 18 predominantly technically trained participants: mlguide, which provides structured, evidence- backed guidance for workflow design, and AI Sandbox, a visual no-code environment for workflow implementation. After using mlguide, participants produced better high-level workflow designs, but their scores on a quiz about specific methodological choices did not improve significantly. Compared with Google Colab, AI Sandbox received higher hedonic user experience ratings, while perceived workload and acceptance did not differ significantly. These findings suggest that different forms of interactive support help with differ- ent aspects of beginner ML experimentation. The paper contributes a reusable evaluation task with instructions, a modified dataset, and a quiz, as well as empirical evidence on structured guidance for workflow design and on the experience of implementing workflows in visual and notebook-based environments.
[HC-24] Ender: Visual Scaffolding for Geometric Proof Comprehension
链接: https://arxiv.org/abs/2611.13368
作者: Hwei-Shin Harriman,Yuchen Jin,Dominik Moritz,Joshua Sunshine
类目: Human-Computer Interaction (cs.HC)
备注: 10 pages, 7 figures
Abstract:Geometric proof is a foundational but notoriously difficult topic in secondary mathematics, primarily due to the high cognitive load required to integrate visual diagrams with logical text. As proofs evolve, tracking the initial givens and an expanding set of derived truths can easily overwhelm working memory. While established scaffolding techniques are often visual and diagram-based, current digital learning environments mostly rely on static, text-heavy interfaces that force students to manage these complex mental models on their own. This paper introduces Ender, an interactive rendering system designed to visualize the logical status of two-column proofs. Ender’s design is grounded in four literature-derived requirements: (1) diagram annotation, (2) structural tracing, (3) support for partial or incorrect work, and (4) synchronization between the diagram and text. To evaluate the system, we conducted a between-subjects, think-aloud experiment with 12 secondary school students in the United States, comparing Ender against a traditional digital proof interface. Our results indicate that students using Ender performed significantly better on proof comprehension tasks–specifically in identifying and correcting logical errors. These findings suggest that dynamic visual scaffolding may ease the working memory burden of digital interfaces, offering a promising building block for future AI-based proof tutoring systems.
[HC-25] MicroLens: Read Trace and Rewire the Residual Stream with Two Lenses
链接: https://arxiv.org/abs/2611.13322
作者: Nika Zahedi,Fangyi Zhu,Anna Wu
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 9 pages, 11 figures. Code: this https URL
Abstract:Lenses that decode hidden states into words are a popular way to look inside language models, but a heatmap of decoded words only shows what a vector reads as and cannot distinguish whether, or how much, the model uses the information. We present an interactive viewer that puts reading, tracing and editing on the same layers-by-tokens grid of the residual stream. In the viewer, every cell can be decoded with two lenses, the logit lens and the Jacobian lens (J-lens), traced back to the tokens it drew from, and edited, with the rest of the model rerun on the spot. We demonstrate three usage scenarios on Gemma-3-4B-it, backed by sweeps over a few hundred prompts, to show what this tool can discover. First, the two lenses give different directions to write with, each suiting a different edit: the logit lens’s word direction sets what a token outputs, while the J-lens’s direction changes what later tokens read. Second, in two-hop trivia questions (the capital of the state or country containing a given city), the state or country is readable at the city token from as early as layer 7, and swapping it there can redirect the answer 92-98% of the time; however, at the answer position, the state or country information is not used. Third, when a chat model answers questions with a step-by-step reasoning prompt, the correct answer is often already the top word in the residual stream before any reasoning is written: for 89-92% of retrieval questions and 39-63% of multi-hop and serial ones, far above their respective distractors (at most 5%). However, ordering puzzles are an exception, as the model works them out only once it writes its final reasoning steps. Furthermore, for answers that are visible in the residual stream, the J-lens reads them a layer or two earlier than the logit lens across the different types of question.
[HC-26] Does Haptic Connection to a Healthy Partner Improve Motor Learning in Chronic Stroke? A Pilot Study of Robot-Based Dyadic Training
链接: https://arxiv.org/abs/2611.13307
作者: Erica L. Waters,Natalia Stroutinsky-Sobotka,Reem M. Azar,Frances S. Shofer,Pamela Z. Cacchione,Rochelle J. Mendonca,Michelle J. Johnson
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC)
备注:
Abstract:Purpose: Robot-based haptic dyads, where two users exchange forces through robots, have been proposed for robot-assisted neurorehabilitation. This intervention could increase motor learning by promoting motor variability, providing an implicit communication channel, and facilitating social interaction. Despite the potential benefits, few studies have investigated robot-based haptic dyads for neurological populations, and none have included a comprehensive clinical assessment. Design/Methodology/Approach: We present a pilot study investigation of individuals with chronic stroke learning a complex tracking task in solo and dyad conditions. Prior to training, participants completed a battery of cognitive, motor, and sensory assessments. We assessed changes in key kinematic outcomes including tracking error, distance traveled, mean absolute power, and sample entropy. We also performed a correlation analysis of clinical scores with baseline tracking errors. Findings: Baseline tracking error was strongly correlated (r 0.6) with lower cognitive scores and higher motor scores. Only the solo group demonstrated lower tracking error at assessment (\Delta = -5.33, 95%CI [-10.25, -0.40], p = 0.027), but this result was tempered by the removal of an outlier participant. Neither group demonstrated clear retention. The dyad group showed significantly elevated performance, effort, and movement complexity during training. Research Limitations/Implications: The selected task was too difficult to reveal learning for either group, which may be related to its high cognitive demand. Sample size and stroke heterogeneity were also limitation. Originality/Value: Dyadic training increased movement complexity and effort, which are associated with better motor learning. Clinical characteristics such as lesion location, cognition, sensation, and chronicity may influence potential benefits of dyadic training.
[HC-27] Project Progress Desk: Know Where All Your Claude Code and Codex Projects Stand at a Glance
链接: https://arxiv.org/abs/2611.13287
作者: Wanghao Ye,Yiting Wang,Shwai He,Guoheng Sun,Ziyao Wang,Yexiao He,Zheyu Shen,Zhaoyi Liu,Bowei Tian,Daeun Jung,Aditya Sarkar,Parastoo Pilevar,Xiaochuan Gong,Yancheng Zhu,Yingying Fan,Qiong Wang,Yuning Zhang,Meng Feng,Siyuan Peng,Zhenle Duan,Sihan Chen,Ziyi Wang,Yifei Dong,Yilong Dai,Ang Li
类目: Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注: 6 pages, 4 figures. Submitted to IUI 2027 Demos
Abstract:Codex and Claude Code let developers work on many projects in parallel, but keeping track of them still means switching between chats, terminal sessions and providers. We present Project Progress Desk, a local desktop interface that brings overall progress, blockers and pending decisions into one view. Users can see which projects need attention, inspect what remains, and compare earlier approaches before choosing how to continue. Agent receipts link reported outcomes to each project’s weighted acceptance checklist; the overview distinguishes receipt-supported progress from dated estimates. We describe daily use across 36 projects, including a choice between two implementations based on their recorded outcomes. At the demo, attendees will explore 30 simulated projects, trace completed work to overall progress, and watch a coding agent’s new result update the overview.
[HC-28] Designing UIs with GenAI: Understanding the Use Benefits and Limitations of Prompt-to-Design Generators
链接: https://arxiv.org/abs/2611.13211
作者: Jingzhou Ye,Yao Li,Xueqiang Wang
类目: Human-Computer Interaction (cs.HC)
备注:
Abstract:The integration of generative AI (GenAI) into user interface (UI) design has led to prompt-to-design generators (P2dGens), tools that enable designers to create UI prototypes from textual prompts efficiently. Despite their growing adoption, little is known about how designers use these tools in everyday practice, their benefits, and limitations. We conducted semi-structured interviews with 17 UI designers to investigate P2dGens use, focusing on workflow integration, attitudes toward generator-assisted design, and the organizational climate. Findings show that designers primarily use P2dGens as ideation accelerators rather than end-to-end solutions, and they adopt a variety of individual and collaborative prompting strategies. Designers expressed complex attitudes, noting gains in efficiency and inspiration while also highlighting concerns about loss of personal creativity and design quality. We also observed emerging governance practices and varying attitudes within organizations toward P2dGens. These insights illuminate how P2dGens reshape UI design workflows and contribute to understanding human-AI collaboration in UI design.
[HC-29] Multimodal Judges Flag Humans Decide: A Review-and-Edit Framework for AI-Generated Product Video Ads WACV2027
链接: https://arxiv.org/abs/2611.13178
作者: Anoubhav Agarwaal,Sukesh Perla,Karthikeyan Mohanraj,Susmita Ghose
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Multimedia (cs.MM)
备注: Accepted at the WVAQ workshop at WACV 2027
Abstract:Generative pipelines can now turn every product page of an e-commerce catalog into a short video ad, but deciding whether such an ad is usable is not one good-or-bad call. We describe how we operationalized the review and editing of these ads so that both can keep pace with generation: reviewers go through each ad scene by scene and answer six questions, on the product, scene quality, pronunciation, script, sound and captions; judges built from vision-language, speech and language models file each finding under the matching question, and reviewers accept or reject each finding and add what the judges missed. Each confirmed issue then opens an edit tool matched to it. The questions and the routing from a finding to its fix do not depend on the models that generate the ad. Across 120 ads from our pipeline, reviewed with judge findings on screen, reviewers marked the product wrong on 60% of scenes, and script, caption or sound problems on under 10% each. On 60 of these ads, they marked the product wrong on 58% of scenes, far more than the 26% marked by two reviewers who later saw the ads with the judges hidden; one reviewer who saw the ads both ways marked 57% with judge findings and 19% without. On 120 blind-labeled scenes, a product judge that watches the clip with the catalog photos reached AUC 0.84, while video-quality and photo-similarity scores were close to chance. We tune the judges for recall: the product judge catches nearly all defects but also flags many clean scenes, so reviewers decide; together, the judges widen what reviewers mark and reviewers add what the judges miss. Without judge findings, one reviewer’s marking faded over a session. We release a review-portal template and our review data (120 ads and 562 scenes with judge findings and human marks, plus 120 blind-labeled scenes with baseline scores) at this https URL.
[HC-30] rust but verify audit and attribute: a human-in-the-loop framework for checking AI-produced research results
链接: https://arxiv.org/abs/2611.13042
作者: György Eigner,Patrik P. Süli,Roland Hollós
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Logic in Computer Science (cs.LO)
备注: 17 pages, 3 figures, 5 tables. Skill files and run record: this https URL
Abstract:Artificial intelligence (AI) systems now produce research results in bulk: in October 2026 a single laboratory released more than 700 AI-generated mathematics manuscripts, only part of them with machine-checked proofs. Such volumes will be checked mostly by AI agents that call specialised software. A passing check certifies only the artefact that was checked. For proofs translated into the proof assistant Lean, it has been shown that this artefact may state something other than the author’s text, and that deciding whether a translation is faithful is undecidable in general. The human researcher therefore stays in the loop, and human and AI contributions must be recorded. We propose a domain-independent, human-in-the-loop framework for checking AI-produced results. Validated, agent-usable tools with different grounds of trust check the results; a fidelity audit asks whether each check addressed the claim as stated; the human decides at fixed points; and a dialog report attributes the work to the human and to the AI. We describe two automatic implementations of the tool layer, for mathematics (Wolfram Language, MATLAB, Lean) and statistics (Python and R), and run the mathematical one on a two-claim example. An ambiguous claim was split into two readings, one refuted and one proved.
[HC-31] Code-Grounded Agent ic Explanations for Static Analysis Vulnerability Findings
链接: https://arxiv.org/abs/2611.13041
作者: Oshando Johnson,Ranjith Krishnamurthy,Rohith Kumar Shanmuganathan,Goran Piskachev,Eric Bodden
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Static application security testing (SAST) tools surface vulnerabilities early and their verdicts are rule-based and auditable. Yet developers routinely dismiss findings they cannot interpret. Large language models can supply the missing explanation, but designs that also hand the model the detection give up the auditability that makes static analysis defensible. We keep detection with the analyser and give the model only the explaining. A preliminary user study with 20 participants and a workshop with 19 practitioners yielded five requirements. Guided by these we redesigned SAFE, an open-source IntelliJ IDEA plugin that explains findings from any analyser reporting in the Static Analysis Results Interchange Format. It tiers explanations by demand, ties them to the reported data flow, and replaces the single-prompt backend with an agent that must consult the repository and the rule that fired before answering. A within-subjects crossover experiment with 24 participants compared the redesign against its predecessor. Perceived usability rose from 77.3 to 85.0 on the System Usability Scale. Actionability gained most among the explanation-quality items, the aspect both earlier studies flagged as weakest, and the odds of a correct remediation were 2.6 times higher. Triage accuracy and time per finding were indistinguishable, which we read as uninformative rather than as equivalence. Trust was the one measure that did not move, and the closing interviews indicate that grounding made findings easier to understand rather than easier to believe. Participants also drew a boundary no requirement anticipated, wanting the tool to find and explain while they judge and apply.
[HC-32] ypedInk: Knowing When Not To Draw
链接: https://arxiv.org/abs/2611.13027
作者: Weiwei Yang
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 8 figures, 10 tables
Abstract:Can a scientific figure generator know when it does not have enough evidence to draw? Complete-source illustrators map a finished methodology section to a schematic, or a table to plotting code, and are never asked this question because their input is assumed complete. The research record is rarely that complete: an outline, a draft paragraph, a table, or a one-sentence intent, in any combination, and painting measured numbers with an image model when the table is missing is a hallucinated result. TypedInk infers a figure type from diagram, plot, plate from a possibly incomplete source and dispatches to the renderer that can honor that type’s evidence contract, refusing dispatch when the contract cannot be met. On the six gold plots in TypedFigBench (5 views, n=30), TypedInk’s code path inks every gold table token when a table is observed (12/12), refuses the 12 hidden-table views instead of inventing numbers, and marks 6 intent-only views illustrative, with no execution failures. An always-draw coder with no such gate inks 30/30 charts, including all 18 with no table, and recovers zero gold numbers on any of them; two vision-language raters (not humans; human replication is future work) still judge those always-draw charts submittable 17/36 times, against TypedInk’s refusal 0/36 times. A diagnostic field ablation of the type router (16 illustrations, 5 views each, n=80) locates where this failure would originate if unchecked: an outline alone yields type accuracy 0.31 and sends every gold plot into an integrity violation, and a chat-model planner narrows but does not close the gap (overall accuracy 0.85, still integrity-failing on 25% of outlines). Diagram-label recall is 0.47 on the full record and 0.19 on an outline. Gold plates are typed at dispatch (oracle integrity-fail 0.00) and rendered as stills.
计算机视觉
[CV-0] Medical Image Segmentation: Foundation Model Adaptation versus Task-Specific Training
链接: https://arxiv.org/abs/2611.14519
作者: Emre Kutlusan,Yongmin Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Foundation models pretrained on large natural-image collections are increasingly adapted for medical image segmentation, in the expectation that they will replace models trained for each task. Most comparisons, however, use data from the training distribution and a single task-specific baseline whose configuration is rarely justified, and therefore provide limited evidence of performance at new clinical sites. It also remains unclear how much of a foundation model must be adapted, and what determines this. We adapted a foundation model, DINOv2, with strategies ranging from tuning only its bias terms to full fine-tuning, and compared it with U-Nets and ResU-Nets trained from scratch. Experiments covered colonoscopy, breast ultrasound and dermoscopy, with testing on held-out data from the training source and on independent datasets from other institutions. The adapted foundation model generalised better on every external dataset. Within the training distribution, the task-specific models remained competitive when training data were abundant, and delineated boundaries more precisely, while the foundation model more often missed small lesions. Only a small fraction of the foundation model required adaptation, and the capacity needed was determined by the imaging domain rather than by the adaptation method. A widely used polyp test set was also found to duplicate images from another benchmark. Foundation-model adaptation is therefore warranted where models must work across sites, with capacity selected on the target domain.
[CV-1] oward Foundation Detection Models via Example-Based Grounding in EM-DETR
链接: https://arxiv.org/abs/2611.14518
作者: Sheethal Bhat,Bogdan Georgescu,Awais Mansoor,Florin C. Ghesu,Sasa Grbic,Andreas Maier
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:DeepLearning (DL)-based Computer-Aided Diagnostic (CAD) systems have shown great promise in supporting clinicians, but their performance heavily depends on the availability of large, well-annotated datasets. In medical lesion detection, the scarcity of comprehensive, stan dardized annotations and the heterogeneity of existing datasets limit the development of robust foundation models. In this work, we introduce a novel approach to building a multi-domain foundation model for le sion detection by integrating diverse medical image datasets annotated for both anatomical structures and abnormalities. We first propose a student-teacher training strategy to effectively combine datasets with heterogeneous label spaces, mitigating catastrophic forgetting, and im proving feature learning. In addition, building on our previously pro posed example-based EM-DETR framework, we adapt it to jointly learn from multiple domain-specific datasets, enabling fine-grained, example driven detection across modalities. Our model achieves state-of-the-art (SOTA) results, with image-level AUC scores exceeding previous SOTA by 1% point on four key findings in CXRs. We achieve a leading mAP50 in CXRs and mammography, surpassing EM-DETR trained on a single dataset by 2-3% points. This approach paves the way for scalable foundation models in medical imaging that leverage heterogeneous data while maintaining robust and generalizable performance.
[CV-2] Measuring Robustness and Efficiency in a Connectome-Constrained Fly Visual System Model: Linking an Adversarial and a Perceptual Illusion NEURIPS2026
链接: https://arxiv.org/abs/2611.14498
作者: Alexandre Garcia-Duran,Alejandro Rodriguez-Garcia,Manuel Molano-Mazón,Alexandre Hyafil,Srikanth Ramaswamy,Janne K. Lappalainen
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注: 39 pages, 24 figures, 2 tables, Accepted at 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
Abstract:Biological visual systems compute reliably under tight metabolic and wiring constraints, whereas artificial vision systems consume far more energy and can be sensitive to adversarial perturbations. Whether networks with brain-like wiring are more robust at lower computational cost is unclear. We address this by benchmarking a task-optimized, continuous-time recurrent neural network of the fly visual system constrained by its synaptic connectome (FlyNet) against artificial neural networks. We compare it against CNNs, RAFT, and continuous-time recurrent neural networks without detailed connectome constraints (sCTRNN, CTRNN) on a collision-detection task within a differentiable 3D rendering pipeline. FlyNet is adversarially vulnerable but, relative to its compute, more robust to common video corruptions than RAFT, while using up to five orders of magnitude fewer FLOPs. The CTRNNs remain more robust than FlyNet across almost all tested perturbations, suggesting that connectome constraints benefit efficiency, through their low edge count, over robustness. Recurrence improves robustness to white-box attacks in RAFT and temporal context improves robustness to common corruptions. FlyNet’s position and velocity representations degrade under adversarial textures but recover under counterfactual textures that restore correct classification while staying equally far from training, ruling out unfamiliar textures as the cause of failure. The reverse-phi illusion similarly disrupts these representations and inverts the same low-level motion statistic as the adversarial textures. Optimizing this input statistic alone, without access to the network, suffices to induce classification failure, attributing both perturbations to the same circuit vulnerability. Connectome-constrained models can thus surface adversarial failure mechanisms as testable hypotheses about biological perceptual vulnerabilities.
[CV-3] RS-JEPA: Remote Sensing Joint-Embedding Predictive Architecture with Scan-Path Views for Cross-Sensor Image Retrieval
链接: https://arxiv.org/abs/2611.14496
作者: Tanishq Rachamalla,Srishti Kaushik,Muhammad Attique Khan,Gustau Camps-Valls,Swalpa Kumar Roy
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cross-sensor remote sensing image retrieval aims to find scenes with the same content across heterogeneous sensors, such as SAR and optical or panchromatic (PAN) and multispectral (MS) imagery, without manual labels. Existing self-supervised methods either reconstruct sensor-specific pixel detail, align each image only with its co-registered twin, or rely on large transformers with 118-173M parameters (and up to 398M). We introduce RS-JEPA, a compact joint-embedding predictive framework built on a Vision Mamba encoder shared by both sensors. Since a state-space encoder reads an image as an ordered sequence of patches, we propose scan-path views, which treat two reading orders of the same image as two views with identical pixels and predict the hidden patches in one order from the visible patches in the this http URL keep this prediction challenging, we propose Adversarial View Coupling (AVC), which learns the hardest split of the visible patches, and a neighbourhood consensus loss that aligns the scene similarities of both sensors. Extensive experiments on SAR-optical (BEN-14K, CBRSIR-VS) and PAN-MS (DSRSID) benchmarks demonstrate that RS-JEPA achieves the highest F1@5 among twelve re-implemented methods (72.92, 80.35 and 94.43) with a retrieval network of only 3.88M parameters (14.57M in training, 8-16times fewer than prior JEPA retrievers). Like prior methods, RS-JEPA trains on co-registered pairs. Our results highlight reading order as a source of self-supervised views. code: this https URL.
[CV-4] BoviShift: A Six-Year Multi-Attribute Audit of Visual Cattle Phenotyping Across Animals and Time
链接: https://arxiv.org/abs/2611.14476
作者: Mobasshir Bhuiya Shagor,Nilambar Halder Tonmoy,Md Saef Ullah Miah
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Computer vision offers a scalable, non-contact means of estimating livestock traits, but correlated images and video frames make reliable evaluation difficult. Image-random splitting can place views of the same animal in both training and test sets, while deployment further requires transfer to animals captured in future years. We introduce BoviShift, a corpus reconstructed from six consecutive annual cohorts of an operational cattle marketplace collected from 2021 to 2026. It contains 2,657 cattle, 10,610 still images, and 2,566 walk-around videos across nine recorded breed categories. We evaluate three splitting strategies through four configurations: image-random and pooled cattle-disjoint splits over the same development cohorts, followed by separate future-2025 and locked future-2026 evaluations. Using a fixed ResNet-50 multi-task baseline, we assess weight, age, height, breed, and coat color. Differences between image-random and cattle-disjoint splitting are task-dependent rather than a universal leakage penalty. Future-cohort behavior is also attribute- and cohort-specific: breed declines relative to pooled evaluation; mean weight and age performance fails to beat constant baselines in 2025; height remains above baseline; and color transfers well to 2025 but degrades in 2026. Multi-task training improves all five point estimates in a single-seed ablation, still-view aggregation helps selectively, and zero-shot video-frame aggregation exposes a still-to-video domain gap. To our knowledge, BoviShift is the longest-spanning cattle-vision resource combining consecutive annual cohorts with physical and visual attributes.
[CV-5] anCRAFT: Context-Reason ed Anticipatory Feedback for Assembly Tasks
链接: https://arxiv.org/abs/2611.14470
作者: Sassan Mokhtar,Lars Doorenbos,Marius Bock,Sina Mokhtar,Katharina Beckh,Dominik Bach,Juergen Gall
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Preventing human error in physical assembly tasks requires assistance systems that can intervene before mistakes are made. This is especially challenging in settings where users are free to choose the construction order and correctness depends on the evolving assembly rather than a fixed next step. We present TanCRAFT, a four-stage real-time predictive assistance system that tracks the scene state, reasons about the current partial assembly, forecasts the near-future motion of manipulated pieces, and intervenes when the predicted placement is incompatible with the target. We instantiate our framework and confirm its effectiveness in a controlled tangram assembly task through offline benchmarks and a within-subject user study. Our findings show that the predictive feedback significantly reduces mistakes relative to unguided and reactive sessions, while yielding the lowest mean perceived workload and shortest mean completion time. Overall, these results demonstrate the value of predictive feedback for error prevention and provide a step toward cost-aware assistance for automation and human-robot workcells.
[CV-6] C2-Routing: Complementary Cropping and Compression for Efficient Vision-Language Inference
链接: https://arxiv.org/abs/2611.14469
作者: Guorui Lu,Qinyu Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Visual Question Answering (VQA) imposes substantial computational overhead on vision-language models (VLMs), motivating model-agnostic input reduction methods that can be readily applied across diverse VLMs, including in black-box settings. Simply enforcing a visual-token cap reduces computation, but it can discard information needed to answer the question. A single reduction strategy also struggles to accommodate different scenarios. Our key observation is that cropping and compression are complementary: cropping highlights small targets and local details, whereas whole-image compression better preserves context for large targets or distributed evidence. Based on this observation, we propose C ^2 -Routing, which adaptively selects cropping or compression according to the input image and question. Since both strategies may still discard key information, we further design a safety gate that uses the predicted joint correctness distribution under original and reduced inputs to guide fallback and protect accuracy. To accommodate consumer GPU resource constraints, all training and evaluation experiments run on RTX 3090 GPUs. To measure gains beyond token capping, we use full-image baselines with the same cap of 1,024 visual tokens. We conduct extensive evaluation on three VLMs and 18 datasets spanning local details, global scenes, scene text, and documents. With the visual budget already capped, C ^2 -Routing further reduces end-to-end FLOPs by 36.53% and visual tokens by 42.36% on average, with a mean relative task-score decrease of only 0.77%.
[CV-7] A Lightweight Differentiable Method for Real-Time Skin Rendering and Digitization
链接: https://arxiv.org/abs/2611.14451
作者: Mario Marquez-Balduque,Carlos Rodriguez-Pardo,Jorge Lopez-Moreno
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 20 pages, 25 figures; includes 6 pages of supplementary material. Accepted to Computers and Graphics
Abstract:Inverse rendering for skin typically relies on offline path tracing to estimate physical properties, which fail to map directly to the approximate shaders utilized in interactive applications, such as game engines. We bridge this gap by directly estimating subsurface scattering parameters tailored for real-time rendering pipelines. We introduce a differentiable Screen-Space Subsurface Scattering (SSSS) framework that enables the joint optimization of spatially varying base textures and scattering distances. To account for local variations in skin thickness, we estimate these parameters across distinct facial regions, generating a blended profile map ready for production runtime engines. Our pipeline utilizes a low-cost, single-light, single-view capture setup designed for accessible and practical appearance acquisition. Optimization is driven by a masked loss function targeting regions where subsurface scattering effects are dominant. We validate our approach on a real-world dataset of 45 individuals across the Monk Skin Tone Scale, ensuring demographic generalization. Furthermore, we complement this evaluation with synthetic data, a multi-light setup validating our single-light assumption, and a tilted-view configuration confirming angular consistency. Results demonstrate that our method effectively recovers scattering parameters from lightweight inputs, yielding realistic skin appearance directly compatible with standard real-time shaders.
[CV-8] UGG: Unified Generative Geometry Model NEURIPS2026
链接: https://arxiv.org/abs/2611.14440
作者: Amani Kiruga,Zhiyi Li,Ruojin Cai,Hansen Lillemark,Xiaoshan Wu,Ayush Tewari,Yilun Du
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: Accepted at NeurIPS 2026. 31 pages, 7 figures, 6 tables. Project page: this https URL
Abstract:Understanding 4D scenes is a core challenge in visual intelligence, requiring models to jointly capture scene geometry, appearance, and dynamics from partial observations. However, existing approaches are subdivided into feedforward models for direct geometric prediction, and generative models for visual synthesis. We introduce UGG, a unified framework for 4D scene understanding that jointly models RGB video and geometric representations such as depth and camera pose. By assigning different noise levels across modalities and time, UGG formulates visual generation and geometric prediction as conditional completion within a single generative model. This flexible formulation enables the model to learn from diverse, heterogeneous datasets with different subsets of geometric and visual annotations. Furthermore, our generative formulation enables inference-time search over multiple plausible scene hypotheses, using geometric evidence extracted from observed RGB frames to improve geometric prediction without ground truth geometry. We demonstrate UGG’s effectiveness over non-unified baselines across camera-controlled video generation, depth estimation, and camera pose prediction, including in challenging sparse observation settings. Code and visualizations are available at this https URL
[CV-9] Metric-Aligned Group Relative Policy Optimization for Explainable Deepfake Detection
链接: https://arxiv.org/abs/2611.14436
作者: Luka Dragar,Peter Peer,Vitomir Štruc,Borut Batagelj
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages. Accepted to MRAC '26 (ACM Multimedia 2026)
Abstract:Deepfake detectors are commonly evaluated on real/fake classification alone, but real-world applications require explanations of the visual evidence behind each prediction. Joint vision-language models struggle with authenticity assessment, producing “real” narratives for manipulated images. To address this, we propose a decoupled predict-then-explain pipeline as our submission to the ACM Multimedia 2026 Explainable Deepfake Detection Challenge, with methods trained and evaluated on the XPlainVerse benchmark. A vision-foundation-model ensemble first classifies the image, then a Qwen3-VL-8B model generates a decision-conditioned explanation of the visual evidence. For fake images, a second Qwen3-VL-8B compresses that text into a short non-expert summary without using the image. We further apply metric-aligned Group Relative Policy Optimization (GRPO) to optimize both explainers against the challenge criteria. Under matched conditions, GRPO raises complex BERTScore-F1 from 0.686 to 0.712 and also improves grounding (entity and evidence F1); our submission ranked first on complex BERTScore-F1 and seventh overall.
[CV-10] One Policy for 100 Household Tasks: Our Solution for the 2026 BEHAVIOR Challenge
链接: https://arxiv.org/abs/2611.14432
作者: Ilia Larchenko,Gleb Zarin,Atindra Nair,Keerti Manney,Vitaly Kleban,Mirko Kiefer,Anjana Yodaiken
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Code: this https URL ; checkpoint: this https URL ; project page: this https URL
Abstract:We describe our solution for the 2026 BEHAVIOR Challenge: 100 long-horizon household tasks in the OmniGibson simulator. We placed 9th on the final leaderboard, the only independent team in the top 10. The core of the solution is a single vision-language-action (VLA) model, one checkpoint for all 100 tasks, built on our 1st-place 2025 solution, which is based on \pi_0.5 . On the 50 tasks shared with 2025, its Q rises from 0.260 to 0.371; across all 100 tasks it scores 0.392. Our main conclusion from 2025 was that the model was undertrained, so most of the 2026 work went into training the model for longer, optimizing some training parameters, making each training step slightly faster and reducing the gradient noise: a migration from 50 to 100 tasks (semantic stages built from the demonstrations’ skill annotations, task and stage embeddings of new tasks warm-started from similar old ones, and a 21-dimensional action space matching the teleoperation controls); variance-reduced multi-sample flow matching with a simple noise-vs-compute model that picks the number of action-expert samples per observation without ablations; and a 200k-step training run, about one pass over the 211M frames of the demonstrations. A cloud evaluation infrastructure keeps a shared record with provenance, episode diagnostics and restorable simulator states, and runs six simulators on one GPU together faster than real time. On top of the learned policy, 14 tasks are routed to a GPT-6 Astra coding agent that controls the robot through observation and motion tools, starting every episode from a frozen copy of its own practice session on training instances. Across these 14 tasks, Astra’s mean Q is 0.639, against 0.108 for the learned policy. The public submission included only part of the Astra results and scored 0.446 Q with 27.3% full-task success.
[CV-11] OnlyAct: Extracting Action Representations from Foundation Models for World Models
链接: https://arxiv.org/abs/2611.14420
作者: Runjia Li,Minghao Chen,Junyu Xie,Philip Torr,Andrea Vedaldi,Tomas Jakab
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video world models promise general-purpose interactive simulators, but their training is limited by action supervision: action labels are scarce, fragmented across incompatible specifications, and do not scale with the unlabeled video pretraining corpus. A common workaround jointly trains an action encoder to compress consecutive frames into a latent action and a decoder to reconstruct the next frame from the previous frame and that latent, forcing the latent to capture only new information. This has two limitations: learning the action space from scratch can limit generalization to unseen domains, and the bottleneck trades expressiveness for separation, with tighter settings losing nuance and looser ones risking scene leakage under domain shifts. We argue for a different starting point: rather than learning actions from scratch, we start from a pretrained foundation model (V-JEPA 2), whose embeddings of a frame pair already carry substantial action information after large-scale, diverse video pretraining. The remaining challenge is to extract the action while discarding the scene. We propose a label-free contrastive adaptation framework that pulls together frame pairs sharing the same motion under a different scene and pushes apart pairs from the same scene with different motion, reshaping the geometry of the pretrained space without imposing a reconstruction bottleneck. We introduce a unified evaluation protocol along three axes (action expressiveness, scene invariance, and action-conditioned video generation quality) across robot manipulation, camera navigation, game, and driving benchmarks, most of which are unseen during adaptation. Compared with annotation-free latent-action methods, our representations are substantially more expressive while matching their scene invariance, and they yield world models that transfer demonstrated actions to new scenes more faithfully.
[CV-12] What Factors Matter for Collection-Level Counting with Human-AI Collaboration?
链接: https://arxiv.org/abs/2611.14380
作者: Seoyun Jeong,Logan Lawrence,Owen L. Martin,Benjamin Koger,Daniel Sheldon,Subhransu Maji
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Many scientific and policy decisions require estimating the total number of target objects across a large image collection. Exhaustive human labeling is often infeasible, while raw model predictions can be biased, especially under domain shift. Recent human-AI estimation methods combine imperfect model predictions with limited human verification to obtain reliable collection-level estimates. However, existing work provides little guidance on how practitioners should choose among models, estimators, and adaptation strategies for a new deployment. We study these choices within a common human-in-the-loop counting framework across 12 tasks spanning biomedical microscopy, ground-level and aerial wildlife surveys, and overhead imagery of urban structures. Our results show that modern multimodal models provide a strong, training-free starting point for many tasks, while fine-tuned detectors become more advantageous at larger labeling budgets and across repeated deployments. Estimation efficiency improves further when model predictions are used to prioritize which units humans label, rather than sampling uniformly. We also find that each model’s ranking quality can be estimated with a small pilot set, yielding practical guidance for selecting strategies on new counting tasks. In a field deployment on aerial pronghorn surveys, a task-specific adaptation of these methods meets the survey’s \pm 5% tolerance after reviewing a median of only 3.2% of collected images.
[CV-13] From Video Frames to Robot Meta-Frames: A Unified Latent Interface for World Action Models
链接: https://arxiv.org/abs/2611.14365
作者: Guoqiang Liang,Yiqi Lin,Yanzhe Chen,Kevin Yuchen Ma,Mike Zheng Shou
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注:
Abstract:Pretrained video diffusion models (VDMs) offer a powerful foundation for World Action Models (WAMs), which jointly predict robot actions and future observations. Building such models requires injecting low-dimensional robot signals (e.g. proprioception, actions, and values) into a latent space shaped by natural videos, and a key design choice is the interface through which these signals enter: existing schemes treat it as a formatting detail, spending a separate latent frame on each signal or bolting on dedicated encoders and decoders. We argue that signal injection is instead a distribution-alignment problem: how well a representation matches the prior of the pretrained VDMs during denoising. Using the relative reconstruction error of a frozen VDM denoiser as a probe, we find that conventional spatial encodings sit far off this prior, whereas frequency-domain packing yields latents that the denoiser corrects far less. Motivated by this diagnostic result, we propose MetaWAM, which folds all robot signals into a single unified meta-frame: each signal is assigned to a disjoint frequency-domain channel band and lifted to the spatial latent by an inverse DCT, yielding a decodable interface aligned with the pretrained VDM prior without any added module or architectural change. A fixed band layout together with slot-aware routing lets the same meta-frame support policy, world-model, and value prediction over a shared set of robot variables. On RoboCasa, MetaWAM reaches 68.9% success while collapsing the per-signal frames into one and cutting inference latency by 39%, and attains 85.0% success on three real-world Franka tasks. Code and models are available at this https URL
[CV-14] Multimodal Abstention That Preserves Acquired Correct Answers
链接: https://arxiv.org/abs/2611.14311
作者: Jinyuan Zhang
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 28 pages. Code and reproduction materials: this https URL
Abstract:Adding abstention to a task-adapted multimodal model can discard correct answers, including capabilities acquired during adaptation. We formulate abstention repair as maximizing correct abstention on unanswerable questions under a budget on newly lost correct answers. We propose ReFrain (Regression-Free Abstention), which combines a linear probe on first-token logits, threshold calibration on held-out image clusters, and a deterministic veto that replaces rejected outputs with the displayed “none of the above” option while preserving all other responses. “Regression-free” denotes zero loss on the calibration set at the zero-budget target. All abstention methods in our main comparison start from the same answering-SFT checkpoint; we separately track lost correct answers, repaired errors, and retained SFT gains. On Qwen2.5-VL-3B, ReFrain at zero budget retains all 57 SFT-gained answers with no losses and improves correct abstention by 7.30 percentage points. At a 0.5% calibration budget, it reaches 74.05% correct abstention with 8 losses and retains 55 gains; CLIP-UP EmbLoRA reaches 64.65% with 62 losses, 41 repairs, and 40 retained gains. The same probe at its default threshold loses 298 correct answers. With models and thresholds frozen, confirmation on 228 independent question clusters yields 66.05% correct abstention and no losses at the 0.5% point, exceeding EmbLoRA by 32.72 percentage points. Ordinary refusal SFT achieves higher correct abstention at greater regression cost. Code and reproduction materials: this https URL.
[CV-15] aoMate-R1: Temporal Role Specialization for Streaming Multi-Reference Video Generation
链接: https://arxiv.org/abs/2611.14307
作者: Qijun Gan,Chenwei Zhang,Xinyang Li,Meiguang Jin,Junfeng Ma
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Streaming multi-reference video generation establishes a scene from specified people and objects, then continues it under changing instructions. We introduce TaoMate-R1, a streaming multi-reference generator that organizes adaptation parameters through temporal role specialization. It assigns separate low-rank adapters to scene establishment and continuation on a frozen backbone. Each adapter performs both denoising and context writing for its role; Self Gradient Forcing jointly trains the roles through the history they construct. This allocation contrasts with operation-based specialization, which separates denoising from context writing across all chunks. A fixed-replay gradient analysis characterizes the update directions admitted by the two sharing schemes. On 32 multi-reference cases, we compare the schemes at the same two-adapter budget. Across ten approximately two-minute streams with changing instructions and shared teacher-audio conditioning, TaoMate-R1 reduces the mean boundary luminance jump from 1.96 to 0.613, with higher boundary feature similarity. It also achieves higher mean CLIP and DINO temporal similarity across short interactions, long streams, and public reference compositions. These results support grouping adaptation parameters by scene establishment and continuation for streaming multi-reference generation. Our code and model are released at this https URL.
[CV-16] ForeseeDrive: Future-Token Conditioning for Anticipatory Generative Driving
链接: https://arxiv.org/abs/2611.14286
作者: Shaokai Wu,Xunchu Zhou,Pinzhi Wang,Bo Dong,Yuting Hu,Yanbiao Ji,Qiuchang Li,Xiao Cui,Qi Kong,Hongtao Lu,Yue Ding
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages (8 pages main text + appendix), 3 figures, 7 tables
Abstract:Generative driving planners can represent diverse ego futures, but a downstream scorer can choose only among sampled candidates; it cannot recover an absent maneuver. We present ForeseeDrive, which turns future scene evolution into a deployable interface for shaping candidate generation. Rather than reconstructing dense future states or scoring a fixed menu, ForeseeDrive derives query-indexed tokens from logged actor motion, occupancy evolution, route interactions, and TTC margins, and predicts the same token space from present context at deployment. The tokens condition trajectory geometry before candidates materialize. A zero-token-gated residual path and candidate-preservation loss limit unnecessary drift, while a TTC-margin guard targets brittle safe-to-unsafe boundary crossings. Under their respective benchmark protocols, ForeseeDrive reaches 89.2 PDMS on NAVSIM navtest, 0.41,m average L2 and 0.05% collision rate on nuScenes validation, and a 76.50 driving score on Bench2Drive. Controlled DiffusionDrive/NAVSIM diagnostics show an exact zero-token no-op, a small aligned-over-shuffled selected-PDM advantage under preservation, mixed oracle-best quality, and localized TTC-boundary failures. Together, the benchmark results and bounded mechanism tests support future-conditioned generation as a practical interface for anticipation. Code is included in the supplementary materials.
[CV-17] UniFlow-RS: Unified Generative Prediction for Heterogeneous Remote-Sensing Tasks
链接: https://arxiv.org/abs/2611.14281
作者: Jianlong He,Liang Liao,Jing Xiao,Chaofeng Chen,Gui-Song Xia
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Remote-sensing foundation models increasingly unify representations across sensing modalities and downstream tasks, yet their prediction mechanisms remain largely task-specific. Separate decoders, prediction heads, and objectives leave prediction fragmented across tasks. This naturally raises a question: can heterogeneous remote-sensing tasks share the prediction process? UniFlow-RS answers this question with a single latent flow-matching predictor shared across tasks. This requires addressing two key challenges: unifying heterogeneous prediction spaces and harmonizing joint optimization across tasks. First, we cast heterogeneous task outputs into structured visual targets in a common latent space, allowing them to be modeled by the same pretrained flow predictor. Task-dependent conditioning further accommodates differences in input configurations. Second, we optimize all tasks with a common Flow Matching objective, eliminating task-specific prediction losses. To mitigate optimization mismatch across tasks and modalities, we introduce Task-Modality Adaptive Timestep Sampling (TMAS), which dynamically allocates training timesteps according to the online learning state of each task-modality group. Using a single shared model, UniFlow-RS handles six tasks and thirteen datasets, surpassing recent task-specific state-of-the-art methods on several benchmarks while remaining competitive on the others. Beyond these benchmarks, the same checkpoint demonstrates strong zero-shot transfer across unseen datasets, task settings, occlusion conditions, and near-infrared false-color imagery, without target-specific adaptation.
[CV-18] AutoThreshold: Joint Confusion-Profile Learning for Adaptive Mask Binarization
链接: https://arxiv.org/abs/2611.14279
作者: Shaokai Wu,Xunchu Zhou,Pinzhi Wang,Bo Dong,Xiao Cui,Qi Kong,Yue Ding,Hongtao Lu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 6 tables
Abstract:A single validation-tuned threshold can become brittle when dense-score distributions shift. We introduce AutoThreshold, a post-hoc policy that predicts the joint mass of positive and negative pixels over score bins. This target directly parameterizes threshold-indexed confusion counts, avoiding products of independently estimated prevalence and class-conditional rates. Softmax normalization and cumulative decoding yield coherent, monotone confusion profiles; a plug-in decoder then selects a threshold for any F_\beta objective without retraining the head. Across three independently trained Kvasir pipelines transferred to four polyp datasets, Joint exceeds continuous Otsu by 0.0080 \pm 0.0046 external macro Dice, with positive gains in 3/3 runs. On the reference pipeline, three head seeds achieve 0.6324 \pm 0.0027 , 0.0085 above Otsu; direct-utility Ridge achieves 0.6342, with the paired difference interval spanning zero. The same Joint checkpoints outperform Otsu for F_0.5 , Dice, and F_2 . On developmental KITTI Road leave-one-corruption-family-out evaluation, Joint improves over scalar regression by 0.0082 [0.0064, 0.0101], while direct utility is 0.0022 higher. These results establish joint confusion prediction as a useful, metric-retargetable inductive bias, without claiming Bayes-optimal F_\beta decisions.
[CV-19] Elastic-WAM: Adaptive Spatiotemporal Imagination for Efficient Robot Control
链接: https://arxiv.org/abs/2611.14257
作者: Haitian Li,Tianfu Li,Xuxi Zhao,Zizheng Liu,Yangcheng Yu,Yiwen Hou,Zeyu Xiao,Mutian Xu,Ziqian Yang,Haonan Chen,Lin Shao
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages. Project page: this https URL
Abstract:World-Action Models improve robot control by predicting future visual dynamics, but iterative video denoising incurs substantial inference overhead. Recent approaches reduce this cost through partial future denoising, yet typically rely on fixed refinement budgets. Our empirical observations reveal that preferred refinement depths vary across tasks and states, while different future regions exhibit varying relevance to action generation. Motivated by these findings, we propose Elastic-WAM, which adaptively allocates world refinement computation across temporal denoising time and spatial future space. A unified spatiotemporal refinement demand field governs when to stop world refinement, how far to advance each denoising step, and which regions to refresh. We parameterize this field with a shared temporal basis and spatial amplitudes, and learn it through a three-stage action-grounded supervised procedure. At inference, the learned field guides recurrent sparse world refinement to improve task success while reducing inference overhead. Evaluations across diverse simulation benchmarks and real-world long-horizon manipulation demonstrate state-of-the-art control performance with reduced inference latency.
[CV-20] Location Matters: Sparse Latent-Variance Attacks for Face Privacy against Diffusion Personalization
链接: https://arxiv.org/abs/2611.14254
作者: Shuting Yang,Shuxin Li,Linyan Dai,Xinwei Zhang,Dong Huang,Renyang Liu,See-Kiong Ng
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注:
Abstract:Text-to-image diffusion models can learn and reproduce an individual’s identity from only a few reference photos, creating risks of unauthorized and misleading content generation. Proactive defenses address this risk by adding adversarial perturbations to facial images before release, thereby disrupting identity acquisition during downstream personalization. However, existing methods typically rely on dense L_\infty -bounded perturbations, whose accumulated distortions can become visible around facial structures. Recent methods improve protected-image quality, but often at the cost of reduced protection effectiveness. In this paper, we propose Sparse Encoder Attack on Latent Variance (SEAL), a sparse adversarial protection framework that modifies only 5% of the spatial locations in a facial image. SEAL combines a differentiable top-k masking scheme that jointly optimizes perturbation locations and values with a variance-oriented objective over the frozen VAE encoder’s latent distribution to disrupt identity-consistent representations. A saliency-guided dispersion penalty further discourages perturbation concentration in perceptually salient facial regions. Experiments on CelebA-HQ and VGGFace2 show that SEAL achieves the best protected-image quality among the compared methods, exceeding 41 dB PSNR with 0.97 SSIM and at most 0.07 LPIPS, while reducing identity similarity to at most 0.15 after diffusion personalization. These results demonstrate that sparse location optimization enables strong identity protection while preserving high visual quality. Code and data are available at this https URL
[CV-21] ViGA-FLAME: Aligning Latent Visual and Geometric Representations for Robust 3D Head Expression Estimation
链接: https://arxiv.org/abs/2611.14218
作者: Wen Hao He,Jiacheng Wu,Jie Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Estimating 3D head expression from a single image requires mapping visual appearance to underlying facial geometry. We propose ViGA-FLAME, a two-stage framework that aligns semantic visual features with a structured geometric latent space. First, a Parametric-Geometric Variational Autoencoder (PG-VAE) learns a unified representation of canonical head surfaces and FLAME parameters, supporting both detailed surface reconstruction and interpretable parameter recovery. Supervision on geometry and parameters, together with consistency between the two decoding branches, connects surface detail with parametric structure. Second, a trainable Visual-Geometric Adapter (ViGA) maps features from a pretrained semantic visual encoder into this latent space. Asymmetric attention enables ViGA to align semantic image features from the visual encoder with the structured geometric information represented in the PG-VAE latent space, without altering the original visual stream. By grounding image evidence in this latent prior, ViGA-FLAME enables robust 3D head expression estimation across severe facial deformations and large viewing angles, efficiently improving robustness over competing feed-forward methods.
[CV-22] DailyWildfireCAN: A Dataset and Benchmark for Next-Day Wildfire Spread Prediction in Canada
链接: https://arxiv.org/abs/2611.14207
作者: Mahacine Ettahri,Hager Radi Abdelwahed,Toluwaleke Olutayo,Daniel Aloise,Gaétan Marceau Caron
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Wildfires represent a critical ecological and socio-economic challenge in Canada, where vast boreal forests and a warming climate have amplified the frequency and severity of extreme fire seasons. Predicting next-day wildfire spread can support fire management, but requires capturing complex interactions among weather, fuels, and terrain. Deep learning has emerged as a promising approach to learning these relationships from heterogeneous environmental data, yet its development and evaluation depend on representative datasets. Existing deep learning research on wildfire spread relies predominantly on US-focused datasets, leaving Canadian fire environments underrepresented. To address this gap, we present DailyWildfireCAN, a multi-modal, multi-temporal dataset and benchmark for predicting next-day wildfire spread across Canada. The dataset tracks continuous fire progression over multi-day events at a high spatial and temporal resolution of 90-m and 24-hour, respectively. This resolution preserves fine-scale information relevant to fire-edge geometry, local fuel variations, and terrain effects, supporting research toward improved daily wildfire spread forecasting. DailyWildfireCAN covers 2,440 wildfire events across eight fire seasons 2017-2024 and integrates 33 features across diverse modalities, including topography, meteorology, land cover, vegetation and forest structure, human influence indices, and satellite imagery. We establish a comprehensive benchmark comparing mono-temporal and multi-temporal prediction models, accompanied by a multi-dimensional evaluation framework assessing robustness across wildfire characteristics, geographic regions, and temporal conditions, spatial prediction quality, and sensitivity to reference label variations.
[CV-23] SpecValley: Aggregation as Spectral Valley in Dense Heterophilic Graphs
链接: https://arxiv.org/abs/2611.14176
作者: Christoph Jahn,Delvin Bacho,Urs Waldmann,Bastian Goldluecke
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Node classification in heterophilic graphs remains an open problem: aggregating over neighbourhoods can damp precisely the part of the signal that carries the labels. The field has answered with ever more expressive propagation operators, leaving the aggregation step that precedes them fixed by degree normalisation. However, degree normalisation acts as a spectral valley in its own right, attenuating a fixed middle band whose position at lambda equal 1 is set by the normalisation rather than by the data, exactly where the eigenvalues of dense graphs accumulate. We therefore begin with the representations rather than the propagation, in two stages: a learnable aggregation followed by an ordinary spectral filter. The first, SpecValley learns the position, sharpness and flank gains of the valley for every graph. We show formally how the aggregation operator and downstream filter interact. We establish that the response 1 minus lambda of degree normalisation vanishes exactly at that accumulation point. A spectral energy budget shows that what aggregation removes from a band, no linear spectral filter can recover. Crowding alone says nothing about the benefit. That is decided by how much label energy the valley removes, which we measure for every graph. SpecValley achieves strong results across heterophilic benchmarks, and a controlled ablation over aggregation operators and downstream filters attributes the gain to aggregation rather than the filter.
[CV-24] GARD: Gradient-Guided Adversarial Representation Defense against Backdoors in Multimodal Contrastive Learning Models
链接: https://arxiv.org/abs/2611.14143
作者: Yuhong Wang,Yujun Cai,Lei Feng,Songze Li
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal contrastive learning models such as CLIP are vulnerable to backdoor attacks with diverse trigger structures, ranging from localized patches to globally distributed perturbations, with recent attacks increasingly optimizing these triggers in the representation space. Existing post-hoc defenses based on clean fine-tuning may leave backdoors active, while trigger-inversion methods depend on accurately estimating the attack target and trigger. We observe that backdoored encoders exhibit structure-dependent representation sensitivity: global perturbations preferentially expose distributed backdoors, whereas local patch perturbations are more effective for localized backdoors. Based on this observation, we propose Gradient-Guided Adversarial Representation Defense (GARD), a target- and trigger-agnostic post-hoc defense. GARD optimizes complementary global and local adversarial probes to maximally displace clean-image representations, and then realigns the perturbed representations with detached clean anchors while preserving image-text alignment. The resulting model requires no additional computation at inference time. Across six visual backdoor attacks and two CLIP visual backbones, GARD reduces the attack success rate below 1% in all 12 evaluated attack-backbone combinations while maintaining competitive clean accuracy. These results demonstrate the effectiveness of adversarial representation probing for repairing backdoored multimodal contrastive encoders.
[CV-25] HaiRuo: Forecasting Ocean Eddies as Fields and Objects with a Satellite-Altimetry Foundation Model
链接: https://arxiv.org/abs/2611.14136
作者: Xukun Qin,Haochen Sun,Yuyang Tian,Ge Chen
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Mesoscale eddies carry most of the ocean’s kinetic energy and move heat, salt and nutrients across basins, yet where an eddy will be two weeks ahead is still read off ocean models with post-hoc tracking heuristics. We introduce HaiRuo, which forecasts eddies as fields and as objects at once: from 30 days of satellite sea-level maps, one forward pass returns 15 days of sea level and geostrophic velocity, daily eddy masks and the trajectory of every eddy alive today. HaiRuo pairs two components with a clear division of labour: (1) HaiRuo-1B, an open-weight 1.05-billion-parameter foundation model pretrained without labels on 24 years of global satellite-altimetry observations, which supplies the 15-day physical fields and masks (+0.14 field skill and +1.1 mIoU points under the same decoder) and further lowers 15-day centre error by 1.2 km frozen (1.35 km over training from scratch); and (2) XuanJi, a Lagrangian object decoder that samples the evolving forecast in each eddy’s co-moving polar frame and refines the 15-day path across three look-ahead rounds, removing 2.5 km on HaiRuo-1B (2.9 km with joint fine-tuning) and 2.5-3.3 km via its object attention across six published backbones. Under one pre-registered global evaluation over the whole ocean, HaiRuo improves on fourteen methods on every endpoint (25.2 vs. 30.6 km 15-day centre error, -18%), holding its margin outside the labelled regions and through both phases of the El Nino-Southern Oscillation (ENSO), while reproducing westward drift, 50-60 deg S eastward reversal and poleward cyclone-anticyclone deflection. Over ten days through one fixed tracker, HaiRuo’s forecast field cuts the eddy-centre error of WenHai and XiHe by 37-43% (-2.8 km vs. day 0 while they degrade +4.3-8.7 km), and its object trajectories cut pipeline error by 77-78% globally. Open-weight checkpoints, code and project page: this https URL
[CV-26] Sim(3)-TurboReg: Robust and Efficient Scale-Aware Registration via Tri-Scale Consistency
链接: https://arxiv.org/abs/2611.14135
作者: Zijun Zhou,Shaocheng Yan,Zhenjun Zhao,Yutong Hu,Ke Yang,Yingdong Gu,Pengcheng Shi,Jiayuan Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 3 figures, 6 tables. Code available at this https URL
Abstract:Feed-forward 3D foundation models recover geometry at high fidelity, yet inference memory constraints keep them from processing long sequences natively. Current streaming systems work around this limit with sliding-window chunking, which introduces severe monocular scale ambiguity. Resolving this ambiguity requires Sim(3) registration that stays accurate and efficient under outlier contamination. We introduce Sim(3)-TurboReg, which reformulates the compatibility graph construction of TurboReg by replacing absolute distance constraints with a strict Tri-Scale Consistency (TSC) criterion. Our analysis shows that TSC suppresses both mismatching and coordinate-distortion outliers. We integrate the resulting solver into existing streaming systems and obtain high-fidelity, kilometer-scale reconstruction for feed-forward models. On controlled unknown-scale registration at 90% outliers, ours matches RANSAC accuracy with 92.5% lower median latency. In DA3-Streaming on KITTI, ours improves mean ATE RMSE by 22.2% and reduces mean end-to-end time by 16.6%. Code: this https URL
[CV-27] Continuous 3D Spatial Transcriptomics Reconstruction via Histology-Informed Implicit Neural Representations
链接: https://arxiv.org/abs/2611.14129
作者: Kaito Shiku,Ryoma Bise,Ahtisham Fazeel Abbasi,Yuichiro Iwashita,Shumpei Takezaki,Andreas Dengel,Muhammad Nabeel Asim
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 5 pages, 2 figures
Abstract:Three-dimensional spatial transcriptomics (3D ST) enables the analysis of gene-expression patterns across tissue depths. However, 3D ST measurements are acquired from discrete 2D tissue sections, which are often sparsely sampled due to high acquisition costs. To address this limitation, we propose a histology-informed multi-task implicit neural representation (HIM-INR) for continuous 3D gene-expression reconstruction from sparse ST observations. HIM-INR jointly reconstructs gene-expression profiles and histological representations using an INR with a shared backbone, leveraging densely sampled histological images as auxiliary spatial supervision. Once trained, HIM-INR predicts gene expression at arbitrary 3D coordinates without requiring histological images. Experiments on two 3D ST datasets demonstrate improved reconstruction over existing methods, particularly as the number of missing ST sections increases.
[CV-28] CroCoMAE: Cross-modal self-supervised collaborative representation learning for SAR-optical remote sensing data
链接: https://arxiv.org/abs/2611.14124
作者: Vito Recchia,Giuseppe Guarino,Cássio Fraga Dantas,Giuseppina Andresini,Annalisa Appice,Dino Ienco
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Remote sensing increasingly captures the Earth’s surface through complementary sensors such as SAR and optical, yet ground-truth annotations remain scarce, limiting supervised approaches. Self-supervised learning (SSL) addresses the label scarcity, and cross-modal SSL further exploits the complementarity between co-located sensors, but existing cross-modal frameworks align modalities mostly through global image-level representations or treat them as largely redundant, leaving their fine-grained complementary structure underexploited. We propose CroCoMAE, a cross-modal SSL framework for SAR-optical remote sensing that unifies contrastive learning and masked image modeling within a ViT-based architecture, operating at two complementary levels. At the image level, an intra-modality contrastive loss on the class token yields globally discriminative, augmentation-invariant representations. At the patch level, masked image modeling drives fine-grained per-modality representations, while a cross-modal contrastive objective aligns a shared subspace across SAR and optical patch tokens, leaving the complementary subspace unconstrained to retain modality-specific information. On two SAR-optical benchmarks covering scene classification and semantic segmentation, CroCoMAE improves over uni-modal SSL baselines and outperforms state-of-the-art cross-modal frameworks in most configurations, with gains in both single-modality and multi-modal evaluation across varying label regimes and the largest improvements on semantic segmentation.
[CV-29] AMEing the Open-World Personalization: Towards Open-Set Personalized MLLM Assistant NEURIPS2026
链接: https://arxiv.org/abs/2611.14118
作者: Rongpei Hong,Jian Lang,Ting Zhong,Fan Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to NeurIPS 2026 Main Track; code and data are available at this https URL
Abstract:Personalizing Multimodal Large Language Models (MLLMs) as intelligent assistants has recently emerged as an active research topic, where an MLLM is expected to go beyond generic, one-size-fits-all replies, and instead generate responses grounded in user-specific objects, entities, and concepts encountered during interactions. Nevertheless, existing studies mainly focus on personalizing MLLMs on a static, predefined set of personalized concepts and require users to manually and frequently extend this set to accommodate newly emerging concepts, which is infeasible in dynamic-world scenarios. To address this limitation, we recast conventional MLLM personalization as an Open-Set problem, and propose TAME-O, the first long-context open-set personalized MLLM assistant. Specifically, TAME-O introduces a training-free, plug-and-play Concept Reconciliation Skill (CR Skill). It enables the assistant to automatically unlock novel concepts during prolonged human-MLLM interactions while preventing confusion with previously unlocked ones, allowing the MLLM to continually grasp more and more concepts and progressively refine the modeling of known ones. Extensive experiments under the open-set personalization setting underscore the efficacy of our method.
[CV-30] Looking One Step Ahead: An Anticipation-guided Agent for Object State Readout in Videos
链接: https://arxiv.org/abs/2611.14113
作者: Yuanhe Tian,Yan Song
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 2 figures
Abstract:Understanding the progress of an activity requires tracking how the objects involved change over time. Object state readout provides this information by describing a target object’s states and transitions across video frames. Although multimodal large language models (MLLMs) generate detailed visual descriptions, these descriptions may leave the attributes needed to distinguish successive states insufficiently specified. In this paper, we propose look-ahead state readout (LASER), an anticipation-guided agent for object state readout that uses possible next-step changes to determine which attributes to inspect in the present. Given temporally ordered frames and a target object, LASER converts anticipated changes into targeted visual questions and inspects the current frame to establish attribute values. It then checks state readouts and forecasts against independent observations of subsequent frames, refining a state record used to generate the final description. Reusable textual observation skills guide this process and are refined by tracing discrepancies with reference captions to the responsible instructions. Candidate revisions are selected on separate videos based on improved caption quality without increased contradictions with reference facts. Model parameters remain frozen throughout adaptation and inference, and the revised skills remain fixed during evaluation. Experiment results and further analysis on OSCaR and PLM RCap show consistent improvements of LASER over strong baselines and existing approaches, which demonstrates the effectiveness of our approach.
[CV-31] Stylization as Geodesic Navigation on Diffusion Manifolds
链接: https://arxiv.org/abs/2611.14097
作者: Yan Li,Zehao Li,Xinlong Sun,Fuchen Long,Xi Chen,Wenhan Luo,Yike Guo
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Facial stylization asks for a delicate transformation: the image should look unmistakably artistic, yet the person should remain unmistakably the same. Existing training-free and instruction-driven methods often fail to expose such control, either drifting away from identity or producing only weak stylization. We introduce Stylus, a training-free framework that treats facial style transfer as navigation on the curved latent manifold of a pretrained diffusion model. Rather than interpolating in flat Euclidean space, \method refines the interpolated content–style point by lowering a ratio-weighted discrete geodesic energy under the pullback metric, made tractable by a rank-one power-iteration approximation of the Jacobian. This principle drives two complementary controls: content–style latent interpolation for smooth semantic fusion, and self-attention query optimization for spatially adaptive style injection. The resulting dual-control mechanism provides continuous, fine-grained control over stylization strength while preserving identity-critical facial structure, all without additional training. Experiments show that Stylus achieves the strongest identity and content preservation at competitive stylization strength.
[CV-32] ZoomGain: Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision
链接: https://arxiv.org/abs/2611.14076
作者: Haozhan Shen,Liangbo He,Jun Lan,Huijia Zhu,Weiqiang Wang,Tiancheng Zhao,Jianwei Yin
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Code: this https URL
Abstract:Fine-grained visual perception requires Vision-Language Models (VLMs) to locate and interpret subtle evidence within complex scenes. Constructing supervision for this ability is challenging: questions must have correct, unambiguous answers that depend on actual fine-grained visual details rather than coarse cues or language priors. Verifying these properties makes manual annotation labor-intensive and automatic generation difficult for models that themselves struggle with fine-grained perception. Yet the same models can often recognize such details once the region is zoomed in. We exploit this global-to-zoom perception gap and introduce ZoomGain, a data construction pipeline that constructs questions about the full image from visual information gained only by zooming in. It verifies each answer on the zoomed view with multiple models, ensures that each question remains unambiguous in the full image and cannot be answered from language priors, and specifically generates high-quality distractors for multiple-choice questions. With this pipeline and human-only screening (no manual QA editing), we build ZoomGainBench, a 1,000-question bilingual benchmark on which the best of 28 VLMs achieves only 61.08. Failure analysis reveals that models favor a particular type of distractor when answering multiple-choice questions incorrectly. Beyond evaluation, training on just 9K ZoomGain samples substantially improves the visual perception capabilities of our 4B/9B models, enabling them to outperform several hundred-billion-parameter models across various perception benchmarks. The trained 9B model achieves SoTA performance on several benchmarks, such as 90.25 on HR-Bench 4K. These results establish global-to-zoom perception gaps as a practical basis for scaling fine-grained visual supervision for both evaluation and training. Our code is available at this https URL.
[CV-33] Obscured Wildfire Flame Detection by Spatio-temporal Analysis of Smoke Patterns Using Frame-wise Transformers
链接: https://arxiv.org/abs/2611.14074
作者: Uma Meleti,Abolfazl Razi,Fatemeh Afghah
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 8 figures. Published in the 2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), pp. 58-65
Abstract:In this paper, we propose a new paradigm in wildfire management for detecting both visible and obscured fire using Deep Learning (DL). The essence of our method is the joint spatial and temporal analysis of RGB videos, embracing the fact that temporal patterns of smoke motions in consecutive video frames can indicate obscured fire flames. This perspective contrasts the traditional approach of independently analyzing images and individual video frames. To this end, we propose a frame-wise transformer architecture that applies an attention mechanism between the consecutive frames while preserving spatial information within frames as opposed to regular attention mechanisms like the non-local block, which combines spatial and temporal information into a single vector. We also optimize the temporal pattern lengths under different spatial resolutions to achieve the highest performance while reducing the computational cost. To investigate the applicability of our method in aerial image processing, we applied it to a curated version of the FLAME2 dataset that includes dual-mode RGB/IR videos. The IR video feed is used to extract ground truth, while the model is trained only on RGB videos to maintain applicability to lowcost commercial drones (which are equipped only with regular cameras) and facilitate processing existing RGB-only datasets. Our proposed method achieves a Segmentation Foreground Dice score of 92.61%, an Object Detection Precision and Recall rate of 93.21% and 91.73%, and clip-level classification accuracy of 98.02%, respectively. We also investigated the efficacy and generalizability of our method through ablation study
[CV-34] Where Do Vision-Language Models Get Left and Right? Position-Code Counterfactuals in Qwen 2.5-VL Qwen 3-VL and InternVL3
链接: https://arxiv.org/abs/2611.14060
作者: Enhao Cui,Junzhi Yang,Yaoshun Lin,Yuhan Feng,Zijian Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 25 pages, 6 figures, 10 tables. Code: this https URL
Abstract:A vision-language model may take left-right direction from the orientation of image content within patches, its vision transformer’s (ViT) position code, or token positions and order in its language model (LLM). Ablations show the ViT code is necessary; we test sufficiency by mirroring only a position code. In Qwen2.5-VL-7B, Qwen3-VL-4B and InternVL3-2B, mirroring ViT width positions reproduces the flipped-image answer on 84.3-93.2% of CV-Bench left-right questions (Qwen2.5-VL-3B: 87.5%, partly a left fallback). With two options, this also counts disruption; a half-grid shift flips most answers too. With the ViT code intact, LLM-side id and token-order mirrors change few left-right answers in all three series (a weak id test in Qwen2.5-VL). In Qwen2.5-VL-7B the effect sits in the global-attention ViT blocks, mostly the last. On What’sUp, flipping content and token order, not the position code, keeps more on/under than left-right answers at the flipped-image answer. Post hoc analyses qualify these results. On CV-Bench items with located objects, random permutations flip 18.9-31.0 points more answers when reversing the objects’ order than when keeping it. On What’sUp, in Qwen2.5-VL and Qwen3-VL, the share of the left-right changes made by random permutations that land on the mirror relation is mostly below chance. Counted as returns to the original answer, the What’sUp contrast of support with left-right relations is clear only in InternVL3-2B. Most remaining errors give the same relation whichever object is asked about, suggesting that repair may target relation binding, object localization or use of the reference object.
[CV-35] CURRENT: A Recurrent Neural Network for Concurrent Spatial and Temporal Super-Resolution of 4D Flow MRI
链接: https://arxiv.org/abs/2611.14058
作者: Luuk Jacobs,Pietro Dirix,Sebastian Kozerke
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注: Under review
Abstract:Objective: To improve the spatial and temporal resolution of aortic 4D flow MRI through a unified deep learning framework for post-acquisition super-resolution (SR) of reconstructed velocity vector fields. Methods: We propose CURRENT, a recurrent neural network that performs spatial and temporal SR with a single network architecture. Supervised training was conducted using paired low- and high-resolution datasets generated by embedding computational fluid dynamics simulations of aortic flow into in vivo thoracic MRI backgrounds, followed by controlled spatial and temporal downsampling. CURRENT was evaluated for two-fold spatial and temporal SR against 4DFlowNet and for two-fold spatiotemporal SR against sequential spatial and temporal SR pipelines based on 4DFlowNet. Image-based and k-space-based quantitative metrics were considered and conventional interpolation were used as baselines. In addition, the impact of CURRENT’s spatiotemporal SR on physics-informed relative pressure estimation was assessed. Finally, the ability of CURRENT to generalize from synthetic training data to in vivo acquisitions was investigated. Results: CURRENT achieved superior spatial SR performance and comparable temporal SR performance to dedicated 4DFlowNet models while providing a unified solution for both tasks. Simultaneous spatiotemporal SR using CURRENT yielded more accurate relative pressure estimates than either spatial or temporal SR alone and outperformed sequential 4DFlowNet-based spatial and temporal SR approaches. Furthermore, the proposed model demonstrated promising generalization to in vivo 4D flow MRI data despite being trained exclusively on synthetic datasets. Conclusion: CURRENT provides a unified framework for spatial and temporal SR of 4D flow MRI. The proposed approach has the potential to enhance physics-based analysis and broaden the applicability of accelerated 4D flow MRI. Comments: Under review Subjects: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph) Cite as: arXiv:2611.14058 [cs.CV] (or arXiv:2611.14058v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.14058 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Luuk Jacobs [view email] [v1] Tue, 10 Nov 2026 13:25:28 UTC (11,839 KB)
[CV-36] Structured Confidence Perturbation via Fisher Geometry for Adversarial Training
链接: https://arxiv.org/abs/2611.14046
作者: Chenlong Wei,Hanling Wang,Chong Zhang,Qinkai Yu,Fangyu Wu,Xiaobo Jin
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 4 figures
Abstract:Adversarial training is often plagued by “pseudo-attacks”, perturbations that significantly increase the loss value but fail to truly alter the model’s confidence structure. To address this, we propose two complementary approaches to directly attack and defend against confidence structure deformation. First, building upon the traditional loss maximization perspective, we introduce a hybrid objective that integrates cross-entropy and KL divergence, encouraging adversarial examples to simultaneously disrupt prediction accuracy and distribution consistency. Second, we geometrically reconstruct adversarial training by generating perturbations that maximize the distribution distance on the confidence manifold. Theoretical analysis shows that the optimal perturbation direction aligns with the principal eigenvectors of the Fisher information matrix. We also propose an efficient algorithm to avoid high-dimensional matrix computations. Experiments on multiple datasets and model architectures demonstrate that our method outperforms existing methods in adversarial robustness and robust transferability, particularly excelling in structural perturbations within high-confidence regions.
[CV-37] Few-Step Streaming Video Generation via Condition Shifting
链接: https://arxiv.org/abs/2611.14033
作者: Deyuan Liu,Jungang Li,Kaiwen Zheng,Kai Jiang,Jintao Chen,Yaole Wang,Ming Lin,Tao Lin,Jianfei Chen,Jun Zhu,Jintao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Few-step sampling reduces the cost of streaming video generation. Distribution Matching Distillation (DMD) supervises few-step generators with a separate fake-score network, which adds training memory and computation and must track the evolving generator. We extend APEX to streaming video by using condition shifting to estimate the velocity field of the generated distribution within the generator. The original text condition selects generation, while a shifted condition selects fake estimation using the same parameters. This removes the independent fake network while retaining S2-Avatar’s frozen reference teacher, Self-Replay Forcing, and perceptual regularization. We evaluate APEX on 180-second videos using three backbone evaluations per block and a one-step spatial Refiner, with a 70-case human study and a 320-case StreamAV-Bench comparison. Compared with S2-Avatar, APEX achieves higher video instruction fulfillment on the Progressive track (3.856 versus 3.831) and higher visual aesthetics across both tracks (0.641898 versus 0.632606). Human raters favor APEX most strongly on instruction following, with +30.0% net preference, followed by consistency and video quality at +4.3% each. Motion quality favors S2-Avatar, audiovisual synchronization is predominantly tied, and usability is unchanged. Separate measurements under matched training settings show 4.4% lower peak allocated GPU memory per GPU and 9.2% shorter mean iteration time than DMD.
[CV-38] HiPFuse: Real-Time LiDAR Semantic Segmentation for Robotic Perception on Edge Devices
链接: https://arxiv.org/abs/2611.14025
作者: Haiyang Wu,George Vosselman,Ville Lehtola
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 13 pages, 6 figures
Abstract:Real-time robotic perception requires accurate LiDAR semantics under limited resources. Bird’s-eye-view (BEV) aggregation is efficient but can mix differently labeled surfaces in columns. We present HiPFuse, a point-BEV fusion framework that exchanges point and height-partitioned BEV features across horizontal scales, combining point-wise detail with 2D spatial context from one LiDAR scan. Fixed height assignments preserve separate vertical aggregation groups, with auxiliary supervision of initial BEV features using targets from the same 3D cells. A dual-scale BEV context module adds broader context from a coarser grid. On SemanticKITTI and MCD, HiPFuse achieves 63.7% and 62.5% mIoU at 14.2 and 22.4 FPS on Jetson AGX Orin, respectively, exceeding the 10 Hz scan rate. Separate training and evaluation show indoor applicability. Replacing only the baseline semantic front end with HiPFuse in fixed robotic systems reduces odometry absolute trajectory error RMSE by 10.6% on average across two outdoor sequences and increases accepted-loop recall by 8.7 percentage points on SemanticKITTI. Qualitative semantic maps show more continuous road labeling. Our code is available at this https URL.
[CV-39] Interleaving anatomical inspection and diagnostic reasoning for explainable radiology report generation
链接: https://arxiv.org/abs/2611.14018
作者: Chentao Huang,Runlong Lin,Zhijun Wang,Yunxiang Li,Yuanyuan Sun,Jian Wang,Hongfei Lin,Ling Luo
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Automated radiology report generation can draft clinically plausible reports, yet the pathway from visual evidence to reported findings remains opaque, leaving the diagnostic basis of these reports unexplained and the generated reports difficult to review or revise. We introduce ARIVE (Anatomy-guided Reasoning with Interleaved Visual Evidence), an explainable report generation framework that interleaves anatomical inspection with diagnostic reasoning prior to final report composition. To support this process, we constructed 159,416 multimodal chain-of-thought (MCoT) trajectories from MIMIC-CXR, wherein visual evidence from inspected anatomical regions informs subsequent clinical interpretation. Trained on these trajectories and further optimized using anatomical and clinical-content rewards, ARIVE achieved superior clinical-efficacy performance compared with strong baselines on MIMIC-CXR and the abnormality-centered MIMIC-ABN. ARIVE’s reports were competitive with those of leading models in blinded expert grading, and its MCoT trajectories earned high ratings for diagnostic traceability. Furthermore, a paired review evaluation suggests that access to MCoT can aid in identifying errors within generated reports.
[CV-40] A Stage-Wise Explainable Pipeline for Arteriolar-to-Venular Ratio Estimation and Retinopathy Screening
链接: https://arxiv.org/abs/2611.14013
作者: Sharan Sharmeela Karuppannan,Yoga Sri Varshan V,Subin Sahayam,Umarani Jayaraman
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for presentation at the 23rd IEEE India Council International Conference (INDICON 2026)
Abstract:The arteriolar-to-venular diameter ratio (AVR) is a clinically motivated marker of retinal vascular health. We present a five-stage pipeline for AVR estimation and exploratory retinopathy screening from fundus images, with inspectable intermediate outputs at each stage. The pipeline combines vessel and artery-vein masks, optic-disc localization, vessel-calibre estimation using a minimal-chord boundary walk, and AVR computation from dominant arterial and venular branches. An explicit threshold-based rule produces the screening decision, enabling errors to be traced to individual processing stages. On an exploratory cohort of 30 HRF images, we report 96.7% accuracy, an F1-score of 0.968 and ROC-AUC of 0.978. Ablation studies and an exploratory comparison with VascX examine the effects of vessel pairing, calibre measurement and aggregation. Interpretation is limited by the small cohort, proxy disease labels and absence of independent reference calibre measurements. The pipeline provides a transparent framework for investigating retinal vascular biomarkers, with further validation needed on larger, clinically graded cohorts.
[CV-41] Lipschitz-Controlled Flow Matching for Robust Image Restoration
链接: https://arxiv.org/abs/2611.13995
作者: Sedjro Salomon Hotegni,Konstantin Sonntag,Paul-Christian Bürkner,Sebastian Peitz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Flow-matching models can produce high-quality image restorations, but their learned dynamics may amplify small input perturbations and make reconstructions unstable. We introduce LipsFlower to reduce this amplification. We derive stability bounds for Flow-from- Y , which initializes the ordinary differential equation from a noise-perturbed observation, as well as for observation-conditioned posterior sampling, which conditions the velocity field on the observation throughout integration. Based on this analysis, we develop LipHDiT, a Lipschitz-aware hourglass diffusion transformer that serves as the vector-field architecture of LipsFlower. LipHDiT incorporates stabilized approximate spectral normalization, bounded modulation, and convex residual mixing. We also use adversarial finite-radius gain regularization at sampled interpolation states to penalize excessive sensitivity to the state and, for conditional sampling, the observation. We test both formulations on blind face restoration, measuring adversarial robustness and the perceptual quality of the reconstructions. LipsFlower reduces perturbation amplification while keeping restoration quality competitive with the evaluated baselines. These results suggest that Lipschitz-aware flow matching can improve the stability of generative image restoration.
[CV-42] Pulse or Provenance? What rPPG-Based Deepfake Detectors Learn
链接: https://arxiv.org/abs/2611.13992
作者: Othmane Harraq,Tamer Aldwairi
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注:
Abstract:Remote photoplethysmography (rPPG) has been proposed as a physiological cue for deepfake detection, based on the premise that synthesized faces lack a genuine cardiac signal. We examine what rPPG-style detectors learn on Celeb-DF++ (22 forgery methods) and FaceForensics++ (FF++), using one identically sampled window per real and fake video. On talking-face forgeries, simple chrominance traces reach an AUC of 0.998, exceeding a dedicated rPPG extractor (0.87). Controlled experiments indicate that this signal is not physiological in origin. On fully synthesized forgeries, it is equally discriminative when extracted from background regions or from full frames without face detection, and it is concentrated above the 0.7 to 4 Hz heart-rate band. It is largely removed by shuffling frame order but persists after normalizing resolution, frame rate and encoding, and an independent reimplementation reproduces these findings. Removing high-frequency content makes most synthesized videos appear real, adding it makes real videos appear fake, and injecting a synthetic pulse has little effect. In controlled self-reenactment experiments with two generators, animating a face from a single frame introduces the same high-frequency signal, whereas reconstructing each frame from its own source does not. Face-based chrominance detectors trained on Celeb-DF++ classify over 99% of real FF++ videos as fake, indicating that they largely encode video source rather than manipulation, and a CLIP-based detector exhibits the same behavior. A frozen CLIP backbone matches or exceeds fusions of CLIP with rPPG and frequency features. Finally, we show that the official Celeb-DF and Celeb-DF++ splits share identities and source-video content between training and test, and that custom protocols reusing test videos and identities in training inflate weaker detectors by up to 0.30 AUC.
[CV-43] RB-LDC: Redundancy-Balanced Latent Coding for Noise-Robust Diffusion Latents
链接: https://arxiv.org/abs/2611.13955
作者: Hyunseok Jeong,Jaeho Jeon,Young-Sik Kim
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 35 pages, 4 figures, including appendices
Abstract:Latent diffusion models decode approximately correct latents, making decoder sensitivity to perturbations relevant to image quality. After whitening, forward diffusion corrupts every direction at the same signal-to-noise ratio (SNR), despite unequal decoder sensitivities. We study an invertible linear layer between the encoder and diffusion process that shifts each direction’s log-SNR. Its linear minimum mean-square-error (LMMSE) recovery risk has an exact spectral expression for any whitened latent distribution. Under an energy budget and a floor ensuring invertibility, minimizing decoder-sensitivity-weighted LMMSE risk integrated over the diffusion schedule yields a unique optimal allocation. The layer aligns with the decoder’s expected pullback metric and equalizes marginal risk reduction across directions, computed by a deterministic spectral solver. At large budgets, the allocation follows a power law in sensitivity whose exponent is determined by the low-SNR tail of the loss weighting: one half for noise prediction and two thirds for data prediction under the cosine schedule, with a crossover on truncated schedules. We instantiate this principle as RB-LDC using Jacobian-based sensitivity estimates. On ImageNet at 256 x 256 resolution with SD-VAE and VA-VAE, trained RB-LDC improves reconstruction from noisy latents over budget-matched isotropic coding in all four metrics and five coding configurations, averaged over 15 SNRs and three pipeline seeds. It reduces average perturbed FID by 17.1 for VA-VAE block-local coding while preserving clean reconstruction. We also report intermediate SNRs where perturbed FID worsens. With a frozen diffusion model, RB-LDC yields lower FID point estimates in all 12 tested combinations of sampling progress and channel SNR under corruption during sampling.
[CV-44] MEOW: Medical Open-World Interactive Segmentation
链接: https://arxiv.org/abs/2611.13935
作者: Philip Matthias Winter,Maria Wimmer,Bernhard Schäfl,David Major,Dimitrios Lenis,Astrid Berg,Theresa Neubauer,Ariharasudhan Muthusami,Johannes Novotny,Georg Zotti,Katja Bühler
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: Preprint. 18 pages, 9 figures
Abstract:In open-world recognition, machine learning models not only make predictions on unknown structures (“generalized open-set”), but also continually learn and improve from those predictions over time. Interactive segmentation, despite its access to user feedback, has so far remained confined to the open-set setting. In this work we introduce MEOW, the first framework that turns non-interactive segmentation backbones into interactive open-world learners without updating learnable model parameters. MEOW combines parameter-free transductive reasoning with memory consolidation to accumulate knowledge and thereby improve future performance. Focusing on medical data, we show that MEOW consistently improves its own predictions on classes never seen during training, and transfers zero-shot to unfamiliar domains, including micro-CT data and other imaging modalities.
[CV-45] he Case for AI-Enabled Gate Monitoring as Farm Security in the UK: A PRISMA-Guided Review of the Evidence on Rural Crime Livestock Predation and Farm Access STOC
链接: https://arxiv.org/abs/2611.13933
作者: Steve Symes,Paul Fergus,Mark Carr,Omar McIver,Arwyn Jones
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Keywords: farm security; rural crime; livestock predation; camera traps; computer vision; PRISMA; evidence review
Abstract:Farm security in the UK rests on thinner evidence than its cost suggests. We ran a PRISMA guided review: twelve searches of Web of Science and Scopus on 2nd -4th November 2026 returned 1,183 records (761 unique), and grey-literature and hand-searches added 35 assessed sources. We synthesised 96 database records and 24 grey sources narratively. UK peer-reviewed evidence is small (16 records), and the main UK survey of crime and farmer mental health has 80 responses. Cost estimates rest on insurer claims: NFU Mutual put UK rural crime at £41.5 million in 2025, with livestock theft up almost 30% to about £4.5 million and dog attacks on livestock costing £1.95 million. The Home Office states that agricultural crime cannot be separately identified in its central data. We found no independent evaluation of cameras, sensors, trackers or marking; light and sound deterrents lost effect within one to five months in a systematic review of anti-predator interventions; and no study examined how often farm gates are left open or what follows. Camera-trap AI papers report accuracy on ecological datasets, none at a farm gate in the records we found, and in one comparison machine learning detected 41% of wildlife against 81% for analysts. We argue, as an inference from these gaps and not as a demonstrated effect, that continuous gate-level AI-mediated monitoring, as implemented in Fieldmark AI, would be highly beneficial for UK farm security. It would create the farm-level, time-stamped record that no official source holds, verify events that periodic human checks miss, resolve whether foxes, dogs or people were responsible, and give every other security measure something to be tested against. No study has yet shown that it reduces loss, and we set out the evaluation needed to show it.
[CV-46] CAppleNet: A Coordinate Attention-Based YOLO11 for Real-Time Apple Stem Cavity and Calyx Detection
链接: https://arxiv.org/abs/2611.13920
作者: Alican Güneş,Robert Selim Hill,Gökhan Hasanoğlu,Murat Kurt
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 6 figures, 15 tables
Abstract:Automated fruit grading, sorting, and orientation systems require real-time, accurate detection of fruit-level morphological landmarks rather than the fruit boundary alone. This paper presents a custom, fully self-collected and self-annotated dataset of 1506 apple images (1054 training, 301 validation, 151 test) captured under controlled, fixed-camera conditions and annotated for three target classes: fruit body, stem cavity, and calyx. A YOLO11s detector trained with default hyperparameters is adopted as the baseline, and a systematic, one-factor-at-a-time ablation study is conducted across input resolution, data augmentation policy, optimizer configuration, and lightweight architectural enhancement to identify the factors that most improve detection of these fine-grained landmarks under high inter-cultivar color and texture variability. Neither increased input resolution, nor any of six tested augmentation variants, nor manual optimizer tuning improved on the untuned baseline (F1 = 0.9047); only a same-slot architectural substitution of the backbone’s C2PSA block with Coordinate Attention (CA) matched the baseline’s overall F1 (0.9038) while reducing parameter count by 10.2% and achieving the study’s best mAP@0.5 (0.935) and best stem-cavity F1 (0.8217). A confidence-threshold sweep identified 0.15 as the deployment operating point for this model, and a confirmatory experiment combining the CA-swap architecture with the best-performing augmentation variant showed the two effects do not combine additively, reversing the architectural gain rather than compounding it. The resulting design recommendations — the CA-swap architecture alone, at a 0.15 confidence threshold — target real-time apple landmark detection for orientation-aware industrial sorting and alignment systems.
[CV-47] MicroBT: Centroid-Guided Synergistic Learning for Multi-Modal Micro Brain Tumor Segmentation and Counting BMVC
链接: https://arxiv.org/abs/2611.13912
作者: Zhenye Lou,Qing Xu,Stefanie Thust,Dorothee P. Auer,Zhen Chen,Wenting Duan
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the 37th British Machine Vision Conference (BMVC), 2026
Abstract:Multi-modal brain tumor segmentation is essential for clinical diagnosis, radiotherapy planning, and longitudinal monitoring. While recent studies have achieved remarkable overall segmentation performance, they rely on overlap-based objectives that inherently favor large regions, causing sub-3 mm micro-lesions to be systematically suppressed during training. Moreover, existing pixel-level segmentation frameworks lack explicit instance-level modeling, leading to unreliable lesion counting that directly impacts clinical staging and therapeutic decisions. To address these challenges, we propose MicroBT, a unified multi-modal framework for micro brain tumor segmentation and counting. Our core insight is that a fundamental geometric property is inherently scale-invariant: regardless of size, each lesion possesses a well-defined centroid. Leveraging this, we first devise a centroid-anchored synergistic paradigm that utilizes predicted centroids as shared anchors to simultaneously condition dense segmentation and instance-level localization. To counteract the tumor size imbalance, we further devise a Volume-Calibrated Loss (VC-Loss) that adaptively amplifies the gradient contribution of micro-lesions, alongside a Size-Aware Curriculum Optimization (SACO) that stabilizes multi-task training by establishing global representations before progressively unlocking fine-grained localization. Extensive experiments demonstrate that MicroBT outperforms state-of-the-art methods, particularly on metastases, where micro-lesion ( \leq 3 mm) localization precision improves by approximately 7% and counting estimation errors decrease by approximately 26%. The code is available at this https URL.
[CV-48] Adapting a Vision Foundation Model for Fracture Characterization in Acoustic Image Logs
链接: https://arxiv.org/abs/2611.13911
作者: Guangyu Wang,Xiaodong Ma,Xinming Wu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Natural fractures (NFs) and drilling-induced tensile fractures (DITFs) in acoustic image logs provide insights into rock mass structure and in situ stress, but manual characterization is time-consuming. Deep learning can automate this task, although expert-labeled data are scarce. We investigate whether adapting a pretrained vision foundation model (VFM) outperforms task-specific models trained from scratch with limited annotations. Using 80 annotated acoustic image samples, we fine-tuned DINOv3, pretrained on satellite imagery, and evaluated it on four boreholes excluded from fine-tuning. The adapted model estimated NF and DITF parameters comparably to human interpretation. Relative to U-Net, YOLOv8, and Swin Transformer models trained from scratch, it yielded fewer false positives and negatives and more accurate fracture counts and orientations. However, its performance advantage generally narrowed as training sample size decreased. We also assessed decoder architecture, fine-tuning strategy, and image enhancement. A progressive upsampling decoder with full fine-tuning performed best among the DINOv3 configurations tested, while fracture-focused image enhancement did not consistently improve accuracy. These results demonstrate the potential of VFM adaptation for fracture characterization in acoustic image logs with limited labeled data and suggest a transferable workflow for other annotation-scarce geoscientific imaging tasks.
[CV-49] EVAThink: Evidence Sufficiency-Guided Training for Adaptive Thinking in Video Understanding
链接: https://arxiv.org/abs/2611.13907
作者: Changti Wu,Ruizhe Fan,Bin Wang,Lei Zhang,Yuxiang Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The code, model, and datasets are available at \href{ this https URL }{EVAThink}
Abstract:Under a fixed frame budget, video understanding with vision-language models (VLMs) depends on whether the sampled frames provide sufficient evidence for the question. Sufficient frame evidence supports direct answering, whereas less informative observations may benefit from reasoning that connects the available cues. We introduce EVAThink, which enhances video understanding through evidence sufficiency-guided training for adaptive thinking. First, Recoverable Evidence Gap Mining constructs evidence-dense and evidence-sparse video views that share the same question and answer under an identical frame budget. It retains pairs where sparse views yield lower direct-answer accuracy than dense views but benefit from explicit reasoning, providing supervision for response-mode selection. Second, Evidence-Guided Two-Stage Reinforcement Learning uses stage-specific rewards to guide training with evidence-based mode preferences and then refine response efficiency, while first-token advantage regularization mitigates mode collapse. Trained on only 6.7K curated samples, EVAThink consistently improves upon the base model across video understanding benchmarks under both 32- and 64-frame budgets, while outperforming strong baselines on average. The code, model, and datasets are available at \hrefthis https URLEVAThink.
[CV-50] Persistent Metric-Semantic Maritime Scene Memory from Open-Vocabulary 3D Scene Graphs
链接: https://arxiv.org/abs/2611.13903
作者: Kim Alexander Christensen,Dina Zimmermann,Martin Steinert,Eleni Kelasidi
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Website: this https URL
Abstract:Three-dimensional scene graphs (3DSGs) can provide robots with persistent metric-semantic representations for spatial reasoning, but the existing methods have primarily been developed for indoor and terrestrial environments. In this paper we investigate how a representative outdoor open-vocabulary method transfers to maritime environments and which adaptations are needed. Our experiments show that zero-shot representative outdoor 3DSG descriptions agree with our labeled ground truth for a majority of geometry-supported objects on our data, but the resulting map exhibits considerable physical-entity fragmentation, and its terrestrial higher-level assumptions don’t capture maritime spatial organization needs. We therefore keep the underlying mapping method and post-process its accumulated evidence. We show a metric-semantic consolidation method followed by geometry-grounded multi-view vision-language model (VLM) reasoning that reconciles remaining fragments into physical entities and groups entities through observations into local places. In parallel, we construct maritime macro regions using map priors and onboard water and structure observations, and associate them with the local places. The resulting representation reduces physical-entity fragmentation beyond both the baseline and our geometry-semantic consolidation component alone, supports correspondence of fixed local places across repeated maritime traversals, and improves contextual retrieval on our reviewed queries over tested baselines. These results show how an outdoor open-vocabulary map can be adapted into persistent maritime scene memory with our methods for object/place identity and spatial abstractions.
[CV-51] A 10K drone image dataset with component-level annotations for rooftop photovoltaic inspection
链接: https://arxiv.org/abs/2611.13892
作者: Penglei Zhang,Wan Zhang,Ruizhe Deng,Bo Chen,Wenhao Duan,Jinhe Zhang,Yanzhen Zhang,Qi Chen,Zhiling Guo,Haoran Zhang,Yuntian Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 4 figures. Supplementary information is included as an ancillary file
Abstract:Component-level information supports rooftop photovoltaic (PV) inspection, but recognizing individual components across complex scenes remains challenging. We present PVIS-10K, a dataset of 10,065 drone images with component-level annotations covering 63,632 physical PV modules across 22 rooftop installations. The high-resolution red-green-blue (RGB) images were acquired by unmanned aerial vehicles (UAVs, also known as drones) in Ningbo, Shaoxing and Shenzhen, China. Multi-angle oblique observations capture installation-height and scale variation, dense arrangements, occlusion, shadows and reflections. Three-dimensional delineation, reprojection and image-by-image review produced 3,516,675 image-level polygon annotations, stored in Common Objects in Context (COCO) format. Station-disjoint partitions support evaluation on unseen stations within and across cities. Four supervised models and text-prompted zero-shot Segment Anything Model 3 (SAM 3) provide full-resolution benchmarks of instance matching and foreground coverage. PVIS-10K supports component localization, delineation and image-based counting for drone-based rooftop PV inspection research.
[CV-52] DroneSegEdit: Adapting Pretrained Generative Editors for UAV Segmentation with Language-Grounded Verification WACV2027
链接: https://arxiv.org/abs/2611.13890
作者: Dehua Chen,Long Xin,Jiangtao Wei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at WACV 2027
Abstract:Recent work has shown that pretrained image generators can support diverse visual tasks. Yet directly applying them to language-guided unmanned aerial vehicle (UAV) segmentation exposes a key limitation: a plausible mask may correspond to the wrong instance among small and repeated objects. We call this mismatch between mask rendering and expression-level target selection the Grounding-Rendering Gap. We present DroneSegEdit, which combines RGB proposal rendering with language-grounded, proposal-constrained verification to select supported foreground while retaining rendered geometry. DroneSegEdit adapts the open-weight LongCat-Image-Edit model using task-specific LoRA and fixed color-to-label decoding. For referring segmentation, a Language-Grounded Spatial Verifier (LGSV) reads hierarchical states from the frozen multimodal large language model backbone to derive expression-conditioned spatial evidence, which is mapped back to the rendered proposal without introducing foreground outside it. On RIS-LAD, DroneSegEdit reaches a new state of the art at 52.78 mIoU, 7.16 points above the previous best. It remains competitive overall on UAVid-RIS and VDD-RIS, with its clearest class-wise gains on compact, application-relevant categories, notably vehicles and humans. A separate verifier-free experiment extends the RGB interface to full-scene parsing: a task-specific adapter jointly renders all classes from one task-level instruction without enumerating class names or palette colors.
[CV-53] Region-Wise Logit Fusion and Utility-Gated Refinement for Brain Metastasis Segmentation MICCAI2026
链接: https://arxiv.org/abs/2611.13884
作者: Zhenye Lou,Qing Xu,Wenting Duan,Zhen Chen
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the Brain Tumor Segmentation Challenge - Metastases (BraTS-METS), held in conjunction with MICCAI 2026
Abstract:BraTS-METS 2026 Task 1 evaluates segmentation of brain metastases and postoperative resection cavities on heterogeneous multiparametric MRI, requiring detection of small enhancing foci while preserving the geometry and hierarchy of enhancing tumor (ET), tumor core (TC), whole tumor (WT), and resection cavity (RC). We present COMPASS-METS (Component-Aware Multi-Trainer Probability Assembly with Structured Segmentation), which combines three five-fold nnU-Net configurations into aligned regional probability ensembles. A structured-probability anchor applies region-specific component decisions, TC boundary completion, RC filtering, and hierarchy-preserving conversion. Omitted ET candidates are recovered additively using learned component confidence and deterministic multi-trainer support for ET and its TC/WT parents; a final utility gate admits only disconnected candidates with sufficient lesion-existence and geometry-safety probabilities. On the reused 179-case challenge development cohort, COMPASS-METS (Team: MicroBT) achieved Small F1 0.354, lesion-wise DSC 0.712, NSD 0.714, All F1 0.779, and HD95 56.6 mm. Relative to ResEncXL, it improved DSC, NSD, and All F1 and reduced HD95, while Small F1 changed from 0.367 to 0.354. Stagewise analysis attributes the principal geometric gains to structured probability construction and shows that guarded ET refinement recovers most of the associated small-lesion detection loss. These 179-case results remain selection-aware development estimates. The source code is publicly available at this https URL.
[CV-54] he BRIDGE dataset: paired brightfield whole-slide images of prostate core-needle biopsies in paraffin after deparaffinization and HE staining
链接: https://arxiv.org/abs/2611.13883
作者: Julia A. Mielcarz,Justyna Durślewicz,Marek Zdrenka,Ahmad Abbadi,Martin Eklund,Mateusz Matusiak,Łukasz Szylberg,Andrea Camilloni
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Computational approaches are increasingly being explored to streamline histopathology workflows, including label-free methods that aim to reduce reliance on chemical staining. Among these, deep learning-based label-free virtual staining uses images of unstained tissue to computationally generate stained images. Despite growing interest, model development and validation remain constrained by the scarcity of paired, clinically annotated whole-slide imaging datasets. We present BRIDGE, a dataset of prostate core-needle biopsies collected at the Prof. Franciszek Lukaszczyk Oncology Centre in Bydgoszcz, Poland. The dataset comprises 869 glass slides from 169 patients, each digitized sequentially after sectioning, deparaffinization, and hematoxylin and eosin staining, yielding in total 2607 whole-slide images. The dataset supports the development and external validation of label-free virtual staining, virtual unstaining, image-registration and segmentation methods. Each slide is accompanied by a Gleason score, enabling downstream analyses of cancer classification and grading concordance. The stained images can be used independently for color normalization and cross-scanner robustness studies. All images are de-identified, distributed in their native scanner format and will be available through the BioImage Archive.
[CV-55] Spatial-Spectral Coupling as Attention: Scaling Shape Correspondence to High-Resolution Surfaces
链接: https://arxiv.org/abs/2611.13870
作者: Bence Demeter,Kilian Ehrenreich,Amelie Kleber,Ioana Oprisiu,Philipp Spohn,Christian Wachinger,Fabian Bongratz,Lennart Bastian
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Establishing dense correspondence between high-resolution 3D shapes remains challenging due to the spatial–spectral coupling at the core of many functional-map methods, which grows quadratically with mesh resolution. We observe that this coupling is equivalent to a scaled dot-product attention formulation, with learned descriptors serving as queries and keys and spectral basis functions as values. This connection makes efficient attention implementations directly applicable to shape matching and an effective mechanism for scaling spatial-spectral coupling. We show that optimized quadratic attention implementations can scale modern deep functional map pipelines to large mesh sizes, improving both runtime and memory consumption, while sub-quadratic methods introduce errors, particularly for non-isometric shape pairs, where an exact coupling proves most necessary. These insights motivate \textitGeo-FlashAttention, which restricts the exact softmax to geometry-selected surface patches and achieves near-linear empirical scaling while preserving correspondence accuracy of exact implementations. Across six standard benchmarks and three functional-map pipelines, rigorous analysis shows that efficient couplings preserve correspondence accuracy. At 60k vertices, these insights reduce peak training memory from 106.08 to 5.67,GiB, with Geo-FlashAttention being 1.5\times faster per training step than FlashAttention. This enables end-to-end learning for correspondence problems on large meshes such as cortical surfaces, where high resolution is required to preserve disease-relevant intricacies of the surface geometry. Project page available this https URL
[CV-56] Mechanistic Interpretability of Deep Neural Networks: Methods Frameworks and Perspectives
链接: https://arxiv.org/abs/2611.13868
作者: Lorenzo Cirillo,Irene Amerini,Andrea Cavallaro
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Deep neural networks underpin fundamental tasks in a wide range of signal domains, such as image, audio, and language processing. However, their internal mechanisms are notably difficult to interpret. Internal activations are structured signals transformed across layers that may encode a multitude of interdependent relationships, as individual neurons frequently represent multiple underlying concepts. Mechanistic interpretability aims to uncover the internal mechanisms of neural networks by identifying components whose activations are causally responsible for specific behaviors. In such a context, this article provides an overview of advances in mechanistic interpretability for the analysis of causal inference in deep neural networks.
[CV-57] owards Text-Only Remote Sensing Scene Classification via Semantic Proxy
链接: https://arxiv.org/abs/2611.13851
作者: Xianping Ma,Kunlin Wu,Xiaofeng Liu,Xiaokang Zhang,Yuanxin Ye
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 4 figures, 14 tables, including appendices
Abstract:Remote sensing scene classification (RSSC) is traditionally formulated as a vision-centric problem, where inference relies on rich visual representations extracted from images. In this work, we revisit this assumption by introducing text-only RSSC, a new problem setting that investigates whether remote sensing scenes can be recognized using only textual semantic representations. To enable this study, we propose SeProRS, which reformulates RSSC as a text-only classification task through semantic proxy generation. Specifically, a vision-language model (VLM) is first employed to convert remote sensing images into textual descriptions, termed semantic proxies, which are then encoded by a text embedding model for scene classification. This decoupled pipeline provides a unified and reproducible benchmark for evaluating text-only scene understanding. Furthermore, we investigate representative prompting strategies and proxy generators, and analyze their impact on classification performance, text embedding behavior, cross-generator transferability, and efficiency. Extensive experiments on three well-known RSSC datasets show that semantic proxies can preserve substantial discriminative information, while also revealing inherent limitations of purely text-based scene understanding. This paper does not aim to replace image-based RSSC, but instead establishes a new research direction by quantifying the feasibility and boundaries of semantic proxy-based scene recognition. The proposed benchmark and analysis provide a foundation for future research on efficient and modality-reduced remote sensing scene understanding. The source code and datasets will be accessible at this https URL.
[CV-58] Satellite Pretraining Is Not One Thing: A Matched-Pair Study of Generic and Earth-Observation Foundation Models for Scene Classification WACV2027
链接: https://arxiv.org/abs/2611.13849
作者: Sandarbh Agrawal,Rakshith Sathish,Ashwathi Nambiar
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the 3rd Workshop on Computer Vision for Geospatial Image Analysis (GeoCV) at WACV 2027
Abstract:Geospatial foundation models are pretrained on satellite and aerial imagery, but whether such an encoder beats a strong generic one for few-label land-use/land-cover mapping remains unclear, as prior comparisons rarely isolate the pretraining data. We study five matched pairs of Earth-observation (EO) and generic encoders sharing objective and architecture (DINO/SatDINO, DINOv3/DINOv3-SAT, MAE/SatMAE, MAE/Scale-MAE, CLIP/RemoteCLIP) within a benchmark of 14 frozen encoders on five RGB scene-classification datasets (0.06-30 m GSD). With native preprocessing, we evaluate k-NN, linear and attentive probes and image retrieval over six low-label splits, using paired per-image significance tests. EO pretraining is not uniformly helpful: on most datasets and metrics, it significantly improves three pairs (SatDINO, Scale-MAE, RemoteCLIP, the latter evaluated only on unseen datasets) and significantly hurts two (linear probe: DINOv3-SAT by up to 22.5 points, SatMAE by up to 8.8), with opposite outcomes even for two MAEs pretrained on the same fMoW-RGB imagery. These effects are robust to patch-token pooling and attentive probing, concentrate on small man-made objects, and are largest with 1-5% labels. Across encoders, the silhouette score of frozen embeddings predicts k-NN accuracy and retrieval mAP almost perfectly (Spearman \rho \geq 0.96 on four of five datasets). Pretraining on satellite imagery is thus not a reliable selection criterion; candidate checkpoints must be validated on target data.
[CV-59] CASCADE: Constraint-Aware SCAffolded DEcomposition for Logical Anomaly Detection ICDM2026
链接: https://arxiv.org/abs/2611.13820
作者: Jinchan Kim,Jinhee Im,Hyunsouk Cho
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at the 26th IEEE International Conference on Data Mining (ICDM 2026). 10 pages, 6 figures, 6 tables
Abstract:Logical Anomaly Detection (LAD) requires verifying whether all category-specific constraints, such as object counts, pairing relations, and measurement ranges, are satisfied by a query image given only a few normal references. Recent LAD methods based on Large Vision-Language Models (LVLMs) formulate this as checklist verification, decomposing the decision into independent textual checks. This decomposition, however, is text-only. Every item still reads the same unmodified query image, so the LVLM must extract different forms of visual evidence from one raw image. We identify this text-image mismatch as a constraint-specific perception gap, the source of two failure modes: instance conflation for binding constraints and scale drift for calibration constraints. To diagnose whether these failures stem from insufficient reasoning or insufficient perception, we compare textual reasoning guides and visual perception guides in a training-free setting. Our analysis shows that reasoning over the same image provides little benefit, whereas adapting the visual input to each constraint substantially improves verification accuracy. Based on this finding, we propose CASCADE, a training-free framework that extends checklist decomposition using constraint-specific visual scaffolds. We further introduce CASCADE++, which adapts these scaffolds at the object level by deriving their configurations from the constraint structure and reference distribution. Across MVTec-LOCO and VID-AD, CASCADE++ surpasses the full-shot state of the art by 3.9 AUROC points with only three references, and improves over the strongest VID-AD baseline by 11.4, transferring across multiple LVLM backbones without any training.
[CV-60] FastWild-GS: Minutes-Scale In-the-Wild Gaussian Splatting via Compact Per-Image Appearance Adaptation
链接: https://arxiv.org/abs/2611.13808
作者: Donghun Ryu,Seokhyun Kim,Minhyeok Lee
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, 13 tables
Abstract:Internet photos of a scene differ in exposure, illumination and transient objects such as passers-by, so one set of Gaussian colours cannot explain all photos (the in-the-wild setting). Recent in-the-wild Gaussian Splatting methods mostly report hours of per-scene optimization. We present FastWild-GS, which describes each photo by a 128-dimensional Photo Appearance Token and a per-channel transform, a scale and an offset applied to the rendered image (six parameters); a small MLP predicts the colour of each Gaussian from this token and a per-Gaussian feature, replacing the spherical-harmonics colour. The same transform is applied to the renders that score Gaussians for densification and pruning, so appearance differences between photos are not counted as reconstruction error. On PhotoTourism, FastWild-GS trains in 8.8 minutes per scene with the lowest LPIPS among the compared methods, 23x faster than AsymGS, the next best in LPIPS. On NeRF-OSR, it has the best average PSNR, SSIM and LPIPS among the compared methods in 9.7 minutes per scene.
[CV-61] Relaxing Cross-Modal Correspondence for Multimodal Remote Sensing Pretraining
链接: https://arxiv.org/abs/2611.13807
作者: Xianping Ma,Yu Zhang,Mingzhe Jiang,Wanxin Xie,Qing Zhu,Nan Xu,Xiaokang Zhang,Yuanxin Ye
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 16 pages, 9 figures, 13 tables
Abstract:Multimodal remote sensing pretraining commonly relies on exact instance-level correspondence across modalities, implicitly treating strictly paired observations as a prerequisite for learning transferable representations. In this work, we revisit this assumption by relaxing cross-modal correspondence from the instance level to the semantic level. We introduce Semantic Pairing (SemPair), a pairing-relaxed pretraining strategy that replaces exact instance pairs with class-consistent cross-instance pairs. To characterize the effect of correspondence relaxation, we formulate a latent-factor model that separates cross-modal dependence into shared semantic structure and instance-specific correlation, showing that SemPair preserves the former while marginalizing instance-specific cross-modal correlations in expectation. We further introduce Mixed Pairing (MixPair) as a controlled interpolation between semantic and exact pairing to study correspondence granularity along a continuous spectrum. Extensive experiments consistently show that SemPair achieves transfer performance comparable to exact pairing (ExaPair), while MixPair reveals a non-monotonic relationship between downstream transfer and the instance-pairing ratio. These results demonstrate that exact instance correspondence is not a prerequisite for effective multimodal remote sensing pretraining and establish cross-modal correspondence granularity as an important design dimension for transferable multimodal representation learning. More broadly, this finding suggests a practical route toward scaling multimodal pretraining with reduced dependence on costly instance-aligned data.
[CV-62] he Target Is a Predicate: Predicate-State Grounding for Semantic-Level Video Reasoning Segmentation
链接: https://arxiv.org/abs/2611.13785
作者: Yuhuan Hong,Yiming Ren,Zhiqiang Lin,Feize Wu,Xingyun Liu,Yitong Xie,Lei Zhou,Lisheng Wu,Junjie Wang,Yujiu Yang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In this paper, we introduce Predicate-State Video Reasoning Segmentation (PS-VRS), where a query defines a dynamic predicate P(o,t) over objects and time rather than a fixed object identity. Existing methods typically treat the target as permanently active once identified, leading to over-emission of masks during vacant intervals and failure to handle target switching. To address this, we construct PS-Bench, a large-scale benchmark with 2,077 videos and 16,250 queries annotated with explicit active/vacant predicate states, temporal intervals, and pixel masks. We further propose Predicate-State Grounding (PSG), a State-Bind-Emit pipeline that: (1) predicts [ACTIVE]/[VACANT] state tokens; (2) aligns them to video frames via dense cross-modal similarity; and (3) emits masks via SAM2 only for active states. Experiments show that PSG outperforms existing baselines by +9.32 JF on PS-Bench and significantly reduces false-positive mask emissions. Our results demonstrate the effectiveness of explicit predicate-state modeling for video reasoning segmentation with dynamic visual witnesses.
[CV-63] Marrying Capacity and Precision for Efficient 2D Gaussian Image Representation
链接: https://arxiv.org/abs/2611.13784
作者: Lin Dong,Qi Ming,Liuqian Wang,Wentao Chao,Yufei Gao,Chengming Liu,Lei Shi
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:2D Gaussian representations provide an explicit and efficient way to represent images, but their rate-distortion performance under limited budgets depends critically on how Gaussian capacity and parameter precision are allocated. Existing methods typically optimize these two resources separately, overlooking content-dependent capacity demands and heterogeneous quantization sensitivity. We present CP-GS, a capacity-precision coupled Gaussian splatting framework for budget-aware 2D Gaussian image representation that jointly optimizes capacity allocation and attribute precision under a shared rate constraint. CP-GS first constructs a content-adaptive Gaussian representation by dynamically reallocating a fixed Gaussian budget from redundant regions to areas with higher representation demand. It then introduces hierarchical distortion-aware representation quantization, using efficient rendering-based calibration to allocate precision across Gaussian attributes and further refine it within heterogeneous attribute groups. Finally, it jointly optimizes Gaussian capacity and attribute precision by constructing candidate representations with different Gaussian counts and precision configurations and selecting operating points according to decoded distortion and actual serialized size. Experiments on DIV2K and Kodak show that CP-GS achieves improved rate-distortion performance over representative 2D Gaussian and implicit neural representations across a range of budgets, while maintaining a fixed overall representation budget and low optimization cost. These results demonstrate the benefit of jointly optimizing spatial capacity and parameter precision for compact Gaussian image representation.
[CV-64] DisasterAg ent: An Evidence-Driven Agent for Verifiable Disaster Report Generation From Remote Sensing Imagery
链接: https://arxiv.org/abs/2611.13776
作者: Zhijian Li,Jiawei Song,Chao Ren,Yidan Zhang,Peijin Wang,Xian Sun,Kun Fu
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Remote sensing disaster assessment is moving from simple damage classification toward comprehensive report-level analysis. In this setting, responders need not only damage descriptions but also verifiable evidence to guide response and recovery. Recent vision-language models (VLMs) and tool-augmented agents offer strong multimodal reasoning and perception, but they struggle to provide the evidence required for high-stakes verification. To address these limitations, we propose DisasterAgent, an evidence-driven agent that uses a Perception-Augmentation-Generation workflow to generate verifiable disaster reports from bitemporal optical imagery. DisasterAgent first invokes a specialized vision expert toolkit in the perception phase to build a Spatiotemporal Perception Graph (STPG), serving as a structured memory to store and organize disaster-induced changes across space and time. Evidence-based Augmentation (EA) then extracts evidence from the STPG, attaching inline citations that form a closed evidence loop from each statement to the supporting visual evidence. Extensive experiments on the DisasterM3 and RSCC benchmarks demonstrate that DisasterAgent consistently outperforms general-domain VLMs, remote sensing-specialized models, and existing remote sensing agents. Notably, compared with the original model, it improves the average score for disaster report generation on DisasterM3 by 13.5% and ST5-SCS on RSCC by 15.34%, providing a more accurate and verifiable solution for disaster emergency response. The code will be available at this https URL.
[CV-65] RF3DGS: Reflection-Free 3D Gaussian Splatting with Self-Priors
链接: https://arxiv.org/abs/2611.13772
作者: Qiang Wen(1),Guotao Meng(1),Junhong Ruan(1),Hongyu Liu(1),Chenyang Lei(2),Qifeng Chen(1) ((1) The Hong Kong University of Science and Technology, (2) Huawei Research)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures, 5 tables. Joint first authors: Qiang Wen, Guotao Meng, and Junhong Ruan
Abstract:3D reflection removal is crucial in applications demanding high-fidelity visualization, such as museum exhibitions. In this work, we propose Reflection-Free 3D Gaussian Splatting (RF3DGS), a novel method for superior reflection removal. RF3DGS exclusively leverages a widely-adopted photometric prior for reflection removal: that the minimum pixel intensity across multi-view images corresponding to the same 3D point strongly correlates with reflection-free information. However, generalizing this principle to a 3D Gaussian Splatting within a multi-view context is non-trivial. To address this, RF3DGS first optimizes a vanilla 3DGS model using a geometric prior to estimate a coarse, reflection-free depth map for each view. Leveraging these depth maps and photometric prior, a 3D warping process aggregates reflection-free information from auxiliary views to construct a clean transmission pseudo-label for a given reference view. This pseudo-label provides a robust self-supervised signal for optimization. We further introduce an Adaptive Twin Rendering (ATR) loss to synergistically utilize this pseudo-label while preserving high-frequency details from the original, reflection-degraded images. By adeptly fusing these internal 3D self-priors with the ATR loss, RF3DGS demonstrates state-of-the-art reflection removal performance across both synthetic and real-world scenarios. Furthermore, we establish the first 3D reflection removal benchmark, based on the Unity rendering engine, for rigorous quantitative evaluation.
[CV-66] From Depth to Safe Depth: Uncertainty-Aware Monocular Perception for Mobile Robot Navigation
链接: https://arxiv.org/abs/2611.13769
作者: Saeid Firouzi Daghigh,Saeed Ayat
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Monocular depth foundation models make it tempting to replace depth sensors on mobile robots with a single RGB camera. We show that, used off the shelf, this is unsafe: in a simulated indoor navigation benchmark, a standard Nav2 stack driven by Depth Anything V2 (metric) collides in 63% of 120 episodes, because the model systematically over-estimates the distance to nearby obstacles. We propose a lightweight plug-in module that turns predicted depth into \emphsafe depth. A 0.58,M-parameter head on top of the \emphfrozen depth model predicts, per pixel, a log-depth correction \mu and an uncertainty \sigma , trained with a \beta -weighted Gaussian negative log-likelihood. The navigation stack consumes the one-sided bound \hat d_\textsafe=d,e^\mu-k\sigma , where k is set by conformal calibration on a held-out world, and uncertainty also decides which pixels may be treated as floor. The correction halves the depth error (AbsRel 0.262\rightarrow0.134 ) and reduces near-range over-estimation from 59.8% to 12.0%. In navigation, safe depth raises the success rate from 37% to 70% and lowers collisions from 63% to 3%, close to the 2% of ground-truth depth. A constant safety margin calibrated to the same pixel coverage reaches only 30% success because it freezes the robot; uncertainty-aware safety wins 52 of 56 discordant paired episodes against it. Gains hold on glass, texture-less walls and dark regions.
[CV-67] Exploring Synchrony for Communication in Modular Neural Networks ICLR2027
链接: https://arxiv.org/abs/2611.13759
作者: Nikolas Willems,Qiyang Sun,Björn W. Schuller
类目: Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI)
备注: Under Review on ICLR2027
Abstract:One motivation for modular neural networks is to divide computation across components that learn distinct, reusable functions, which could then be recombined to support compositional generalisation. In practice, modular structure alone does not guarantee that such roles develop, and unrestricted communication between modules can undermine any specialisation that does emerge. This raises the question of how modular communication should be organised. The problem can be viewed as an analogue of the perceptual binding problem, where synchrony has been proposed as a mechanism for coordinating distributed representations. We introduce SyncNet, a modular architecture that separates module content from a dynamical phase state: learnt Kuramoto dynamics evolve the phases, while phase alignment determines communication through a shared channel. We evaluate SyncNet on Sort-of-CLEVR for relational reasoning and on SQOOP for systematic generalisation, where lower pair variety demands more recombination at test time. SyncNet gives the best held-out accuracy of the models compared at the three lowest pair varieties, by 5.1 percentage points at the hardest, and is competitive on Sort-of-CLEVR though below the strongest attention baselines. Test-time overrides show that performance depends on phases that evolve: fixed phases cost as much as no addressing at all, and permuting which module holds which region is nearly as damaging. A content-addressed variant trained from scratch nonetheless matches it, so the benefit appears to come from the modular decomposition rather than synchrony itself.
[CV-68] Effects of Laser Beam Width and Measurement Noise on Tree Diameter Estimation in Laser Scanning
链接: https://arxiv.org/abs/2611.13739
作者: Jesse Muhojoki,Nuutti Ervasti,Eric Hyyppä,Aapo Erkkilä,Eppu Honkanen,Tuomas Yrttimaa,Mikko Vastaranta,Juha Hyyppä
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Laser scanning has been utilized for decades to measure tree structure and geometry. Despite continuous advances in sensor technology, uncertainties arising from scanner characteristics and measurement conditions has remained relevant sources of error. In particular, factors such as laser beam diameter can introduce both systematic and random errors in the reconstructed tree geometry. This study investigates the main sources of these errors in mobile laser scanning (MLS) and quantifies their effects on stem curve (stem diameter) estimation. We discovered through simulations and experiments that the heteroscedasticity (difference in variance in ranging and scan angle) in the measurement noise is a key factor along with the beam width of the lidar, which cause the bias of the diameter estimates to depend not only on distance, but also on the tree diameter. We demonstrated that this bias can be reliably predicted and negated for trees with a diameter at the breast height greater than 10 cm using technical specifications of the sensor without any additional calibration. In experiments, the proposed bias compensation method reduced the root-mean-square error of stem diameter estimates to sub-centimeter level on boreal forest test sites. However, for small trees with diameter below 10 cm, the diameter bias becomes sensitive to the diameter of the tree \unicodex2013 an unknown factor \unicodex2013 and other factors, such as different bark and out-of-roundness of the stem, limiting the precision of lidar for measuring geometry of trees and applicability of the presented method.
[CV-69] Hint Not Hole: Localization-Robust Video Overlay Removal
链接: https://arxiv.org/abs/2611.13711
作者: Dongyang Zhang,Wei Zhang,Renjie Zou,Zhiwei Huang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video overlay removal recovers the scene beneath burned-in subtitles, karaoke lyrics, titles, credits, logos and watermarks. Generative video models now fill a known region convincingly, but in practice the region comes from a detector or a segmenter, and it is wrong in both directions: it misses strokes, halos, whole frames and whole objects, and it covers background between characters, around boxes and on genuine scene text. Erasers that treat the mask as a hole, regenerating inside and copying outside, inherit every one of these errors. We present HintErase, which treats localization as a hint, not a hole. The model sees the full, unmasked video together with a hint channel; during training the hint is corrupted the way real localizers fail, while supervision follows the true overlay. The result is a single model that removes overlay the hint missed, keeps background the hint wrongly covered, and is trained to keep genuine scene text even when the hint lands on it. For full-length releases, a streaming pipeline handles interlaced sources, shot cuts, missed and jittering hints, paste-back and audio, with memory independent of the length of the video. This report describes the approach, the pipeline that turns it into full-length deliveries, and qualitative results; video results, with input, hint and output side by side, are on the project page.
[CV-70] CJRR-Net: A Candidate-Joint Reprojection Ranking for Future-View Selection in Human Pose Estimation
链接: https://arxiv.org/abs/2611.13700
作者: Huiting Xi,Marsha Mariya Kappan,Eduardo Benitez Sandoval,Francisco Cruz
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10pages, 4 figures, 4 tables. Accepted for oral presentation at the Australasian Conference on Robotics and Automation (ACRA 2026)
Abstract:Multi-camera systems support performance recording, sports and human-movement analysis, where selecting an informative viewpoint is important for reliable human pose estimation. We propose the Candidate-Joint Reprojection Ranking Network (CJRR-Net) for future-view selection among eight calibrated cameras in MPI-INF-3DHP. The model combines current-view pose observations, camera configuration, and candidate-specific geometric features obtained by reprojecting the estimated 3D pose and its uncertainty into each candidate view. CJRR-Net achieves a selected-view 2D mean per-joint position error (MPJPE) of 105.18 pixels, compared with 114.64 pixels for the shared-input MLP and 117.56 pixels for adapted PoseErrNet. It also achieves the lowest mean error among the listed policies in each evaluated occlusion group. A blender rollout illustrates repeated camera selection. These findings support candidate-specific geometry for single-view selection in the evaluated fixed eight-camera setting.
[CV-71] Ego Guidance: Enhancing Robot Policy Learning with Action-Free Human Egocentric Videos
链接: https://arxiv.org/abs/2611.13685
作者: Lin Long,Tianren Ma,Zhong Chu,Di Wu,Maosong Cao,Qixiang Ye,Junbo Zhao,Shanghang Zhang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:In this paper, we investigate how human egocentric videos can benefit robot policy learning without action supervision. Building on a pretrained VLM, we propose Shared Prediction on Action and Representation (SPAR), an MoT architecture in which future visual state prediction can provide independent and complementary learning signals alongside action prediction. We find that representations learned from egocentric videos improve downstream performance on out-of-distribution robot tasks, with further gains as video data scales. We also study the effects of objective choices and data curation, providing insights into how to leverage action-free videos for scalable robot learning.
[CV-72] EgoHMP: Achieving Precise Human Motion Prediction in 3D Scenes via Egocentric Cues NEURIPS2026
链接: https://arxiv.org/abs/2611.13654
作者: Xin Zhao,Pengzhan Zhou,Ziyi Li,Riheng Jia,Zhida Qin,Yu Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026 (Main Conference)
Abstract:The complex interactive dependencies between human behavior and 3D scenes make accurately predicting human intent a major challenge for human motion prediction (HMP). While recent studies use human gaze to infer intent, gaze signals suffer from ambiguity, drift, and a reliance on eye-tracking hardware. Research in cognitive science indicates that egocentric views inherently encode potential interactions, while head motion serves as a crucial precursor in motion planning. Motivated by this, we propose EgoHMP, a novel framework that uses egocentric views and head motions as robust carriers of interaction intent. By translating this intent into an envisioning of future interactions, EgoHMP achieves precise HMP in 3D scenes. EgoHMP first employs a vision-motion alignment encoder to align the visual and motion feature spaces. Based on these aligned features, an egocentric intent predictor with a modality-aware modulation mechanism adaptively suppresses invalid interaction cues to predict an accurate interaction probability map. Furthermore, to enhance the stability and realism of the generated motions, we propose a contact-aware motion decoder. Specifically, inspired by the human cognitive process of planning trajectories prior to action execution, we first predict a global trajectory to serve as a self-prompt, effectively mitigating the optimization conflict in diffusion models. Subsequently, we introduce a multi-stage denoiser, which relieves motion artifacts by incorporating human contact modeling through a multi-stage optimization strategy. Finally, a GCN-based motion decoder is employed to synthesize physically plausible and semantically consistent motions. Extensive experiments demonstrate that EgoHMP achieves state-of-the-art performance.
[CV-73] Predicting Single-Cell Spatial Gene Expression from HE using Pathology Foundation Models NEURIPS2026
链接: https://arxiv.org/abs/2611.13649
作者: Ashley P. Tsang,Evan Liu,Conrad Foo,Bob Chen,Hassaan Maan,Donny Chan,Elisa Penna,Veronica Ibarra Lopez,Hector Corrada Bravo,Aicha BenTaieb,Lisa M. McGinnis,Runmin Wei,Kenneth T. Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: NeurIPS 2026 Workshop on AI at Scale for Clinical Impact. Project page: this https URL
Abstract:Predicting gene expression directly from hematoxylin and eosin (HE) images offers a scalable and cost-effective approach to deriving spatial molecular insights from routine histology. Pathology foundation models, typically pretrained using tile-level objectives, may not adequately capture the fine-grained cellular features required for single-cell molecular prediction. In this work, we systematically evaluate and establish domain-informed and task-specific strategies for adapting pathology foundation models to single-cell spatial gene expression prediction. Using paired HE and Xenium 5K data, our approach improves mean gene-wise Pearson correlation by 2.67x over the standard zero-shot baseline and achieves state-of-the-art performance on this task. We demonstrate strong gene-detection performance across the full gene panel (AUROC = 0.800) and importantly identify that predictive performance varies substantially across biological contexts, with our model achieving its highest Pearson correlation for spatially variable genes (r = 0.643). Our findings demonstrate that, despite the challenges of sparse single-cell measurements, HE-derived representations can recover biologically meaningful spatial expression patterns, highlighting both the opportunities and limitations of morphology-based molecular inference at cellular resolution.
[CV-74] picoEmergencyNet : Attention-Enhanced Ultra-Small Deep Learning Model for Aerial Image Classification in Emergency Monitoring IJCNN2026 IJCNN
链接: https://arxiv.org/abs/2611.13638
作者: Christos Kyrkou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, Appeared in IJCNN2026 C. Kyrkou, “picoEmergencyNet: Attention-Enhanced Ultra-Small Deep Learning Model for Aerial Image Classification in Emergency Monitoring,” 2026 International Joint Conference on Neural Networks (IJCNN), Maastricht, Netherlands, 2026, pp. 1-6
Abstract:Real-time disaster recognition from aerial imagery requires deep learning models that operate under strict memory, compute, and latency constraints. While recent approaches achieve high accuracy, their computational cost limits deployment on resource-constrained UAV platforms. This work introduces picoEmergencyNet, an ultra-lightweight convolutional neural network designed for aerial image disaster classification. The proposed architecture combines depthwise dilated convolutions with efficient channel attention and channel shuffling to enable multi-scale feature extraction at minimal computational cost. picoEmergencyNet has 23k parameters and 7M FLOPs, requiring only 0.1 MB of model storage. Evaluated on the AIDER and AIDERv2 disaster datasets, it achieves up to 96.1% accuracy, matching or exceeding state-of-the-art lightweight models that are orders of magnitude larger. Deployment experiments on the NVIDIA Jetson AGX Orin show that, without hardware-specific optimizations, picoEmergencyNet achieves 258 FPS on GPU and 46 FPS on CPU with a 7x lower activation memory footprint compared to competing models. These results demonstrate that careful architectural specialization, rather than model scaling, enables highly efficient and accurate TinyML-ready disaster recognition for real-time UAV-based emergency monitoring.
[CV-75] DepthGS: Depth-Guided Gaussian Splatting with Adaptive Regularization for High-Quality Surface Reconstruction
链接: https://arxiv.org/abs/2611.13628
作者: Xiao Ren,Yu Liu,Ning An,Jian Cheng,Xin Qiao,You-Fu Li,He Kong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: To appear in IEEE Trans. on Pattern Analysis and Machine Intelligence
Abstract:Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient representation for novel view synthesis, offering fast differentiable rendering and high visual fidelity, and has increasingly been extended to surface reconstruction. However, the relationship between depth information and reconstruction error remains insufficiently explored, limiting the principled design of geometric optimization strategies. To fill the above gap, we propose DepthGS, a depth-guided Gaussian splatting framework. We derive theoretical relations between depth discrepancies and reconstruction error under single-view and multi-view settings, and rigorously establish corresponding upper bounds on reconstruction error. The above analysis informs the design of adaptive regularization strategies in both single-view and multi-view optimization. More specifically, for single-view optimization, we propose a depth-difference-guided adaptive normal regularization strategy that modulates the normal constraint strength using a geometric reliability weight computed from local depth differences. For multi-view optimization, we introduce a cross-view depth consistency regularization strategy that promotes consistency between neighboring-view depth maps and the corresponding depth predictions obtained by transforming the current view’s depth predictions into those neighboring views. Extensive experiments on DTU, Mip-NeRF360, and Tanks and Temples demonstrate that DepthGS achieves competitive performance in both surface reconstruction and novel view synthesis.
[CV-76] FlashMotion: Ultra-Fast Long-Term 3D Motion Generation
链接: https://arxiv.org/abs/2611.13626
作者: Kaixing Yang,Xulong Tang,Ronghui Li,Xiangyue Zhang,Ziqiao Peng,Mingshuang Luo,Chenxi Zhao,Haoyu Wu,Ju Fan,Hongyan Liu,Puwei Wang,Zijiao Zeng,Jun He
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 7 figures, 10 tables. Code: this https URL
Abstract:3D human motion generation plays a crucial role in game development, extended reality (XR), digital humans, and embodied simulation. Beyond motion generation, practical pipelines include rigging, retargeting, and rendering, making ultra-fast long-term 3D motion generation without compromising quality valuable for real-time interactive applications. Despite promising progress, high-quality long-horizon motion generation under tight inference budgets remains challenging, owing to 2 coupled bottlenecks: (1) generative strategies that require many sampling steps for high-quality 3D motion generation and (2) model architectures with substantial per-step computational and memory costs for long sequences. We present FlashMotion, a co-designed framework for high-quality long-horizon 3D motion generation under a strict inference budget, covering text-to-motion, music-to-dance, and speech-to-gesture tasks. To enhance trajectory-level efficiency, we introduce a 3D motion-space MeanFlow formulation with kinematic and dynamic consistency constraints for few-step generation, together with an Adaptive Soft masking mechanism for temporally coherent motion editing. To improve per-step efficiency, we design a lightweight non-autoregressive BiMamba architecture with condition-guided state selection and AdaIN-based condition fusion mechanism, enhancing cross-modal alignment and generalization to longer motion sequences. Extensive experiments demonstrate that FlashMotion achieves overall state-of-the-art performance in both quality and efficiency across text-to-motion, speech-to-gesture, and music-to-dance generation. Code is available at FlashMotion (this https URL).
[CV-77] PUMA: Probability-Constrained Sequential Interaction for Forensic Localization
链接: https://arxiv.org/abs/2611.13621
作者: Zhouli Yu,Mengyuan Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, 1 table
Abstract:Image manipulation localization must jointly exploit semantic structure and noise-sensitive forensic evidence. However, the effects of spatial correspondence, matching quality, and interaction order are often conflated. Existing semantic-forensic fusion methods provide complementary representations, but do not explicitly preserve quality-mismatch information during spatial correspondence or adaptively select the order in which information is propagated across branches. We propose PUMA (Probability-constrained Unbalanced Matching and Aggregation), which combines spatially constrained unbalanced optimal transport with position-wise convex selection between two sequential operators through a shared positional interface. Before normalization, PUMA retains signed transport-mass residuals, constructs row-stochastic cross-branch and within-noise-branch operators, and updates noise features through a gated residual path. PUMA achieves strong localization results on four test sets, with scores on COVERAGE and NIST16 exceeding the published results of the representative methods listed in our comparison. The experiments support the utility of local matching states and adaptive sequential interaction for cross-source localization and several post-processing conditions.
[CV-78] When to Start Denoising? Adaptive Denoising Entry for Generative Video Compression
链接: https://arxiv.org/abs/2611.13620
作者: Zifu Zhang,Yuqiao Li,Ding Ding,Shengxi Li,Mai Xu,Daowen Li,Kai Li,Ying Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 8 figures
Abstract:Generative video compression offers a promising solution for compressing and reconstructing videos under extremely low bitrate constraints, among which the diffusion-based solutions still achieve the best performance by providing the powerful generative latent prior. However, their effectiveness critically depends on when the compressed latent is introduced into the diffusion process, which directly governs the trade-off between reconstruction fidelity and perceptual realism. To address this limitation, we identify denoising start-timestep selection as a key factor governing this trade-off and formulate it as a geometric optimization problem. Based on this formulation, we propose a novel adaptive diffusion entry method for generative video compression (ADE-GVC), the first timestep-aware generative video compression framework, featuring a lightweight predictor that estimates an adaptive denoising start point from the compressed latent. A temporal adapter further extracts compression-aware conditions and injects them into a pretrained video diffusion model, enabling the reconstruction of videos with both high fidelity and realism. More importantly, we observe a strong quantitative relationship between the predicted timesteps and the distortion between the original and compressed latents, providing empirical support for our optimization formulation. Extensive experiments demonstrate that ADE-GVC achieves the state-of-the-art performance on perceptual metrics and substantially improves reconstruction realism over existing generative video compression methods. Notably, our ADE-GVC achieves an average bitrate savings of 40.99% (on DISTS) and 95.24% (on PSNR) compared to the latest open-source baseline GLC-Video.
[CV-79] Lightweight Deep Learning Framework for Astrocyte-Centric 3D Reconstruction across Normal and Stress-associated Ultrastructural States
链接: https://arxiv.org/abs/2611.13581
作者: Bowen Deng,Gwang-Jin Jeong,Seulgi Noh,Ji Young Mun,Na Young Kim
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注: Accepted for publication in npj Imaging. 19 pages, 4 figures, 6 tables, including Supplementary Material. Code is available at this https URL
Abstract:The morphology of astrocytes is closely associated with their ability to support and modulate neural circuit functions; however, its reconstruction from volume electron microscopy (vEM) data remains labor-intensive and computationally resource-intensive, placing substantial barriers on its broader adoption and scalability despite the high spatial resolution of vEM. We present AstroSeg-vEM, a lightweight, astrocyte-centric framework for morphology-preserving astrocyte segmentation, 3D reconstruction, and quantitative morphological analysis in vEM images. AstroSeg-vEM combines transformer-based 2D segmentation with dedicated post-processing methods and operates directly on high-resolution EM sections, making it well suited to anisotropic vEM data and downstream proofreading workflows while preserving the full in-plane resolution of the original EM data. Across four mouse hippocampal datasets representing normal and morphologically defined stress-associated astrocyte ultrastructural states, AstroSeg-vEM demonstrates robust performance and generalization, achieving average Dice and weighted Dice values of 0.92 and 0.95, respectively, on a held-out test dataset. Its lightweight architecture enables efficient, high-fidelity reconstruction on consumer-grade hardware, providing a scalable and accessible platform for large-scale astrocyte ultrastructure analysis and future astrocyte-neuron interaction studies.
[CV-80] PhyRubric: Rubric-Grounded Diagnostic Evaluation of Physical Commonsense in Text-to-Video Generation
链接: https://arxiv.org/abs/2611.13576
作者: Huiyi Chen,Weitai Kang,Shicheng Fan,Xinshuo Wang,Zhixiang Shen,Yan Yan,Philip S. Yu,Baoxin Li
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 34 pages, 19 figures
Abstract:As text-to-video (T2V) models are increasingly positioned as world simulators, it is essential to evaluate whether their videos conform to physical commonsense. However, existing benchmarks rely on annotators who rate each video with a single score based on intuition, yielding ratings that are subjective and do not tell which physical principle is violated. We introduce PhyRubric, a rubric-grounded diagnostic framework for evaluating physical commonsense in T2V generation. For each caption, PhyRubric constructs a caption-specific rubric before any video is seen, decomposing the caption into semantic and physical items, and derives scores from item-level verdicts grounded in timestamped evidence. We instantiate this framework as PhyRubric-Bench, which spans 34 categories across five physical domains, and scale it up to PhyRubric-Train with 3,788 videos, on which we fine-tune the open-source PhyRubric-Judge. Empirical results across ten T2V models show that physical commonsense remains far from solved, and that PhyRubric-Judge correlates more closely with human judgments than existing open-source judges while matching the frontier proprietary Gemini 4 Argon. Further analysis provides actionable insights for T2V models.
[CV-81] Agent icConflictLens: From Long Traffic Videos to Verifiable Conflict Evidence
链接: https://arxiv.org/abs/2611.13540
作者: Boshra Khalili,Sonia Moshfeghi,Jinwoo Jang,Andrew W Smyth
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traffic cameras continuously record urban road-user behavior, providing proactive safety evidence beyond crash reports. Yet safety-critical events - conflicts, near misses, unsafe turns, sudden braking, and crosswalk interactions - are rare and brief, buried in hours of routine footage. Existing traffic-video pipelines extract detections, trajectories, and risk scores but process footage uniformly, without adapting to an analyst’s query. Vision-language methods improve scene understanding but often lack traffic-specific verification of participant-level conflicts. We introduce AgenticConflictLens, an agentic, query-conditioned framework for efficient, auditable conflict extraction from long fixed-camera traffic videos. Given a natural-language safety query, a Traffic-Aware Controller combines structured traffic memory with specialized conflict-analysis tools to choose where to search, which road users to examine, and which evidence to verify or reject. A coarse-to-fine search retrieves candidate intervals and applies detailed verification selectively. The framework produces conflict records with refined time intervals, participant pairs, semantic regions, trajectories, motion evidence, uncertainty, rejection reasons, and supporting clips. On 96 annotated hours from Bellevue and NYC DOT, AgenticConflictLens achieves the highest conflict-existence F1 and temporal IoU on both datasets: 0.84 F1/0.77 tIoU on Bellevue and 0.80 F1/0.73 tIoU on NYC DOT, while inspecting only 8.7% and 11.9% of video, respectively. These results support scalable, evidence-grounded urban traffic-safety analysis.
[CV-82] UniWeaver: Harnessing Unified Multimodal Models for Interleaved Text-Image Generation
链接: https://arxiv.org/abs/2611.13530
作者: Hongxu Chen,Zixuan Zhong,Donghao Zhou,Haodong Li,Xiaomin Yu,Tianhao Li,Shiyao Yu,Zhongwei Wan,Pheng-Ann Heng
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:Unified multimodal models (UMMs) have opened up new possibilities for generating rich interleaved text-image content. However, they generally lack native support for coordinating the two modalities, and translating coarse user requests into coherent multimodal compositions also requires explicit planning. We propose UniWeaver, a structured, model-agnostic harness framework that integrates multimodal planning, generation, and visual feedback to produce coherent interleaved outputs. To train UMMs for this task, we construct UniWeaver-10K, a dataset of 10,000 tasks across 20 task families, paired with execution trajectories guided by UniWeaver. We also introduce UniWeaver-Bench, a task-oriented benchmark systematically assessing text coverage, writing quality, visual coverage, and image-text alignment. Experiments show that UniWeaver achieves state-of-the-art performance and improves interleaved text-image generation across multiple UMM backbones.
[CV-83] GEMOR-GS: Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM
链接: https://arxiv.org/abs/2611.13528
作者: Yuchen Yang,Ming Ding,Xinyu Wu,Hesheng Wang
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Photorealistic and metrically consistent world representations are a primary requirement for LiDAR–inertial–visual SLAM systems that serve robots over continuous operation. 3D Gaussian splatting makes such representations attainable by supplying an explicit, renderable, and queryable scene model. We present GEMOR-GS (Global Extrinsic-calibration-refined Mapping and Online Relocalization for 3D Gaussian-Splatting SLAM) to make the Gaussian map geometrically accurate, globally refined, and re-enterable, providing a better appearance for robot perception and decision-making. To improve the 3DGS map, the LiDAR–camera extrinsic is first estimated offline by direct-method alignment over dedicated calibration segments and reused by all following online and offline processes. Under the calibrated geometry, keyframe poses and Gaussian parameters are refined alternately under a gated differentiable-rendering and LiDAR–projection objective. The final global reconstruction is then rebuilt from the refined keyframe schema with all online constraints lifted. For the robot SLAM lifecycle, the method recovers the pose from the 3DGS map after tracking loss through an XFeat bypass map, RANSAC-PnP coarse localization, and Gaussian RGB-D warp refinement. Experiments on one self-collected and five public sequences quantify each element: the offline reconstruction wins all paired keyframes against the online map by 14.3–19.9 dB, improves PSNR by approximately 9 dB over Gaussian-LIC2, and achieves 86–100% held-out relocalization success tied to extrinsic quality. The results show that GEMOR-GS improves the geometric accuracy, appearance fidelity, and re-enterability of the 3DGS map, serving as a dependable robot world model for SLAM and downstream tasks.
[CV-84] Seeing the Small: Deformable Duality Mamba for Retinal Lesion Segmentation
链接: https://arxiv.org/abs/2611.13514
作者: Chang Yu,Siqing Li,Yuanchen Liu,Zhenyu Bu,Luming Yang,Alper Yilmaz
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 5 pages, 4 figures, 1 table
Abstract:Diabetic retinopathy (DR) lesions vary widely in shape, and microaneurysms (MAs) span only a few pixels. We hypothesize that modeling the spatial organization of lesion regions, with global context and shape-adaptive scanning, improves the segmentation of such small and irregular lesions. To test this, we propose D ^2 Mamba, which couples non-causal state space duality with deformable Mamba scanning. We evaluate it with five-fold cross-validation on the DDR dataset, comparing against representative segmentation baselines under an identical training protocol, and conduct ablations on each component. D ^2 Mamba achieved the best overall performance, with 33.87% mIoU, 2.99 percentage points above the strongest baseline. The largest gain appeared on MAs, the smallest and most easily missed lesions, where Dice improved by 10.06 percentage points. These results confirm our hypothesis that spatially adaptive state space modeling benefits tiny-lesion segmentation. Code and pretrained weights are available at this https URL.
[CV-85] Evidential Deep Learning with conditional entropy for glaucoma detection TAAI2026
链接: https://arxiv.org/abs/2611.13513
作者: Hai Siong Tan,Kuancheng Wang
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 4 figures. To appear in TAAI 2026
Abstract:We propose a novel conditional entropy function as a basis for quantifying predictive uncertainty in Evidential Deep Learning (EDL) for glaucoma detection from retinal fundus images. Using the EyePACS-AIROGS-light-V2 and RIM-ONE-DL datasets for validation, we found that our proposed EDL model regularized by conditional entropy function produced uncertainty estimates with substantially lower calibration errors than Deep Ensembles, MC Dropout, post-hoc calibration and the conventional EDL framework based on Dempster uncertainty. To further probe our models’ interpretability, we analyzed Grad-CAM heatmaps using a focus index representing spatial concentration. Compared with the other uncertainty quantification methods, EDL models exhibited greater separation between the index distributions of correctly and incorrectly classified fundus images. In particular, false negatives were associated with localized Grad-CAM activations that were displaced from the optic disc, consistent with its established clinical importance in glaucoma assessment. Overall, our findings suggest that our proposed EDL framework for glaucoma detection provides not only enhanced uncertainty calibration but also admits a more physiologically consistent interpretability.
[CV-86] MAL: Topology-preserving Multi-offset Affinity Learning for Curvilinear Structure Segmentation WACV2027
链接: https://arxiv.org/abs/2611.13511
作者: Suho Han,Soungsill Park,Kanghyun Ryu,Yoseob Han
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
备注: 15 pages, 11 figures, submitted to WACV 2027
Abstract:Curvilinear structures such as retinal vessels and surface cracks need both pixel-level accuracy and structural continuity. Pixel-wise supervision does not model the connections between pixels, and topology-based losses still produce ordinary masks. Connectivity-based methods predict these connections directly, but they use binary connectivity targets over a fixed local range. Such labels give the same value to thin segments and to thick regions, and they cannot describe connections at larger offsets. To address these limitations, we propose Topology-preserving Multi-offset Affinity Learning (TMAL). TMAL uses soft affinities that emphasize thin regions near the boundary, and it predicts directional affinities at several offsets to reach further. It then combines these affinities with weights that come from the decoder features, so local continuity and wider context are used together. An auxiliary segmentation branch adds region-level supervision and keeps the overlap accuracy high. On the retinal vessel dataset DRIVE and the crack inspection dataset DeepCrack, TMAL reaches the highest Dice, IoU, and clDice scores, and it greatly reduces the connected-component error \Delta\beta_0 . Code is available at this https URL.
[CV-87] Distribution Shift Detection in Remote Sensing Imagery using Machine Learning Models WACV2027
链接: https://arxiv.org/abs/2611.13510
作者: Abdullah Almansour,Ozan K. Tonguz
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
备注: Accepted as a short paper at the 4th Workshop on Computer Vision for Earth Observation (CV4EO), held in conjunction with WACV 2027
Abstract:Remote sensing imagery acquired by Unmanned Aerial Vehicles (UAVs) supports wide range of applications; however, deploying machine learning (ML) models on resource-constrained aerial platforms introduces challenges beyond predictive accuracy, particularly when deployment data differ from the distributions observed during training. Such shifts are common in remote sensing due to geographic, sensor, seasonal, and other visual variations. A practical solution is to equip deployed models with mechanisms that detect emerging distribution shifts and support subsequent responsive actions. This paper presents an empirical case study of three open-source detection tools: (i) Alibi-Detect; (ii) NannyML; and (iii) PyTorch-OOD. Using cross-dataset remote-sensing shifts, we evaluate both detection effectiveness and computational latency of these tools. Our results reveal differences in detection behavior and processing cost across methods and implementations, highlighting trade-offs between detection capability and response-time. These findings motivate further work toward lightweight, and latency-aware monitoring mechanisms for resource-constrained systems.
[CV-88] Dark Matters: VAE-Aware HDR Encoding for SDR-to-HDR Video Reconstruction
链接: https://arxiv.org/abs/2611.13504
作者: Donghoon Cha,Bomseok Park,Hansl von Kwon,Ku DoHyung,Hanwool Sul
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 13 pages, 4 figures
Abstract:SDR-to-HDR reconstruction is an ill-posed problem in which clipped highlights, crushed shadows, and quantization artifacts must be recovered while preserving the original content. Latent diffusion models provide powerful generative priors for restoring missing detail, but their effectiveness is constrained by how HDR signals pass through the pretrained VAE. We find that common log-like encodings suppress low-luminance chromatic differences before generation, producing a dark-side collapse in the frozen-VAE latent. We introduce EquivFlow, a compact, invertible exposure-equivariant encoding that maps exposure changes to additive shifts while retaining a scale-independent chromatic component, without modifying the VAE. Encoding-level tests across pretrained image and video VAEs show that its exposure structure and deep-dark chroma transport persist across the tested hosts. Integrated into SDR-to-HDR video diffusion, EquivFlow delivers consistent chromatic-fidelity gains across the tested settings while retaining competitive reconstruction fidelity.
[CV-89] Reasoning -Aware Relational Representation Learning for Open-Vocabulary Scene Graph Generation NEURIPS2026
链接: https://arxiv.org/abs/2611.13496
作者: Jiawei Xiao,Boya Wang,Longtian Qiu,Shan Ning,Xuming He
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by NeurIPS 2026, codes are available at project page this https URL
Abstract:Open-Vocabulary Scene Graph Generation (OV-SGG) requires models to recognize visual relationships beyond the training vocabulary, yet existing methods often rely on dataset-specific object–predicate co-occurrence patterns. Under long-tailed distributions and noisy object localization, such reliance leads to biased, entangled relation representations, limiting both rare-relation recognition and generalization to unseen predicates. To address these challenges, we propose RaRe, a reasoning-aware relational representation learning framework that transforms MLLM-generated chain-of-thought (CoT) rationales into structured relation embeddings. Rather than using CoT only as an intermediate reasoning trace for final prediction, RaRe directly optimizes the rationale-derived representation space to improve inter-relation discriminability. Specifically, RaRe first aligns rationale embeddings with textualized relation descriptions through self-supervised contrastive learning, and then refines rationale generation with a specificity-oriented reinforcement learning objective that encourages semantically object-pair-specific relational evidence. Experiments on VG150 and PSG demonstrate improved category-balanced recognition over existing baselines, while the additional gains from learning vary across datasets and proposal settings. Code available at: this https URL.
[CV-90] When Reconstruction Helps Event-Camera Person Triggers
链接: https://arxiv.org/abs/2611.13488
作者: Yifan Shuai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 3 figures, ancillary score tables and recomputation script
Abstract:Event cameras record asynchronous changes in brightness. To use a general visual model as a recording trigger, a system must represent these measurements in a form the model can interpret. We compare direct grayscale accumulation, two temporal encodings, and two intensity reconstructors on paired real event data. On 192 windows from four held-out DSEC recordings, thresholds calibrated only on development data yielded 40 of 52 person detections and 12 of 140 background positives with E2VID. The strongest direct temporal encoding at an admissible fixed cutoff yielded 14 detections and seven background positives. A retrospective comparison at a common 10% background-positive cap yielded 40 detections for E2VID and at most 19 for any direct encoding. FireNet+ had lower preparation cost and produced 21 detections with zero observed background positives at its frozen cutoff. On the same 192 windows, Qwen3-VL-4B yielded 33/52, 2/140 with E2VID and 9/52, 9/140 with the highest-recall direct rendering, the three-bin image (person detections, background positives). A 96-second exploratory replay illustrates the sensitivity of episode capture and recording starts to cutoff selection. The experiments identify a detection benefit from reconstruction under controlled input history and distinguish it from the cost and false starts of continuous triggering.
[CV-91] Hand-Object Contact Transition Detection in Egocentric Video ACCV2026
链接: https://arxiv.org/abs/2611.13486
作者: Huy Anh Nguyen,Chang Dong,Feras Dayoub,Minh Hoai
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACCV 2026
Abstract:This paper tackles the task of hand-object contact transition detection in egocentric video, which aims to localize the two temporal boundaries of a contact episode: the moment a hand first establishes contact with an object (touch) and the moment contact is released (release). We propose a unified framework that jointly models the interacting hand and object to accurately localize both transition types. The proposed framework augments hand-centric spatiotemporal representations with object-aware features learned directly from the video and introduces a transition-aware event spotting formulation tailored to contact transitions, without relying on external object or hand-object interaction detectors during inference. To support this task, we present ContactTransition, a dataset comprising 4,021 egocentric videos with 8,498 touch and 8,077 release annotations, together with object-of-interest supervision. Experiments demonstrate that the proposed method consistently outperforms strong two-stage and end-to-end event-spotting baselines on both transition types. Finally, we show that detected contact transitions provide effective temporal anchors for downstream egocentric hand-object reasoning.
[CV-92] Camera3R: Feed-Forward 3D Reconstruction from Heterogeneous Camera Models NEURIPS2026
链接: https://arxiv.org/abs/2611.13453
作者: Ruxiao Duan,Yunwen Zhou,Erin Hong,Dongxu Zhao,Eric Turner,Federico Tombari,Alex Wong
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
Abstract:Feed-forward 3D reconstruction (3R) from unconstrained multi-view images has recently emerged as a prominent research direction in 3D computer vision. Various foundational reconstruction models have been proposed with different architectures and reconstruction paradigms. Despite being trained on large-scale perspective datasets with substantial computational resources, these models generally struggle to generalize to non-perspective images. This limitation hinders their deployment in applications involving arbitrary, unknown, and potentially heterogeneous camera models. To build on the capabilities of existing reconstruction models with minimal retraining, we propose Camera3R, a low-rank adaptation framework that efficiently extends perspective-pretrained 3R networks to any other camera models. Extensive experiments demonstrate that, with only 0.5% additional parameters and less than a day of training, Camera3R adapts a foundational 3D reconstruction model to non-perspective cameras while fully preserving its original performance on perspective inputs. Code is available at this https URL.
[CV-93] Prompt2Echo (P2Echo): Text-Prompted Multi-View Echocardiography Segmentation with One Model
链接: https://arxiv.org/abs/2611.13425
作者: Moein Heidari,Alvin Kimbowa,Mahdi Tabatabaei,Afshin Bozorgpour,Sina Ghorbani Kolahi,River Jiang,Wenjin Chen,David J. Foran,Jasmine Grewal,Dorit Merhof,Ilker Hacihaliloglu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 5 figures, 7 tables
Abstract:Automated segmentation in multi-view echocardiography is essential for comprehensive cardiac assessment, yet remains challenging due to substantial anatomical and appearance variability across standard acoustic windows. In this paper, we present P2Echo, a unified framework capable of segmenting diverse cardiac structures across multiple standard echocardiographic views using a single model controlled by structured view-aware text prompts. Unlike recent foundation model adaptations that rely on implicit latent priors, P2Echo explicitly conditions the segmentation process via a novel Dynamic Image-Text Alignment (DyITA) module. DyITA integrates a differentiable routing mechanism and token differential operators to dynamically modulate decoder features according to the requested view and target structure. This allows the network to adaptively switch its processing pathways for different views without architectural changes. Extensive experiments on a large-scale internal dataset (seen during training) and held-out evaluation on three external datasets demonstrate that P2Echo matches or exceeds both view-specific specialists and state-of-the-art universal models at a fraction of their computational cost, offering an efficient, controllable solution for multi-plane cardiac analysis. Code is available at: this https URL.
[CV-94] How Hyperbolic Are Hyperbolic Vision-Language Models?
链接: https://arxiv.org/abs/2611.13384
作者: Freek Byrman,Partha Das,Stijn Verdenius,Pascal Mettes
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Hyperbolic vision-language models (VLMs) have emerged as a rapidly growing area of research, reporting strong zero-shot performance alongside an interpretable embedding space in which radius orders representations by generality. But how hyperbolic are hyperbolic VLMs in practice? We investigate this at the exponential map, the single operation through which curvature enters an otherwise Euclidean network. We show that across MERU, HyCoCLIP, ARGENT, and PHyCLIP, the exponential map is well approximated by the identity function, i.e., pairwise distances and angles in the pre-map Euclidean space barely change under projection, and removing the map at inference leaves evaluation scores unchanged. Motivated by this observation, we train Euclidean VLMs on CC3M from scratch under the same hierarchy losses. Over roughly the final 90% of the training steps, the training statistics are nearly indistinguishable from those of their hyperbolic counterparts, and the resulting models match the evaluation scores and recover the same radial distributions. Our findings establish Euclidean space as a sufficient substrate for hierarchical vision-language learning as it is currently practiced, and motivate new paradigms in which hyperbolic geometry can deliver on its theoretical promise. Code is available at this https URL.
[CV-95] Uni-Loc: Weakly Supervised 3D CT Pathology Localization via Spatially Grounded Vision-Language Classification
链接: https://arxiv.org/abs/2611.13330
作者: Hao-Chih Lee,Zahi A. Fayad,Xueyan Mei
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate localization of abnormalities in three-dimensional (3D) CT scans is essential for linking automated predictions with clinically relevant pathological findings. We propose Uni-Loc, a vision-language framework that unifies pathology classification and 3D localization using scan-level supervision. Uni-Loc derives both predictions and localization maps from spatial similarities between pathology-conditioned queries and image features. A token-to-voxel upsampling module enables high-resolution localization, while gradient-guided thresholding identifies pathology regions without additional learned parameters. Evaluated on five localization tasks comprising 355 annotated CT scans, Uni-Loc achieves macro-averaged voxel-level AUROC/AUPR of 96.1%/34.5%, outperforming relevance propagation (80.7%/13.9%) and gradient saliency (88.7%/8.1%). Uni-Loc also achieves classification AUROC of 88.39% on CT-RATE and 78.03% on RAD-ChestCT, improving over a matched baseline. These results demonstrate effective 3D pathology localization without requiring pathology-specific voxel-level supervision.
[CV-96] Robust Flow Matching under Target Corruption and Label Noise NEURIPS2026
链接: https://arxiv.org/abs/2611.13324
作者: Mert Can Kurucu,Erik Englesson,Hossein Azizpour
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at NeurIPS 2026. 37 pages, 10 figures
Abstract:Flow Matching (FM) is a strong framework for generative modeling due to its stable training and efficient sampling. However, FM assumes clean target data and labels, an assumption often violated in practice. We formulate FM training as a time-dependent regression problem over conditional vector fields and analyze how the two corruption types affect the objective: i) target corruption adds a time-dependent residual term that grows with time, ii) label noise causes updates to the wrong class-conditional vector field. Based on this analysis, we propose to improve FM robustness by mapping corruption-specific residual scores to sample weights. For target corruption, we propose the Temporal Residual Score (TRS), which emphasizes residual magnitudes at intermediate-to-late FM times, where clean and corrupted samples are more separable. For label noise, we propose the Comparative Conditional Score (CCS), which compares the residual of the observed class-conditional vector field against alternative class-conditional vector fields for the same trajectory and velocity target. Experiments across controlled corruption settings and real-world noisy labels show that the proposed methods improve robustness over standard FM and robust-training baselines, supporting corruption-specific residual structure as a practical basis for robust FM.
[CV-97] ABRR: Transforming Temporal Sentence Grounding with Wasserstein-Adaptive Refinement and Precision Ranking
链接: https://arxiv.org/abs/2611.13310
作者: Xiang Fang,Wanlong Fang,Junhao Dong,Daizong Liu
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: Accepted by IEEE Transactions on Multimedia
Abstract:Temporal Sentence Grounding (TSG) aims to localize temporal segments in untrimmed videos that correspond to natural language queries, a critical task for video understanding with applications in video retrieval, summarization, and human-computer interaction. Despite significant progress driven by transformer-based models, existing TSG methods suffer from two key limitations: quantization errors due to temporal downsampling, which misalign segment boundaries, and suboptimal ranking of predictions based on semantic matching rather than localization precision. To address these challenges, we propose the Adaptive Boundary Refinement and Ranking (ABRR) framework, a novel, model-agnostic post-processing approach that enhances the performance of any TSG model without retraining. ABRR integrates three innovative components: (1) Temporal Distribution Smoothing, which mitigates noise in predicted score distributions using Gaussian kernel convolution; (2) Boundary Refinement via Optimal Transport, which leverages the 2-Wasserstein distance and Sinkhorn iterations to align coarse segment boundaries with true temporal moments, grounded in Monge-Kantorovich optimal transport theory; and (3) Quality-Driven Re-Ranking, which reorders predictions using a composite score balancing localization precision and semantic relevance. By correcting quantization errors and prioritizing accurately localized segments, ABRR achieves substantial improvements over state-of-the-art TSG models. Extensive experiments on benchmark datasets, including Charades-STA, ActivityNet Captions, and QVHighlights, demonstrate consistent gains at stringent IoU thresholds (e.g., IoU=0.7), alongside enhanced ranking performance. The framework’s computational efficiency and compatibility with existing models make it a versatile and impactful solution for real-world TSG applications.
[CV-98] Domain-Adversarial Adaptation from MURA to In-House Radiographs for Improved Adult Elbow Fracture Classification
链接: https://arxiv.org/abs/2611.13308
作者: M. Bolhassani,W. Furtado,D. Duvall,MJ Negahdar,J. Joshi,S. Contractor,A. Amini
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 3 figures, Accepted and presented on SPIE Medical Imaging 2026
Abstract:We studied domain shift in adult elbow fracture classification from radiographs when training on a large public dataset and deploying on a small, institution-specific dataset (UofL). We implemented a domain-adversarial neural network (DANN) to align feature distributions across source (MURA) and target (UofL) domains while optimizing fracture classification. A pretrained convolutional backbone was adapted to grayscale inputs; the task head predicts fracture presence, and a gradient-reversal-based domain head encourages domain-invariant features via adversarial learning. We compared four settings: (1) Source-only, (2) Target-only (limited labels), (3) Naive merge (pooled training), and (4) DANN (MURA to UofL). Evaluation on UofL test data uses AUC-ROC, and accuracy. DANN outperformed Source-only and Naive merge baselines by reducing domain bias without overfitting to the small target set. Ablations showed that progressive adversarial weight, dataset-size-aware domain loss, and domain-balanced sampling improved stability and target performance. Results demonstrated that domain alignment is a practical path to robust clinical generalization from public to local data.
[CV-99] DUO: Dual Readouts of Diffusion Models for OOD Detection
链接: https://arxiv.org/abs/2611.13304
作者: Alireza Javid,Shirin Shoushtari,Nuria González Prelcic
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Under review
Abstract:Diffusion models are powerful generative methods for learning and sampling from complex data distributions. Safely deploying these models requires detecting inputs that fall outside their training distribution. Because diffusion models recover clean samples from noisy inputs, reconstruction error is often used for out-of-distribution (OOD) detection. This approach assumes that in-distribution (ID) inputs are reconstructed more accurately than OOD inputs. However, OOD inputs may be reconstructed equally well or even better, making this score unreliable. We therefore propose the leading sensitivity spectrum of intermediate denoising features as a complementary OOD signal and demonstrate that it remains informative when reconstruction error is misleading. We introduce DUal-readout of diffusion models for OOD detection (DUO), a fully unsupervised, training-free OOD detector that combines reconstruction error and feature sensitivity. DUO improves average AUROC by approximately 6% points over the strongest baselines while substantially reducing inference cost, and achieves SOTA on near-OOD and high-resolution benchmarks.
[CV-100] Domain adaptation of a video Foundation model for Heart Failure Diagnosis from Echocardiography
链接: https://arxiv.org/abs/2611.13297
作者: Mahyar Bolhassani,Tijana Geroski,Narayana S. Singam,Amir Amini
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 6 figures, accepted and presented at Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026)
Abstract:The rapid advancement of foundation models has transformed computer vision, yet their adaptation to medical imaging, particularly purpose built foundation models for spatio temporal echocardiography videos, remains limited. In this work, we explore the feasibility of fine tuning a pretrained foundation model for video (TimeSformer) for automated diagnosis of reduced ejection fraction heart failure (HFrEF) from apical four chamber videos. The model is finetuned on the publicly available RVENet A4C dataset with a phase aware clip generation strategy that captures complementary systolic and diastolic dynamics. We employ divided space time attention, layer wise learning rate decay, and multi clip inference to enhance robustness under limited data. Experimental results show that full fine tuning of the proposed model outperforms classifier only tuning and a 3D CNN baseline. These findings highlight the potential of leveraging foundation video models for echocardiography based heart failure diagnosis and motivates further domain adaptation studies in medical imaging.
[CV-101] RetinexGS: Retinex-Inspired Gaussian Splatting for Low-Light RAW Reconstruction
链接: https://arxiv.org/abs/2611.13280
作者: Yulang Fei,Huaiyuan Weng,Chul Min Yeum
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Standard 3D Gaussian Splatting (3DGS) pipelines struggle with severely underexposed images, producing unstable colors, floating artifacts, and incomplete reconstructions in dark regions. To better account for noise in these low-light observations, RAW images offer a natural basis for noise-aware reconstruction by preserving linear sensor measurements and providing access to noise metadata. We present RetinexGS, a RAW-domain 3DGS framework that combines Retinex-inspired appearance modeling with sensor-informed optimization for low-light novel-view synthesis. We factorize each Gaussian’s color into a view-independent base color and a shared, position- and view-dependent modulation field, regularizing the two components separately to constrain appearance learning from noisy observations. We use DNG noise metadata to weight the reconstruction loss for signal-dependent noise, while distortion-aware rendering avoids spatially resampling RAW targets. To address sparse geometry in dark regions, we introduce depth-guided initialization. We evaluate RetinexGS on three public RawNeRF scenes and two custom-captured low-light RAW scenes. With all methods trained on the same RAW inputs for each scene, RetinexGS outperforms LE3D and RawNeRF in mean sRGB PSNR by 2.18 dB and 2.71 dB respectively, across the five scenes, while supporting real-time novel-view rendering.
[CV-102] RaMOD: Moving-Object Detection from Raw mmWave Radar Cubes under Ego Motion
链接: https://arxiv.org/abs/2611.13271
作者: Yuan Fang,Long Zhuang,Lingzi Xing,Xijia Wei,Jingqi Jiang,Fangzhan Shi,Simon Julier,Kevin Chetty
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Millimeter-wave (mmWave) radar provides weather-robust sensing and direct Doppler measurements, yet automotive perception pipelines often reduce its output to sparse point clouds or two-dimensional projections, leaving raw range-azimuth-Doppler (RAD) cubes underutilized. Ego motion induces Doppler responses from world-static structures, while Doppler wrapping aliases radial velocities, complicating moving-object detection and velocity estimation. We present RaMOD (Radar-based Moving-Object Detection), a real-time framework for moving-object detection and velocity estimation from raw RAD cubes acquired by a vehicle-mounted single-chip radar. RaMOD explicitly estimates frame-wise ego motion and uses the estimated radar translational velocity to predict the Doppler region of world-static structures and suppress returns within it. It temporally encodes the suppressed cubes into multi-scale features to predict RAD-space boxes with class labels. For each detected object, it predicts radial and forward velocities under raw-Doppler and Doppler-unwrapped hypotheses, together with Doppler-wrapping probabilities. On our BRIDGE-Liv dataset, RaMOD outperforms all compared raw-radar methods across RAD-cube, RA, and RD detection settings, achieving 80.8% cube mAP at IoU 0.30, 15.7 percentage points above the strongest RAD-cube baseline. Velocity MAE is 1.128 m/s, with 99.87% Doppler-wrapping accuracy.
[CV-103] Adaptive Residual Quantization for Memory-Efficient Temporal Action Segmentation
链接: https://arxiv.org/abs/2611.13262
作者: Gerard Donahue,Guven Gergerli,Ayush Gupta,Reza Ghoddoosian,Enna Sachdeva,Faizan Siddiqui
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Temporal action segmentation (TAS) is commonly trained on large banks of pre-extracted frame features, but storing and replaying those features becomes a practical bottleneck for long-form video, especially in incremental TAS where past-task data must remain available to mitigate catastrophic forgetting. Recent TAS replay and condensation methods reduce this burden, but they rely on dense frame-wise annotations and allocate storage through fixed segment-level structures, limiting their flexibility for long procedural videos whose frame complexity varies substantially over time. We introduce Adaptive Residual-Quantized Variational AutoEncoder (ARQ-VAE), a compact and label-free memory representation for TAS features that learns a discrete residual quantization of frame-level video features and then adaptively truncates the residual chain on a per-frame basis, retaining only the residual prefix that best reconstructs each frame feature. This yields a practical compact feature memory that does not depend on frame-wise labels and can therefore support multiple TAS settings with the same stored representation, while a lightweight fine-tuning stage further improves reconstruction quality for the truncated codes that are actually retained. Across standard TAS benchmarks, ARQ-VAE achieves a substantially improved storage-performance trade-off over prior replay and condensation baselines. On Breakfast, our final representation reduces training-set storage from 28 GB for the original I3D feature bank to 10 MB while retaining strong downstream segmentation performance; with ASFormer, it achieves 71.5 Edit and 51.2 F1@50 in the supervised setting. We further show that the same label-free memory remains effective for incremental and unsupervised TAS. We position ARQ-VAE as a practical memory representation for TAS and a promising direction for broader dataset condensation applications.
[CV-104] Rethinking Evaluation for Video Capsule Endoscopy: Validation Protocol Source-Video Diversity and Cross-Dataset Shift
链接: https://arxiv.org/abs/2611.13245
作者: Smriti Regmi,Debesh Jha
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 4 figures and 9 tables
Abstract:Automated video capsule endoscopy (VCE) classification remains challenging due to severe class imbalance, limited source-video diversity, and variations in image appearance across datasets. Although deep learning models have demonstrated promising performance on established benchmarks, their reliability under different validation protocols and external dataset shift remains insufficiently understood. In this work, we introduce DMSNet, a deformable adaptive multi-scale edge-guided attention network integrating adaptive multi-scale feature fusion, deformable lesion refinement, edge-guided attention, and sparse feature selection. We benchmark DMSNet against 18 representative convolutional, transformer-based, and pretrained vision architectures on Kvasir-Capsule under random frame-level and source-video-disjoint validation protocols. We further identify source-video overlap between the original training and test splits and evaluate model performance on both the complete test set and a source-video-disjoint subset. Under random frame-level validation, DMSNet achieves an MCC of 0.442 \pm 0.020 on complete test set and 0.456 \pm 0.037 on the source-video-disjoint subset. On the external CV2024 dataset, ConvNeXt-Tiny achieves the highest MCC of 0.207, while performance degrades substantially across architectures relative to in-domain evaluation. We further investigate source-video diversity using a per-class cap of 200 training frames. With the total training-set size held fixed, increasing the maximum source-video coverage from four to eight improves MCC for 17 of 19 evaluated architectures. Overall, these findings demonstrate that model rankings and classification performance depend substantially on evaluation protocol, training-source diversity, and cross-dataset shift, emphasizing the need for source-aware validation and rigorous external evaluation of VCE classification systems.
[CV-105] Before Words Beyond Speech: Evaluating Nonverbal Social Reasoning in Early Childhood NEURIPS2026
链接: https://arxiv.org/abs/2611.13213
作者: Marie Amale Huynh,Laura Bravo-Sánchez,Lauren Klein,Nick Haber,Philip Fisher,Serena Yeung-Levy
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted as NeurIPS 2026 Spotlight paper. Project page at \hyperlink{ this https URL }{this URL}
Abstract:Children’s earliest years are a period of intense social learning, during which meaning is built through gaze, gesture, and physical contact. Yet, this critical aspect of social interaction remains under-evaluated in multimodal video understanding, as existing benchmarks prioritize the conversation-driven interactions of adults. We address this gap with \bench ( \textbfN aturalistic \textbfE arly-childhood \textbfS ocial in \textbfT eractions), the first child-centric benchmark for nonverbal social reasoning. \bench comprises \totalvids manually annotated clips ( \sim 12s) curated for diversity across naturalistic settings, cultural contexts, and developmental stages. Beyond a lack of data, targeted model improvement in this domain is hindered by coarse evaluation schemas that conflate basic perception with higher-order reasoning. To resolve this, \bench introduces a compositional schema that builds atomic interaction cues into progressive levels of social reasoning: contextual, behavioral, interpersonal, and open-ended descriptions. Extensive analysis of state-of-the-art Vision-Language Models (VLMs) reveals a stark performance collapse: despite strong contextual recognition, models exhibit substantial gaps in decoding precise behavioral and interpersonal cues. We trace these failures to specific biases, including poor consequence grounding and temporal sensitivity. Finally, we ground \bench in real-world applications by evaluating VLMs using domain adaptation strategies. Ultimately, \bench provides a rigorous testbed for advancing socially grounded artificial intelligence in early childhood. Project page: this https URL.
[CV-106] Image Quality and the Reliability of Fabric-Density Measurement: Counting Threads from Smartphone Photos Through a Pick Glass WACV2027
链接: https://arxiv.org/abs/2611.13200
作者: Anoubhav Agarwaal
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Accepted at the WVAQ workshop at WACV 2027
Abstract:Thread density, the threads per inch along and across a woven fabric, is a basic specification that buyers check whenever fabric is sold. Without laboratory instruments, inspectors count it slowly, by eye, through a pick glass, a folding 10 \times magnifier with a one-inch window. A smartphone photo through that glass could make the check faster and leave a record. We judge such a photo by what it is used for, as in task-based image quality: it is good if the thread count from it is within \pm 5% of the true count, a common tolerance in fabric specifications. We introduce PickGlass-TPI: 76 factory scenes of 44 fabric patches, each counted in an open annotation tool by a textile-trained annotator, whose counts are the reference. On it we benchmark frontier and open vision-language models, CNNs, counting specialists and a training-free spectral counter (PG-Lattice). No method agrees with the reference within the tolerance. The best methods are off by about a quarter: a small open model that we tuned only on synthetic images (Molmo2-4B, 25%) is level with the best frontier model, GPT-6.1-sol (27% when asked directly, 25% given four magnified close-ups of the window). Both err less than a non-expert annotator (37%), but only the tuned model also beats, on both error measures, a baseline that guesses each scene’s density as the average of the others. PG-Lattice most often repeats its count within 5% across two photos of the same patch, but its misses, like GPT-6.1-sol’s errors, are often a factor of two. Even the reference repeats a count across such re-shoots only to a median 9%, so one photo cannot yet be shown to meet \pm 5%; the image quality that matters is reliability across re-shoots. Generic no-reference quality scores chosen before labeling do not rank PG-Lattice’s error, and generative enhancement raises perceptual scores but does not reliably improve the count.
[CV-107] Structured Kernel Surrogate Models for Multimodal Attribution NEURIPS2026
链接: https://arxiv.org/abs/2611.13198
作者: Ziniu Zhang,Zhenshuo Zhang,Jianglin Lu,Ruoxuan Xiong,Yun Fu,Hongyang R. Zhang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages. To appear at NeurIPS 2026
Abstract:We study the problem of cross-sample modality attribution, which involves measuring how one modality in one sample affects the learning outcome in other multimodal samples. This problem is crucial in a multimodal system because a data modality (such as a text description) may be redundant for one sample (a self-explanatory image), but the same data modality would otherwise be uniquely informative for another sample, or informative when combined with other modalities. Prior work focuses on influence estimation at the level of one sample, and it remains an open question how one should capture the interactions across both samples and modalities. To capture scenarios like the above, we propose structured kernel surrogate models, which are surrogate mappings from both samples and modalities to the prediction outcome when they are trained together. We design an algorithm, SKS, which applies kernel ridge regression with a structured, compositional kernel on the selection states of individual samples and modalities. Specifically, the kernel function multiplies kernel-based similarity scores between every pair of data modalities for one sample and a pair of two samples. Additionally, we design a gradient-based estimation algorithm to efficiently estimate the structured kernel surrogate model. We evaluate our approach on two multimodal benchmarks, spanning a range of data modalities including satellite images, road networks, text, audio, and tabular data. We find that SKS achieves 57.8 % higher correlation with the true prediction outcomes than existing methods. In downstream data selection, SKS improves the average accuracy by 1.93 % over baselines. Models with data selected by SKS have lower Hessian traces, indicating a flatter loss surface. Using SKS to select satellite images identifies road segments, where accident scenes commonly occur, as opposed to background images.
[CV-108] Multimodal Road Closure Mapping via Cross-Modal Graph Attention
链接: https://arxiv.org/abs/2611.13192
作者: Izzeddin Teeti,Vaibhav Kango,Nidhi Sridhar,Mohamed Moustafa
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Published at ACM SIGSPATIAL 2026. 11 pages, 4 figures
Abstract:Accurate and timely identification of road closures is critical for large-scale navigation and logistics systems, yet remains challenging due to the need to jointly reason over visual evidence, spatial context, and road network topology. Existing approaches either rely on manual map updates or focus on detecting closure-related objects without resolving which road segment is affected. In this work, we introduce a unified multimodal and multitask framework that jointly performs road closure classification (no, partial, complete) and road segment association within a single learning architecture. Our model integrates graph-structured map data, satellite imagery augmented with projected street-level detections, and street-view images using modality-specific encoders and cross-modal transformer attention, enabling explicit reasoning over visual cues and network topology. Evaluated on nearly 20K real-world scenarios collected from a large-scale commercial vehicle fleet, our approach achieves 72.7% closure classification accuracy and 60% road segment association accuracy, outperforming frontier vision-language models by over 28 percentage points on closure classification while being over 4,000x smaller and 100x faster at inference. Extensive ablations demonstrate the importance of cross-modal attention, edge-aware pooling, and graph message passing for accurate geospatial reasoning. This work establishes the first benchmark for joint road closure classification and segment-level attribution, and shows that task-specific architectural inductive biases substantially outperform general-purpose multimodal models for structured geospatial understanding.
[CV-109] Comparative Evaluation of Deep Learning Models for Geographic Atrophy Segmentation in En Face OCT Images
链接: https://arxiv.org/abs/2611.13191
作者: Utkarsh Doshi,Adarsh Gadari,Sharat Chandra Vupparaboina,Joseph DeCicco,Giulia Gregori,Nicola Valsecchi,Nasiq Hasan,Elham Sadeghi,Josè-Alain Sahel,Jay Chhablani,Kiran Kumar Vupparaboina,Sandeep Chandra Bollepalli
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 30 pages, 11 figures
Abstract:Background: Geographic atrophy (GA) is an advanced form of age-related macular degeneration (AMD). It causes irreversible vision loss due to progressive degeneration of the retinal pigment epithelium (RPE) and photoreceptors. Accurate segmentation of GA in en-face image is crucial for early diagnosis, monitoring disease progression, and developing personalized treatment strategies. However, existing deep learning approaches are typically evaluated using single architectures and single-device datasets, limiting insight into relative performance and cross-device robustness. Methods: We trained and systematically compared five deep learning architectures for GA segmentation: Nested U-Net, ResU-Net, nnUnet, Swin-Unet and MedSegDiff model, together with a thresholding based classical baseline. The models were trained on a dataset of en-face OCT images derived from 269 OCT volumes from two different devices, following a three-step preprocessing technique involving normalization and inpainting to enhance the quality of the en-face images. Results: On the fixed held-out validation set comprising OCT scans from both Cirrus ® (Carl Zeiss Meditec, Inc., USA) and Spectralis ® (Heidelberg Engineering, Germany) devices, nnUnet achieved the highest mean Dice similarity coefficient (DSC) of 0.8882 +/- 0.0033. The other models yielded the following Dice scores: Nested U-Net (0.7932 +/- 0.0423), ResU-Net (0.8453 +/- 0.0074), Swin-Unet (0.6886 +/- 0.0294), and Diffusion model (0.7959 +/- 0.0242). Conclusions: nnUnet framework, demonstrated most reliable performance for GA segmentation in en-face OCT under limited data conditions. It offers the best balance of accuracy, stability, and efficiency for GA quantification.
[CV-110] X Marks the Spot: Diffusion for Image Geolocalization with Location Representations
链接: https://arxiv.org/abs/2611.13180
作者: Brian Wei,Chih-Chuan Hsu,Tianyi Gao,Daniel Cher,Eric Xing,Danielle Beaulieu,Nathan Jacobs
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages, 9 figures
Abstract:Image geolocalization predicts the location at which a photo was taken. Accurate geolocalization requires discriminating between both physically and semantically adjacent areas on Earth. However, prior diffusion-based approaches predict locations by generating coordinates, which are semantically unstable over space. We avoid this limitation by learning geolocalization behavior in the pretrained representation spaces of location encoders, which provide both positional and semantic understanding. To make this space usable for coordinate prediction, we introduce a Location Autoencoder (LAE), which pairs a frozen location encoder with a learned decoder mapping embeddings back to coordinates with kilometer-level accuracy worldwide. In the LAE space, we train DIG, a latent diffusion model that transports noise to location embeddings conditioned on image features. DIG consistently outperforms coordinate-based generative models and sets a new state of the art for parametric geolocalization on three benchmarks: OSV-5M, Im2GPS3k, and YFCC4k. Notably, DIG is the first generative model to outperform retrieval-based methods, and further improves retrieve-and-rerank systems when used as a prior. Code and models will be released.
[CV-111] GroundCascade: Selective Second Opinions for GUI Grounding
链接: https://arxiv.org/abs/2611.13177
作者: Adrian Phoulady,Hady Ahmady Phoulady
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Graphical user interface (GUI) grounding maps a screenshot and a natural-language instruction to the screen location where an agent should act. Zoomed views can reveal small targets but may lose the context needed to identify them. We introduce GroundCascade, a training-free controller that uses agreement between a grounder’s full-screen and zoomed predictions to decide when to consult a complementary model. It selects between zoomed predictions while preserving corroborated full-screen locations. An optional Finalizer predicts from an unmarked joint crop when disagreement remains (Full); omitting it yields the two-stage Selective variant. GroundCascade reaches 85.33% on ScreenSpot-Pro with Indeed-UI-32B/8B/32B and 88.43% on the visible-target OSWorld-G-Refined subset with Qwen3.6-27B/3.8-27B/3.8-27B, gains of 2.66 and 2.35 percentage points over the stronger standalone two-pass grounder. On UI-Vision, GroundCascade reaches 72.20% category-balanced accuracy, with Selective slightly higher at 72.28%. Across these three evaluations, GroundCascade in Full mode uses 2.42-2.73 calls per request, 31-39% fewer than unconditionally acquiring both models’ two-pass predictions. At matched second-model call budgets, selective acquisition gains 1.52-2.56 points over standalone Two-pass, compared with 0.22-0.69 points from stratified random acquisition. GroundCascade leads the official ScreenSpot-Pro leaderboard and exceeds reported GUI-specialist comparisons on OSWorld-G-Refined and UI-Vision, subject to differences in external evaluation protocols.
[CV-112] Continuous Angular Control in Text-to-Image Generation via Geometric Token Encoding
链接: https://arxiv.org/abs/2611.13172
作者: Furkan Güzelant,Basavaraj Sunagad,Aysegul Dundar,Adam Kortylewski
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models can render a photorealistic analog clock but cannot control the time it displays. Across four pretrained models, fewer than 1% of generated clocks place both hands within 6 degrees of the requested time; the models instead collapse onto visually dominant configurations such as the canonical 10:10 pose. The same failure appears when a specific fraction of a circular object is requested, where models return complete or conventionally sliced objects. We observe that these two failures share a geometric structure: both target quantities can be parameterized by angular variables. We introduce a geometric token encoding framework that conditions generation directly on these angles. Each angular component is encoded as a periodic sine-cosine feature and projected into a role-specific token embedding that is injected into the prompt before a frozen text encoder. To spatially ground these tokens without requiring localization input at inference, we introduce Stochastic Localization Relaxation (SLR), which stochastically relaxes attention supervision from component-level to object-level to unconstrained localization during training. For clocks, we additionally introduce a hand-swap preference objective that discourages assigning the correct angles to the wrong hand roles. On analog clocks our method reaches 63-78% Acc@6 degrees across evaluation methods, compared with below 1% for the baselines. On circular-fraction generation, it achieves 89.5% Acc@0.10, while maintaining 88.8% on unseen object categories and 84.9% on unseen target values, compared with roughly 20% for pretrained baselines. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2611.13172 [cs.CV] (or arXiv:2611.13172v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2611.13172 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[CV-113] VIGOR: Benchmarking Visual Rationale Correctness in Multimodal Large Language Models NEURIPS2026
链接: https://arxiv.org/abs/2611.13150
作者: Yanlin Chen,Tang Li,Xi Peng
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: NeurIPS 2026
Abstract:Multimodal large language models (MLLMs) are typically evaluated by whether they produce correct answers, but correctness alone does not reveal whether their outputs are supported by the right visual evidence. We introduce VIGOR, a benchmark for evaluating VIsually GrOunded Rationales in MLLMs. VIGOR represents visual rationales as explicit concepts paired with pixel-level groundings, covering multiple abstraction levels including low-level visual properties, intermediate structures, objects, parts and high-level motion, relations. To construct VIGOR at scale, we develop an automatic annotation framework that reuses existing dense supervision when available and generates missing concept groundings through proposal-based annotation followed by conservative multimodal verification. Using VIGOR, we evaluate whether MLLMs “look at what they say” by comparing model-derived visual evidence for each expressed concept against annotated ground-truth rationales. Our results show that current MLLMs exhibit limited visual rationale correctness and reveal distinct grounding failures across concept levels. VIGOR provides a benchmark and scalable data construction pipeline for studying whether MLLM outputs are supported by appropriate visual evidence. Code is available at this https URL
[CV-114] Just Image Transformers with Late-Stage Pixel Refinement
链接: https://arxiv.org/abs/2611.13120
作者: Denis Lukovnikov,Andreas Müller,Asja Fischer
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work on latent-free diffusion transformers has demonstrated the feasibility of training image generators without a latent VAE. This avoids compression artifacts from the VAE and simplifies the training pipeline. In particular, JiT showed that even a standard transformer can be trained to generate images when the model is trained to predict the final image ( x -prediction). Even though JiT unlocks training efficiency comparable to latent models, it still suffers from reduced image quality likely arising from modeling the image as a collection of large patches. In this work, we revisit JiT and show that adding simple convolutional pixel-pathway layers in a post-training phase significantly improves generation quality. Moreover, we show that the added pixel-pathway does not need to be active in the early denoising stages, and can be activated as late as in the last 20% of the denoising process. Our experiments on ImageNet@256 show improvements compared to the original JiT results, achieving an FID of 1.78 (from 2.34 with corrected sampling), without using additional losses.
[CV-115] SpatialSR: Spatial Resolution-Centric Pipeline for Arbitrary-Scale Remote Sensing Super-Resolution
链接: https://arxiv.org/abs/2611.13046
作者: Yan Zhang,Runfeng Luo,Liangke Gao,Lin Yuan,Bin Xiao,Xinbo Gao
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 39 pages, 20 figures and tables,
Abstract:Although remote sensing image arbitrary-scale super-resolution (RSI-ASSR) methods support reconstruction at arbitrary or continuous scales, they predominantly condition on the relative scale factor and have not sufficiently investigated the absolute spatial resolution itself as a reconstruction prior, thereby disregarding the inherent spatial context of objects, which frequently leads to spatial-related distortions and compromised performance in downstream remote sensing applications. To address these challenges, we introduce SpatialSR, a SRe-centric arbitrary-scale RSISR method that leverages the spatial resolution (SRe) of remote sensing images (RSI) as an essential prior for super-resolution reconstruction. Distinct from existing SR methods, SpatialSR aims to restore high-resolution remote sensing images with a target SRe (typically set to 0.1m/pixel to align with the metadata of commonly used downstream task datasets). Specifically, we design a Spatial Resolution Estimation Module (SREM) to estimate the SRe of low-resolution (LR) images, thereby dynamically determining the required arbitrary scaling factor. Next, we employ a pretrained SRe-to-image diffusion model to extract SRe-specified texture-style codes, which are further incorporated into a carefully designed Resolution-aware Style Consistency Module (RSCM) to constrain the SRe-aware texture consistency of reconstructed images. Furthermore, to overcome the high computational expense inherent in diffusion-based SR methods, we wrap the iterative diffusion process as a Trajectory Consistency (TC) loss, which enables comparable reconstructed texture quality of the generative model in SpatialSR within a single step. Extensive experiments demonstrate that the proposed SpatialSR achieves state-of-the-art (SOTA) performance on commonly used RSISR and downstream (image classification and segmentation tasks) datasets.
[CV-116] MICRO: A Surgery-Phase Retinal Image Rigid Registration Benchmark and One-Step Frequency-Structure Similarity Registration Learning
链接: https://arxiv.org/abs/2611.13045
作者: Mingdao Gong,Yan Hu,Mingzhen Yuan,Jialu Zhang,Junming Feng,Yiming Jiang,Jing Ma,Jinghua Liu,Huazhu Fu,Hai Lu,Xiaoqing Zhang,Jiang Liu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Precise multimodal retinal image rigid registration (MRIRG) is essential for aligning cross-modal vessel structures, offering critical guidance for ophthalmic surgery. Despite its significance, the advancement of MRIRG is hindered by the lack of publicly available multimodal retinal image rigid registration datasets spanning various ophthalmic surgery phases (e.g., preoperative, intraoperative, and postoperative). To address this issue, we introduce the MICRO dataset, a large-scale MRIRG benchmark, comprising 1,365 cross-modal retinal image pairs with standardized annotations across all ophthalmic surgery phases. Moreover, existing deep registration methods achieve competitive MRIRG results via a two-step segmentation-then-detection framework, but segmentation quality heavily constrains their performance. We argue that this dependency stems from an implicit need for keypoint topology constraints, which have not been exploited for MRIRG through a one-step registration framework. Inspired by the above analysis, we propose a novel One-step Frequency-Structure Similarity Registration (OFS2Reg) learning framework for MRIRG that leverages the potential of cross-modal vessel structure similarities to construct keypoint topology constraints under a shared detection network. To be specific, OFS2Reg applies a well-designed frequency-guided structure preservation descriptor to construct coarse keypoint topology constraints in descriptors by highlighting significant vessel structures. Additionally, we develop a new cross-modal vessel structure similarity learning method that refines cross-modal keypoint topology constraints to precisely align pixel-level cross-modal vessel structures by leveraging cross-modal vessel structure similarity priors.
[CV-117] Preserving Cultural Records: A Large-Scale Benchmark and Visual-Prior Routing Network for Ancient Book Image Restoration
链接: https://arxiv.org/abs/2611.13010
作者: Shumin Fan,Tianyu Song,Hongming Chen,Xiaodong Zhou,Xiang Chen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Ancient books preserve invaluable cultural heritage, yet their images often suffer from complex degradations caused by long-term ageing, environmental exposure, and physical damage. Existing ancient book image restoration benchmarks are limited in scale and rely on simplified degradation simulations, which cannot fully represent real-world degradation patterns and restrict the generalization ability of restoration models. To address these challenges, we introduce AncBench, a large-scale synthetic paired benchmark containing 100K aligned clean-degraded image pairs. AncBench models realistic composite degradations through a comprehensive degradation pipeline that considers material ageing, paper texture variation, blur, staining, water-induced geometric distortion, and local occlusion. Furthermore, we propose AncRestore, a degradation-aware visual-prior routing network for ancient book image restoration. AncRestore first learns degradation representations from degraded images and then dynamically selects and integrates suitable visual priors according to different degradation characteristics. Guided by these degradation cues, a dynamic routing mechanism combines expert-wise and region-wise routing strategies adaptively adjusts the contribution of these priors across different regions, enabling the model to recover both global appearance and local text details. Extensive experiments show that AncRestore effectively suppresses composite degradations while preserving text structures and enhancing restoration quality against other methods, highlighting its application potential for intelligent ancient book image restoration in cultural heritage preservation.
[CV-118] Instance-preserving graded semantic reporting for surgical action triplets
链接: https://arxiv.org/abs/2611.13009
作者: Yanchong Xue,Wanting Zhang,Lingtao Yu,Guochao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Complete-or-reject reporting of surgical action triplets can discard a correctly identified instrument and action when the target is uncertain. We introduce instance-preserving graded semantic reporting, which separates instance selection from the choice of semantic detail. Each selected instance retains its predicted mask and labels while hierarchical correctness scores guide instrument (I), instrument-verb (IV), or complete instrument-verb-target (IVT) reporting. The utility-increment rule DELTA weighs the expected gain from finer reporting against its error cost. We compare three decision rules on 5,641 test frames from eight surgical videos using fixed predictions and scores. At the 10% development calibration target, DELTA retains correct partial reports for 80.0% of candidates rejected by a matched complete-or-reject baseline (5,329/6,662). Adding IV increases source-normalized weighted correct-report yield by 19.13 to 23.13 per 100 frames across all three rules relative to separately recalibrated I/IVT controls. Weighted reporting risk changes by -0.77 to +1.01 percentage points, with complete correct-triplet counts unchanged or reduced. Graded reporting makes partial surgical predictions available as localized descriptions, with an explicit trade-off between report utility and completeness.
[CV-119] G-STAR: Group-Scaled Token Capacity for Deeply Compressed Autoregressive Generation
链接: https://arxiv.org/abs/2611.13004
作者: Xu Huang,Ye Huang,Xiaotong Li,Zhengrong Yue,Jialv Zou,Minhao Jing,Haodong Li,Wenhao Yan,Yaxin Luo,Duomin Wang,Daquan Zhou
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Discrete autoregressive models offer a scalable framework for image generation. However, they face a fundamental trade-off: long token sequences preserve representation quality but slow generation, while aggressive spatial compression reduces representation capacity. Based on product quantization, we show that, under a fixed nominal information budget, group size should scale quadratically with the per-axis spatial compression factor. Building on this principle, we propose GSTAR, a spatial–group co-scaling framework that enables (32\times) and even effective (64\times) compression while preserving strong reconstruction, understanding, and generation performance. Since larger groups introduce intra-group dependencies that must be modeled efficiently, we analyze their impact on inference latency and generation quality and introduce a semi-autoregressive decoder that combines parallel feature computation with lightweight causal modeling. With these designs, our highly compressed models perform competitively with leading models across five representative text-to-image benchmarks (GenEval, DPG-Bench, PRISM, LongText-Bench, and Qwen-Image-Bench). At (1024\times1024) resolution, our model delivers a (20\times) speedup over prior discrete autoregressive models, generating each image in 2.5 seconds on a single NVIDIA H200. Together, these results show that highly compressed discrete representations can enable high-quality autoregressive generation at low latency.
[CV-120] PhysMoE: Fast Spatial Grounding with Single-Token Coordinate Representation in Modality-Decoupled VLMs
链接: https://arxiv.org/abs/2611.13000
作者: Ruimeng Liu,Denan Liang,Ruofei Bai,Fuming Zhang,Kuan Xu,Shenghai Yuan,Lihua Xie
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Embodied Vision-Language Models (VLMs) are backbones for robotic planning and manipulation. However, for coordinate prediction in grounding tasks, existing architectures suffer from two distinct bottlenecks: discrete tokenization disrupts spatial continuity with high decoding latency, while standard point regression fails on multi-contact affordances and irregularly shaped targets. To resolve these challenges, we propose PhysMoE, introducing three core contributions: (1) sharing lower layers for multimodal context while routing upper layers to specialized spatial (pixel) and linguistic (text) experts by physical modality, eliminating gradient interference; (2) encoding each coordinate as a single token by reusing the visual encoder’s learned positional embeddings, injecting 2D spatial priors to natively align coordinates with visual features and ease training; and (3) differentiable Signed Distance Field (SDF) supervision on pointing, accommodating affordances and irregular geometries without costly mask decoders. Compared to the base model, PhysMoE achieves a 15.7% average gain and an 8.31x throughput speedup across 9 coordinate-based grounding benchmarks (trajectory, affordance, and localization), while preserving multimodal understanding and reasoning across 11 text-based embodied benchmarks.
[CV-121] IB: Sample-wise Tempered Information Bottleneck for Multimodal Attribution beyond Alignment Assumption NEURIPS2026
链接: https://arxiv.org/abs/2611.12997
作者: Qiming Huang,Peixi Liu,Jianbo Jiao
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: Accpeted to NeurIPS2026
Abstract:Multimodal (e.g. vision-language, in this work) attribution aims to interpret the models without access to ground-truth explanatory supervision. Existing multimodal attribution methods typically rely on paired modalities as proxy supervision, implicitly assuming that image-text pairs are semantically aligned. Information bottleneck (IB)-based attribution methods follow this general paradigm and instantiate it through a global sufficiency-compression objective. In realistic multimodal datasets, however, this assumption is frequently violated due to partial semantic misalignment and unreliable cross-modal correspondences. Through theoretical analysis, we show that, under such misalignment, information-bottleneck-based attribution objectives exhibit a structural limitation: by assuming equal reliability across all proxy supervision signals, they induce posterior over-contraction and forced explanations. To address this issue, we propose TIB, a sample-wise Tempered Information Bottleneck framework that adaptively modulates attribution strength according to cross-modal reliability, without assuming semantic alignment. Extensive experiments on controlled benchmarks and large-scale datasets show the effectiveness of the proposed approach, while also validating the identified failure mode.
[CV-122] Evidence-Grounded Iterative Reasoning for the Diagnosis of Diffuse Cystic Lung Diseases
链接: https://arxiv.org/abs/2611.12981
作者: Haoqing Li,Jun Shi,Tou Li,Xianmeng Chen,Rui Wang,Yating Peng,Qiwei Jia,Li Gao,Qianyi Chen,Hong An,Ruoyun Ouyang,Xiaowen Hu
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Overlapping imaging manifestations complicate the differential diagnosis of Diffuse Cystic Lung Diseases (DCLDs), while limited annotated data constrain supervised model development. Multimodal Large Language Models (MLLMs) can integrate imaging and clinical information, but their diagnostic reasoning remains limited by incomplete volumetric perception and insufficient grounding in disease-specific evidence. We propose DCLDs-EIR, an evidence-grounded iterative reasoning framework coupling diagnostic hypothesis refinement with multimodal evidence acquisition and verification. Multiview renderings of predicted lung and cyst segmentations characterize global cyst distributions and initialize the differential diagnosis. Competing hypotheses guide axial CT inspection to identify local findings that, together with clinical context, inform retrieval from a disease-specific knowledge graph. Accumulated patient observations and retrieved knowledge are jointly assessed for diagnostic support and refutation to revise the diagnostic state. Updated hypotheses and unresolved distinctions guide subsequent inspection, forming a feedback loop between evidence acquisition and hypothesis verification. Multicenter evaluation demonstrates that DCLDs-EIR consistently improves diagnostic accuracy over baselines and enables traceable differential diagnosis through explicit links between diagnostic hypotheses, patient observations, and disease-specific knowledge.
[CV-123] rajectory-Regularized Distribution Distillation for One-Step Generation
链接: https://arxiv.org/abs/2611.12978
作者: An Zhao,Yixiang Zhou,Zejian Li,Yi Guo,Pei Chen,Lingyun Sun
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Distilling flow matching models for one-step generation generally follows two paradigms. Trajectory alignment supervises the student with the teacher’s probability flow ODE, but its performance degrades under very small sampling budgets. Distribution matching directly optimizes the generated distribution, but estimating its reverse Kullback-Leibler gradient typically requires a separate network to track the evolving student score. We propose trajectory-regularized distribution distillation (TRD), which combines distribution-level and trajectory-level supervision through a momentum-mixture policy predicted in a single student forward pass. This policy serves simultaneously as a one-step generator, an estimator of the student’s marginal velocity that removes the need for a second trainable network in distribution matching, and a closed-form time-dependent velocity field supervised along the teacher ODE trajectory. Experiments on SANA-1.6B and Stable Diffusion 3.5 show that TRD outperforms representative consistency and distribution matching baselines in generation quality and training efficiency for both one-step and few-step generation, with a trade-off in sample diversity.
[CV-124] Selective Surgical Action-Center Localization:Retaining Localization Gains through Supervised Scoring
链接: https://arxiv.org/abs/2611.12977
作者: Yanchong Xue,Wanting Zhang,Lingtao Yu,Guochao Zhang
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Surgical action-center localization requires both an accurate reference point and a decision about whether to report it. When frames with and without a reportable center are mixed, the benefit of improved localization depends on which correct candidates survive selection. We present a selective localization pipeline combining heatmap and coordinate supervision with a logistic-regression scorer. The scorer learns to identify predictions with a reportable center and a localization error within a specified tolerance. The internal evaluation uses 5,000 frames from 50 surgical sources, five source-grouped folds and two random seeds. Matched locator and scoring comparisons track correct candidates and their rejection. At an 80% acceptance quota per source and a tolerance of 0.10 times the image diagonal, our method retains 46.08 correct outputs per 100 inputs. The baseline locator with the same scoring approach retains 44.20. The gain is 1.88 (descriptive 95% source-paired bootstrap interval, 0.94 to 2.79). Under entropy-based selection, increased rejection of correct predictions offsets the higher supply of correct predictions on average. For the coordinate-supervised locator, supervised scoring reduces these rejections from 6.15 to 2.99 per 100 inputs. Evaluating retained correct outputs reveals whether a selection rule preserves or erodes an improvement in localization.
[CV-125] Partition Refine and Detect: Preserving Novel Category Capacity in Open-Vocabulary Object Detection
链接: https://arxiv.org/abs/2611.12976
作者: Wei Zhang,Zhu Teng,Zhixiang Chi,Junnan Zou,Yi Liu,Jianping Fan
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: IEEE Transactions on Multimedia (TMM)
Abstract:Open-vocabulary object detection (OVD) expects the model trained on base classes to recognize both base and novel classes at inference. Typical methods rely on CLIP-style vision-language embeddings, yet the learned space is often biased toward base categories, with limited effective capacity preserved for novel concepts. Such a base-dominated representation can compress novel semantics, leading to semantic collapse and mis-calibrated confidence at inference. To address this issue, we present PR-OVD, a partition-and-refine framework that explicitly reserves capacity for novel categories and strengthens category separation, while being seamlessly integrated into different detector architectures. Specifically, PR-OVD first performs coarse partitioning to build global divisions for base, novel, and an explicit background partition in both visual and textual modalities, preventing object-irrelevant embeddings from leaking into category-specific representations. It then applies category-level refinement within each partition to sharpen decision boundaries and alleviate the lack of fine-grained region-level pretraining in CLIP. To further provide reliable visual anchors for novel categories, we design a cognitively inspired Structure Prompt (SP) that encodes both egocentric attributes and allocentric context, facilitating stable partition-and-refine learning. Extensive experiments on two OVD benchmarks show that PR-OVD delivers an average 6.5% proportional improvement on novel categories over the state-of-the-art methods. Notably, PR-OVD also benefits other open-vocabulary tasks, including segmentation and classification, further confirming its effectiveness and generality.
[CV-126] Attribute-Evidence Learning with Multimodal Prompt for Generalized Category Discovery
链接: https://arxiv.org/abs/2611.12975
作者: Wei Zhang,Zhu Teng,Zhixiang Chi,Junnan Zou,Wenxin Luo,Linfeng Ye,Jianping Fan,Konstantions N. Plataniotis
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Abstract:Generalized category discovery (GCD) aims to discover novel categories from unlabeled data by leveraging limited labeled samples from old classes. Existing methods that rely solely on visual cues often neglect the multimodal nature of human perception, prompting recent approaches to incorporate textual information such as category tags and attributes. However, these approaches typically entangle global tags and local attributes within a shared global image representation, rendering attributes unreliable and potentially providing misleading cues that can drive the model toward incorrect predictions. To this end, we propose AptM-GCD, a novel multimodal framework that exploits apt textual guidance through attribute evidence learning and multimodal prompt reasoning. Specifically, we design an Attribute-Evidence Learning (AEL) mechanism that treats attributes as localized visual evidence rather than global category descriptors. By interacting attribute embeddings with patch-level visual tokens, AEL identifies reliable attribute-related regions and extracts discriminative local evidence, thereby reducing the impact of noisy or irrelevant attribute cues. Building upon the extracted evidence, we further develop a Multimodal Prompt Learning (MPL) mechanism to bridge local attribute semantics and global visual understanding. MPL converts fine-grained textual evidence into visual prompts and injects them into a multimodal prompt transformer, enabling attribute information to enhance category discrimination through contextual reasoning without enforcing direct global alignment between modalities. Extensive experiments on six datasets demonstrate that AptM-GCD consistently achieves SoTA performance across both fine-grained and generic benchmarks. Moreover, the proposed framework exhibits strong generalization ability, delivering consistent gains under unimodal and class-imbalanced GCD settings.
[CV-127] Evaluation protocols for historical script classification: the cost of manuscript-level leakage and the role of soft labels
链接: https://arxiv.org/abs/2611.12969
作者: Daniil I. Naryzhnyi(ITMO University),Roman A. Popkov(ITMO University),Daniil A. Bakalin(ITMO University)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 29 pages, 8 tables, 2 figures
Abstract:A paleographer applies a script type classifier to unseen manuscripts. We measure how much testing on other pages of training manuscripts inflates accuracy. On VML-HP-ext (715 pages, 171 Hebrew manuscripts, 14 script classes) we compare page-level and manuscript-level splits. Page-level accuracy drops from 97% to 54% (59% at manuscript level), and is 39% on the official blind test set. The gap of 40 to 47 percentage points (pp) at patch, page and manuscript level is statistically significant and reproduces with ResNet18; on binarized patches (ink and background only) it is 34 to 42 pp, also significant. On unseen manuscripts the model is overconfident: mean confidence 70% against 54% correct pages; patch-level expected calibration error (ECE, mean gap between confidence and accuracy) rises from 0.065 to 0.444. Binarization leaves patch-level overconfidence but cuts it markedly at page level. Confidence still helps triage: in the more confident half of pages 67% are correct, 41% in the rest. Training on expert soft labels showed no evidence of an accuracy difference, though a small page-level loss cannot be ruled out. The predicted distributions are closer to the expert’s assessment. ECE is lower at patch level (0.21 to 0.31) and in most runs at page level, but not significantly at manuscript level. Label smoothing (blurring the label, no expert input) also lowers ECE but comes no closer to the expert. Temperature scaling (post-training confidence correction) helps significantly only at patch level. On 97 pages of 16 manuscripts with mixed expert assessment, soft labels cut divergence from the expert by 22 to 30% (significant in four of five runs); the point estimate of accuracy is 16.5 to 23.7 pp below hard labels, but with 16 manuscripts the loss can neither be confirmed nor ruled out. We propose recommendations for the evaluation protocol; code will be released with the paper
[CV-128] EarthRecord: From Pixels to Land Change with Multimodal Agents over Four Decades of Satellite Imagery
链接: https://arxiv.org/abs/2611.12934
作者: Haoran Zhang,Huizhen Sun,Hanbin Wen,Jie Leng,Jiaxin Zhu,Zhaohua Sun
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 9 figures, 18 tables. Project page: this https URL
Abstract:While multimodal agents now carry out complex data analysis with tools, their ability to turn decades of satellite imagery into reliable land-change analysis, crucial for Earth observation science, remains unmeasured. Existing benchmarks fall short: they pose questions on curated images or give agents preprocessed inputs, leaving out the raw multi-sensor archives where such analysis begins. We introduce EarthRecord, a benchmark built on the raw record of 56 sites: four decades of Landsat, MODIS, VIIRS, and Sentinel-2 data, with cloud masking and cross-sensor harmonization left to the agent. It poses 238 land-change questions across ten analysis tasks, scored against withheld references from global products, agreement among methods, and visual interpretation. Two controls test what answers rest on: the closed book removes the record, and instrument pairs alter one satellite’s data while nothing changes on the ground. Evaluations of twelve multimodal agents reveal that leading agents complete most analyses end to end but falter when an answer requires mapping from imagery. Further analysis shows that the record moves answers from prior knowledge to the pixels but not yet to the real change on the ground: when one satellite’s data are altered, half of the right answers follow the alteration. EarthRecord thus provides a principled platform for measuring and advancing agents from pixels to land change.
[CV-129] URL-Sim: Unified and Regulated LiDAR Simulation for High-Fidelity Reconstruction of Dynamic Driving Scenes
链接: https://arxiv.org/abs/2611.12932
作者: Kefei Qian,Xipeng Feng,Jie Li,Long Cao,Yuhan Chen,Xiangfei Huang,Guofa Li
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing high-fidelity LiDAR simulation scenes from real-world driving logs is essential for cost-effective safety validation of autonomous driving systems. While simulators based on Neural Radiance Fields (NeRF) achieve realistic scene reconstruction, their large-scale adoption is limited by slow rendering speeds. Recent 3D Gaussian splatting (3DGS) approaches have improved rendering efficiency through explicit primitive representation and differentiable rasterization. However, existing 3DGS-based methods remain largely restricted to rigid vehicles and produce redundant primitives through unconstrained densification. To address these limitations, we propose URL-Sim, a unified and regulated dynamic LiDAR simulation framework. URL-Sim constructs a dynamic Gaussian scene graph that decouples the modeling of traffic participants, including vehicles, pedestrians, and cyclists, enabling accurate reconstruction of heterogeneous dynamic objects within a single framework. It further introduces a regulated Gaussian densification strategy that ranks primitives with multi-dimensional importance scores and allocates Gaussians to the most critical regions while keeping the total primitive count bounded. Experiments on the Waymo and KITTI-360 datasets demonstrate that URL-Sim achieves higher quantitative accuracy and visual quality than existing baselines under comparable primitive budgets, and accurately recovers the geometry and poses of diverse dynamic traffic participants.
[CV-130] opology-Aware Reinforcement Learning for CAD Decomposition toward All-Hexahedral Meshing
链接: https://arxiv.org/abs/2611.12930
作者: Jiaming Peng,Zhihao Zheng,Rui Wang,Qinglin Wang,Jing Xiao,Jie Liu,Na Lei,Xinhai Chen
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 16 pages, 18 figures
Abstract:Automatic hexahedral meshing for complex CAD models has long been regarded as the “holy grail” of mesh generation. The fundamental challenge arises from feature misalignment, where orthogonal or interacting features coexist, making direct hexahedral meshing infeasible and requiring decomposition into simpler, meshable blocks. While existing decomposition methods, including frame-field pipelines and learning-based techniques, have reduced manual intervention, they can remain sensitive to geometric and topological complexity and may fail to produce valid decompositions for challenging feature configurations. Specifically, their action spaces are typically restricted to surface-based splitting, which often yields invalid or unmeshable decompositions. To address these limitations, we propose DecompRL, a deep reinforcement learning framework for CAD decomposition toward hexahedral meshing. We design a set of topology-aware internal-splitting actions that leverage both global topological structure and local geometric property to effectively separate misaligned features. Furthermore, we introduce geometric rewards together with meshability feedback to guide the learning process toward valid decompositions and high-quality meshes. Extensive experiments on ABC, Fusion360, NIST, and DeepCAD show that DecompRL achieves the highest success rate among the evaluated learning-based methods on three of the four benchmarks, while maintaining competitive mesh quality. The measured LMFF-to-DecompRL runtime ratio reaches up to two orders of magnitude; this compares the full LMFF procedure with DecompRL decomposition alone, excluding subsequent mesh generation. The largest SR improvement is observed on Fusion360, where DecompRL reaches a success rate of 59.6%.
[CV-131] Large-area Spatially Aligned Anchors
链接: https://arxiv.org/abs/2611.12912
作者: Joni Vanherck,Brent Zoomers,Lode Jorissen,Isjtar Vandebroeck,Eric Joris,Nick Michiels
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at XR Salento 2024
Abstract:Extended Reality (XR) technologies, including Virtual Reality (VR) and Augmented Reality (AR), offer immersive experiences merging digital content with the real world. Achieving precise spatial tracking over large areas is a critical challenge in XR development. This paper addresses the drift issue, caused by small errors accumulating over time leading to a discrepancy between the real and virtual worlds. Tackling this issue is crucial for co-located XR experiences where virtual and physical elements interact seamlessly. Building upon the locally accurate spatial anchors, we propose a solution that extends this accuracy to larger areas by exploiting an external, drift-corrected tracking method as a ground truth. During the preparation stage, anchors are placed inside the headset and inside the external tracking method simultaneously, yielding 3D-3D correspondences. Both anchor clouds, and thus tracking methods, are aligned using a suitable cloud registration method during the operational stage. Our method enhances user comfort and mobility by leveraging the headset’s built-in tracking capabilities during the operational stage, allowing standalone functionality. Additionally, this method can be used with any XR headset that supports spatial anchors and with any drift-free external tracking method. Empirical evaluation demonstrates the system’s effectiveness in aligning virtual content with the real world and expanding the accurate tracking area. In addition, the alignment is evaluated by comparing the camera poses of both tracking methods. This approach may benefit a wide range of industries and applications, including manufacturing and construction, education, and entertainment.
[CV-132] Uncovering the Blind Spots of OKS-mAP: A Unified Protocol for Occlusion Joint-Angle and Temporal Evaluation of 2D Pose Estimators
链接: https://arxiv.org/abs/2611.12911
作者: Yifan Fan
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures
Abstract:The dominant metric for 2D human pose estimation, OKS-mAP, has three blind spots: it does not distinguish visible from occluded keypoints, does not measure joint-angle error, and is silent on the temporal stability of tracking-free per-frame inference. We propose a supplementary protocol with three modules: (M1) OKS-mAP stratified by ground-truth visibility, with Delta_occ = mAP(v=1) - mAP(full), quantifying how much the aggregate metric masks occlusion-specific differences; (M2) joint-angle error (RMSE/MAE/P95) plus knee-valgus detection consistency; (M3) frame-to-frame displacement, median-filter residual jitter, and jump rate, before and after OneEuro smoothing. We ran it on four datasets (COCO val2017, CrowdPose, OCHuman, PoseTrack18) and four models from two architecture families (YOLO11s-pose, an occlusion-domain fine-tuned variant, ViTPose-B/L), under a common detector-free oracle protocol and an open-source harness. Delta_occ is negative in every combination (-0.19 to -0.47): aggregate mAP overestimates occluded-scene performance in every tested combination. Joint-angle error is amplified in occluded layers; angle metrics reveal differences mAP cannot localize, e.g., the smaller occlusion-trained model significantly outperforming a larger transformer in occluded elbows. On PoseTrack18 val, raw jitter matched between the two YOLO models; pixel-domain OneEuro smoothing cut jump rates 9-10% at no matched-scope AP cost; residual jitter persists after detector removal; the transformer pair’s jump rate is significantly lower; smoothing benefits replicate across architectures. Stratified mAP tracks full mAP closely (sign-consistency 1.0, 12/12), serving magnitude diagnosis rather than re-ranking; angle-metric difference vectors (especially knee) partially diverge, suggesting independent information. The protocol and harness are open-sourced, complementing rather than replacing OKS-mAP.
[CV-133] xtual Response-Aware Guidance for Zero-Shot Adversarial Robustness of Vision-Language Models
链接: https://arxiv.org/abs/2610.40309
作者: Yubo Cui,Xianchao Guan,Zheng Zhang
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
备注:
Abstract:Vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial attacks. Existing adversarial fine-tuning methods often use pre-trained model guidance to preserve image–text alignment while improving robustness. Our analysis of visual representation drift reveals complementary limitations of two representative forms of guidance. Matching visual features constrains all directions equally, providing broad coverage without semantic emphasis. Matching prediction distributions prioritizes semantically relevant directions, but remains limited by the candidate texts and prediction probabilities. These limitations can hinder preservation of pre-trained image–text relationships and degrade zero-shot performance. We therefore propose Textual Response-Aware Guidance (TRAG), which constrains visual representation drift using the pre-trained text encoder’s responses to local variations in input embeddings. TRAG constructs anchor-level constraints from the response directions and magnitudes around semantic anchors, then aggregates them using pre-trained image–anchor relevance, yielding an image-adaptive constraint. Extensive experiments across architectures and tasks demonstrate state-of-the-art clean and robust performance. Further analysis shows that TRAG helps preserve pre-trained image–text relationships.
[CV-134] Predicting Time-to-conversion for Dementia of Alzheimers Type using Multi-modal Deep Survival Analysis
链接: https://arxiv.org/abs/2205.01188
作者: Ghazal Mirabnahrazam,Da Ma,Cédric Beaulac,Sieun Lee,Karteek Popuri,Hyunwoo Lee,Jiguo Cao,James E Galvin,Lei Wang,Mirza Faisal Beg, theAlzheimer’s Disease Neuroimaging Initiative
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Dementia of Alzheimer’s Type (DAT) is a complex disorder influenced by numerous factors, but it is unclear how each factor contributes to disease progression. An in-depth examination of these factors may yield an accurate estimate of time-to-conversion to DAT for patients at various disease stages. We used 401 subjects with 63 features from MRI, genetic, and CDC (Cognitive tests, Demographic, and CSF) data modalities in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We used a deep learning-based survival analysis model that extends the classic Cox regression model to predict time-to-conversion to DAT. Our findings showed that genetic features contributed the least to survival analysis, while CDC features contributed the most. Combining MRI and genetic features improved survival prediction over using either modality alone, but adding CDC to any combination of features only worked as well as using only CDC features. Consequently, our study demonstrated that using the current clinical procedure, which includes gathering cognitive test results, can outperform survival analysis results produced using costly genetic or CSF data.
[CV-135] ransGHOST: Real-Time Synthesis-Free Reconstruction of Instrument-Occluded Regions in Ophthalmic Microsurgery
链接: https://arxiv.org/abs/2611.13971
作者: Niran Nataraj,Maina Sogabe,Masamichi Yamashita,Norihiko Ito,Kenji Kawashima
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 21 pages, 10 figures, 11 tables
Abstract:Intraoperative scene perception in ophthalmic microsurgery is limited by instrument occlusion: when a tool enters the high-magnification field, it conceals tissue from both the surgeon and the perception components of robot-assisted systems that process the video. Generative inpainting can remove the occluder but replaces the missing image content with synthesis, which is unsuitable when visual provenance matters. Many video inpainters also depend on future frames and cannot operate online. We present TransGHOST, a causal reference-guided reconstruction pipeline whose non-generative safe mode operates in real time. Every restored value is obtained by resampling a recorded frame using geometric transforms fitted only to visible regions. The system provides per-pixel confidence and provenance and marks unsupported pixels as abstentions. We evaluate TransGHOST on a new veterinary clinical corpus of canine cataract surgery with pixel-level instrument and anatomy labels. Only the occluded region is scored, and each scoring target is verified to differ from the reference supplied to the system. Safe mode reaches 31.89 dB masked PSNR and 0.915 masked SSIM at 51.7 ms median and 85.5 ms 95th-percentile latency. On a pre-declared held-out cohort using instrument cutouts excluded from segmentation training, it reaches 29.83 dB masked PSNR, while matched-mask evaluation on validation shows a 3.45 dB advantage over LaMa. Region-aware reference selection improves masked PSNR by 3.60 dB over recency when candidate references can themselves be occluded. Reference-age analysis further defines the operating interval for the brief, repeated occlusions targeted by TransGHOST.
[CV-136] Counterfactual Analysis of Extent Leakage from Annotation-Derived Prompts in Vision-Language Medical Image Segmentation
链接: https://arxiv.org/abs/2611.13970
作者: Niran Nataraj,Ankita Das,C. Krishna Mohan,Kenji Kawashima
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 23 pages, 10 figures, 13 tables
Abstract:Text-guided segmentation models receive an image and a short caption about the lesion, and on public benchmarks the caption improves accuracy considerably. On colonoscopy and breast ultrasound, however, the size, count and location words of each caption were computed from the mask that later scores the prediction, and the chest X-ray captions were written by benchmark annotators. We tested what two published models take from them. On colonoscopy and chest X-ray, it is mostly the lesion’s extent. Keeping only the size word or the lung-zone phrase recovers 84 to 98 percent of the improvement over an empty caption, and removing it loses 52 to 77 percent. The rest matters little, and a polyp called a rock is segmented just as well. We call this extent leakage. On breast ultrasound, nearly half the captions also name the diagnosis, which carries part of the benefit. Retraining on three datasets shows that models pick up this habit from their training captions, though only partly on breast ultrasound, and that it weakens their segmentation from the image alone. The leak can be measured without training a model, since caption words alone predict lesion area (coefficient of determination 0.54 to 0.77 on six of seven test sets). Without the test mask, the best method we tested, which estimates extent from the image, reaches 74.59 Dice on colonoscopy against the published 77.23. We propose an evaluation protocol that keeps what a caption says about the image apart from what it copies from the label.
[CV-137] A probabilistic model of visual segmentation explains early visual cortical dynamics NEURIPS2026
链接: https://arxiv.org/abs/2611.13316
作者: Tridib K. Biswas,Ruben Coen-Cagli
类目: Neurons and Cognition (q-bio.NC); Computer Vision and Pattern Recognition (cs.CV)
备注: 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
Abstract:Neural dynamics are a central feature of biological neural systems. In the visual cortex, the functional role of neural dynamics is poorly understood, particularly for static stimuli. The statistics of still natural images have long been used by models that successfully predict firing in early visual cortex; those models assume that neurons represent inferences about latent image features, but often ignore the dynamics of said inference. Here, we test if inferential dynamics in images explain visual-cortical dynamics. To do so, we present a biologically plausible model of cortical computation which assumes that the inference of features is coupled with inference about how those features are organized into meaningful parts, a function also known as segmentation. Given an input image, our model iterates between inferring the features and the segments, through recurrence and feedback, thus inducing dynamics in the neural representation of features. For these neural dynamics, we derive theoretical and exact predictions. Our model predicts both gain and variability in evoked responses to natural images, and we show that it captures classical single-neuron experimental findings such as gain dynamics and variability decay during stimulus presentation. The model also predicts that those dynamics are organized around inferred segment structure and more heterogeneous than previously thought, reflecting the complexity of inference for natural scenes. Finally, our analytical formulation provides a clear interpretation of our results. Insights from our normative model could generalize to segmentation problems in other experimental modalities, and help address the cost of inference in artificial networks with iterative computation.
[CV-138] Is a CNN-Transformer Ensemble Worth It When a Chest X-Ray Foundation Model Is Available?
链接: https://arxiv.org/abs/2611.13014
作者: Aditya Seelaboyina,Irfan Khan
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 2 figures, 10 tables. Code: this https URL
Abstract:Ensembles of ImageNet-pretrained convolutional networks and Vision Transformers are a common way to improve multi-label chest X-ray classification. We ask whether such an ensemble is still worth its cost when a model pretrained on chest radiographs is available. On the official patient-disjoint split of NIH ChestX-ray14 (25,596 test images, 2,797 patients), we train five ImageNet-pretrained backbones and the EVA-X-S foundation model with validation-only model selection, compare five fusion rules, and estimate every difference with a patient-level paired bootstrap. EVA-X-S alone reached a test macro-AUROC of 0.828; the five-model ensemble reached 0.814 (difference -0.0136, 95% CI -0.0178 to -0.0098) at about 7.8 times the summed backbone latency and ten times the parameters. Averaging the five ImageNet models with EVA-X-S changed AUROC by -0.0056 (-0.0095 to -0.0021). With all five ImageNet backbones trained with the EVA-X-S recipe, their ensemble scored 0.817, still -0.0109 (-0.0142 to -0.0076) below EVA-X-S, and across three seeds EVA-X-S varied by at most 0.001 AUROC. Under the ImageNet models’ recipe, however, EVA-X-S fell to 0.798. Our full-stacking configuration never outperformed calibrated averaging. In this setting, the tested ImageNet ensembles did not improve on a single chest X-ray foundation model fine-tuned with its own recipe.
[CV-139] D-TINs for Track-Sampled Ice Sheet Surface Reconstruction from Satellite Altimetry Data ICDM2026
链接: https://arxiv.org/abs/2611.12894
作者: Xiaoyu Xia,Noah Bergam,Abani Patra
类目: Geophysics (physics.geo-ph); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at the IEEE ICDM 2026 Workshop on Spatial and Spatiotemporal Data Mining (SSTDM 2026)
Abstract:Satellite altimetry samples ice sheets densely along tracks but sparsely across tracks, with frequent gaps in spatial coverage. We study the impact of this irregular support on surface reconstruction and introduce a new method, D-TIN, that applies Delaunay triangulation and barycentric interpolation directly to observed locations. A spatial holdout removes contiguous track intervals and nearby same-pass observations. We compare D-TIN with two common workflows, kriging and tile-based polynomial interpolation, to evaluate its accuracy and computational efficiency. Across seven ICESat-1 campaigns, D-TIN reduces RMSE by 12.1 m to 23.6 m relative to independent 5 km tile and polynomial based reconstruction. Its aggregate accuracy is close to the ordinary kriging workflow using a 1 km grid on the commonly used GStatSim platform while its median runtime is 252 times lower. A fixed-weight multiscale extension of D-TIN, called MS-D-TIN, gives the lowest aggregate RMSE, 67.5 m, while retaining the low computational cost of the D-TIN family.
[CV-140] Differential Diagnosis of Frontotemporal Dementia and Alzheimers Disease using Generative Adversarial Network
链接: https://arxiv.org/abs/2109.05627
作者: Da Ma,Donghuan Lu,Karteek Popuri,Mirza Faisal Beg
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Frontotemporal dementia and Alzheimer’s disease are two common forms of dementia and are easily misdiagnosed as each other due to their similar pattern of clinical symptoms. Differentiating between the two dementia types is crucial for determining disease-specific intervention and treatment. Recent development of Deep-learning-based approaches in the field of medical image computing are delivering some of the best performance for many binary classification tasks, although its application in differential diagnosis, such as neuroimage-based differentiation for multiple types of dementia, has not been explored. In this study, a novel framework was proposed by using the Generative Adversarial Network technique to distinguish FTD, AD and normal control subjects, using volumetric features extracted at coarse-to-fine structural scales from Magnetic Resonance Imaging scans. Experiments of 10-folds cross-validation on 1,954 images achieved high accuracy. With the proposed framework, we have demonstrated that the combination of multi-scale structural features and synthetic data augmentation based on generative adversarial network can improve the performance of challenging tasks such as differentiating Dementia sub-types.
人工智能
[AI-0] Re-Search: Dissecting LLM Ideation Through a Two-Player Questioning Game
链接: https://arxiv.org/abs/2611.14487
作者: Muhan Gao,Hangxiao Zhu,Xinqi Su,Yu Zhang,Daniel Khashabi,Kuan-Hao Huang
类目: Artificial Intelligence (cs.AI)
备注: Preprint. 15 figures, 10 tables
Abstract:Research ideation is iterative: researchers refine their ideas step by step based on feedback. Yet existing evaluations obscure this search process, making it difficult to understand how LLMs explore the space of research ideas and what limits their progress. We introduce Re-Search, which adapts the format of Twenty Questions to research ideation: given only the research problem of a published paper, a model must rediscover the paper’s core idea by asking yes-or-no questions. Built from 200 ICML 2026 papers, Re-Search tracks how a model’s idea evolves as it asks questions and receives binary oracle feedback, isolating ideation from execution. Across six recent LLMs, nearly all progress happens within the first 30 of 100 questions, which largely decide how the game ends; after that, most models plateau. Through controlled interventions on the candidate questions and the hypothesis space, we find that the bottleneck is generation, not judgment: models recognize a good idea or a good question when one is offered, but often cannot produce it on their own. We further analyze the ideation traces and find that models fail by either over-exploiting one direction or over-exploring too many. These findings point to concrete design principles for autoresearch systems: since models can judge candidates they cannot produce, enriching the search with externally sourced ideas and questions may be more effective than improving any single model’s generation ability.
[AI-1] Quantifying the Sampling Frequency Invariance of Time-Series Foundation Models for Sensor Data Applications
链接: https://arxiv.org/abs/2611.14425
作者: Sourish Gunesh Dhekane,Abinaya Kumar,Torsten Woertwein,Yan-Bo Lin,Thomas Ploetz,Shabnam Ghaffarzadegan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Time-Series Foundation Models (TSFMs) are increasingly used in sensor applications, where sampling frequency (SF) varies across sensors, devices, and operating modes (e.g., power saving). Ideally, TSFMs must remain effective during such SF changes, provided the SF retains task-critical bandwidth. In this paper, we stress-test 4 prominent TSFMs: MOMENT, Chronos-2, Moirai-MoE, and TimesFM, to quantify their SF-invariance and investigate whether their behavior tracks the resulting loss of information bandwidth rather than the SF value itself. Our stress-test probes 3 dimensions: (i) the shift in embeddings, (ii) the degradation in forecasting performance, and (iii) the degradation in downstream performance, quantified by three novel metrics: FSI, FDI, and CDI, respectively. To our knowledge, this is the first study to place SF-invariance at the core of TSFM evaluations. Our results characterize: (i) how SF-invariance depends on the margin between signal’s native-SF and the bandwidth its task requires, (ii) how much of that invariance the choice of TSFM explains, and (iii) how much of TSFM’s performance degradation under a lower SF reflects information loss rather than a limitation of their abilities. These findings reframe SF-invariance as a measurable, signal-aware property of the (signal X TSFM) pair, and guide practitioners on when TSFMs display SF-invariance.
[AI-2] Evaluating LLM -Generated World Models for Neuro-Symbolic Reasoning
链接: https://arxiv.org/abs/2611.14419
作者: Souhaila Djaffal,Amna Jamal,Gagana Madhugiri Devaraja,Anidipta Pal,Jobst Heitzig
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Formal world models can support planning, verification, and safety reasoning, but constructing them from open-ended natural-language situations is difficult, particularly in multi-agent settings where several formalizations may be plausible. We study whether five LLMs can generate such world models as partially observable stochastic games and whether mechanical validity is sufficient to establish that the resulting model faithfully represents the intended decision problem. We introduce a benchmark of 44 scenarios spanning game, situational, and cultural settings and evaluate generated world models along three dimensions: mechanical validity, reference-relative structural agreement, and semantic criteria. Across three empirical tracks, we analyze 410 first-pass generations. In the reference-backed game track, 134/135 generated world models pass a strict mechanical audit, yet mean permutation-invariant structural F1 is only 0.366, with just 28/134 mechanically valid models exactly matching the authored reference. In the cultural track, explicitly naming the country changes the generated structural representation for most LLMs while producing only small changes in automated semantic scores. These results show that executability is a necessary but insufficient criterion for LLM-generated world models: models that are mechanically valid can still differ substantially in the structure used by downstream neuro-symbolic reasoning and planning.
[AI-3] Precision on Demand: Escalating Weight Bits at Uncertain Decoding Steps in a Small Local Model
链接: https://arxiv.org/abs/2611.14418
作者: Mohammed Nayeem Khan,Logesh L,Aliyas Ahmad Misri
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Local decoding usually fixes one weight precision for the whole generation. We study a different allocation: store 8-bit weights, decode with their top 4 bits, and redo a step at 8 bits only when the low-precision output distribution looks uncertain (entropy above a threshold or a small top-two margin). Quantization is simulated, the model is Qwen3-0.6B, and the benchmark is GSM8K. Cost is counted in weight bytes read per token. On 36724 replayed steps the entropy of the low pass ranks argmax flips with AUC 0.93. At a 15% escalation budget it catches 78% of flips and the margin gate 94%, against the 15% expected for a random gate of equal rate (a computed expectation, not a run). In 150 live problems all-high scores 64.0% and all-low 55.3% (McNemar p=0.053). An entropy gate with a 16% escalation rate scores 62.7% at 0.87 of the all-high weight bytes. Its paired difference is -1.3 (-8.0 to +5.3) points to all-high and +7.3 (+0.0 to +14.7) points to all-low (p=0.080), so the live data do not separate the gate from either end. Verified speculation reaches zero KL at similar bytes in the same cost model. A live random control, a thinking-mode phase rule, a second model and live KL were not run.
[AI-4] Causal Concept Explanations for Deep Neural Models NEURIPS2026
链接: https://arxiv.org/abs/2611.14407
作者: Josh Rountree,Pulkit Verma,Oswin So,Chuchu Fan,Julie A. Shah
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026. 28 pages, 1 figure, 9 tables. Code: this https URL
Abstract:Concept-based explanations in neural models are important for the development, certification, and adoption of autonomous systems. Yet, interpretability remains correlational. Without methods to guide and quantify causal relationships between explanations, a concept explanation can be provided without being necessary or sufficient for the observed output thereby appearing correct while misleading. We introduce Causal Concept Wrapper Network (CCW-Net), the first use of interventional mediation analysis from fields such as economics, medicine, and epidemiology as a training objective to align causal effects of concept-based explanations with a model’s output. We further apply mediation analysis as an evaluation tool to quantify, per concept label combination, how much of any model’s output is causally attributable to each concept explanation. As a training objective, CCW-Net shapes a model’s concept representations such that they are both necessary and sufficient for the outputs they are credited with and independent of other concepts’ contributions. Counterfactual concept representations anchor a set of causal alignment criteria: necessity, sufficiency, and independence. Necessity drives irrelevant concept effects toward zero, sufficiency ensures relevant concept representations fully reconstruct a model’s output, and independence enforces the total effect decomposes linearly across concepts. We evaluate CCW-Net across aircraft control, driving, and image classification. CCW-Net provides a step toward concept-based explanations that are not merely interpretable, but quantifiably causal, supporting safer evaluation, certification, and deployment of transparent and trustworthy neural models.
[AI-5] An Energy-Based Perspective on Machine Learning
链接: https://arxiv.org/abs/2611.14383
作者: Bo Dai,Yilun Du
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Energy-based models (EBMs) define a distribution through a scalar energy, p_\theta(x)\propto\exp(-E_\theta(x)) , and are usually presented as one model family among many. This tutorial has a broader goal: beyond introducing EBMs, it uses them as a single lens that connects the different aspects of machine learning. Any EBM is specified by three choices, the energy, which determines what the model represents; the inference procedure, which samples from or optimizes the energy; and the learning method, which fits the energy to data. We first develop these components, including sampling, variational inference, and learning by maximum likelihood, noise contrastive estimation, pseudolikelihood, score matching, and adversarial training, and show that the learning methods share one contrastive structure. We then show that discriminative models, generative models such as GANs, normalizing flows, diffusion and autoregressive models, representation learning methods such as contrastive learning, word2vec, BERT, denoising auto-encoders, and diffusion representations, reinforcement learning with EBM policies and world models, inverse reinforcement learning, and planning, as well as inference-time computation with verifiers, are all EBMs that differ in these three choices, mostly in how they handle the partition function and sampling. This view explains existing methods, organizes them into a common framework, and suggests new algorithms by replacing one component while keeping the others, with composition of energies as a principled way to combine learned models at inference time.
[AI-6] Cognitive Mapping of Learning by Thinking Mechanisms in Large Language Models
链接: https://arxiv.org/abs/2611.14382
作者: Dong Han,Jin Zhang,Kunyue Yang
类目: Artificial Intelligence (cs.AI)
备注: Theoretical framework paper; 24 pages, 3 tables, 1 figure
Abstract:Large language models (LLMs) increasingly exhibit capabilities that resemble human Learning by Thinking (LbT), the acquisition of new insight through internal processes such as explanation and reasoning, without new external input. A systematic account of the cognitive mechanisms behind these capabilities, and of their limits, remains underdeveloped. This paper proposes a Cognitive LbT Framework that maps four mechanisms (Reflective Reasoning, Synthetic Memory Formation, Internal Simulation, and Conceptual Self-Organization) onto computational processes in LLMs. Drawing on representative methods, including chain-of-thought prompting, self-consistency, self-reflection, Tree-of-Thoughts search, Self-Instruct, and self-taught reasoning, the analysis argues that selected methods can functionally approximate aspects of these mechanisms and can improve performance on some tasks without additional labeled examples, although this does not always constitute durable learning in model parameters. For example, Self-Instruct generated approximately 52,000 instructions from 175 seed tasks and reported a 33% absolute improvement on Super-NaturalInstructions (Y. Wang et al., 2023). The same mechanisms also produce characteristic failures, including hallucination diffusion, model collapse, concept drift, and high computational cost. These failures are not isolated technical defects but follow from four structural asymmetries between LLMs and human cognition: the identity of generator and monitor, closed learning loops without external grounding, symbolic rather than sensorimotor simulation, and the cost of inference-time computation. The framework yields testable propositions about when LbT in LLMs should succeed or fail and offers a structured basis for designing more reliable autonomous learning systems.
[AI-7] CLADE: Category-Level Answering from Deficient Evidence in Retrieval-Augmented Language Models
链接: https://arxiv.org/abs/2611.14355
作者: S M Rayeed,Charles V. Stewart,Yao Ma
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Retrieval-augmented systems often retrieve passages that are accurate and relevant but state the answer at a coarser level of granularity than the question requires. For example, a question may ask which specific disease a drug treats (familial hyperlipidemia), while the retrieved passage mentions the broader disease family (hyperlipidemia). Prior work on context sufficiency treats such coarse passages simply as insufficient. We argue that they form a distinct case, which we call resolvable insufficiency: text alone cannot settle the fine-grained answer, but the broader category it mentions maps to a short list of candidate answers in a domain ontology. When language models read these coarse, family-only passages, they exhibit a phenomenon we call Clade (Category-Level Answering from Deficient Evidence): they answer instead of abstaining or searching further. On biomedical questions from PrimeKG, family-only passages reduce abstention by 13.8 points compared to uninformative passages, and the resulting guesses are wrong about half the time. A counterfactual test confirms the cause: replacing an exact disease name with its broader family name lowers abstention by 17.4 points compared to deleting the sentence. Simple fixes like confidence filtering, scaling to a larger model, and explicitly instructing the model to abstain cannot resolve Clade, and this replicates on ecological species interactions in GloBI. Finally, we test whether an ontology can turn coarse passages into exact answers. Given the correct family, an ontology list includes the true answer in 98.5% of cases, but string matching finds that family on the page for only 19.8%. Letting the model choose the family raises this to 30.0% and end-to-end recovery from 10.0% to 26.8%. Overall, our results show when language models should stop guessing and consult an ontology, and highlight which steps of that process remain unsolved.
[AI-8] Forward-Value Discretization: Faster Stochastic Diffusion Sampling Without High-Order Approximation
链接: https://arxiv.org/abs/2611.14328
作者: Yuchen Jiao,Na Li,Changxiao Cai,Yuting Wei,Gen Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Stochastic diffusion samplers often require substantially more neural function evaluations (NFEs) than deterministic samplers to achieve comparable sample quality. Although higher-order discretization accelerates deterministic sampling, its benefits for stochastic sampling remains unclear. We establish matching upper and worst-case lower bounds showing that stochastic samplers with higher-order approximations remain first-order convergent, even with exact score functions. Thus, increasing the approximation order does not generally improve the convergence order. Motivated by this limitation, we introduce forward-value discretization, a training-free framework that evaluates the data/noise prediction model at a lookahead estimate constructed from previous predictions. For deterministic sampling, the resulting Forward DPMSolver (F-DPMSolver), improves sample quality at low NFE budgets through its DDIM-based variant, despite remaining first-order convergent. For stochastic sampling, we develop Forward DDPM (F-DDPM), which evaluates the model at a cleaner lookahead point adjusted for subsequent noise injection. This estimate reuses both the previous model prediction and the previously injected noise. Both variants admit first-order convergence guarantees with one model evaluation per iteration. Across CIFAR-10, ImageNet, FFHQ, and LSUN, F-DDPM consistently achieves lower FID than the evaluated stochastic baselines at matched NFE budgets. With 1.5 times as many NFEs as deterministic baselines, it achieves competitive sample quality, narrowing the practical efficiency gap between stochastic and deterministic sampling. Code is available at this https URL.
[AI-9] When Should Agents Remember? Falsification-Gated Self-Evolution for LLM Agents
链接: https://arxiv.org/abs/2611.14326
作者: Runxuan Tang,Haoyu Gao,Yuyan Ding,Junyi Yao,Zihao Zheng,Zecheng Sheng,Liwei Hou
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:LLM agents increasingly operate in long-horizon, verifier-rich environments, but current self-improvement mechanisms often write episode-derived lessons directly into memory, making local success an unreliable signal for reusable and safe knowledge. This paper addresses the core question of when an experience-derived behavioral update should be admitted into persistent agent state. We propose Falsification-Gated Self-Evolution (FGSE), a verifier-grounded framework that represents each candidate lesson as a structured hypothesis with an explicit precondition, behavior change, expected effect, and verifier. FGSE applies the hypothesis only in a temporary state, tests it on target-transfer, falsification, and archived-regression probes, and then commits, refines, or rejects it according to measured gain and risk. Across web, app, tool-use, and long-term memory benchmarks, FGSE achieves strong task performance, including 39.8 WebArena success rate, 78.8 AppWorld average completion, and 0.724/0.487 Pass ^1 on \tau -Retail/ \tau -Airline, while reducing falsification failures to 5.0%, regression damage to 1.3%, and harmful committed updates to 3.6%. These results suggest that testing memory updates as hypotheses supports more reliable long-term adaptation.
[AI-10] LM-SimBench: A Unified Framework Dataset and Benchmark for Large Language Model Training Simulation
链接: https://arxiv.org/abs/2611.14285
作者: Yu Dong,Hong Li,Qiang Sun,Xu Zhou,Jinbiao Huo,Qixiu Cheng,Zhiyuan Liu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Evaluating candidate parallelization strategies through distributed training runs makes strategy selection prohibitively expensive at scale. Performance simulation offers a lower cost alternative, yet controlled comparisons among existing simulators remain difficult because they differ in the training stages they model and the workloads on which they are evaluated. We introduce LM-SimBench, a unified framework, profiling dataset, and benchmark for evaluating LLM training simulators. The framework decomposes simulation into functional stages from model compilation to schedule level aggregation, connecting execution representations and cost estimators to measurable evaluation targets. Following this framework, we profile 23 model settings from 10 dense and Mixture-of-Experts (MoE) LLM families on an Ascend Neural Processing Unit (NPU) cluster. The resulting dataset contains 348 feasible configurations that jointly vary five parallelism dimensions and activation recomputation, with six views spanning configuration, rank, operator, event, and kernel granularity. Evaluations of representative training simulators demonstrate how LM-SimBench supports comparisons of prediction accuracy, assessment of configuration selection, and diagnosis of prediction errors. Matched GPU-NPU experiments validate the cross-platform applicability of the collection and evaluation protocol and demonstrate NPU-guided GPU configuration selection.
[AI-11] Recurrent Modular Structure in Spiking Neural Network Reinforcement Learning
链接: https://arxiv.org/abs/2611.14262
作者: Vikram Ramavarapu,Himanshu Udupi,Ashish Gautam
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注: 19 pages, 5 figures
Abstract:Biological neural circuits are both modular and recurrent, with dense communities sparsely linked to one another. Spiking neural networks (SNNs) for reinforcement learning are usually feed-forward, and it is unclear how to obtain functional modules in a recurrent SNN or what they buy. In this work, we introduce a class of spiking networks, called Community Recurrent SNNs (CoRe SNNs): recurrent networks partitioned into communities with fixed roles. A controller community receives the stimulus, each action has its own community, and latent communities carry internal state. Spiking activity is then a walk between communities, and the action is the community the walk reaches. All learning is local and three-factor, and the recurrent communities are closer to cortical microcircuits than a feed-forward actor. We compare three regimes for obtaining the communities: dynamic modularity, in which a new learning rule we introduce, Map-STDP, encourages modular structure during learning; partial modularity, in which a stochastic block model (SBM) initialization enforces it partly; and full modularity, in which sealing enforces it fully. On CartPole, across three spiking neuron models, Map-STDP improves learning over the same network without it matches full modularity (sealed communities) on two of the three models, and makes the observed spike walk more compressible. Replacing the feed-forward actor of a TD-STDP actor-critic with recurrent action communities improves on the feed-forward network on CartPole and LunarLander, and matches it on Acrobot. How strongly to enforce modularity depends on the task: full modularity helps most on Acrobot, while on LunarLander the dynamic, partial and full regimes perform alike. Overall, these recurrent modular networks can match or even improve upon feed-forward approaches, with improvements in bio-plausibility and energy efficiency.
[AI-12] A Cross-Scenario Survey and Analysis of Self-Evolving Multimodal Agents
链接: https://arxiv.org/abs/2611.14256
作者: Jingjing Jiang
类目: Artificial Intelligence (cs.AI)
备注: 44 pages, 15 tables, 4 figures
Abstract:Recent work shows that multimodal agents increasingly self-improve without recurring human annotation. However, each self-improvement mechanism is designed and evaluated within a single scenario, leaving its preconditions unverified under cross-scenario transfer. This survey investigates the structural source of asymmetric mechanism transfer across four scenarios: multimodal reasoning, multimodal generation, GUI and Web navigation, and embodied manipulation. We first formalize the self-evolving multimodal agent by four criteria and a differentia of self-reference, and adopt the three-stage self-evolving loop of experience generation, feedback acquisition, and update consolidation as our analytical framework to position the scenarios along ceilings on rollout count and feedback fidelity. Comparing self-evolving with fixed loops within each scenario separates the limitations that the scenario imposes from those inherent in the self-evolving design. We then recast each mechanism as a trade-off that raises a scarce factor by presupposing a task-structural resource, such as output equivalence or checkable outcomes, and judge it portable, conditional, blocked, or redundant across scenarios. We attribute the asymmetry to divergent combinations of scarce factors and resources across opposite transfer directions, and identify the matched-budget measurements that could overturn these still untested verdicts.
[AI-13] Verifiable Agent ic Traffic Class: A Policed Quality-of-Service Mechanism for Agent ic AI Traffic Evaluated from Simulation to Real-Kernel Adversarial Testing
链接: https://arxiv.org/abs/2611.14211
作者: Naveen Babu Narayanan Nair,Sajay Kurup
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: 20 pages, 3 figures, 15 tables; submitted to IEEE Access; code and data at doi: https://doi.org/10.5281/zenodo.23865136
Abstract:Software agents built on large language models generate traffic unlike human web traffic, yet routers have no class for it and cannot verify a sender’s claim to be an agent. This paper proposes the Verifiable Agentic Traffic Class (VATC), which combines a reserved Differentiated Services Code Point (DSCP) value declaring agent traffic, a router-side classifier that demotes flows contradicting the declaration to a class served after best-effort traffic, and a rate-limited priority queue. VATC was compared with static Differentiated Services (DiffServ), a dedicated class that trusts the marking, and fq_codel, a per-flow fair-queuing scheduler, in two simulators and on a real Linux kernel. VATC matched DiffServ under steady load. In a constructed burst it cut mean agent queuing delay by 98% relative to DiffServ in simulation (assuming an immediate verdict), at a 12% increase in human delay; most of the gain came from the separate class. Under a spoofed flood in the kernel, the rate limit held human latency 11 times below DiffServ’s. Without verification, DiffServ and a tightly configured fq_codel gave legitimate agents lower latency than VATC; a live four-feature classifier then demoted all 20 bulk-shaped spoofers and gave agents 0.30 ms, but missed six spoofers that copied the agent pattern. With emulated network delay, a timing feature of the classifier stopped separating agent from human flows. In real agent harnesses on a congested link, router queueing took 13% to 63% of task time, depending on harness and assumed model latency. The traffic was synthetic or sandbox-scale.
[AI-14] Joint Quantization and Dynamic Partitioning for Context-Aware Edge Vision Transformers
链接: https://arxiv.org/abs/2611.14196
作者: Zhu Jiaqi,Jain Prakriti,Katare Dewant,Kumar Saurabh
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deploying vision transformer models across connected and automated vehicles (CAVs) requires joint management of computation and Vehicle-to-Vehicle (V2V) communication. Static split-inference methods use fixed partition points and cannot adapt to time-varying link bandwidth, processing load, and concurrent multi-stream traffic. This paper proposes an edge orchestration framework that jointly selects model partition points, per-layer quantization bitwidths, and execution placement across heterogeneous vehicular compute nodes. The joint partitioning and mixed-precision allocation problem is formulated as a Mixed-Integer Linear Program (MILP) that minimizes compute and communication energy subject to end-to-end latency and task-quality constraints. The framework is evaluated on Jetson Orin Nano (8~GB), Jetson Orin NX (16~GB), and Jetson AGX Orin (32~GB) nodes using OPV2V-driven multi-stream workloads with K \in \2,4,6,8\ . Across the evaluated configurations, the proposed formulation reduces V2V communication energy by 33.3 – 36.0% relative to a Uniform INT8 split. At K=8 , end-to-end task completion latency is reduced by 34.0% on the Orin Nano and 33.1% on the Orin NX relative to the same baseline, while limiting the reduction in 3D detection accuracy to 3.1 – 4.1 percentage points in \mathrmmAP_50 relative to FP16 execution.
[AI-15] An Augmented Lagrangian Approach to Lifted Bellm an Linear Programming
链接: https://arxiv.org/abs/2611.14183
作者: Jongchan Park,Hyukjun Yang,Narim Jeong,Donghwan Lee
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Temporal-difference critics regress onto a bootstrapped target and usually need a target network. Lifted Bellman Linear Programming (LBLP) instead expresses the in-sample optimal values of offline RL as the solution to a linear program over the dataset. Its neural solver ALBUM relaxes the constraints into hinge penalties with fixed coefficients, and its guarantees are conditions on coefficients chosen in advance. We solve the same program with the method of multipliers: the coefficients become multipliers learned by dual ascent, with one penalty parameter \rho . In the convex case the fixed points are the KKT pairs of LBLP, and at every stationary point the applied constraint weights balance the objective exactly. The critic can serve a Gaussian actor, a flow actor, and best-of-N sampling, and for the Gaussian actor we propose an advantage-normalized DDPG+BC objective. With a single Q and V and no target network, our AL-LBLP with best-of-N reaches 60.1% average success on the 50 state-based OGBench tasks. On long-horizon tasks, it reaches 81.5% on humanoidmaze-giant and 79.8% on antmaze-giant. It does not collapse up to batch size 8192.
[AI-16] Cross-Border Fulfillm ent Center Selection via Doubly Robust Estimation and Linear Programming
链接: https://arxiv.org/abs/2611.14174
作者: Kriti Mahajan,Patrick Bloebaum
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce the cross-border (XB) fulfillment center (FC) Selection Model: a multi-stage optimization framework combining forecasting, doubly robust estimation, and linear programming to select optimal source FCs. For each source-destination shipping arc, the objective is to choose a subset of source-country FCs that minimize delivery time and cost while maximizing profit and shipped units under a fixed domestic network. We evaluate the model on three production-scale international shipping corridors for a large e-commerce company using 12 months of shipment data, employing both aggregate offline replay and shipment-level counterfactual nearest-neighbor matching. Across all shipping corridors, the XB FC Selection Model achieves 8.5-51.1% reductions in delivery time and 4.4-48.3% reductions in logistics cost relative to human expert-defined FC selection policies, with up to 6.5 percentage-point improvements in 5/5 target compliance (5 days, 5 USD). Pareto analysis shows the model consistently dominates single-objective ranking strategies and unconstrained LP on the efficiency-reallocation frontier, with all results statistically significant.
[AI-17] BARS: Budget-Aware Sequential Sampling with Refusal for Majority Identification under Unknown Distributions
链接: https://arxiv.org/abs/2611.14171
作者: Shenxi Liu,Yiwei Li,Mingyang Zhao,Yuhang Tian,Kan Li
类目: Artificial Intelligence (cs.AI)
备注: 23 pages (17 pages without appendix), 7 tables, 8 figures
Abstract:Repeated sampling with majority selection is an effective means of enforcing self-consistency in large language models. Recently, a variant has also been introduced that terminates early to save costs when the output distribution is favorable. But it still cannot yield a correct confidence estimate from the answer distribution. For this issue, we formalize this as majority identification under an unknown distribution with a budget and a target error rate, and propose BARS, a sequential rule certifying the top-two gap and returning an early stop, a budgeted majority vote, or a refusal when feasibility cannot be established. A refusal is a statement about the rule and its budget, not a claim that a question is unanswerable, and it tells practitioners when to widen the budget or switch models. A certified mode carries proven bounds on the joint answering error and on refusing an instance the budget could handle, while a practical mode, used in our experiments, replaces them with empirical calibration. On synthetic instances drawn from known distributions over thousands of seeds, the difficulty-driven behavior is recovered and the price of the certified mode is this http URL GSM8K the practical mode draws 22.3% fewer samples than fixed-8 sampling over the same pool, refused questions included, at the cost of declining 18.8% of the questions. On MATH, it greatly improves the accuracy of its output answers into 82.5% at the cost of honestly refusing to answer half of the difficult questions that the model itself is unable to solve.
[AI-18] Hx2Dx: From Anamnesis to Diagnosis - A Multimodal Arena for Conversational Clinical Reasoning in LLM s
链接: https://arxiv.org/abs/2611.14122
作者: Federico Ravenda,Mirko Cesarini,Daniele Montagnani,Volodymyr Karpenko,Antonietta Mira,Andrea Raballo
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) achieve expert-level diagnostic accuracy in static evaluations, where the model receives a curated vignette or case report that already condenses the diagnostically relevant findings. Such evaluations measure inference over already selected evidence; the evaluations do not account for the inquiry used to obtain it. In clinical practice, however, diagnosis is an iterative process of inquiry. Clinicians take the patient’s history, or anamnesis (Hx), through targeted questioning, examine the patient, and request and interpret tests, progressively narrowing the differential until they reach a final diagnosis (Dx). We introduce Hx2Dx ARENA, an interactive benchmark of 1’000 clinical cases from the New England Journal of Medicine (NEJM). Each case is a simulated encounter that proceeds from initial presentation through history taking and physical examination to diagnostic workup. We evaluate open- and closed-source LLMs under multiple configurations. Performance varies considerably across models in the interactive setting, and every model loses diagnostic accuracy relative to the static setting, where the full case is presented upfront. The ability to ask the right questions accounts for much of both the gap between models and the loss of accuracy: diagnosis improves as questions become more targeted and build a more complete clinical picture. LLMs thus reason well once the relevant data are available but remain limited in hypothesis-driven information gathering, in knowing what to ask in order to know. Closing this gap is a prerequisite for their safe integration into real-world diagnostic workflows.
[AI-19] Model-Free Runtime Measurement of Task-Relevant Information
链接: https://arxiv.org/abs/2611.14111
作者: Dimitris Panagopoulos,Adolfo Perrusquía,Yang Xing,Weisi Guo
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures, 2 extended data tables, appendix included. Code available
Abstract:Autonomous systems increasingly monitor physical kinematics, environmental states and task completion outcomes. While existing work provides methods to plan information seeking actions and interpret acquired observations, current approaches do not monitor whether the live process of information gathering is functioning properly. Consequently, such systems can remain operationally active without resolving outstanding mission requirements. To address this limitation, we introduce Acquisition-State Telemetry (AST), a model free architecture that evaluates ongoing information acquisition from observable evidence relative to declared task requirements. Specifically, a domain interface maps raw observations into task resolution coordinates and normalized comparative representations, which are processed by a fixed causal monitor. This monitor emits two continuous real time signals that quantify (i) prospective progress across unresolved requirements and (ii) informational friction generated when recurrent evidence yields negligible informational gain. We evaluated a single, unadjusted monitor configuration across eight diverse domains including search and rescue dialogue, spatial grid exploration, hierarchical capability acquisition, continuous robotic manipulation, and probabilistic intent estimation. We show that across these domains the friction channel distinguished unproductive stagnation from purposeful exploration prior to task completion, while remaining muted during post completion activity. Furthermore, in closed loop reinforcement learning experiments under early termination constraints, we demonstrate that penalizing informational friction allowed agents to sustain high mission success, whereas unpenalized policies and baselines with matched operational costs failed to complete required tasks.
[AI-20] ProofCoder: Continuous Reasoning for Automated Software Verification
链接: https://arxiv.org/abs/2611.14106
作者: Martim Afonso,Inês Lynce,Maurizio Morisio,João F. Ferreira
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Formal verification using proof assistants provides strong correctness guarantees for software, but constructing proofs remains labor-intensive and requires significant expertise. Recent approaches based on large language models (LLMs) have shown promise in automating proof synthesis. However, these approaches typically constrain the model to a rigid tactic-generation loop, in which the surrounding system controls premise retrieval, proof-state management, and backtracking. We introduce ProofCoder, an agentic proof-synthesis system for Rocq that approaches theorem proving from a coding-agent perspective. Rather than generating tactics within a fixed loop, ProofCoder reasons continuously throughout proof search and uses proof-assistant feedback to decide when to gather additional information, inspect proof states, revise previously generated proof fragments, or backtrack to an earlier point in the proof. This allows it to iteratively construct, repair, and refine proofs, at either tactic-level or whole-proof granularity. We evaluate ProofCoder on 280 challenging verification tasks from the AutoRocq Benchmark. On the best-performing configurations, ProofCoder increases proof success from 47% to 60% over the state-of-theart AutoRocq system, corresponding to a 27.7% relative improvement at approximately half the cost. Our ablations identify continuous reasoning as a key design choice: without it, the agent solves fewer lemmas at substantially higher cost, backtracks four to six times more often, and more frequently discards its own revisions. Whole-proof and tactic-level generation achieve similar success rates but follow distinct search and repair strategies. These results suggest that effective automated software verification requires more than improved tactic selection: continuous reasoning and feedback-guided proof revision are critical ingredients for robust proof synthesis.
[AI-21] Sable: A Small-Field Sumcheck Argument for Bit-Exact Inference of Large Language Models
链接: https://arxiv.org/abs/2611.14083
作者: Tomer Solberg
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We present Sable, an argument system and single-GPU prover for the statement that a committed, integer-quantized transformer produced a given token sequence from a given prompt, bit for bit. Sable fixes an integer function F_O with no floating-point step, in which every rounding, normalization, softmax and sampling decision is determined by its inputs, and proves it with a constraint system whose relations provably never wrap modulo the 31-bit KoalaBear prime. The proof batches zerochecks and LogUp-GKR lookups into one sumcheck per domain, and commits to every witness tensor and weight in one multilinear commitment built from Reed-Solomon codes, BLAKE3 Merkle trees and a mixed-length BaseFold opening. The prover streams the model layer by layer on one GPU. On one NVIDIA RTX PRO 6000 Blackwell GPU, Sable proves all 64 layers of Qwen2.5-32B in 92.5 s for 4,096 tokens and 398 s for 16,384 tokens, and covers Qwen3-32B and the mixture-of-experts Qwen3-30B-A3B (4,096 tokens in 121.8 and 84.7 s). A proof costs 41 to 50 times the serving time, or 6.4 cents per 4,096-token request at public GPU prices, and 40 to 64 times less time per parameter and token than zkLLM. On WikiText-2, F_O’s greedy token matches the BF16 model’s at 86.3% of positions; on Qwen3, F_O is within 1.6 points of BF16 on MMLU and 1.7 points on GSM8K. A verifier with a compiled core checks a proof on one CPU core in 19 to 56 s. In the random-oracle model, the interactive proof has round-by-round soundness error 2^-103. The proof is not zero-knowledge, so the system targets open-weight models. Code: this https URL
[AI-22] Can KV Cache Be Reused Across Model Updates? A DeepSeek -V4-Flash Case Study
链接: https://arxiv.org/abs/2611.14051
作者: Zhikun Tao,Fangyong Jin,Junping Zhao
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Reusing key–value (KV) caches across language model updates could avoid repeated computation of shared context, but changes in model weights raise a fundamental question about what these caches preserve. We investigate direct cache reuse across four model updates, with a detailed study of DeepSeek-V4-Flash to Flash-0731. Our central finding is that caches can retain useful task information without faithfully reproducing the updated model’s behavior. Retrieval accuracy varies substantially across updates, ranging from near-native performance to large losses. With source caches already available, reuse achieves median time-to-first-token speedups of 2.10 – 5.08\times on three longer-prefix QA benchmarks. To characterize the behavioral differences behind these outcomes, we introduce Functional Cache Divergence, which compares next-token distributions under reused and native caches. Direct KV cache reuse better preserves native model behavior in natural-text continuation, whereas compatibility degrades with increasing prefix length in single-token retrieval tasks. Answer-margin analyses help explain how correct retrieval decisions survive substantial internal state changes. Yet even agreement on the first generated token does not ensure agreement in answer content or stopping behavior. These findings establish task utility and fidelity to native inference as distinct criteria for evaluating cross-version KV cache reuse.
[AI-23] Personalized Memory Rubrics: Learning What to Remember per User via Rubric-Guided Credit Assignment WSDM2027
链接: https://arxiv.org/abs/2611.14017
作者: Yupeng Han,Kai Zhang,Shuochen Liu,Ze Liu,Xianquan Wang,Zhihong Pan,Xiangyu Zhao,Qi Liu,Enhong Chen
类目: Artificial Intelligence (cs.AI)
备注: WSDM 2027 Main Conference
Abstract:Long-horizon personalized agents maintain an evolving memory by continuously deciding what to add, modify, or discard. However, learning an optimal update policy remains challenging. Conventional training relies solely on terminal question-answering accuracy, yielding a coarse feedback signal that treats all intermediate updates identically. Furthermore, update utility is inherently user-specific, as identical memory actions may yield opposite effects across different user personas. Existing methods fail to address this heterogeneity, relying on globally shared evaluation standards with at most a single scalar intermediate reward. To address these challenges, we propose \textbfPersonalized Memory Rubrics (\textbfPMR), a framework that translates terminal feedback into fine-grained, per-step, and per-user credit assignment. PMR decomposes evaluation into a universal layer capturing cross-persona failure patterns and a per-user adaptive layer modeling individual traits as a specialized delta over the universal baseline. Through \textbfRubric-Guided Credit Assignment, PMR redistributes credit at the token level, selectively amplifying validated decisions on successful trajectories while falling back to standard policy optimization on failed ones to avoid unreliable attribution. The rubric and memory policy \textbfco-evolve in a closed loop with anti-circularity guarantees. Crucially, PMR incurs zero inference overhead, operating without any rubric or external judge during deployment. Extensive experiments across four benchmarks demonstrate consistent gains over strong memory-augmented baselines.
[AI-24] Margin-Guided Token Credit for Reinforcement Learning with Verifiable Rewards
链接: https://arxiv.org/abs/2611.14015
作者: Chi Liu,Xin Chen,Yuchuan Weng,Srinivasan Manoharan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In reinforcement learning with verifiable rewards, a language model is rewarded for a correct final answer, and GRPO passes that reward to every token of the solution in equal measure. We instead place the credit where the model hesitated, in a method we call MCPO (Margin-Credit Policy Optimization). We measure hesitation by the top-2 margin M=p_1-p_2 of the policy’s next-token distribution, map it to a score with two fixed thresholds, and spread each solution’s advantage over its tokens so that the mean stays unchanged. The margin is read from a forward pass the trainer already performs, so the method needs no extra model evaluation and no batch statistics. We train MiniCPM5-2B-Base with LoRA for 200 steps on 2,048 DAPO-Math problems and evaluate on six competition-math benchmarks. MCPO beats an unmodified GRPO baseline for all five data seeds, by 6.55 points of macro avg@32 on average (95% CI [4.1, 9.0] ), and a Qwen3-4B-Base replication improves for all three seeds ( +4.75 ). The gain is largest on problems that GRPO solves only some of the time and grows with k in pass@ k , reaching 13.8 points at pass@32. MCPO models also write two to three times longer answers, and runs that write longer score higher; our experiments do not separate the two effects.
[AI-25] From Attack Surface to Defense Architecture: Compositional Security for Agent ic Coding Harnesses
链接: https://arxiv.org/abs/2611.13960
作者: Simon Hegelich,Bardh Prenkaj,Habiba Sarhan,Morteza Shahrezaye,Kolja Hegelich,Christian Montag,Ralf Schneider,Erik Cambria
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 21 pages, 3 figuers, 1 sublement
Abstract:AI coding harnesses couple language models to shells, filesystems, and network sockets, turning the harness into an automated Living-off-the-Land (LotL) platform: once an adversary injects instructions into retrieved content, dual-use binaries such as bash, curl, and find become the payload. We argue that security in agentic systems is a structural property of the cognitive architecture and permission composition rather than an emergent property of individual models. Static regex blacklists miss structural evasions, whereas LLM-as-a-Judge guardrails incur high latency while over-blocking benign work. We present ActiveSecHarness, a neurosymbolic reference-monitor architecture built around Separation of Intelligence and Authority. It combines (1) Macaroon-inspired HMAC capability tokens across four Trust and Authority Rings, (2) a 2D product taint lattice \mathcalL = \mathcalL_\mathrmtrust \times \mathcalL_\mathrmsens and a workspace provenance ledger that prevents secret laundering across disk, (3) deterministic Ring-0 OS/tool guards, and (4) bytecode certificates, split-horizon audit logs, self-healing security test synthesis, and an adaptive UserWikiManager. Across N=252 fixtures ( 120 attack, 120 benign, 12 kill chains), a live local LLM judge (qwen3:4b) refuses 98.3% of benign work at 142.3,\mathrmms latency, whereas Ring 0 achieves \mathrmABR=1.000 and \mathrmFPR=0.000 in 12.7,\mu\mathrms ( 11,200\times faster). In a multi-step trajectory ablation, an oracle Conseca-style stateless policy misses 4/12 kill chains ( \mathrmABR=0.667 ) and coarse Boolean taint blocks 83.3% of benign workflows, while our 2D product lattice achieves \mathrmABR=1.000 and \mathrmFPR=0.000 at \approx 20,\mu\mathrms per step. Comments: 21 pages, 3 figuers, 1 sublement Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) MSC classes: 68M25, 68T42, 06B35, 68Q60 ACMclasses: D.4.6; K.6.5; I.2.11; D.2.4 Cite as: arXiv:2611.13960 [cs.CR] (or arXiv:2611.13960v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2611.13960 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-26] reeAgent : Structure-Aware Memory Agent for Processing Long Hierarchical Data
链接: https://arxiv.org/abs/2611.13946
作者: Wei Gu,Weiwei Qi,Jiajun Tang,Tianhang Zheng,Bo Chen,Zhibo Zhang,Jinfei Liu,Kui Ren
类目: Artificial Intelligence (cs.AI)
备注: 13 pages, 3 figures, 5 tables, including appendices. Code: this https URL
Abstract:Practical Web applications increasingly require agents to interpret long conversations or documents. For inputs exceeding the context windows of large language models (LLMs), memory agents process segments sequentially and retain past information in bounded memory. However, they typically divide inputs into flat token segments without preserving structure or hierarchy, causing three limitations for structured data such as API responses or HTML documents. First, fixed-length segmentation can split fields of the same record, breaking their relationships. Second, segments lack hierarchical information, obscuring their structural context and positions. Third, successive memory updates may discard details of earlier sibling records needed for reasoning on later segments. To address these limitations, we propose TreeAgent, a structure-aware agent that uses the input hierarchy to guide both segment-wise context processing and access to past information. Specifically, TreeAgent constructs segments from complete subtrees and groups of adjacent sibling subtrees to preserve local structural relationships. We provide theoretical guarantees on segment counts to guide token budget selection toward matching the segment count of fixed-length segmentation. We further attach structural paths to the segments for the agent to identify their ancestors and hierarchical positions. We also introduce residual memory links from the segments of low-level children to the segments of high-level parents, enabling direct information flows beyond successive memory updates. We evaluate TreeAgent with four representative memory agents on eight retrieval and aggregation tasks. The inputs are derived from real-world application data in JSON, HTML, YAML, and JavaScript and range from 8K to 1M tokens. TreeAgent improves the average score across the four datasets for all four agents, with 3.61% performance gain on average.
[AI-27] Chem World: A Large-Scale Benchmark and Physics-Aware Framework for Trustworthy Molecular Property Prediction
链接: https://arxiv.org/abs/2611.13945
作者: Tianyou Bai,Huan Wang,Mingchen Gao,Tongrui Yang,Fangyue Lin,Pinze Ren,Zhenlin Zhao,Siming Dong
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Molecular property prediction is fundamental to computational chemistry and drug formulation research, yet existing benchmarks remain fragmented across datasets, properties, and evaluation protocols, limiting systematic evaluation of model generalization. We introduce Chem World, a large-scale benchmark integrating 17 public datasets with over 800,000 molecular and chemical-system instances across 10 property tracks, covering single-molecule and multi-component systems, including drug solubility. Chem World provides a unified testbed for evaluating molecular representations and compositional modeling across diverse chemical properties. Building on the benchmark, we propose PACM (Physics-Aware Compositional Modeling), which combines molecular representations with composition-aware modeling and domain-specific physical constraints. Extensive experiments show that molecular language models provide strong and transferable representations, while PACM consistently improves prediction across diverse chemical systems. Chem World and our PACM-powered drug-property prediction Agent support generalizable chemical modeling and offer a foundation for AI-assisted biomedical research and drug formulation development.
[AI-28] ailoring Optimization Approaches with Domain Knowledge in Industry Process for Meta-heuristic Development
链接: https://arxiv.org/abs/2611.13940
作者: Jakub Cebula,Konrad Gmyrek,Paweł B. Myszkowski,Marko Đurasević,Łukasz P. Olech,Adam Paliszek-Saładyga
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:The paper presents an Industry Process for Meta-heuristic Development (IPMD) schema, a systematic methodology for applying meta- and hyper-heuristic applications to optimization problems. In this context, we present a case study of Balancing Non-dominated Tournament Genetic Programming (BNTGP) applied to solve two NP-hard multi-objective optimization problems: routing (Capacitated Vehicle Routing Problem with Time Windows) and scheduling (Multi-Skill Resource Constrained Project Scheduling Problem). The BNTGP method is based on a metaheuristic design. Its genetic programming-based representation makes BNTGP a hyper-heuristic, as it enables the exploration of a search space of heuristics for solving problems in general. The resulting heuristic can then be applied across a set of problem instances.
[AI-29] APCO-G: LLM -Guided Design-Adaptive Timing and Power Co-Optimization with Graph-Based Physical Priors
链接: https://arxiv.org/abs/2611.13931
作者: Wuqian Tang,Zixiao Wang,Che-Chien Lin,Fong-Hua Fu,Laura Angélica Shion-Korsak,Xinyun Zhang,Bei Yu,Chun-Yao Wang
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注: 27 pages
Abstract:Post-placement timing and power co-optimization is critical to meeting setup timing targets and reducing power within placement constraints. A cell replacement can improve timing along one path while increasing power or reducing slack on others. Conventional optimizers follow fixed rules for scheduling and accepting repairs, limiting their ability to address design-specific bottlenecks. A large language model (LLM) can propose changes to these rules, but on its own it cannot reliably estimate how a cell replacement affects endpoint slack and power. TAPCO-G combines LLM-guided optimizer adaptation with graph-based physical priors to evaluate candidate cell replacements. The outer loop tunes tool settings using physical-design feedback and revises executable repair policies when tuning stalls. The inner loop anchors delay estimates to static timing analysis snapshots, accounts for upstream driver-load changes, and propagates delay shifts to timing endpoints. It combines these estimates with coarse-to-fine power screening to prioritize candidates for physical trials. TAPCO-G produced placement-legal, logic-equivalent implementations and outscored CERBERUS on all eight ASAP7 benchmarks from the MLCAD 2026 Contest, with a mean quality score gain of 5.516 points, and scored 21.648 points higher than TAPCO on average. These results demonstrate the effectiveness of the proposed co-optimization framework.
[AI-30] Beyond Proxy Metrics: MLLM s as Human Surrogates for Explainable AI Evaluation NEURIPS2026
链接: https://arxiv.org/abs/2611.13926
作者: Eoin M. Kenny,Salim I. Amoukou,Tom Bewley
类目: Artificial Intelligence (cs.AI)
备注: Neural Information Processing Systems. NeurIPS 2026. Sydney
Abstract:Explainable AI (XAI) has expanded into a broad and impactful research area, but the fundamental question of how best to evaluate explanation techniques has proven frustratingly elusive. Methodologies typically revolve around either proxy metrics which tell us little about the utility of an explanation, or human evaluation which—while generally considered the gold standard—is expensive, difficult to scale, and challenging to reproduce. In this paper, we investigate if Multimodal Large Language Models (MLLMs) can act as surrogate human participants to automate the evaluation of XAI. To do this, we first propose a ``purpose-grounded’’ evaluation framework with metrics applicable to both MLLMs and human users alike. Then, we offer some conceptual insights as to why and when we can trust these models to automate this process. Lastly, we conduct extensive empirical testing across five purpose-grounded areas such as model improvement and fourteen popular MLLMs. Results indicate that all models trend positively, but OpenAI and Qwen models generally show higher alignment compared to other variants, which would be the recommended choices in practice. We expect our insights not to replace user testing, but rather to help XAI researchers complement their current evaluation methodologies and move the field away from heavy reliance on proxy metrics for measuring explanation utility.
[AI-31] Domain-Invariant and Language-Model-Assisted Acoustic Side-Channel Attacks on Keyboards
链接: https://arxiv.org/abs/2611.13922
作者: Bikrant Bikram Pratap Maurya
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Acoustic side-channel attacks (ASCA) recover typed text from the sound of keystrokes, using only a commodity microphone and no access to the victim’s device. Prior work establishes that such attacks succeed under idealized laboratory conditions - a single keyboard, a fixed microphone, a quiet room, and training and testing on the same device but leaves open the question that matters for security: whether keystroke inference survives the open world of unseen keyboards, diverse users, noisy environments, and compressed transmission channels. The answer developed here is that it does, provided the acoustic model is made explicitly domain-invariant and its noisy predictions are corrected by a language model.
[AI-32] ransparency Disclosures for Chain-of-Thought Monitorability
链接: https://arxiv.org/abs/2611.13873
作者: Jacob Davies,Theo Bearman
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 27 pages (including references and appendix), 3 figures
Abstract:Chain-of-thought (CoT) monitoring is one of the most valuable tools for monitoring advanced AI systems, but it is under threat. We argue that frontier AI developers should report specific indicators to make these threats trackable and to better facilitate coordination to preserve CoT monitorability. We identify five indicators that are both highly relevant and reasonably feasible to disclose, one for each link in a five-step chain on which CoT monitoring depends. The first, third-party CoT access and base architecture specifications, also underpins external verification of the rest. The others span CoT quality (naturalness and faithfulness) and monitoring effectiveness (detection and uplift). An audit of current disclosures by Anthropic, OpenAI, and Google DeepMind shows that no developer covers the full set, but that every indicator has been reported by at least one frontier developer. Because current disclosures rely largely on developers’ own evaluations, we also recommend that a cross-developer audit be repeated with deeper access than public disclosures allow, either by embedded evaluators working within developers or by an Independent Verification Organization.
[AI-33] axonomy-Agnostic Neurosymbolic Grounding of Robot Kinematics
链接: https://arxiv.org/abs/2611.13850
作者: Bastien Dussard(LAAS-RIS, LAAS),Aur{é}lie Clodic(LAAS-IDEA, LAAS-RIS, LAAS),Guillaume Sarthou(LAAS-RIS, EPE UT, LAAS)
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:While symbolic reasoning for task allocation requires grounding a robot’s physical capabilities, standard Unified Robot Description Format (URDF) files provide only low-level kinematics without the formal semantic context needed for reasoning. We present URDF2Ontology, an automated, taxonomy-agnostic neurosymbolic framework that transforms raw URDF files into populated, grounded OWL ontologies. It leverages a Large Language Model (LLM) for targeted semantic reasoning, coupled with an iterative sequential graph-purging mechanism to prevent context window saturation. To process complex kinematic structures, it employs a morphology-aware dual-track architecture featuring a bottom-up Tree Track for branching assemblies and a top-down Chain Track for serial limbs. Guided by neurosymbolic guardrails, the framework extracts functional sub-graphs and asserts populated ontologies aligned with arbitrary user-provided taxonomies. Empirical evaluation across a diverse fleet of heterogeneous robots demonstrates strong schema alignment, valid structural extraction, and a significant reduction in manual domain authoring overhead.
[AI-34] MusicFlux: Flexible Conditioning for Text-Guided Music Generation and Editing ICASSP2027
链接: https://arxiv.org/abs/2611.13834
作者: Teysir Baoueb(IP Paris, LTCI, IDS, S2A),Leonardo Boulitreau de Menezes Martins Marques(IP Paris, LTCI, IDS, S2A),Paraskevas Stamatiadis(S2A, LTCI, IDS),Ga{ë}l Richard(S2A, IDS, IP Paris, LTCI)
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP)
备注: Submitted to ICASSP 2027
Abstract:Music editing transforms existing recordings while preserving their original attributes. Recent approaches adapt pretrained text-to-music models for tasks like inpainting, event insertion, or restoration by conditioning them with context audio. It was recently proposed for image editing to unify generation and editing by treating the source image as in-context information. However, applying this directly to music is computationally expensive due to long audio sequences. In this work, we develop MusicFlux, a text-guided music editor built on an efficient pretrained flow-matching text-to-audio model. We introduce two conditioning approaches that trade off editing performance and computational efficiency. Experiments show that both are competitive with strong baselines, with one achieving better editing quality and the other preserving the original sequence length and substantially improving computational efficiency.
[AI-35] Ceci nest pas une pipe – Toward a Symbolic Solver for the Abstraction and Reasoning Corpus Using Evolutionary Computation
链接: https://arxiv.org/abs/2611.13831
作者: Luca Favole,Giorgio Mongardi,Giovanni Squillero,Alberto Tonda
类目: Artificial Intelligence (cs.AI)
备注: Submitted to the ARC Prize 2026 Paper Track (Fairly Symbolic)
Abstract:The Abstraction and Reasoning Corpus (ARC) is a benchmark designed to assess the capability of Artificial Intelligence (AI) systems to generalize starting from a few samples, a task humans excel at. The ARC-AGI contests held since 2020 have been the focus of intense competition, with participants proposing approaches ranging from traditional symbolic AI to Transformer architectures. Despite a long history of successes, evolutionary algorithms (EAs) were notably absent from the approaches appearing in the main contest. As score-and-search program synthesis approaches, EAs suffer from issues related to choosing the proper representation in the Domain-Specific Language applied to the problem (for example, pixel-level or object-level), and distinguishing between two candidate solutions producing the same outputs. In this work, we present the first fully symbolic, evolutionary-based program synthesis approach submitted to the ARC-AGI-2 main contest. The main contributions are: evolving the grid parser and the output projector together with the program that transforms the grid, so that a task-specific language emerges during program synthesis; grounding the symbols the parser can extract in core knowledge theory from cognitive psychology; and an object-aware fitness that grades partially correct outputs beyond pixel-level discrepancies.
[AI-36] Swanada: Split-Encoder Capability-Routed Conformer CTC and Sub-30 MB On-Device Speech Recognition Across Twelve Scripts
链接: https://arxiv.org/abs/2611.13814
作者: Hemanth HM
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 7 pages, 2 figures, 3 tables. Open-source browser, NodeJS, and Python implementation with interactive demo at this https URL and this https URL
Abstract:Client-side automatic speech recognition (ASR) across Indic and Classical scripts eliminates cloud transcription costs, network tail latency, and server-side audio retention, yet neural exports face four systems bottlenecks in browsers and mobile runtimes: (1) monolithic per-language Conformer checkpoints require ~177 MiB per script ( 2.1 GiB across 12 scripts), exhausting CacheStorage quotas; (2) static relative positional encoding tables embed 10.2-20.5 MB of FP32 constants while 1x1 Conv1d layers trigger slow or unsupported ConvInteger kernels in WebAssembly SIMD; (3) sliding-window CTC flickers on boundary tokens; and (4) mobile WebAudio pipelines suffer 44.1/48 kHz sample-rate mismatches and silent AudioWorklet stalls under iOS WKWebView. We present Swanada, an open-source 16 kHz streaming ASR system spanning Classical Sanskrit, Indian English, and ten Indian languages (sa, en, hi, kn, mr, bn, ta, te, ml, gu, pa, or) across browser WebAssembly, this http URL, and Python. Swanada pairs capability-routed shared INT8 Conformer encoders (a 17-layer 177.38 MiB Studio tier at d = 512 for desktop/servers and a distilled 14-layer 25.35 MiB Mobile tier at d = 256 for phones) with twelve hot-swappable 515.0 KiB native-script linear CTC heads ( 257 \times 512 ), cutting 12-language storage by 11.6\times (183.4 MiB) and 67.8\times (31.39 MiB; 25.85 MiB first load). Runtime protobuf tail surgery exposes the shared representation in 2 ms, while dynamic positional synthesis, MatMul pointwise convolutions, and post-quantization QInt8 head calibration preserve sub-30 MB accuracy. Across N = 480 held-out and N = 1,280 open public test clips (FLEURS, LibriSpeech, GLOBE, AMI, wild Sanskrit), Swanada achieves 5.43% held-out and 8.87% 12-language open CER on Studio (18.56% and 19.23% on Mobile) at 0.0097\times - 0.0264\times RTF, with 30.6 ms median TTFT and 0.00% committed-prefix churn. Comments: 7 pages, 2 figures, 3 tables. Open-source browser, NodeJS, and Python implementation with interactive demo at this https URL and this https URL Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI) ACMclasses: I.2.7; H.5.5; D.3.4 Cite as: arXiv:2611.13814 [cs.SD] (or arXiv:2611.13814v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2611.13814 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-37] Integrating Action Prefix Prediction and Alignment Across Modalities for Multimodal Reinforcement Learning
链接: https://arxiv.org/abs/2611.13782
作者: Sijie Xu,Francisco Cruz
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the Australasian Conference on Robotics and Automation (ACRA 2026)
Abstract:Learning representations from multimodal observations is difficult in reinforcement learning with limited interaction data. Multimodal Joint Predictive Representation combines multimodal latent prediction with contrastive learning across modalities, but its recursive rollout depends on earlier predictions and its pairwise alignment focuses on correspondence between modalities. We propose Improved Predictive Representation, which combines prediction from action prefixes with alignment at the observed anchor based on Contrastive Multimodal learning. At each horizon, it predicts future representations directly from the observed anchor and the corresponding sequence of actions. We evaluate two crop configurations: IPR-S with a minimum retained crop area of 10%, and IPR-H with 50%. Across five DeepMind Control Suite tasks, IPR-H achieves an average return across tasks that is 10.0% higher than the baseline with visual and depth images and 3.4% higher with visual images and position vectors. The relative performance of the two configurations varies across tasks. In Webots, both IPR configurations achieve higher mean final returns than the baseline without LiDAR, but not when LiDAR is available.
[AI-38] Softness is something you also need! Why compliance helps control: information metrics of a passive walker with compliant ankle
链接: https://arxiv.org/abs/2611.13779
作者: Fabio Bonsignorio,Elisabetta Barletta,Sorin Dragomir,Francesco Esposito,Enrica Zereik
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: main: 11 pages, 6 figures; supplemental: 7 pages, 1 figure
Abstract:A great gap in adaptivity, flexibility, and dexterity still exists between intelligent robots, whether they are made of rigid or (partially) compliant components, and their naturally evolved animal and vegetal counterparts. One reason could be of paradigm' nature. On the one hand, most robots (both rigid and soft) are still designed by following a Cartesian’ approach, with top-down control and a clear mind-body separation. Designing artificial agents on the basis of the principles that we observe in biology is not an easy task. The principles of organization of animals and plants rely on morphological computation and emergence of fit behaviors from loosely coupled networks of embodied agents. Modeling and control require radically new approaches. In this paper we briefly recall and explain some seminal mathematical results linking information metrics and dynamics of the agents, already published years ago; we integrate them with some recent methods developed by us and by the community, we highlight some brief remarks about their relevance and we hint to our current and near future work towards finding concrete and efficient ways for the modeling and control of completely or partially compliant artificial agents and for a better understanding of physical intelligence in nature. We present a technical note summarizing phase-space volume calculations for inverted-pendulum and passive-dynamic walking models, following our proposed framework. Starting from the McGeer compass gait, we introduce knee-ankle compliance, foot-rocker effects, and a Ruina-style compass gait with distributed leg inertia. Standard heel-strike maps are derived and used in the estimation of viable phase-space volumes W(\gamma) , based on a state-dependent Monte-Carlo and Reproducing Kernel Hilbert Spaces methods. We show how the methods based on RKHS allow deeper and more subtle analysis. Comments: main: 11 pages, 6 figures; supplemental: 7 pages, 1 figure Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI) Cite as: arXiv:2611.13779 [cs.RO] (or arXiv:2611.13779v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2611.13779 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-39] Mixed Signals: Weight-Space Programming for EEG and ECG
链接: https://arxiv.org/abs/2611.13758
作者: Dara Varam,Taraneh Aminosharieh Najafi,David Atienza
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: 9 pages, 7 figures, 5 tables. Project website: this https URL
Abstract:Weight-space programming allows language models to read a trained neural network’s weights and edit them. This opens the door to ``training’’ by reasoning rather than through gradient descent. Prior work has demonstrated its functionality and promise for game-playing networks and fixing specific failures in language models, even extending to diffusion models. In this work, we present its applicability to models trained on biosignal data, specifically EEG and ECG data. We show that frontier models are not only able to debug specific faults planted in biosignal-based models without being told what these faults are, but are even capable of writing all the weights of a network from scratch, given only the architecture, the task, and a minimal set of training subjects. In particular, in one set of experiments, a language model was able to match standard gradient-descent training using 6\times fewer training subjects, through reasoning alone and without any gradient descent step. Networks written by Claude Opus 5.5 also scored higher than classic hand-crafted EEG pipelines (alpha band power, and common spatial patterns with linear discriminant analysis) when built from the same 5 subjects. We also highlight some of the limitations, which include the inability of language models to improve already well-trained classification models, and their incapacity to reliably recognize, from the weights alone, the task a network was trained on. Overall, our results indicate that weight-space programming can be useful for small networks, such as those deployed on edge devices and wearables, but the concept still requires further investigation before it can be used in practice.
[AI-40] oward Autonomous RTL and Synthesis Optimization: Benchmarking and Self-Improving Agents
链接: https://arxiv.org/abs/2611.13713
作者: Wenji Fang,Yao Lu,Jinghua Wang,Zhiyao Xie
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注: ASP-DAC’27 Invited Paper
Abstract:Recent advances in large language models (LLMs) and autonomous agents are creating new opportunities for hardware design optimization. This paper examines three connected directions toward autonomous RTL and synthesis optimization: (1) Realistic benchmarking. We discuss reference-based and open-ended RTL optimization benchmarks that evaluate optimization opportunities under realistic EDA workflows. (2) Tool-grounded RTL optimization. We present Dr. RTL, a multi-agent framework that automates critical-path analysis, RTL rewriting, and implementation-level evaluation, while continuously improving through reusable optimization skills. Evaluated on 20 real-world RTL designs, Dr.~RTL achieves average WNS/TNS improvements of 21%/17% with a 6% area reduction over commercial synthesis. (3) Design-specific synthesis optimization. We explore agent-driven synthesis script tuning and the discovery of executable synthesis algorithms across intermediate representations, technology-independent optimization, and technology mapping. Finally, we envision AI-native design automation that jointly explores architectural, RTL, and EDA algorithm design spaces, guided by verification constraints and implementation feedback.
[AI-41] Broad-Persistent Advising in Interactive Reinforcement Learning via Latent State Encoding
链接: https://arxiv.org/abs/2611.13712
作者: Zhuoran Liang,Francisco Cruz
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures, 3 tables, Accepted for oral presentation at the Australasian Conference on Robotics and Automation (ACRA 2026)
Abstract:Deep Interactive Reinforcement Learning (Deep IntRL) accelerates reinforcement learning training by incorporating external advice. However, conventional Deep IntRL uses each piece of advice only in the current step and discards it after a single interaction. Persistent Advising and Broad-Persistent Advising (BPA) extend this process by storing advice in persistent memory and reusing it in similar states through state generalisation based on K-means clustering. This research proposes Encoder BPA, which employs a latent encoder to retrieve and reuse advice. In this framework, a trained encoder is used to map a state sequence into a latent representation at every time step. Advice can be retrieved through latent comparison, together with a top-(k) aggregation and a consistency condition to improve reuse stability. Experimental results show that Encoder BPA improves learning efficiency and advice utilisation at an early stage of training compared with different Deep IntRL approaches, especially in complex and partially observable environments.
[AI-42] AutoManifold: Agent ic Design of Data Visualisation Algorithms via Manifold Embedding NEURIPS2026
链接: https://arxiv.org/abs/2611.13710
作者: Burak Susam,Ruiyuan Kang,Tingting Mu
类目: Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026 (40th Conference on Neural Information Processing Systems). 37 pages, 5 figures. Code: this https URL
Abstract:Manifold embedding for data visualisation is dominated by a small set of hand-designed state-of-the-art (SOTA) algorithms. Their design targets are fixed at development time, resulting in inflexible algorithmic behaviour that cannot be readily steered towards user-specified structural priorities. Hand-designing new embedding algorithms to address user priority requires highly specialised expertise and is time consuming. To address autonomous algorithm design tailored to user preference, we introduce an agentic algorithm generation pipeline AutoManifold. It composes new manifold embedding algorithms from a constrained vocabulary of affinity, cost, and optimisation primitives, supported by multi-agent large-language-model (LLM) orchestration. AutoManifold conditions every stage of its design on user-specified structural preservation preferences, and iteratively refines the algorithm configuration through an LLM-guided, metric-grounded iterative loop. We compare the generated algorithms against the three strongest and most frequently used SOTA methods (t-SNE, UMAP, and PaCMAP) on real-world datasets spanning different difficulty regimes under identical evaluation infrastructure. The results demonstrate that LLM-driven, priority-conditioned algorithm synthesis can move beyond hyperparameter tuning, producing genuinely new algorithms that outperform traditional hand-designed methods in their ability to steer towards user-specified properties without compromising the intrinsic structure in the original data. The code of AutoManifold is available at this https URL.
[AI-43] Agent ic AI Meets Formal Verification in the Era of Agent ic Hardware Development
链接: https://arxiv.org/abs/2611.13705
作者: Wenji Fang,Yuchao Wu,Jing Wang,Zhiyao Xie
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注: ASP-DAC’27 Invited Paper
Abstract:As AI agents become increasingly capable of generating and optimizing hardware circuits, ensuring the correctness of AI-generated designs becomes more important. Meanwhile, advances in AI lower the barriers to developing and applying formal methods, creating new opportunities to automate and improve hardware verification. This invited paper presents an overview of the methodologies and applications of hardware formal methods in the AI era. First, we examine how agentic AI advances hardware formal methods across three levels: specification, proof construction, and reasoning engines. Second, we discuss how formal methods support reliable AI-driven hardware development through RTL generation, debugging, and correctness-preserving optimization. We review representative research alongside our recent contributions, highlighting key technical advances and insights from experimental results. Finally, we discuss future research opportunities toward proof-native hardware development, where formal reasoning could become an integral part of autonomous hardware design.
[AI-44] SeqFold: Timestamp-Addressable Compressed States for User Action Sequence Backbones
链接: https://arxiv.org/abs/2611.13704
作者: Mianwei Zhou,Duo Zhang,Henry Tsai,Yuqi Zheng,Jian Li,Juexin Lin,Sujay Khandagale,Vijay Rajaram,Dong Jiao,Tao Cheng
类目: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注:
Abstract:Existing sequential user modeling methods mainly focus on improving the backbone encoder or learning task-specific user representations for a single prediction objective. We instead study how to export the multi-layer user states of a frozen long-history backbone as compact, timestamp-addressable representations for downstream training backfill and serving. In this setting, the user state at a given timestamp summarizes the user’s actions before that time, so downstream systems need only the state aligned to each target timestamp rather than the full hidden-state timeline. To this end, we present \textttSeqFold, a compressed-state interface that combines learned multi-channel fusion, channel-specific temporal decay, and Lookup-Free Quantization (LFQ) to produce compact discrete codes from frozen backbone states. Task-specific decoders then map these shared codes into flexible embedding spaces for downstream models. To make timestamp-addressable export practical at scale, we further develop a fused streaming implementation that updates the fusion recurrence after every action while materializing states only at target timestamps. On large-scale proprietary datasets, SeqFold reduces per-state representation storage by 3,400\times while preserving most of the downstream ranking performance of the uncompressed model, showing that SeqFold is an effective interface for reusing long-history sequence backbones in downstream models.
[AI-45] opological Dynamics Under Non-Stationary Supervision: Persistent Homology of Neural Network Weight Evolution
链接: https://arxiv.org/abs/2611.13701
作者: Mitra Varun
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We investigate how the persistent topology of neuron- weight point clouds evolves under stable and non-stationary supervision. An identical multilayer perceptron is trained on Fashion-MNIST with correct labels, a fixed random permutation of the training-label vector, or a fresh permutation every epoch. Five paired seeds share initialisation and minibatch order, yielding 15 models trained for 100 epochs. Raw and row-normalised incoming weight clouds are analysed with H0 and H1 Vietoris Rips persistence at 22 checkpoints. Correct-label models reach 89.39% mean true-label test accuracy; fixed-random models fit 67.94% of their assigned training targets without generalising to the original task. Correct-label models have descriptively greater time-averaged normalised fc3 H1 persistence than either random-label condition, but the configured exact comparisons do not reach conventional significance after Holm correction. Early condition classification achieves 60.0% mean balanced accuracy using topology, 66.7% using conventional diagnostics, and 60.0% using both. Thus, no incremental predictive improvement is demonstrated in this setting. The archive contains all 3,300 configured topology cases. Interpretation is limited by five seeds, a single architecture and dataset, 282 capped H1 distance comparisons, and deferred bottleneck analysis.
[AI-46] On Private Knowledge Retrieval and Private Inference for secure multi-agent systems
链接: https://arxiv.org/abs/2611.13696
作者: Vicenç Torra,Maria Bras-Amorós
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Multi-agent systems provide a convenient framework for the massive deployment of agents based on Large Language Models (LLMs). It provides a sound framework where agents can interact with existing software to perform actions in an autonomous way. Agentic AI is the new term to denote these AI-based autonomous systems. In real multi-agent systems, agents need to deal with malicious agents some of which can exploit security vulnerabilities to do harm. A memory poisoning attack is an example of exploiting a vulnerability to modify agents knowledge and, thus, affect its behavior. In an agent, there are different types of memory systems (semantic, episodic, and short-term) which have their own characteristics and for which different memory poisoning attacks may be developed. In this paper we focus on semantic memory, we discuss its attack, and mitigation strategies. We introduce private knowledge retrieval and private inference. We propose a signature system in the knowledge base to prevent from undesired manipulation. This should avoid memory poisoning attacks by introducing security by design in the construction of the semantic memory. In our architecture, an agent contains a local Prolog-like inference system (i.e., using Horn clauses to represent knowledge) which accesses an external knowledge base to get the required knowledge. This externally signed knowledge base is accessed privately. We provide different implementations in Python using different privacy levels including k-anonymity and information-theoretical privacy (IT-PIR). Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) ACMclasses: I.2.11 Cite as: arXiv:2611.13696 [cs.CR] (or arXiv:2611.13696v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2611.13696 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-47] AURA: Auditory Spatial Intelligence via a Unified Representation from a General-Audio Encoder through Complete Alignment
链接: https://arxiv.org/abs/2611.13687
作者: Boyan Han,Xiaoyan Wang,Zhe Sun,Ziqi Wang,Yujun Cai,Hongli Cao,Yiwei Wang,Ming-Hsuan Yang,Liang An
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Answering spatial questions about multi-source scenes requires a model both to identify each sound and to locate it. Existing spatial audio-language models obtain spatial information from a dedicated encoder, which separates spatial and semantic processing into distinct feature spaces. We study whether a general-audio codec trained without spatial supervision can serve both purposes. A controlled layer-wise analysis shows that the frozen codec retains spatial information, with lateral cues carried by the difference between the two ear channels. This information, however, is not readily accessible: in dual-source scenes, a model trained on these features predicts left/right at chance and, on the remaining axes, assigns the detected direction to the queried and the non-queried source equally often. We attribute this failure to an audio–text information asymmetry, in which the audio contains both sources while the answer describes only the queried one. A controlled 2\times2 experiment shows that either completing the scene description or exposing the inter-channel difference lifts left/right accuracy above chance, whereas binding directions to the queried source requires both. Building on these findings, AURA keeps the codec and the language model frozen and exceeds the strongest dedicated system on SpatialSoundQA by 10.31 points in dual-source direction accuracy, with about 40% of its trainable parameters.
[AI-48] Bootstrapped On-Policy Distillation
链接: https://arxiv.org/abs/2611.13680
作者: Yue Wu,Xueyu Zhou,Yangrong Hu,Jian Huang
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:On-policy distillation (OPD) improves language models by distilling teacher predictions on student-generated trajectories, while on-policy self-distillation (OPSD) removes the need for an external teacher by using a reference-conditioned copy of the model as the teacher. Despite its great early improvements, we find that OPSD does not remain consistently effective under continued training and can eventually suffer substantial performance degradation. We study this training behavior and link the degradation to a growing mismatch between the evolving reference-free student and the frozen privileged teacher. In the early stage of OPSD, the privileged teacher remains closely aligned with the student and provides useful guidance for improving the student’s generation trajectories. However, as training progresses, the growing distributional mismatch between the privileged teacher and reference-free student can cause the teacher to provide misleading supervision, ultimately degrading student performance. This phenomenon motivates us to first leverage privileged reference supervision to acquire useful knowledge and then transfer it to a fresh student. We introduce Bootstrapped On-Policy Distillation (BOPD), a two-stage framework that first uses OPSD to internalize task-relevant information from reference-conditioned supervision into an intermediate model, and then distills this reference-free intermediate model into a fresh copy of the initial model. Experiments across models show that BOPD improves mathematical reasoning and code generation beyond both the initial models and the constructed OPSD teachers. These results suggest that privileged reference supervision can be more effectively exploited by first transferring its task-relevant signal into a task-adapted model and then distilling from that model without reference conditioning.
[AI-49] Benchmarking LLM Next-Step Recommendations with Knowledge Space Theory
链接: https://arxiv.org/abs/2611.13667
作者: Peter Steiner,Jan Hochweber
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Selecting an LLM for next-step recommendation requires benchmarks that reflect the intended educational task and domain. We present a benchmarking method that uses Knowledge Space Theory (KST) as a formal evaluation reference. Given a task catalogue and an existing prerequisite relation, the method derives admissible knowledge states and the complete set of immediate next steps for each state. Recommendations generated by a model under a specified prompt are compared with these reference sets. A sample evaluation study uses 2,400 responses from six models across four mathematics domains, four prompt conditions, and two catalogue orders. Exact-set accuracy was 61.3% with descriptions only, 62.8% when models were asked to reconstruct prerequisites, 96.3% with supplied prerequisite relations, and 98.2% with the complete knowledge space. Performance varied across models and domains, and recommendation sets agreed across the two presentations in 85.25% of pairs. The benchmark supports comparisons of model-prompt combinations against a context-specific reference. Its scores describe structural recommendation accuracy under the evaluated conditions; they do not establish learning benefit or the necessity of using an LLM for selection.
[AI-50] MOAI: Module-Optimizing Architecture for non-interactive secure transformer Inference ICLR2026
链接: https://arxiv.org/abs/2611.13641
作者: Linru Zhang,Xiangning Wang,Jun Jie Sim,Zhicong Huang,Jiahao Zhong,Huaxiong Wang,Pu Duan,Kwok-Yan Lam
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Published elsewhere. Minor revision. ICLR 2026
Abstract:Privacy concerns have been raised in Large Language Models (LLM) inference when models are deployed in Cloud Service Providers (CSP). Homomorphic encryption (HE) offers a promising solution by enabling secure inference directly over encrypted inputs. However, the high computational overhead of HE remains a major bottleneck. To address this challenge, we propose MOAI, an efficient HE-based, non-interactive framework for secure transformer inference. MOAI gains significant efficiency improvement from: (1) a novel evaluation flow that combines column and diagonal packing with consistent strategies across all layers, eliminating expensive format conversions. (2) rotation-free algorithms for Softmax and LayerNorm that significantly reduce the number of costly HE rotations, removing 2,448 HE rotations in BERT-base inference. (3) Column packing removes rotations in plaintext-ciphertext matrix multiplications and interleaved batching further reduces the rotations in ciphertext-ciphertext matrix multiplications. MOAI uses at least 1.7x fewer HE rotations compared to the state-of-the-art works across all matrix multiplications of BERT-base. As a result, we achieve a 52.8 % reduction in evaluation time compared to the state-of-the-art HE-based non-interactive secure transformer inference, THOR (Moon et al., CCS 25). We then apply MOAI on the Powerformer’s framework and achieve a 55.7 % reduction in evaluation time compared to Powerformer (Park et al., ACL 25), which approximates Softmax and LayerNorm with simpler functions in transformer and proposes HE-based non-interactive transformer inference. We report an amortized time of 2.36 minutes per input on a single GPU environment. We show the extendibility by applying MOAI in LLaMA-3-8B. Our implementation is publicly available as open source.
[AI-51] Megrez: A Knowledge-Guided Agent for Formal Mathematics over MK Set Theory
链接: https://arxiv.org/abs/2611.13613
作者: Tiancheng Xu,Wensheng Yu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:General-purpose coding agents equipped with proof-assistant tools have shown strong performance on standalone competition problems, yet cumulative textbook formalization inside a low-resource mathematical library still faces three obstacles: aligning textbook concepts with library declarations, keeping long-horizon search synchronized with live proof states, and integrating locally verified proofs reliably into a growing codebase. We present Megrez, a knowledge-guided formalization agent for Rocq libraries built on Morse–Kelley (MK) set theory. Megrez coordinates a general coding agent through three mechanisms: (1) a source-rebuildable knowledge graph merging formal declarations, compiler references, and curated metadata to support retrieval via Chinese aliases, concept clusters, proof dependencies, and multi-lane full-text ranking; (2) a feedback-driven 1–5 tactic short-burst proving loop paired with a goal-local branch ledger and exponential-interval structured reflection; and (3) a three-check acceptance pipeline (statement fidelity, axiom auditing, and full builds) coupled with a multi-corpus Provider stack and a candidate-bound recoverable batch state machine. Deployment and evaluation on a point-set topology corpus demonstrate effective cross-chapter formalization and scalable premise retrieval.
[AI-52] Earth Embeddings in Environmental Remote Sensing
链接: https://arxiv.org/abs/2611.13612
作者: Yuchi Ma,Jiaqi Yang,Mengyu Liang,Ivan Higuera-Mendieta,Caleb Robinson,Anthony Ortiz,Min Chen,David B. Lobell,Inbal Becker-Reshef,Juan M. Lavista Ferres
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Earth observations from satellite platforms have profoundly transformed research and applications across numerous domains. However, uneven observation coverage and frequent data gaps continue to limit their ability to capture Earth’s surface dynamics. Moreover, processing multi-modal Earth observations requires substantial computational resources and sophisticated data harmonization frameworks, which may not be readily accessible to many remote sensing practitioners. Earth embeddings have emerged as promising solutions to these challenges. As general-purpose representations converted from multi-source Earth observations, Earth embeddings can support a wide range of downstream tasks with no requirement of task-specific feature design and preprocessing. In this study, we first introduce the theoretical foundations of Earth embeddings. Then, we review several representative Earth embedding products. We next examine the applications of Earth embeddings across six major domains of environmental remote sensing. Finally, we systematically assess the performance of Earth embeddings in four downstream tasks, including hyperspectral image classification, individual tree crown segmentation, crop yield prediction, and aboveground biomass mapping. Our review and case studies suggest that, despite several advantages over conventional Earth observation data, current Earth embeddings do not always outperform remote sensing imagery or hand-crafted features. Earth embeddings should not be viewed as a direct replacement for conventional EO data, but rather as representations that provide stable information across space and time. Remote sensing practitioners may consider evaluating Earth embeddings’ suitability for specific tasks and using them strategically, rather than assuming superior performance by default.
[AI-53] ransformers Provably Learn Graph Search: Training Dynamics and the Exponential Power of Depth
链接: https://arxiv.org/abs/2611.13594
作者: Xutao Ma,Somayeh Sojoudi
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) have achieved remarkable progress on complex reasoning tasks, yet the mechanisms by which they acquire reasoning capabilities and the efficiency of their reasoning remain poorly understood. In this paper, we investigate the path-finding problem over directed graphs, a symbolic abstraction of multi-step reasoning. We first characterize the architectural requirements for this task, proving that one-layer transformers require \Omega(N^2) size on dense graphs to implement the key DFS child-selection primitive. While a two-layer transformer implements full DFS with O(N\log(N)) size. Thus, a second layer is necessary and sufficient for near-linear DFS reasoning. We then extend this expressivity analysis to deeper models: for every k\geq3 , a k -layer transformer implements a bounded-reachability DFS policy with look-ahead horizon 1+2^k-3 , establishing an exponential increase in reachability horizon with depth. Finally, going beyond expressivity, given appropriately curated training data, we show that two-layer transformers can provably learn to execute DFS via gradient flow and generalize to unseen graphs. Together, our results provide a mechanistic account of how transformers implement graph search, how depth expands bounded look-ahead, and how such reasoning algorithms can emerge through training.
[AI-54] Market-Conditioned JEPA: Learning from Unpredictable Market Shocks
链接: https://arxiv.org/abs/2611.13572
作者: Yuqi Wang,Fengyuan Liu,Mukai Li,Haochen Luo,Lei Li,Qi Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 23 pages, 4 figures, 14 tables
Abstract:Much of the variation in financial time series is driven by market-wide shocks that cannot be predicted from an asset’s own history. Per-asset self-supervised learning treats this variation as irreducible noise. Under a latent factor model, we show that conditioning prediction on the realized market factor makes the asset’s market exposure necessary for optimal prediction, while reducing the noise against which the asset-specific component is learned. Building on this insight, we introduce Market-Conditioned JEPA (MC-JEPA), a joint-embedding predictive architecture for financial time series. During training, we construct a leave-one-out market condition for each asset from the future target embeddings of other assets and incorporate it through factor loadings derived from the asset’s own representation. On 11 years of minute-level Chinese A-share data, MC-JEPA improves over per-asset JEPA across nine targets spanning returns, market exposure, risk, and liquidity, with the strongest and most statistically significant gains in exposure, risk, and liquidity. It achieves the best RankIC on all liquidity targets and on return horizons from two hours to five days, outperforming time-series foundation models and self-supervised baselines, and delivers the best portfolio performance in a historical backtest. Ablations show that the return and exposure gains require the condition to be aligned with the realized market movement. After restoring the volatility scale removed by normalization, the predictor’s factor loadings rank stocks by market beta nearly as well as a supervised probe, without labels. A synthetic factor-model study with known parameters shows that conditioning improves the recovery of both market exposure and the asset-specific component.
[AI-55] Metamorphic testing of learned AC optimal power flow surrogates reveals representation dependence NEURIPS2026
链接: https://arxiv.org/abs/2611.13566
作者: Stephen Peng,Vineet Jagadeesan Nair
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Optimization and Control (math.OC)
备注: Accepted to the NeurIPS 2026 Workshops on (i) Trustworthy AI Evaluation, (ii) AI Foundations for Power Grids, (iii) Tackling Climate Change with Machine Learning, (iv) Sim2Science, and (v) Representations for the Physical Sciences
Abstract:Evaluating a learned AC optimal power flow (AC-OPF) surrogate on one grid serialization cannot show whether its accuracy depends on that encoding. We test this dependence with thirteen metamorphic relations, each specifying when a transformation is valid and how to compare its outputs. Our audit covers 17 model and pipeline configurations: two released trained checkpoints and 15 random-weight architecture or mechanism controls. Every configuration violates at least six relations, even at reporting tolerances ten times tighter or looser. For GridSFM-Open v1.1, source inspection and intermediate-tensor interventions point to an indexing bug and three noncanonical or numerically unstable preprocessing choices; a repaired checkpoint remains untested. In a separate case500 training experiment, augmentation lowers transformed-view error in three of four models and median discrepancy and constraint-violation P95 in all four. These benefits depend on the training protocol: some relations worsen, and every evaluated prediction remains AC infeasible. A GPU/CPU eigensolver mismatch accounts for most of the large raw-Laplacian-baseline gain. We recommend reporting representation consistency alongside accuracy, feasibility, and cost. These results establish neither deployment readiness nor universal invariance.
[AI-56] Benchmarking Vendor-Guided Optimizations on Edge NPUs: From NPU Only to End-to-End Performance. Extended technical report
链接: https://arxiv.org/abs/2611.13563
作者: Youngjin Jo,Jihwan Kim,Minseok Choi,Kwanghoon Choi,Jusung Kang,Jaein Kim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Edge NPU performance depends not only on hardware capabilities, but also on model transformation and the overall deployment pipeline. We propose a benchmarking methodology that separates these effects by comparing three configurations: Baseline, Vendor Guided NPU Only (VG-NPU), and Vendor Guided End-to-End (VG-E2E), where VG-NPU preserves common model I/O and host-side processing while VG-E2E follows the vendor-native deployment pipeline. We apply this methodology to four edge NPUs and three vision workloads. VG-NPU improves both accuracy and Runtime-call latency in 10 of 12 NPU–workload combinations, whereas VG-E2E reduces System-level latency in all combinations but improves accuracy in only four. Moreover, NPU-level performance rankings do not always match End-to-End rankings, and VG-E2E provides an additional 10.4–70.7% reduction in System-level latency over VG-NPU. These results demonstrate the importance of evaluating vendor-guided optimization separately at the controlled model-transformation and full deployment levels when comparing heterogeneous commercial Edge NPU platforms in practice.
[AI-57] Massive Activations Confound Sparse Autoencoders NEURIPS2026
链接: https://arxiv.org/abs/2611.13548
作者: Nathan Delisle,Chenhao Tan
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at NeurIPS 2026 (poster). Code: this https URL . SAEs: this https URL
Abstract:Sparse autoencoders (SAEs) are commonly evaluated by variance explained and ablation damage. We show how massive activations undermine both evaluations, and propose a correction. Massive activations cause up to 99.9% of activation variance to lie along the first principal component (PC1). Because the PC1 component has no unique sparse decomposition, each SAE run rebuilds it from a different set of features. These features dominate importance rankings: an average of 47 of the 50 most important features are unmatched in a separate run, compared with about 27 of a random 50. A dictionary can also report 99.96% of variance explained while leaving 33% of the variance outside PC1 unexplained. We bypass PC1 by training the SAE on activations with PC1 removed, then adding PC1 to the reconstruction. In affected models, the top-50 feature sharing rate rises from 4-39.5% to 73-99.5%. At the same TopK sparsity, the resulting SAE degrades the model up to 44% less and reduces content reconstruction error by up to 33.9%.
[AI-58] Agent Benchmarks Stop Ranking Before They Run Out of Tasks
链接: https://arxiv.org/abs/2611.13541
作者: Zihan Dong,Qishi Zhan,Minxuan Hu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Data-science and ML-engineering agents are evaluated on benchmarks such as MLE-bench, ALE-Bench, SWE-fficiency and AlgoTune, whose tasks yield a quantity (a Kaggle metric, contest score or speedup). When one stops separating the best models, the usual remedy is a harder version. Re-scoring released runs of eight benchmarks, including SWE-bench Verified and Terminal-Bench for breadth, we find much of this saturation lies in the measurement, not the tasks. Most agent benchmarks reduce a run to one pass/fail bit, which fails in two ways. It is too coarse: neighbouring top-ten agents on SWE-bench Verified differ by less than its tests’ errors, and correcting them reorders neighbours no more than deleting a random sixth of each agent’s passes. It is capped at the reference: ALE-Bench’s top ten, separated by contest score, tie when graded against the median human, although nearly half the runs beat that human. On fixed tasks, a higher bar only moves the problem; graded credit fixes it. We derive a clipped log-ratio score with dated anchors and cost as a separate axis. In a Terminal-Bench re-run, pass/fail-tied models differ over tenfold on the quantity the tests discard.
[AI-59] Developing an Operational Model for an Internet of Data Based Trusted Data Space for Vocational Education
链接: https://arxiv.org/abs/2611.13520
作者: Yalan Dong,Hao Tian,Hongyang Jia,Chunxue Wang,Wenchang Lei
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:Data circulation in vocational education has long been constrained by unclear ownership, a lack of trust, and insufficient incentives. The Internet of Data provides a new underlying technology for enabling trusted cross-domain data circulation. Building on an Internet of Data based trusted data space for vocational education, this paper develops a multi-stakeholder operational model encompassing a value network, a product portfolio, revenue allocation, and governance rules. Emphasizing data sovereignty protection and incentive compatibility, the model provides a reference for the sustainable operation of regional vocational education data ecosystems.
[AI-60] Nepali-English Code-Mixed Speech Recognition: Data Preparation and Adaptation of a Speech Foundation Model
链接: https://arxiv.org/abs/2611.13512
作者: Jeevan Neupane,Janardan Bhetwal,Kshitiz Paudel,Anup Aryal,Bipin Bashyal
类目: Artificial Intelligence (cs.AI)
备注: 9 pages, 5 figures, 13 tables. Code and model checkpoints: this https URL
Abstract:Nepali speakers regularly mix English words into conversation, but multilingual speech recognisers often struggle with language switches and the writing script used for English words. We study how to adapt SraVaani, a 449M-parameter hybrid TDT-CTC model, using a 172.8-hour corpus covering six Nepali speech domains. Its pretrained tokenizer does not support digits and several common symbols. Rather than replace it or discard these recordings, we generate Nepali and English readings for numbers and use zero-shot ASR output to select a plausible form. Full fine-tuning reduces test word error rate (WER) from 48.6% to 17.1%; a 12-epoch run reaches 16.3%, compared with 16.4% for fine-tuned Whisper large-v3-turbo. Freezing the encoder increases WER to 25.5%, while excluding numeric clips raises WER on numeric test speech from 14.3% to 21.3%. We also evaluate on FLEURS using both its original references and mixed-script alternatives. The comparison shows that transcription conventions explain part, but not all, of the out-of-domain performance loss.
[AI-61] he WoW: A Large-Scale World Knowledge Corpus for Full-Lifecycle LLM Training
链接: https://arxiv.org/abs/2611.13487
作者: Tianze Sun(1),Yongyao Duan(2),Sam Chen(2) ((1) Harbin Institute of Technology, (2) OpenCSG)
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Existing Chinese educational corpora often suffer from fragmented coverage and a lack of systematic organization. In this paper, we propose WoW (\textbfWorld Kno\textbfwledge), a large-scale dataset designed for the full-cycle training of foundation models. The construction of this dataset is driven by an Educational Knowledge Graph, which serves as a taxonomic backbone for the systematic collection and completion of missing educational modules across all levels. Compared to previous versions, this iteration provides enhanced systematicity, superior quality, and a larger scale, while incorporating multimodal signals and supporting every stage of model development. Experiments demonstrate that WoW significantly improves model performance across diverse benchmarks. To facilitate community research, we further provide an interactive platform for graph-based data selection and retrieval.
[AI-62] Sharp Sample Complexity of KL-Regularized Reinforcement Learning
链接: https://arxiv.org/abs/2611.13480
作者: Andrew Liu
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:KL-regularized reinforcement learning, the objective behind maximum-entropy RL and RL from human feedback, is variational inference in the graphical model of “control as inference”: the regularized optimal policy is a posterior over trajectories, and the suboptimality of a policy is \beta times a KL divergence between path measures. However, it is not known how many samples it takes to learn this posterior, even with a generative model. Earlier 1/\epsilon guarantees scale with H^5 in the effective horizon H , and no lower bound for MDPs depends on the temperature \beta . In this work, we show that for a discounted MDP with K state-action pairs, the plug-in estimator finds a policy that is \epsilon -optimal from every initial state with \widetildeO(KH^3/(\epsilon\max\epsilon,\beta) + KH) samples, and that no algorithm, even one that chooses where to sample, can do with fewer when \epsilon \lesssim \min\H, H^2/\beta\ . For \epsilon \beta this is a 1/\epsilon rate with the same horizon dependence as unregularized RL. We also find the exact asymptotic constant of the plug-in estimator, a Fisher-weighted variance of the estimated soft Q-function. The estimator is locally asymptotically minimax among methods that sample every pair equally often, and we give the allocation of samples that is optimal among adaptive designs. Simulations on small MDPs match the rates and the constants.
[AI-63] SafeRoute-MAS: Coverag e-Aware and Risk-Calibrated Message Routing for Multi-Agent Scientific Reasoning
链接: https://arxiv.org/abs/2611.13454
作者: Zhiming Xue,Jianan Liu,Weiran Yan,Aicheng Xie,Jing Yang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce SafeRoute-MAS, a coverage-aware, risk-calibrated procedure that keeps all specialist messages or removes one using only pre-integration information. On a sealed cache of 5,328 committees, its source-compatible operating point gains +1.80 percentage points (95% CI [+1.13, +2.52]) while removing 12.76% of message tokens. We also run 4,580 controlled integrations over a stratified 458-committee sample on a public gpt-oss-120b endpoint. The sample estimate is -0.09 points; design weighting to the original workload gives +0.94 points (95% CI [-1.31, +3.50]) and 18.72% acted-input reduction. A weighted token-matched comparison likewise does not establish a selector advantage (+1.68 points, CI [-1.27, +5.10]). Only 13 of 88 cached non-neutral labels (14.8%) retain direction by repeated-call majority. Expected-corruption UCL is 4.60% after weighting, but only 2 of 11 prespecified marginals pass the support/risk rule. We therefore propose a numerical qualification with Keep-All fallback, applied here retrospectively rather than validated prospectively.
[AI-64] ForeSpark: Joint Draft and Verification Budget Scheduling for Parallel Speculative Decoding
链接: https://arxiv.org/abs/2611.13442
作者: Jaeyong Song,Daniel Wang,Pratyush Patel,Jonny Kong,Preyas Shah,Shobhit Kanaujia
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages
Abstract:Parallel drafters accelerate LLM decoding by proposing a whole block of draft tokens in a single forward pass, making them attractive for LLM serving. Recent systems optimize goodput (i.e., accepted tokens per second) because rejected drafts steal compute from other requests. However, these systems control only how many tokens are verified, so every request still drafts a full block. At high serving load, we find that drafting consumes up to 40% of step time, of which a majority is discarded because it is never verified. Yet, draft-length control from autoregressive drafters does not carry over, because a parallel drafter commits to its length before its single pass. Moreover, draft and verification lengths are coupled across the batch, so they must be selected jointly for every request while keeping the decision computationally cheap at every step. In this paper, we propose work-conserving speculation (WCS), a design that jointly allocates draft and verification budgets per request in LLM serving, and present ForeSpark as its prototype. ForeSpark realizes WCS with (i) a context-aware pre-draft estimator that predicts acceptance for every candidate length from the target model’s context features, (ii) a lightweight joint draft-verify scheduler that selects both lengths through a global sort, and (iii) a post-draft budget distribution that spends verification on the most promising tokens. Implemented on vLLM, ForeSpark improves throughput by up to 1.77x over the state-of-the-art DSpark and by 2.49x over autoregressive decoding at a matched per-request generation rate, without changing the output distribution. It also tolerates higher load, sustaining up to 1.51x the request rate of DSpark under latency service level objectives (SLOs).
[AI-65] You Dont Need Feature Learning to Do In-Context Learning
链接: https://arxiv.org/abs/2611.13431
作者: Joseph Turnbull,Jamie Simon
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:How should we understand memorization vs. generalization in in-context learning (ICL) tasks, and when does one happen vs. the other? To investigate, we study how rotation-invariant kernel machines learn the ICL linear regression task proposed by Raventos et al. (2023). By decomposing the task in the kernel eigenbasis, we obtain a spectral picture of learning in this ICL setting. This picture reveals that as sample size grows, kernel machines learn successively-higher-degree components of the memorizing solution, which depend upon successively-higher-degree statistics of the training task set. The generalizing solution is approximately a low-degree truncation of the memorizing solution, and so the model first generalizes, then degrades into memorization as training proceeds. The takeaways are threefold. First, if this case is indicative, learning a “generalizing” solution on an ICL task is really just underfitting the memorizing solution! Second, the fact that kernel machines can generalize on this task means that feature learning is inessential to ICL. Third, transformers empirically trained to do ICL are shown to memorize tasks through the higher-order statistics of the training tasks that the kernel-based theory predicts, suggesting it applies even to models capable of feature learning. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2611.13431 [cs.LG] (or arXiv:2611.13431v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.13431 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[AI-66] Placing LLM Lookup Tables by Per-Layer Prefetch Deadlines
链接: https://arxiv.org/abs/2611.13413
作者: Wen Huang,Jack Liu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 5 figures, 10 tables
Abstract:DeepSeek-V4.1’s 203 GB of Engram n-gram tables sit in host DRAM today, taking 190 of the 223 GiB a 4-GPU serving process uses. Row indices depend only on input tokens, so reads can start with each step and overlap earlier layers. V4.1’s two Engram layers have windows that differ by 30x (0.3 vs. 9.3 ms). We show that this deadline, together with session reuse and contention on a shared device, decides which tier each table can use and which layers need cache, and we give a model that predicts the resulting slowdown from measured read times. Agent and chat traffic reuse rows within sessions, and static hot sets transfer poorly. We build SlackTier, a table tier for SGLang that issues all lookups at step start. It reads the short-window layer first and splits a small GPU row cache across layers. The path runs inside the decode CUDA graph and is bit-exact with the original gather. Layer 14’s table can go to NVMe uncached with no measurable decode cost. With both tables on four NVMe disks, mean time per output token on agent and chat replays rises at most 1.4% over unmodified SGLang, and host memory falls from 212 to 34 GiB. Both tables need no cache when read over RDMA (remote direct memory access) from another node’s memory; one copy serves two instances. On a KV-constrained agent workload, using the freed memory as host KV cache at the same memory budget raises median throughput by 36% and cuts median p90 time to first token from 38 to 24 s.
[AI-67] Bursts That Outlast Themselves: Persistent Degradation in Prefix-Cached Agent Serving
链接: https://arxiv.org/abs/2611.13412
作者: Wen Huang,Jack Liu
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 8 figures
Abstract:LLM agents drive inference engines with closed-loop sessions whose context grows every turn, and serving them economically depends on prefix caching: a hit costs an incremental prefill of about a thousand tokens, a miss recomputes a 10–40K-token context. We show that this makes prefix-cached agent serving metastable. With an identical base workload, a few minutes of extra arrivals leaves a vLLM server in a degraded regime–a ~10% hit ratio, about half the throughput, and a backlog that grows for the remaining 70 minutes–while its no-burst twin stays healthy (9 of 12 pairs; none in the reverse direction). The regime appears at loads the cache-warm server sustains, is absent without prefix caching, and also appears with Qwen3-32B on SWE-bench agent trace shapes and on SGLang, which tolerates larger triggers. Slower service keeps more sessions in the system, their working set outgrows the KV pool, and every turn becomes a recomputation; a first-order workload-balance condition, calibrated independently, brackets when the regime can drain. Getting out is the harder problem: throttling new admissions recovered 1 of 7 matched collapsed runs, while suspending admitted sessions against a capacity-based limit recovered all of them within minutes. A CPU offload tier delays the collapse but does not prevent it. A budgeter that bounds the resident KV demand of active sessions to the pool, with thresholds that need no per-workload tuning, prevented the failure in every run we tested.
[AI-68] SteerDistill: Quantizing LLM Agents via Closed-Loop Teacher Steering
链接: https://arxiv.org/abs/2611.13410
作者: Genming Cui,Xiangyu Li,Zixu Hao,Xin Ding,Yunxin Liu,Ting Cao
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint
Abstract:Low-bit quantization reduces the inference cost of LLM agents but can compromise task success. Examining failed rollouts, we find that quantized agents overlook task requirements or environmental conditions, producing environment transitions that diverge from successful full-precision references. Correcting these behavioral divergences remains difficult: offline data may miss the student’s failure contexts, while its own rollouts may provide few correct responses in those contexts. We introduce SteerDistill, a quantization-aware distillation method in which a full-precision teacher guides the student to revise failed responses and iteratively refines its guidance using feedback from student execution. This closed-loop teacher steering produces locally verified behavioral corrections. We internalize these corrections by training the student without guidance to match the steered student’s predictions at corrected turns and the full-precision teacher’s at other turns, enabling unassisted inference. Across five agentic benchmarks and three models, SteerDistill exceeds the strongest quantized baseline by 1.67–2.18 points in average score under W4A16 and stays within one point of BF16. On \tau^2 -Bench, it outperforms QeRL by 1.84 percentage points in success rate using 67% fewer training GPU hours and produces shorter successful trajectories than the quantized baselines.
[AI-69] Stinging the Swarm: Extracting Attacker Intelligence from a Twin-Controller OT Honeypot
链接: https://arxiv.org/abs/2611.13406
作者: Canyon Grey Dell’Omo,Joao Pedro de Carvalho Voltani
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures, 2 tables
Abstract:Autonomous language-model agents can drive industrial controllers over fieldbus protocols, probing a plant at machine speed and in coordinated swarms. We present Talos, an inline gate that judges every agent request to a controller in milliseconds and routes agents it flags as hostile to a device-faithful decoy – a mirror twin of the real plant – where their attack continues harmlessly and is read for intelligence. We evaluate the full pipeline (detect - quarantine - mirror - extract) against 2,400 autonomous attacker agents, 600 in each of four tiers that escalate the attacker’s effort to stay hidden, from no concealment to an explicit non-disclosure rule and a trained cover story. No write reached the real plant in any tier: the gate denies the damage path and a device-level envelope check backstops it, while unmapped registers are denied without exception and the gate still permits safe-direction stop commands. Decisions are model-driven in every tier with no inference timeouts, closing a common objection to small on-device judges. Beyond blocking, the pipeline reads the attacker. We find a behavioral structure along two axes – how persistently an agent attacks and how much it discloses – and show that the system has a capture channel for three of the four resulting regions: hardened, silent attackers escalate most and are read on the mirror; compliant, talkative attackers volunteer their identity to a diagnostic form before they are ever quarantined; only the paranoid, low-yield region resists both. Attacker identity fields are self-authored cover stories, but involuntary tells – the self-reported toolchain fingerprint and the attacker’s own list of targeted registers – accumulate across the swarm into a high-confidence profile drawn from agents under explicit orders to stay silent. The gate, mirror, and harness are released under Apache 2.0 with a synthetic device family.
[AI-70] Living constitutions: Participatory alignment for sovereign foundation models
链接: https://arxiv.org/abs/2611.13402
作者: Stine Lyngsø Beltoft,Jonas Vistrup,Peter Schneider-Kamp,Lukas Galke Poech
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In Constitutional AI, a plain-text “constitution” encodes values and gets incorporated into a model via post-training. However, the procedure for creating such a constitution poses significant challenges: who contributes, who decides which norms are included, and to what extent must the process be participatory or democratic? These challenges can be paralyzing. Yet, we argue that taking action is essential: without a deliberate constitution, sovereign models would risk misalignment or default to norms set by frontier model providers. We propose treating the development of a constitution as an evolving, participatory process akin to citizen science, iteratively drafted, tested, and refined. We therefore formulate five desirable principles: iterative, modular, justifiable, positional, and legitimate. We further argue that such a document must remain responsive not only over time but across the people it governs, and propose that provisions be marked as settled, contested, or underdetermined, so that durable disagreement is recorded in the constitution rather than resolved away. Empirically, we show via agent-based modelling that proposed process for maintaining living constitutions is robust against 50% bad actors among citizens - assuming a share of 75% participants with good intent in the deliberative body. This indicates that the proposed process tolerates a substantial share of adversarial participants.
[AI-71] he AI Eras Lines of Code? A Case Study on What an Engineers Token Spend Measures
链接: https://arxiv.org/abs/2611.13396
作者: Shyam Agarwal,Olga Lvova,Agnia Sergeyuk,Anastasiia Serova,Timofey Bryksin,Thomas Zimmermann,Bogdan Vasilescu
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Organizations use engineers’ token spend to monitor AI adoption, set budgets, and measure productivity, but the metric’s relationship with engineering output is unclear. We link five weeks of token usage in coding agents to the commits of 404 engineers at JetBrains, and analyze a company survey of 412 employees. We find that token spend varied mostly between engineers, and which engineers spent the most stayed largely the same over the five weeks. Among weeks with similar commit counts, the 90th percentile of spend was 39 to 240 times the 10th. Within an engineer, doubling spend was associated with about 10% more commits. Available work metrics explained 38% of the variation in spend between engineers and 13% of the week-to-week variation within an engineer. In the survey, respondents in technical roles reported in nearly nine of ten agent evaluations using agents for tasks beyond coding, such as analysis, research, and planning. Token spend therefore measures how much engineers use agents across many kinds of work and not the outcome and value of that work. We recommend using token spend at most for resource accounting, and evaluating agent-assisted work with evidence about tasks and outcomes.
[AI-72] Online-Adaptive Fuzzy Cognitive Maps for Neuro-symbolic Financial Forecasting ICML
链接: https://arxiv.org/abs/2611.13388
作者: Brandon Rodriguez,Abel Deulofeu,Olaoluwa Adigun
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures. Accepted at ICMLA 2026
Abstract:We present a neuro-symbolic framework that pairs online-adaptive Fuzzy Cognitive Maps (FCMs) with long short-term memory (LSTM) networks for financial forecasting. Financial markets exhibit regime shifts and nonlinear dynamics. Existing models often trade off one of these key properties: these models capture temporal patterns but hide causal drivers, or they recover causal graphs that cannot adapt post-training. We close this gap with two frameworks. Framework I constructs an FCM from live dual-source price series and macroeconomic indicators, and adapts its edges online through an error-driven Hebbian rule. We introduce the Asymmetric Additive Residual Fusion method to seamlessly combine the FCM and LSTM predictions under Framework I. Framework II constructs a literature-grounded, LLM-augmented FCM where every edge carries a verifiable citation that gives an auditable map of factor interactions. On NASDAQ Composite data, Framework I matched the performance of the LSTM baseline on state-directional accuracy and outperformed LSTM by 7.6 percentage points on the return-sign accuracy. Framework I outperformed standalone FCM models across both evaluation metrics. The adaptive maps converged to new regimes within 10-15 trading days, and Framework II center-node FCMs scored 68.9% to 85.7% on the state-directional accuracy.
[AI-73] Enforcing Axioms for AI Alignment under Loss-Based Rules ICLR2026
链接: https://arxiv.org/abs/2611.13369
作者: Alexandros Hollender,Sonja Kraiczy
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Published in the Proceedings of ICLR 2026
Abstract:Many alignment methods, most notably reinforcement learning from human feedback (RLHF), train reward models by minimizing a loss on pairwise comparisons of model responses. Inspired by principle-guided methods such as Constitutional AI, we treat each principle as a voter that compares responses according to that principle, for example, preferring the more helpful or less toxic response. We represent these voters as linear directions in a fixed embedding space, a simplifying assumption motivated by the Linear Representation Hypothesis. In this linear social choice model, Ge et al. (2024) showed that an optimal linear reward model can violate Pareto optimality (PO): From the principles-as-voters lens, this means a response A can be less helpful and more toxic than B, yet still receive a higher reward. We ask whether richer reward models or changes to comparison data can recover PO within loss-based training. We show that PO can still fail for polynomials of any fixed degree and one-hidden-layer ReLU multilayer perceptrons (MLPs) of any fixed width d\ge 2 with suitable finite parameter bounds. We then recover PO for loss-based training of linear rewards under suitable assumptions in an idealized setting with uniform comparison directions and equal comparison lengths. These results motivate dataset design as an approach to recovering PO in constitutional-style alignment while retaining loss-based training.
[AI-74] No finite rulebook can certify every safe program: an impossibility theorem for gatekeeping AI agents
链接: https://arxiv.org/abs/2611.13367
作者: Tomas Perez-Acle
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 34 pages, 4 figures
Abstract:An AI assistant writes small programs, and a gatekeeper must decide, from the code alone, whether each one may run. Suppose the rules are fixed: never perform a forbidden action, never let the output depend on a secret, always answer eventually. Can two rulebooks, one listing what is allowed and one listing what is not, both be complete, if every rule must be backed by evidence that a machine can check in finite time? We prove that they cannot. Checkable evidence can only establish what a finite record settles, and never'' and eventually’’ are never settled by a finite record. For prohibitions the failure is one-sided: every violation can be caught by watching, but no safe program can be cleared for good. For confidentiality and responsiveness, neither side can be. We characterise exactly which rules about visible behaviour evidence can confirm, and we show that any sound rulebook of n bits omits a compliant program of about n bits. A time limit makes everything checkable, but only by running the program for essentially the whole limit, so a finite machine is not a feasible one. Finally, we show what a gatekeeper can still guarantee: not a verdict on every program, but a checkable list that contains everything it must allow and nothing it must refuse. Simulations on 2.2 million random programs and on self-referential adversaries show that the obstruction is adversarial rather than typical, and that the one error a longer test never removes is the liveness part of confidentiality.
[AI-75] When Are Predictions Enough? An Evaluation Protocol for Frozen Expert Composition NEURIPS2026
链接: https://arxiv.org/abs/2611.13356
作者: Alessandro Pereira,Keith J Ransom,Lewis Mitchell
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted, NeurIPS 2026, ED track, poster
Abstract:Practitioners often combine independently trained models while keeping their weights frozen. These experts are commonly combined through their final predictions, although their internal features may retain useful information that those predictions omit. We ask when combining features improves on combining predictions, and how to make that comparison fair. We introduce a reusable evaluation protocol, apply it to 21 frozen deepfake detectors from DF40 (Yan et al., 2024), and audit how evaluation choices change the conclusions. The protocol uses strong prediction baselines, compares simple and larger feature combiners, varies architecture and training data separately, and reports results by held-out generator with train/test overlap controls. Under leave-one-generator-out evaluation, logistic regression on the features of six architecture-diverse experts improves macro AUROC by 0.081 over the best tested prediction combiner in each fold, with gains on seven of eight generators. The larger network shows no clear improvement over the linear model (approximately 3.5M versus 10K parameters), suggesting that access to richer information matters more than additional combiner capacity on this pool. Features may help when they retain information that the predictions omit and that transfers to new generators. Predictions may be enough when the strongest prediction baseline already performs well, while neither approach reliably compensates for poor expert transfer. Weakening prediction baselines on the overlap-affected standard split inflates the apparent feature advantage by up to 0.172 AUROC. Overlap-aware evaluation also changes the estimated gain, though the contrast does not isolate overlap. Results by generator and expert pool show where the advantage holds, weakens, or reverses. Without these controls, the same benchmark can support different conclusions.
[AI-76] Multi-Stage Phase-Aware Self-Supervised Reconstruction for Multi-Echo Gradient-Echo MRI
链接: https://arxiv.org/abs/2611.13352
作者: Juan Liu,Nian Wang
类目: Artificial Intelligence (cs.AI)
备注: Submit to a journal; under review
Abstract:Purpose: To develop a phase-aware self-supervised framework for accelerated high-resolution multi-echo gradient-echo (mGRE) MRI that preserves anatomical detail and complex echo information for quantitative mapping. Methods: We developed multi-stage phase-aware self-supervised reconstruction (MSPR-GRE), integrating progressive k-space expansion with phase estimation and refinement. Sequential unrolled networks reconstruct complex multi-echo images and update echo-dependent phase priors. A relaxation-informed temporal subspace is used for acquisitions with at least four echoes, while shorter echo trains are reconstructed in the image domain. Training uses held-out acquired k-space samples without fully sampled targets. Evaluation included 9.4 T ex vivo mouse-brain data 30um eight-echo and 18um dual-echo) and retrospectively undersampled 7 T AHEAD human-brain data, with supervised ablations. Results:MSPR-GRE reduced artifacts in regions of rapid phase variation and preserved fine mouse-brain anatomy. Across five AHEAD test datasets at R = 4, 8, and 12, it generally improved magnitude-image, T2* map, and QSM metrics, particularly at higher acceleration. At R = 12, fourth-echo magnitude NRMSE was 12.09 \pm 0.60% versus 21.55 \pm 1.12% for SSDU. Ablations indicated benefits of phase refinement, while supervised experiments supported multi-stage phase-aware reconstruction. Conclusion: MSPR-GRE improves accelerated mGRE reconstruction and quantitative-map agreement without fully sampled training targets. Further validation of generalizability and phase-estimation accuracy is needed.
[AI-77] Veritas: A Verifier Generation Harness for Agent Tasks
链接: https://arxiv.org/abs/2611.13336
作者: Daoan Zhang,Qinbo Bai,Dehai Min,Xingyu Mu,Ziyi Chen,Jianing Yin
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 31 pages, 1 figure
Abstract:The effectiveness of a self-improving system relies heavily on the quality of its verifier, which judges the outcome of each improvement round. However, the verifiers of most tasks are written by hand, which is expensive and does not scale to tasks that come without one. We propose Veritas, a harness that generates verifiers automatically. Unlike general-purpose agent harnesses, Veritas adds three mechanisms for verifier generation: an evidence header stating what the verifier will observe, a library of domain rules that tasks leave unstated, and authoring principles, partly refined by a self-improvement loop. We evaluate on six benchmarks: four scored by execution, whose official verifiers we first certify, and two that compare pairs of responses. With the model fixed, the verifiers Veritas generates agree better with the official verdicts than those of seven other harnesses on all four execution-scored benchmarks. On the two pairwise benchmarks they are at or near the best. They also agree better than those of the vendor’s own harness for two of three other model families. Its mechanisms also improve 9 of 12 harness-benchmark pairs scored by execution across three harnesses.
[AI-78] Disentangling Multimodal Rotary Positional Embedding in Vision-Language Models
链接: https://arxiv.org/abs/2611.13334
作者: Zhaoyi Liu,Haichuan Zhang,Xiaochuan Gong,Yancheng Zhu,Bowei Tian,Wanghao Ye,Yufan Zhang,Ang Li
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language models widely adopt Multimodal Rotary Position Embedding (MRoPE), an extension of Rotary Position Embedding (RoPE) that incorporates temporal, height, and width axes to encode the spatial structure of visual tokens. However, MRoPE also assigns height and width coordinates to text tokens that lack inherent spatial structure, introducing spatial positional effects into text-vision attention. Existing MRoPE variants explore different frequency allocations and coordinate layouts, with their designs suggesting different intuitions about these cross-modal effects: they may provide useful spatial references for associating language with visual regions, or introduce unnecessary positional preferences that hinder text-vision interactions. In this work, we disentangle intra-visual spatial structure from cross-modal spatial effects to investigate this question. We identify a constraint of shared coordinate layouts that prevents eliminating cross-modal spatial effects while preserving intra-visual spatial encoding, and develop controlled experiments to isolate their respective contributions. Our results across multiple model scales, MRoPE variants, and benchmarks show that cross-modal spatial effects provide little additional benefit as spatial references and can even impair model performance, while intra-visual spatial encoding remains useful for fine-grained grounding.
[AI-79] Stochastic Projections and Rank-Aware Learning Rates for Federated LoRA
链接: https://arxiv.org/abs/2611.13331
作者: Saber Malekmohammadi,Kevin Kuo,Virginia Smith,Golnoosh Farnadi
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Fine-tuning large-scale foundation models across resource-constrained, distributed clients presents significant computational and communication challenges. While merging Low-Rank Adaptation (LoRA) with Federated Learning (FL) enables rank-based flexibility, the interdependence of the adaptation matrices ( \mathbfA and \mathbfB ) often complicates optimization. In this work, we theoretically demonstrate that LoRA fine-tuning with Gaussian initialization and a frozen \mathbfA matrix acts as a stochastic approximation of full fine-tuning, where batch gradients are subject to a random, rank-dependent perturbation. Motivated by this insight, we propose freezing and periodically resampling \mathbfA matrices at the client level. This strategy decouples the \mathbfA and \mathbfB gradients, diminishes LoRA low-rank constraints and reduces both computational and uplink communication overheads by 50%, allowing for the reallocation of resources toward higher adaptation ranks. Furthermore, we show that in this frozen- \mathbfA regime, the adaptation rank r directly governs the signal-to-noise ratio (SNR) of the perturbed gradients. Specifically, we find that the SNR increases linearly with r , implying that clients’ learning rates must be dynamically scaled with their adaptation ranks for higher utility. Building on these foundations, we introduce SPRA-LoRA (Stochastic Projection Rank-Aware LoRA), a federated rank-aware fine-tuning strategy that utilizes frozen, resampled \mathbfA matrices and rank-dependent learning rates for clients. We also prove its convergence with heterogeneous client resources. Our experiments across benchmark datasets demonstrate that SPRA-LoRA consistently outperforms state-of-the-art baselines with half the overhead on clients.
[AI-80] Agent -Guided Discovery of Exchange-Correlation Functionals with Physics-based Validation
链接: https://arxiv.org/abs/2611.13329
作者: Achuth Chandrasekhar,Amir Barati Farimani
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate, compact exchange–correlation functionals are central to predictive density functional theory. Symbolic search and language-model agents can propose new functional forms, but it remains unclear whether their gains survive self-consistency and external evaluation, whether the new terms carry physical content, whether agents outperform evolutionary search, and how language-model backbones compare. We connect frontier LLM agents to a GPU evaluator through the Model Context Protocol, with training-only fitting, persistent memory, and self-consistent and external evaluation. Opus-5.5 agents passed the screening gate with about 40-fold fewer tokens than Codex (GPT-6-Astra) agents and found a candidate with lower validation and held-out errors; only this candidate advanced to self-consistent evaluation. The resulting 13-parameter functional, O159, multiplies same-spin correlation by a smooth density window. Refitted controls favor a window that scales the entire same-spin factor, reducing it by 15–30% at its peak, and that acts on reaction energies mainly in near-iso-orbital regions; against a refitted no-window control, it lowers heavy-atom total-energy errors on GSCDB137 by 29%. With about 400 fits each, SyFES evolution did not improve on its GAS22 seed, whereas the agents found forms 0.10–0.13 \kcal\ lower in validation error by adding an explicit density scale. Self-consistent O159 lowers the held-out MGCDB84 test error of \omega B97M-V by 11.7%, and its mean normalized error ratio on 36 GSCDB137 datasets (1.139) is comparable to \omega B97M-V (1.144) and below GAS22 (1.190). Frontier-LLM agents thus produced O159, a compact functional whose improvement over \omega B97M-V survives self-consistent, physics-based validation.
[AI-81] F3-AGI v4.0: Bioenergetically-Gated Active Inference Metaplastic Preconditioning and Residual Program Synthesis for Sample-Efficient Abstract Reasoning
链接: https://arxiv.org/abs/2611.13325
作者: Henrique Oliveira
类目: Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures. Code and reproducibility materials: this https URL
Abstract:Few-shot abstract reasoning on ARC-AGI requires inducing algorithmic transformations from 2 to 5 demonstration pairs without task-specific pretraining. Because few-shot demonstrations under-constrain the hypothesis space, program synthesis suffers from the Rashomon effect (predictive multiplicity): heuristic search finds spurious programs with low training error that fail on test inputs, while unconstrained library growth degrades latency. We introduce F3-AGI v4.0, a CPU-native neuro-symbolic and active inference architecture comprising three mechanisms: (1) F3-Metabolic-Optim, combining mistake-gated plasticity, Fisher-EMA preconditioning, and virtual ATP gating; (2) Adaptive Complexity-Guided Program Synthesis (AACR) with a Cycle-3 ResidualPatchSynthesizer and smallest-pair early rejection; and (3) a 512D L2-normalized random projection driving Expected Free Energy (EFE) exploration with dead-signature pruning. Across three empirical studies: (i) on a synthetic 5-task class-incremental stream without replay, F3-Metabolic-Optim achieves 91.67% final accuracy with 9.17% forgetting, skipping 93.47% of updates, cutting L1 plasticity cost by 65.7%, and running 2.19x faster than EWC; (ii) on the 400-task ARC-AGI-1 public evaluation set (offline CPU, zero LLM calls), F3-AGI v4.0 solves 57/400 tasks (14.25% pass@2; 56/400 pass@1) with a mean latency of 1.62 s/task, decomposed into 19/50 (38.00%, 1.19 s/task, a 42.5x paired speedup over Horizon 2) on the 50-task calibration slice and 38/350 (10.86%) on the 350 strictly uninspected hold-out tasks (with 7 autonomous Cycle-3 patch solutions); and (iii) in a local 5-game (15-level) ARC-AGI-3-inspired interactive suite, our agent completes 15/15 levels with 89.44% level-weighted RHAE (90.72% unweighted).
[AI-82] owards Scalable Network-Based Interventions for HIV Treatment and Prevention via R2C-Agent
链接: https://arxiv.org/abs/2611.13312
作者: Akseli Kangaslahti,Milind Tambe,Alastair van Heerden,Cheryl Johnson
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Human Immunodeficiency Virus (HIV) remains a pressing public health challenge worldwide. Targeting limited HIV prevention and treatment resources effectively with network-based algorithms is a long-standing problem with a rich history at AAMAS and other computational venues. However, optimizing targeted biomedical interventions for minimizing spread of HIV often requires observability of the contact network of HIV transmission risk behavior among individuals, which is prohibitively expensive to collect at scale. Peer referral networks, i.e., networks collected through a recursive peer referral process, are an alternative network class often used to study HIV in hidden populations that can be collected digitally at scale, but they deviate from the underlying contact network: referrals can follow non-risk social ties and participants accept only one referral, hiding contacts among already-recruited individuals. In this work, we first formalize the novel problem of planning network-based interventions to minimize HIV spread in the hidden contact network while observing only the peer referral network. Second, we implement R2C-Agent, a decision making agent for interventions that is designed around our new conditional graph diffusion model called Referral-to-Contact Diffusion (R2C-Diff). R2C-Diff positions the peer referral process as incremental corruption of the underlying contact network and learns to reverse it. R2C-Agent plans its interventions over contact networks sampled from R2C-Diff, so its decisions account for uncertainty about the hidden contact network. Finally, we conduct simulated experiments using real-world HIV network data that demonstrate that R2C-Agent empirically plans more effective interventions than baselines from interdisciplinary literature and highlight key tradeoffs and assumptions driving performance.
[AI-83] PegasusAI Studio: Legible Cyberinfrastructure and Accountable AI Agents for Scientific Workflows
链接: https://arxiv.org/abs/2611.13309
作者: Komal Thareja,Mats Rynge,Berent Aldikacti,Angelina Ju,Ian Lumsden,Anirban Mandal,Rajiv Mayani,Kin Wai Ng,Hamza Safri,Michael Stealey,Sai Swaminathan,Michela Taufer,Karan Vahi,Michael Zink,Ewa Deelman
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:AI coding agents can now construct scientific workflows, but operating them on shared cyberinfrastructure (CI) raises problems that code generation does not solve. Agents need current knowledge of the workflow system and its execution sites and access to the runtime state of distributed executions, while facilities need to attribute every agent action to a user and bound it by that user’s privileges. We present PegasusAI Studio, an AI-assisted science gateway built on the Pegasus and HTCondor stack. Studio makes CI legible to agents by projecting one facility-curated knowledge base into the native configuration of several coding agents, by introspecting workflow interfaces and execution sites, and by grounding diagnosis in workflow and scheduler telemetry. Agent actions operate within a trust envelope that combines federated authentication, per-user execution environments and scheduler credentials, provider-neutral model access, observability, and approval controls for state-changing actions. The same application runs as a JupyterHub-based gateway on a research cloud and as an Open OnDemand application on HPC systems. We evaluate Studio through case studies of legacy CASA weather workflow modernization, a gravitational-wave reanalysis, an EEG analysis built with an open-weight model, and an Event Horizon Telescope (EHT) M87 image reconstruction that recovered from two failed runs to a zero-failure execution. The evaluation establishes that these mechanisms operate as designed.
[AI-84] rain High Ship Low: Efficient LoRA Synchronization for Distributed On-Policy Post-Training
链接: https://arxiv.org/abs/2611.13306
作者: Shenghui Li,Li Ju,Thiemo Voigt
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 7 figures
Abstract:Transmitting LoRA adapters to rollout workers can bottleneck distributed on-policy post-training over bandwidth-constrained networks. Lowering the training rank may limit the capacity a workload needs, while adaptive optimizer updates are difficult to compress. Rollout workers can instead reconstruct updates from gradients by maintaining replicated optimizer states. We introduce HiLo, a compressor-agnostic framework that replaces parameter transmission with compressed gradient dissemination and local optimizer replay. By applying the same decoded gradients, the learner and workers maintain consistent policy states at the same version under reproducible execution. We further develop Spar, a budget-aware gradient compressor that combines spectral truncation, adaptive rank allocation and error feedback to decouple training rank from communication cost. Experiments across on-policy distillation and reward-guided post-training show that HiLo with Spar maintains learning quality close to full synchronization while transmitting as little as 1.6% of the adapter bytes, and accelerates end-to-end updates by up to 2.2\times under bandwidth constraints.
[AI-85] Off-policy Learning with Excursion Policies NEURIPS2026
链接: https://arxiv.org/abs/2611.13275
作者: Jiamin He,Mark Rowland,Zhaohan Daniel Guo,Hado van Hasselt,Diana Borsa
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to NeurIPS 2026 (camera-ready)
Abstract:Off-policy reinforcement learning (RL) tries to learn the value of a target policy while data comes from a different behavior policy. This often requires trading off bias for variance or stability. One way to trade these off is to linearly interpolate between the behavior and target policies, but this represents only one point in a broader space of interpolation mechanisms. We introduce excursion policies–a structured alternative where the agent follows the target policy for a multi-step “excursion” before reverting to the behavior policy. We derive dynamic programming results for these policies, establishing the theoretical basis for their use in RL. We further develop provably sound off-policy temporal-difference (TD) algorithms for excursion value estimation. We find that excursion policies provide an efficiency-stability trade-off comparable to mixture policies in continuous control benchmarks, and our analysis illustrates conceptual differences in their behavior.
[AI-86] Dependable Long-Horizon LLM Agents : A Trajectory-Centered Empirical Study of Where Errors Enter and Which Controls Reach Them
链接: https://arxiv.org/abs/2611.13254
作者: Shaohuang Wang,Jinghao Wen,Xulei Zhang,Xun Jiao
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:When an LLM agent fails part-way through a long task, the usual analysis records a symptom: a failure-mode label, or the agent and step judged responsible. It does not say where the error entered, which state carried it to the symptom, whether anything in the trajectory contradicted it, or which control could have caught it. We define an episode record that ties a candidate fault to its origin, propagation, detection, response and consequence, located by operational phase and state domain, and that names the control families whose observation and reach cover it. We turned the record into a written annotation protocol and applied it to 95 failed runs of six multi-agent systems from the public WhoWhen and MAD corpora, reading each trajectory in full. Faults enter early (median at 0.18 of the run), mostly as wrong decisions or fabrications over task state, and in 37 of 88 WhoWhen runs before the step the corpus’ human labels mark as decisive. In 72% of failed runs the trajectory already contained evidence against the error before the failure; in 88% of those no agent acted on it. Something flagged a deviation in 31% of runs, but no response removed the deviated state. Independent judges reading the unused evidence, task contracts fixed at admission, and re-checking beliefs against the environment reach most episodes; restoring the agent’s own state would have sufficed in 35 of 95 runs. A text scan of the raw corpus also shows that 45% of the hand-crafted WhoWhen failures ended with a provider content-filter crash after which the harness emitted a final answer no agent produced, a failure class that neither symptom taxonomies nor responsible-agent labels represent. Protocol, records and scripts are included.
[AI-87] Curies Jarvis: Autonomous Robotic Chemistry Experiments from Printed Protocols
链接: https://arxiv.org/abs/2611.13251
作者: Rumi Loghmani,Bozhang Song,Victor Alvarado,Aliyah Redding,Amir Barati Farimani
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures, 4 tables. Rumi Loghmani and Bozhang Song contributed equally
Abstract:A person running a bench experiment works from a printed instruction sheet, tells identical containers apart by the labels written on them, and looks at the bench again before each step. Lab automation usually works the other way around: the procedure is written as a script, and containers stand at fixed positions or carry fiducial markers. We present Curie’s Jarvis, a system that lets a robot arm carry out a chemistry experiment on a bench set up for people. It reads the steps from a photo of the printed sheet, finds containers on an ordinary bench with no fixed positions or markers, tells identical-looking containers apart by reading their labels in four camera views, locates its target again before every step, and sets how each step is carried out, including its speed, from what is being handled. On a Franka Panda arm, Curie’s Jarvis completed 5 of 5 runs of one protocol from a children’s chemistry kit and 4 of 5 runs of another, with no human intervention, and in 160 locates on printed and handwritten labels it identified every container correctly. This brings robots a step closer to doing bench chemistry the way people do.
[AI-88] ExpertCrowd-SWE: Measuring Frontier Coding Agents on Expert-Authored Engineering Tasks in Unfamiliar Codebases WWW
链接: https://arxiv.org/abs/2611.13246
作者: Josip Vrdoljak,Akash Singh,Bas Steunebrink,Alex Yarotsky,John Lorenzo,Achille Nicoletti,Sam Pigott
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 17 pages, 5 figures, 6 tables. Dataset: this https URL ; leaderboard: this https URL
Abstract:ExpertCrowd-SWE (EC-SWE) is a benchmark of 100 expert-authored, multi-file software engineering tasks for evaluating autonomous coding agents in large, unfamiliar codebases. Each task is a change request written by a practising engineer against a pinned commit of a real repository, paired with a sealed reference solution and a task-specific grader whose weighted, mostly executable criteria award partial credit; must-pass blockers and pass/fail gates encode what a maintainer would refuse to merge. The tasks span 87 open-source projects and 11 languages. The median ticket is 148 words, while the median reference solution changes 471 lines. Every task image is calibrated before release so that the reference solution scores 1.0 and both the untouched repository and an inert change score 0; nearly half of the tasks also ship alternative, lazy and adversarial control patches that the grader must classify correctly. The final 100 tasks were selected for difficulty against current frontier models. We evaluate seven frontier models, five runs each (3,495 scored runs), in a single platform harness that gives each model its provider-native tool interface. Claude Opus 5.5 (67.3%) and GPT-6 Astra (63.7%) lead without a statistically significant gap, followed by Claude Fable 5.1 (46.5%), Gemini 3.8 Flash (40.0%), and a statistically indistinguishable group of Qwen 3.8 Max, DeepSeek V4.1 Flash and Grok 4.6 (32-34%). No task is fully solved by every model, and on 80 tasks no model earns full credit on all five runs. Scores span 35 points. Depending on the model, agents take a median of 33 to 166 steps per run, with little relation to score. We release 24 tasks with their environments, graders and results, and hold out 76 to limit contamination.
[AI-89] NCP: Verifier-Guided Optimization of Weight-Space Control Policies
链接: https://arxiv.org/abs/2611.13236
作者: Ekrem Aksoy
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present the Neural Control Plane (NCP), a closed-loop system that observes language-model execution, synthesizes candidate weight-space intervention rules, evaluates them, and maintains a versioned table of admitted policies. NCP separates a pretrained model’s inference data plane from the control policy that selects temporary parameter interventions. W-ISA supplies transactional execution and parameter restoration; NCP supplies telemetry, candidate synthesis, admission criteria, priorities, and rule lifetime management. The optimization state consists of active rules and an archive of evaluated candidates, while base-model parameters are restored after each request. In a local SmolLM-135M-Instruct experiment, NCP generated three candidates from development telemetry and evaluated them on separate task examples and a 4,095-target WikiText-2 slice. One projection reduced validation token repetition from 0.4133 to 0.2191, with a perplexity change of -0.0574%. All candidates produced zero newly correct exact answers, so the configured admission gate archived all three and retained the original active policy. Fresh-process repetition and saved-table resumption reproduced the measured outcomes. NCP provides an executable framework for studying recursive self-improvement at the control-policy level through explicit synthesis, verification, and state transitions.
[AI-90] Decisions by Discrimination: Differential Evidence for Medical Reasoning AACL2026
链接: https://arxiv.org/abs/2611.13233
作者: Yunpeng Xiao,Yuzhang Xie,Junhao Huang,Carl Yang,Xiao Hu,Kai Shu
类目: Artificial Intelligence (cs.AI)
备注: Accepted by AACL 2026 (main)
Abstract:Large language models (LLMs) are increasingly studied for clinical decision support, and medical question answering provides a testbed for evaluating this capability. In particular, medical multiple-choice questions (MCQs) reflect a core challenge in clinical decision-making: plausible options often share overlapping evidence, while only a few decisive factors distinguish the correct option from incorrect ones. However, existing retrieval-based and reasoning-oriented methods mainly focus on supplying broadly relevant knowledge or eliciting reasoning paths, rather than identifying the compact differential evidence that separates confusing alternatives. In this work, we present a retrieval-grounded differential evidence framework for medical MCQs. Our method extracts option-specific pieces of differential evidence and organizes them into concise evidence prompts for answer selection. This design better aligns external knowledge with differential reasoning while reducing irrelevant context. Experiments show consistent accuracy gains across multiple datasets and target LLMs. Ablation studies further show that our framework outperforms both directly using retrieved evidence chunks and closed-source LLM-generated general hints, and that its improvements are not solely explained by answer-proximal guidance, but are consistent with differential evidence serving as a compact aid for reasoning over competing alternatives. Our findings also offer insights for medical reasoning and other decision-making settings with multiple possible choices.
[AI-91] W-ISA: Transactional Execution of Weight-Space Intervention Programs
链接: https://arxiv.org/abs/2611.13232
作者: Ekrem Aksoy
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We present W-ISA, a software instruction set and transactional runtime for executing weight-space intervention programs on pretrained language models. Shape-checked instructions express parameter updates, projections, ablations, and rank-one transformations. The runtime applies these programs within request-scoped execution contexts and restores saved parameters after completion or interruption. Activation-based match-action rules connect measured model states to intervention selection. We evaluate restoration and replay on SmolLM-135M-Instruct and Qwen2.5-1.5B-Instruct, and compare materialized rank-one updates with equivalent PyTorch hooks on the latter. In 45 timed requests, both intervention implementations produce identical greedy sequences at 32, 128, and 512 tokens; their maximum prompt-logit difference is 0.000184. On the complete WikiText-2 raw test split, the combined SmolLM attention-head ablation and MLP projection changes perplexity from 22.2353 to 22.3188, a relative increase of 0.3754%. W-ISA separates intervention specification, execution, and measurement, making parameter-level experiments reproducible and composable.
[AI-92] Wrong the Same Way: Cross-Prompt Sharing Both Suppresses and Amplifies Reward Hacking in RLVR NEURIPS2026
链接: https://arxiv.org/abs/2611.13218
作者: Michael Li,Rohan Pandey,Ray Chen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at the Transitioning from Pre-Training to Post-Training Workshop at NeurIPS 2026
Abstract:Two accounts of how reinforcement learning with verifiable rewards (RLVR) interacts with an imperfect verifier coexist: in one, reward hacking is inherited from the base model; in the other, RL amplifies it. We study both in a controlled environment small enough to compute the entire response distribution exactly. We vary the universality of a leak, or the fraction of prompts that accept the same wrong response, and the verifier’s static false-positive rate. At its KL-regularized optimum, RLVR acts as a filter that reweights the accepted set without altering its composition, so the hack rate is inherited exactly. Taking that as a null model, a known decomposition writes the drift from it as a sum over prompts of covariances between reward and the empirical tangent kernel. The terms from other prompts vanish without parameter sharing. With sharing, every prompt that accepts a wrong answer pushes it up and every prompt that rejects it pushes it down through the baseline, in proportion to the model’s probability of producing it there. A shared-parameter policy thus drives a leak that few prompts accept below the base rate and amplifies one that most prompts accept. At every convergent learning rate under Adam, SGD, and sampled GRPO, hacking ends higher at high universality than at low, but whether either end crosses the base rate depends on the step size. Independent per-prompt copies remove the dependence. The direction of this push depends on neither the verifier’s error rate nor universality alone, but on how much of the model’s kernel-weighted probability of a wrong answer falls on prompts that accept it, which frozen base model controls support. Verifier fixes should target the mistakes the model repeats across prompts: two patches that remove equally many errors differ by up to 38 points of true accuracy. We propose this alignment as a hypothesis for why earlier studies disagree.
[AI-93] Reliability of Claude Code as an Agent ic AI Grading System for Handwritten University Exams
链接: https://arxiv.org/abs/2611.13217
作者: Rayed AlGhamdi
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:This study examines the reliability of Claude Code, an agentic deployment of the Claude Opus 4.6 large language model, as a grading system for handwritten university exams. Four research questions addressed agreement with expert human grading on total scores (RQ1), agreement across five question types from multiple choice to evaluative judgement (RQ2), consistency across two independent grading sessions one week apart (RQ3), and the accuracy of AI-generated feedback (RQ4). Claude Code graded the 20-mark mid-term papers of 60 consenting students in a Saudi computer graphics course in a single agentic session, guided by three rubric files. Reliability was estimated on disjoint samples: 20 papers for RQ1, RQ2 and RQ4, and 15 papers for RQ3. Analyses used Pearson correlation, mean absolute error, Bland-Altman analysis, weighted Cohen kappa, intraclass correlation, and a four-category coding scheme separating diagnostic from confirmation feedback. The author, as the sole subject-matter expert for the course, provided the human benchmark, so the design assesses agreement with a domain-expert reference rather than inter-rater reliability. AI grading agreed strongly with human grading on totals (r = 0.97; MAE = 0.85 marks) with a small but significant AI-positive bias of 0.68 marks, showed excellent test-retest consistency (ICC = 0.96), and produced accurate feedback (92.8% diagnostic; 97.8% confirmation). Drawing items were the outlier on three of four measures. Three failure modes recurred: keyword matching without conceptual depth, selection-state ambiguity on corrected multiple-choice answers, and region-of-attention drift on hand-drawn diagrams. The findings suggest that AI grading at this capability level can assist instructors on objective and short-form items under structural safeguards but is not yet ready to replace instructor judgement at boundary cases or on drawing items.
[AI-94] Counting Under Disagreement: Dollar-Weighted Measurement Selective Machine Labelling and Budgeted Human Review from Disagreeing AI Readers of Administrative Text
链接: https://arxiv.org/abs/2611.13215
作者: George Koomullil(Ascendr, PBC)
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
备注: 61 pages, 8 figures, 46 tables. Replication record (calibration sample, population aggregates, prompts and analysis code): this https URL
Abstract:We study AI-assisted measurement when one text label applies to many administrative records, the target is a monetary total, and the human readings that anchor the machines disagree. The testbed is 121,070 grant-purpose texts from US tax filings (799,675 records, \ 126.4B), labelled by a rule-based classifier and three language models, with a designed sample of 651 texts read independently by three people under one codebook. We state exact conditions for a fixed-predictor difference estimator and for a cross-fitted predictor whose folds are assigned to the whole frame before sampling, under Poisson sampling, and assess the implemented fixed-size procedures by simulation: 8 predictors whose own totals span \ 11.2-15.0B agree to within \ 0.4B once rectified. Two rules that accept machine labels at a stated dollar-weighted error, weighted conformal p-values under a dollar measure and Learn-then-Test on the design estimate of the error, are evaluated on nested held-out folds and on re-draws of the design with synthetic truths; their error control in deployment is not established, and the count-based rule accepts no text under a dollar-drawn sample. In a replay that charges every human label, a two-phase review policy whose calibrator is fitted on its own first phase reduces the RMSE of the total by 28% at a budget of 100 texts relative to dollar-proportional sampling, while pure Neyman allocation fails with an uncalibrated predictor. Independent readers shift the estimated total by \ 3.93B, 2.2 times the within-reader sampling interval, and institution-level intervals under-cover at every size. Every headline result is re-run under each reader. The results identify both the benefits and the limits of using learned predictions to decide what a person should verify.
[AI-95] XAI-Driven Confidence Estimation for OOD-Resilient Runtime Prediction
链接: https://arxiv.org/abs/2611.13199
作者: Mahmoud Sanad,Mohammed Bakr Sikal,Osama Abboud,Heba Khdr,Jörg Henkel
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Machine learning-based resource management (RM) models degrade under out-of-distribution (OOD) inputs at runtime, yet existing approaches in the literature rely on a single global error margin that is overly conservative and fails to distinguish reliablepredictions from catastrophic failures. We propose Lightweight Runtime EXplainability (LEX), the first post-hoc XAI framework for input-specific runtime confidence estimation in ML-based RM. LEX clusters the embeddings of the training input space using a Gaussian Mixture Model and associates per-cluster prediction error bounds to input samples, enabling fine-grained runtime confidence signals without retraining. Evaluated on a state-of-the-art LSTM-based IPS forecasting model on Intel Alder Lake, across 34 benchmark applications, LEX achieves ~94% containment on unseen workloads and produces ~17% tighter per-input confidence bounds than global baselines. With its negligible runtime overhead, LEX advances the field by providing explainable and reliable input-specific confidence signals of ML predictions, thereby making ML-based resource management resilient to OOD at runtime.
[AI-96] AgentS cript-Eval: Benchmarking LLM s and Agents for Code Generation in an Enterprise DSL NEURIPS2026
链接: https://arxiv.org/abs/2611.13183
作者: Dipin Khati,Shubham Mehrotra,Bin Bi,Zhou Yu,Denys Poshyvanyk,James Zhu,Sitaram Asur,Phil L. Mui
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted to 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: 2nd Workshop on Agentic AI Benchmarks and Applications for Enterprise Tasks (AABA4ET), NeurIPS 2026
Abstract:Domain-specific languages (DSLs) are widely used in enterprise software, giving a compact interface to complex tasks, from data access to infrastructure automation. Platforms increasingly extend them to agents, where a developer declares the topics, permitted actions, and guardrails of an agent rather than writing general-purpose code. Large language models (LLMs) write general-purpose code well, but that ability has been measured almost entirely on languages abundant in public pretraining data, and enterprise DSLs are neither abundant nor public. We introduce AgentScript-Eval, an execution-graded benchmark of 100 real tasks for AgentScript, the declarative DSL that Salesforce uses in Agentforce to build conversational agents. Tasks span authoring an agent from a specification, debugging a broken definition, and optimizing a working one. Evaluating five models zero-shot, we find that the regime is hard: the best model reaches only 25.0% pass@3, and every model scores 0% on authoring from scratch. We further find that model scale and price are weak predictors of success – a 31B open-weight model attains the highest compilation rate of any model tested (48.0%) and matches the strongest frontier model on partial-credit accuracy at the lowest token cost. Adding an agentic compile-and-edit loop is the single largest lever, roughly doubling partial-credit accuracy. Beyond that gain, the wording of domain guidance matters more than its packaging. The same skill pack scores 28% as plain documentation but only 14% as a live plugin. Rewriting one skill inside that plugin then raises it to 37%, our best result, with catalog and tooling untouched. Larger models did not reliably outperform smaller ones; what mattered was the harness surrounding the model and the guidance supplied to it.
[AI-97] AI Alignment Is Neither Solvable nor Sufficient
链接: https://arxiv.org/abs/2611.13167
作者: Gerrit Großmann
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Humans and AI systems increasingly act within the same social and economic environments. Alignment, the goal of ensuring that AI systems act in accordance with human values, is widely regarded as a central challenge for ensuring that AI benefits society while avoiding harm. We argue that this endeavor is misguided. First, alignment is not well defined, as it presumes a sufficiently stable and identifiable set of human intentions or values. Second, where a concrete notion of alignment can be specified, it is not solvable. Third, where alignment appears solvable, solving it would not necessarily mitigate the negative consequences of AI. A key reason is that alignment is typically framed as a property of individual AI systems, while the resulting hybrid societies of humans and machines are complex systems shaped by interactions and feedback loops. Emergent risks, and not only those posed by individual misaligned systems, are therefore likely to become a major source of harm. We conclude that AI safety is an ongoing challenge and not a problem that can be solved once and for all by verifying a particular model property.
[AI-98] Cognitive Multi-Agent Search and Rescue: Language-Grounded Hierarchical Reinforcement Learning for Collaborative Robotics
链接: https://arxiv.org/abs/2611.13161
作者: Sepideh Ravanbakhsh,Hamidreza Moradi
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 33 pages, 8 Figures
Abstract:Effective search and rescue (SAR) operations require autonomous systems capable of coordinating multiple heterogeneous agents, adapting to dynamic hazards, and aligning with human mission intent. Existing language-guided reinforcement learning (RL) methods primarily focus on single-agent instruction following and lack mechanisms for team-level coordination in complex, partially observable environments. This paper presents a Cognitive Multi-Agent Framework based on Language-Grounded Hierarchical Reinforcement Learning (LG-HRL) that integrates natural-language understanding, hierarchical policy decomposition, and a shared cognitive workspace to achieve cohesive multi-agent behavior in SAR scenarios. Human instructions are interpreted by a large language model (LLM) and transformed into structured semantic cues that guide high-level strategy selection and role assignment across UAV and UGV agents. A formal Language-Grounded Multi-Agent Hierarchical Decision Process (LG-MAHDP) is introduced to model the interplay between semantic grounding, hierarchical control, and distributed coordination. Comprehensive experiments in a simulated disaster environment show that LG-HRL improves learning speed by 2.3 times, increases coordination coherence by 37%, and enhances subgoal accuracy and hazard avoidance by 46% and 31%, respectively. The system achieves 20-30% higher victim rescue rates, demonstrates emergent role specialization, and adapts immediately to human verbal updates. Ablation studies confirm that both the shared cognitive workspace and language grounding are critical to overall system performance. Overall, this work establishes a new paradigm for language-guided multi-agent SAR robotics, demonstrating that semantic grounding combined with hierarchical reasoning enables safer, more efficient, and more interpretable autonomous rescue operations.
[AI-99] How Much of Your VLA Is kNN
链接: https://arxiv.org/abs/2611.13160
作者: Denis Grachev,Damien LaRocque,Omar Moured
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 21 pages, 10 figures, accepted to the CoRL 2026 G2A/TAG workshop
Abstract:Vision-language-action (VLA) models now exceed 95% success on the LIBERO simulation benchmark, yet it remains unclear how much of this performance requires generalization beyond the training demonstrations. In this work, we show that retrieving nearby demonstrations and reusing their action chunks achieves a success rate of 91.7 % on LIBERO without policy training. Under LIBERO-Plus perturbations, retrieval shows the smallest drop of all evaluated policies. Across nine publicly available VLAs, fine-tuning mostly makes the vision-language backbone a better retrieval key, and the actions of the VLAs remain close to the demonstrations. In addition, we introduce Local Action Consistency (LAC), an offline metric that measures whether nearby observations in representation space are also associated with similar future actions. We show that LAC tracks the success rate of retrieval and can guide the training of a retrieval key. Our evaluation and source code are available at this https URL.
[AI-100] aching the Harness: Student-Aware Skill Transfer for LLM Agents
链接: https://arxiv.org/abs/2611.13144
作者: Chang Liu,Bo Wu
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Effective teaching requires reasoning about the learner as well as the task, a capacity that Theory of Mind describes as inferring another agent’s latent state from its behavior. We ask whether a stronger LLM teaches a fixed student better when it applies this principle. The student is an LLM agent acting through a harness that fixes its tools, context, and action budget, so a procedure that helps one model can exhaust the budget of another. We introduce Student-Aware Teaching (SAT). The teacher builds a learner model that pairs an empirical profile with hypotheses about the student’s limitations, tests these hypotheses with diagnostic probes, and writes reusable skills for the diagnosed failures. A skill enters the library only after it raises success on matched development tasks. With Claude-Sonnet-5 teaching DeepSeek-V4-Flash, SAT reaches 86.46% task success across four primary runs, leading both a profile-free teacher with the same framework (81.04%) and the untaught student (75.00%) in each run. Removing the analytical learner model lowers both prediction and success, and under misleading learner profiles none of the 48 candidate skills passes admission. When DeepSeek-V4-Flash and Gemini-3.8-Flash exchange libraries, each performs better with its own. Saved libraries keep average gains of up to 11.1 points after the teacher leaves. The benefit depends on a student that can follow instructions and a deployment route that delivers them.
[AI-101] DOBB: Dynamic Oriented Bounding Boxes for Multi-Agent Collision Avoidance under State Uncertainty and Heterogeneous Dynamics
链接: https://arxiv.org/abs/2611.13142
作者: Pedro Antonio Alarcon Granadeno,Jane Cleland-Huang
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Learning-based decentralized collision avoidance must remain reliable under state-estimation uncertainty and heterogeneous agent dynamics. Existing methods typically address these factors through training-domain variation or by exposing uncertainty and capability estimates to the policy at runtime. However, both leave the policy to infer how these quantities should alter the physical separation required for safe interaction, which can couple collision-avoidance behavior to relationships learned from the training distribution. We introduce the Dynamic Oriented Bounding Box (DOBB), an online geometric representation that maps the current state estimate, uncertainty, and agent-specific response characteristics into a dynamic, direction-dependent safety margin. We derive geometric features that jointly characterize the relative configuration and severity of interactions between agents’ DOBBs, along with a reward function that preserves the physical interpretation of those interactions. Across four evaluation environments, DOBB achieves the lowest mean total failure rate of 2.6%. This performance persists under out-of-distribution shifts in actuation dynamics and state-estimation uncertainty, with collision-based failure remaining at or below 2.3% in every tested condition. We attribute this robustness to the geometric abstraction of the safety margin, which is less coupled to the specific uncertainty and actuation regimes encountered during training.
[AI-102] Building World Models from Intrinsic Motivation
链接: https://arxiv.org/abs/2611.13113
作者: Reece Keller,Declan Kutscher,Xaq Pitkow,Aran Nayebi
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 27 pages, 11 figures
Abstract:In the absence of extrinsic rewards or task supervision, human behavior is guided by intrinsic motivation—neural signals that drive curiosity, creativity, play, and other essential facets of intelligence. Intrinsic motivation plays an important developmental role in building robust world models that enable generalization and adaptation. Yet existing studies leave open how a broad range of intrinsic motivations interacts with world-model representation learning to support downstream control. This limits our understanding of which intrinsically collected experiences build reusable models. To address these gaps, we integrate 24 intrinsic objectives, including established methods and explicit variants spanning curiosity, entropy, and competency, into a common model-based reinforcement learning framework. We evaluate the world models they build through zero-shot, few-shot, and multi-task control in locomotion, manipulation, and open-ended gaming environments, using three representation-learning strategies: observation reconstruction, latent prediction, and redundancy reduction. Curiosity-driven exploration with observation reconstruction yields the strongest aggregate family profile, although the best individual pairing depends on the experiment. In compositional vision-based tasks, intrinsic motivation derived from both episodic and lifelong novelty—providing a complementary memory to the world model’s generic recurrent state—achieves the best zero-shot transfer and few-shot adaptation. These findings motivate designing intrinsic motivation and representation learning jointly. Our results demonstrate world models as a powerful tool for measuring the quality of intrinsically motivated experience, and positions intrinsic motivation as a key ingredient for building world models autonomously.
[AI-103] Elicitation of the parameters of a multi-criteria sorting model
链接: https://arxiv.org/abs/2611.13062
作者: Eda Ersek Uyanik
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注: PhD Thesis defended at University of Mons, Belgium, July 07 2023. 216 pages this https URL
Abstract:Multiple-criteria decision aiding (MCDA) proposes models and methods for different types of decision problems such as choice, ranking or sorting. Such models involve various parameters that must be elicited. In the elicitation process, all necessary information has to be taken from the decision-maker. For this, several direct and indirect elicitation methods are suggested in the literature. In this work, we concentrate on direct elicitation: we aim at providing a rigorous elicitation procedure for the parameters of the Non-Compensatory Sorting model (NCS), which has been characterized by an axiomatic analysis. A basic version of the NCS model is considered, i.e., a model with two categories and no vetoes. The parameters are the set of sufficient coalitions of criteria and the profile separating the two categories. Regarding the sufficient coalitions, we propose several questioning algorithms that engraft questions on classical graph exploration algorithms such as depth-first search. Such algorithms are implemented and evaluated by simulation. For eliciting the limit profile, we design a questioning algorithm using dichotomy. Questioning algorithms can be optimal in the worst case or on average. An optimal algorithm in the worst case has been proposed by Hansel in the framework of monotone Boolean functions. We implement and evaluate this algorithm as a variant of the depth-first search approaches. Regarding optimal questioning strategies on average, we evaluate decision trees for up to four criteria. On the other hand, we assess how more general is NCS as compared to MR-Sort, a particular case of the NCS model in which sufficient coalitions can be represented by additive weights.
[AI-104] Continuous-Time Set-Transformers for Asynchronous WiFi-IMU Indoor Localization
链接: https://arxiv.org/abs/2611.13057
作者: Mohamed Bachar,Ilyass Abouelaziz(LINEACT),Yuehua Ding(LINEACT)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Indoor localization commonly combines WiFi received signal strength indicator (RSSI) fingerprints with inertial measurements. However, these sensor streams are sampled asynchronously at different rates; therefore, observations rarely align in time and may be missing or stale. Most existing methods resample the data onto a shared clock and process each modality using a separate branch. We instead present a continuous-time transformer that represents observations as an unordered set of timestamped tokens. Each token encodes its measurement, modality, and elapsed time since acquisition. A single attention block jointly fuses information across modalities and time, avoiding both resampling and modality-specific branches while remaining invariant to token order. During training, we randomly mask entire modalities and individual observations, enabling the model to operate under missing or outdated inputs. We validate the modality-specific encoders on their respective benchmarks and evaluate the fused model on both simulated and real datasets, including a crosssession dataset collected on unseen days. The proposed approach improves upon an open WiFi fingerprinting baseline under cross-session drift, remains competitive with learned fusion and dead-reckoning baselines, and degrades gracefully as observations become missing or stale. These results suggest that a single continuoustime transformer provides a practical framework for indoor localization from asynchronous sensors.
[AI-105] APS-VLA: Action-Guided and Future-Aware Token Pruning for Efficient Vision-Language-Action Models
链接: https://arxiv.org/abs/2611.13030
作者: Xiaoyang Xing,Chun-Mei Feng,Anthony Cohn,Xiaoliang Gong,Asoke Nandi
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures, 6 tables
Abstract:Vision-language-action (VLA) policies have demonstrated strong generalization in robotic manipulation, yet processing dense visual tokens often incurs high inference latency. Visual token pruning offers a practical way to reduce this cost, but identifying a compact set of tokens that preserves reliable action generation remains challenging. Existing methods largely rely on static or immediate heuristic cues to estimate token importance. We introduce APS-VLA, a framework that learns visual token selection from robot demonstrations while keeping the pretrained VLA backbone frozen. A lightweight Decision Influence Estimator scores tokens conditioned on current visual observations, language instructions, and robot proprioception. Its training combines two complementary signals: action supervision matches both the full-input policy predictions and demonstrated actions, while an auxiliary visual transition objective uses demonstrated actions to predict subsequent visual features from the selected representation. At deployment, the estimator performs a single hard selection and packing step before the policy’s native action-generation procedure; the auxiliary predictor is removed. Experiments with OpenVLA-OFT on LIBERO and with \pi_0.5 and XR-1 on RoboCasa365 demonstrate inference acceleration with modest changes in task success. Across five real-world tasks on two dual-arm platforms, APS-VLA reduces mean policy inference latency from 137.25 to 93.25 ms, achieving a 1.47\times speedup with an average success rate of 61%, compared with 60% for the unpruned baseline. Together, these results support APS-VLA as a modular, plug-in framework for efficient robotic control across VLA architectures, preserving the underlying policy parameters and native action-generation procedure.
[AI-106] GRAP: Gender Rubric-Aware Probing for Analyzing Gender Bias in LLM s ECML-PKDD2026
链接: https://arxiv.org/abs/2611.13024
作者: Seona Seo,Geonhee Ahn,Hyunsoo Cho,Sookyung Kim
类目: Artificial Intelligence (cs.AI)
备注: 3 pages. Accepted at the ECML-PKDD 2026 Workshop on Bias and Fairness in AI
Abstract:As LLMs increasingly mediate everyday interactions, subtle gender biases in open-ended responses pose growing societal risks. Existing probing methods have two key limitations: they rely on fixed-format tasks, such as pronoun resolution or occupation association, that alignment-tuned models can easily handle; and they compare raw group-level rubric scores, ignoring per-dimension variance and obscuring which bias dimensions reliably distinguish gender groups. We introduce GRAP (Gender Rubric-Aware Probing), a framework that uses a lightweight logistic probe to identify the most discriminative bias dimensions from responses scored across 10 rubric dimensions, operating task-agnostically on open-ended instruction following.
[AI-107] Understanding-First AI Engineering: Purposes Reference Architecture and Architectural Patterns for Human Understanding
链接: https://arxiv.org/abs/2611.13022
作者: Liming Zhu,Qinghua Lu,Ming Ding
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:AI systems can produce reliable results without developing human understanding. However, human understanding remains valuable for shaping subsequent AI-supported work, forming preferences, governing collective choices and satisfying curiosity, even when human participation is unnecessary for correcting errors. We propose understanding-first AI engineering, which treats purpose-specific human understanding as a first-class design principle guiding requirements, design and operation. The purposes of understanding are organised into four complementary rationales: reflective agency, effective action, collective governance and epistemic value. A reference architecture connects production, verification, use, and learning and exploration through shared records, supporting understanding through explanation and participation. Five candidate architectural patterns guide choices about parallel learning, explanatory models, goal exploration, audience-specific views and distributed expertise. Qualitative scenario analysis of two hypothetical use cases demonstrates how the purpose classification, reference architecture and selected patterns guide investigation, participation and decision processes while retaining operational controls. The cases illustrate design consequences and limitations rather than establish empirical effectiveness. The approach makes human understanding an explicit engineering commitment whose justification need not depend on improving the original AI task.
[AI-108] ChipOmni: A General-Purpose AI Agent for Electronic Design Automation
链接: https://arxiv.org/abs/2611.13020
作者: Pengkun Wang,Rui Mao,Zhe Zhao,Dongting Xie,Haibin Wen,Song Xu,Di Wu,Xu Wang,Zhengyang Zhou,Binwu Wang,Xiaofeng Cao,Yang Wang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Electronic design automation (EDA) workflows remain highly fragmented, requiring engineers to coordinate heterogeneous representations, specialised software tools, and iterative verification across multiple design stages. Although large language models have brought more general reasoning and code-generation capabilities to EDA, many existing approaches remain focused on isolated tasks and struggle to plan, verify, and repair designs autonomously within executable engineering environments. Here we introduce ChipOmni, a knowledge-enhanced, context-evolving AI agent framework for EDA. ChipOmni integrates multimodal understanding, dynamic planning, multi-agent collaboration, and verification-driven tool interaction into a unified autonomous system. By continually incorporating feedback from external engineering tools, the framework maintains an evolving task context, integrates domain knowledge, and iteratively refines intermediate designs. Across benchmarks for analog circuit, RTL, testbench, and formal-assertion generation, ChipOmni improves multiple task-specific measures over the evaluated baselines and solves all 30 AnalogCoder-Pro tasks. In a long-horizon case study, ChipOmni advances a natural-language RISC-V specification through three design levels to a micro-SoC GDSII layout that passes physical sign-off, without human intervention after initialization. Together, these results show that ChipOmni can coordinate verification-driven design across heterogeneous tasks and sustain long-horizon engineering workflows, advancing a shift from fragmented, tool-centred processes towards agent-centred, closed-loop systems.
[AI-109] Multi-Future Drafting: Mixture Block Drafters for Tree-Verified Speculative Decoding
链接: https://arxiv.org/abs/2611.13017
作者: Haoyu Huang,Jialiang Kang,Linlin Yang,Xiao Yang,Baochang Zhang
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Block drafters such as DFlash draft a block of future tokens in one forward pass, and draft trees such as DDTree verify the prefixes ranked highest by the product of the drafter’s per-position distributions. Under tree verification, the expected number of accepted tokens is the probability mass the tree covers. We show that a product misallocates it: when the continuation can follow several futures, it splices them into chimeras, prefixes stitched together from different futures that the target never produces, and gives them as much budget as the futures themselves. Barring ties, no product-ranked tree can hold two futures that disagree in m positions without 2^m-1-1 chimeras, however well the drafter is trained, and in practice chimeras make up 27-33% of a 64-node tree. We propose Multi-Future Drafting (MFD), which restores the coupling with a latent variable instead of the chain rule: K learned mode embeddings at the drafter’s input draft K coherent blocks in the same pass, and a mixture DDTree lets the verifier pick the future the target takes, losslessly and with K+1 extra vectors. On four Qwen targets from 4B to 27B parameters, at batch size 1, MFD reaches up to 7.8 \times speedup, 5-8% faster than the best single-mode tree trained identically and, on Qwen3-4B and Qwen3-8B, 9-16% faster than the released DARTree trained on the same prompts.
[AI-110] From Questions to Decisions: Benchmarking How LLM Agents Acquire Decision-Relevant Information from Human Beings
链接: https://arxiv.org/abs/2611.13015
作者: Yuhan Tang,Ke Fan,Xinkai Zou,Baichuan Mo,Kangxin Cui,Ao Qu,Ou Zheng,Jiangbo Yu,Haris N. Koutsopoulos,Jinhua Zhao,Yuan Zhou
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:LLM agents are increasingly asked to make plans for people, such as scheduling drivers or assigning nurses. However, people’s requests are often incomplete, and the supporting information for decision-making may be outdated, requiring agents to ask for clarification. Existing benchmarks typically provide fully specified problems or rely on uncalibrated LLM-simulated users, leaving this ability insufficiently tested. We introduce Ask2Decide, a benchmark of 43 planning tasks that evaluates whether agents ask for missing information that matters for their decisions. An operator holds facts absent from the agent’s request and records, and optimization solvers determine which facts change the optimal plan. We also include similarly worded facts that leave the optimal plan unchanged to test whether agents distinguish useful information from irrelevant details. We collect 79 interaction sessions from 19 participants and find that, after an incorrect plan, human replies omit the correcting fact far more often than replies from an uncalibrated simulated user (43% versus 7%). Using these interactions, we calibrate a simulated operator that more closely matches human reply patterns. Evaluating four frontier LLMs, we find that their initial plans are infeasible on 58-98% of tasks when information is missing, compared with 5-28% when all facts are provided upfront. Agents uncover facts that change the optimal plan only slightly more often than facts that leave it unchanged (39-52% versus 32-41%). These results show that obtaining decision-relevant information through interaction remains a major bottleneck for LLM planning. This is ongoing work and will be updated. The date of this version is shown on the first page.
[AI-111] Compile-Gated Lean Proof Refactoring with Enterprise Agent Practices: A Model per Step under a US3 Cap Followed by Programmatic Rewrite Rules NEURIPS2026
链接: https://arxiv.org/abs/2611.13011
作者: Bobber Cheng
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: 20 pages. Technical report of team Vexorium-Agent for the closed track of the Lean Refactor Arena, a competition at the NeurIPS 2026 Workshop on AI for Verifiable Coding
Abstract:Our closed-track entry (Arena username Vexorium-Agent) scored 86.31 points on the final board (L = 77.07, H = 81.86, Z = 100) over the 60 final problems, against 33.33 for the reference proofs, 8th at the deadline (top: 92.57), for US\ 166.33 at list prices with every call counted (per problem: mean US\ 2.772, at most US\ 2.874; US\ 3 cap). Three practices carry the entry. First, each paid stage runs the model that measured best per dollar for it among the 9 we tested in our own harness: GPT-6.1 Sol the early sessions, GPT-6.1 Terra the later ones and the tail at 70% of Sol’s cost per turn, and Gemini 4 Argon the review and repair; Sol also runs the style rewrite. Second, what our development and research runs learned ships as compiler-checked programmatic rewrite rules rather than prompt text (+0.93 points from rules fixed before the release; +0.26 and +0.30, in sample, from passes built or switched on after the release). Third, every call is recorded and reviewed before each change (all 9 board scores came within -0.063 to +0.015 points of our meter’s prediction). The harness, gate and many of these rules also serve our open-track entry. We report the full bill and the spread of three runs.
[AI-112] An Open Implementation Flow for the Xilinx Spartan-3E and Spartan-6 that Runs in the Browser Checked Byte for Byte Against ISE
链接: https://arxiv.org/abs/2611.13008
作者: Pedro Malo
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE); Systems and Control (eess.SY)
备注:
Abstract:The author describes an open implementation flow, from HDL to a programmable bitstream, for the Xilinx Spartan-3E (XC3S100E to XC3S1200E) and Spartan-6 (XC6SLX16, XC6SLX45), devices that only the discontinued ISE 14.7 supports. The flow runs unchanged in a web browser or in this http URL: Yosys and nextpnr compiled to WebAssembly, with nextpnr’s Himbaechel framework given Spartan-3E and Spartan-6 architectures generated from Silinx’s own device descriptions, bit database and timing model, and Silinx’s own bitstream writer. The bit database was built by black-box observation of ISE’s documented XDL and bitgen tools, using codeword-batched experiments; the router is given only switches whose bits were measured, and the writer refuses a feature it has not measured. ISE is the oracle: for the same placed and routed design, the bitstream is byte-identical to the one bitgen writes and ISE’s DRC is clean. Placement and routing were first done by an in-house annealer and PathFinder router, which nextpnr replaced once it matched them. On 67 open-source designs for the XC3S1200E, compared with ISE at the same optimisation goal (45 designs built by both), the flow uses 15% fewer slices and has 14% shorter critical paths at goal Area, 21% fewer slices and 8% shorter paths at Speed, and 20% fewer slices but 12% longer paths at Fast (geometric means). XST cannot read 15 of the designs. The flow is published as the npm package silinx-fpga (Apache-2.0).
[AI-113] Just AI Governance and the Forbidden-Boundary Principle
链接: https://arxiv.org/abs/2611.13007
作者: Na Young Ahn
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: submitted in IEEE Access, Nov. 7, 2026
Abstract:Artificial intelligence (AI) increasingly shapes consequential decisions, yet predictive accuracy is not a sufficient justification for delegated authority. This conceptual study develops an integrated framework for assessing just AI across normative, data, model, and governance layers. Drawing on plural theories of justice, sociotechnical fairness research, and publicly available governance standards, it maps six justice requirements to observable evidence, decision rights, and remedies. It proposes a two-stage evaluation rule: exclude uses that fail independently justified minimum rights constraints, then compare eligible alternatives across multiple performance and governance criteria. As an explicitly limited normative analogy, Genesis chapter 2 motivates a Forbidden-Boundary Principle (FBP), under which an AI agent must not execute externally consequential actions outside pre-authorized boundaries. The resulting First-Principle Safe-Stop Protocol (FPSSP) comprises an independently governed policy monitor, a mandatory execution gate, a restricted or alternative safe-state controller, immutable-oriented audit trails, and accountable human authorization for recovery. We distinguish this normative and traceability contribution from existing Simplex and runtime assurance architectures; we do not claim a novel safety switch or formal proof of universal ethical compliance. A policy schema, threat model, test matrix, outcome definitions, and a prospective comparison design make the proposal falsifiable. No FPSSP implementation, experimental dataset, numerical improvement, or verified latency bound is reported. The framework supports future evaluation rather than constituting evidence of operational safety.
[AI-114] he Shape of a Program: Path Signatures for Trace-to-Program Induction
链接: https://arxiv.org/abs/2611.13005
作者: Mohamed Ghanem,Bernd Finkbeiner
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Synthesizing programs from execution traces is a fundamental challenge in algorithmic interpretability and the reverse engineering of complex systems. We bring a rough-path-theoretic perspective to program induction, treating execution traces as high-dimensional rough paths and encoding them with the path signature transform, a tool from stochastic analysis whose expectation provably characterizes the distribution of traces a program induces. On three program-induction domains, Karel, List, and RASP, our log-signature trace encoder outperforms LSTM, Transformer, and Fourier baselines in almost every setting, with its largest margins when training data is scarce. We further observe that the trace-to-program pipeline is naturally contractive: it often produces valid programs that are shorter than the ground truth. We turn this into a bootstrapping procedure that exposes redundancy in the ground-truth supervision without any external oracle, which we demonstrate on redundancy-injected versions of our datasets. Finally, the log-signature encoder induces a markedly more interpretable latent geometry than the baselines, in which the nearest neighbors of every state lie on its own execution trace.
[AI-115] Specializing Meta Multi-Objectivization as Baseline for Software Engineering Optimization
链接: https://arxiv.org/abs/2611.12991
作者: Chengjie Chuan,Pengzhou Chen,Tao Chen
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted by IEEE Transactions on Software Engineering (TSE)
Abstract:Given the discrete nature of many software engineering (SE) optimization problems, trapping at local optima remains a crucial issue in the optimizer designs. Meta Multi-Objectivization (MMO) is an optimization model for overcoming local optima by introducing an extra, auxiliary objective. Yet, MMO is originally designed only for configuration tuning, leaving its suitability for other SE problems in doubt, particularly regarding the choice of the auxiliary objective. In this paper, we study the possibility and resolution for specializing MMO as a general baseline across a range of SE optimization problems, making it a fundamental counterpart for future SE optimizers that seek to mitigate the issue of local optima. We do so by providing both theoretical remit and empirical evidence: we analytically show how the choice of the auxiliary objective is crucial while demonstrating the effectiveness of MMO under nine SE optimization problems comprising 212 cases when the right one can be chosen, i.e., MMO is able to outperform the other state-of-the-art and general-purpose optimizers in 79% and 94% of the cases, respectively. Drawing on the theoretical understanding and empirical findings, we then propose L2RAC, a tool that automatically recommends the best auxiliary objective choice for a given SE problem/case, hence helping to specialize MMO therein. Through extensive experiments, we reveal that, in general, L2RAC can more accurately rank the choice of auxiliary objective for MMO under a case than other guesses by up to 60%, saving as much as 99% of the cost that would otherwise be required to conduct trial-and-error when using MMO.
[AI-116] hird Eye: Beyond Immediate Gains in Recursive Self-Improvement
链接: https://arxiv.org/abs/2611.12988
作者: Sujato Dutta,Padala Indira Sneha,Kypu Mahitha,Shashank Vanga,Neeraj Choudhary
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Does a useful self-update remain useful after the model updates again? Third Eye separates immediate performance from the outcome of a further native update. We evaluate five 3-8B backbones on math and code: 64 fresh runs, three candidates per run, and three continuations per candidate. Selection and final scoring use disjoint items. In the balanced Qwen3-4B/Llama-3.2-3B comparison, immediate-selected utility is near zero, but continuation reduces it by 1.78 percentage points (simultaneous 95% interval [0.68, 2.88]). Candidate-averaged immediate utility is already negative: selection partly accounts for the apparently neutral first step. Continuation also lowers utility for SmolLM3-3B and Llama-3.1-8B; Qwen3-8B remains inconclusive. Target and out-of-distribution losses dominate retention changes. Matched recipe and recipient-donor controls identify neither a reliable mitigation nor a single mechanism. The results support evaluating persistent self-updates as adaptation transitions, with independent scoring and a no-update baseline, rather than interpreting immediate performance as their full value.
[AI-117] ICLayout-Bench: Benchmarking LLM Agents for Custom Circuit Layout Design
链接: https://arxiv.org/abs/2611.12982
作者: Zhangmai Li,Tianrui Ma,Zheng Weng,Mo Zou,Zidong Du,Yifan Hao,Yongwei Zhao,Wei Li,Jing Li,Qi Guo,Yunji Chen
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: 12 pages, 7 figures, 3 tables, including appendices. Project page: this https URL Code: this https URL Dataset: this https URL
Abstract:Large language model (LLM) agents are being explored for circuit design, but their ability to produce high-quality custom layouts has not been extensively evaluated. ICLayout-Bench is a benchmark in which agents use electronic design automation (EDA) tools to construct and optimize GDSII layouts from transistor-level circuit netlists. These layouts are evaluated using physical verification, parasitic extraction, and post-layout simulation. Layouts that pass the physical and functional checks are scored on electrical performance and area. The benchmark includes 165 tasks across 13 process design kits (PDKs), covering technology nodes from 3 nm to 180 nm. The tasks span analog, mixed-signal, and digital circuits in open-source and commercial EDA flows. The experiments evaluate 22 configurations spanning seven models on 21 public and 19 private tasks. Mean layout scores range from 4.22 to 80.26 on public tasks and from 1.43 to 67.01 on private tasks, on a 100-point scale. Higher reasoning effort does not consistently improve mean scores, and configurations with comparable scores can incur substantially different model costs.
[AI-118] MADSNet: A Multi-Scale Axial-Attentive Dense Spiking Network for Spike Camera Image Reconstruction
链接: https://arxiv.org/abs/2611.12980
作者: Sirui Li,Rui Zhao,Siqi Cai,Haizhou Li,Tiejun Huang,Yajing Zheng
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Spike cameras encode dynamic scenes into high-temporal-resolution binary spike streams for efficient high-speed vision. Although spiking neural networks (SNNs) can process such signals with sparse and accumulate-only computation, existing SNN-based reconstruction methods are typically shallow and still lag behind ANN-based models in reconstruction fidelity. This paper proposes MADSNet, a Multi-scale Axial-attentive Dense Spiking Network for spike camera image reconstruction. MADSNet adopts a hierarchical spiking encoder–decoder, where a multi-scale spike stem extracts early features, axial spike attention captures long-range dependencies, a dense spiking bottleneck enhances feature reuse, and a spike-to-continuous decoder reconstructs continuous intensity images from spike-domain representations. Experiments on gray-scale and color spike reconstruction show that MADSNet outperforms SSIR by 4.3 dB PSNR and Spk2ImgNet by 1.94 dB on SREDS. Compared with Spk2ImgNet, it achieves about 15 \times lower latency and uses only about 1/40 of the estimated energy, demonstrating that carefully designed SNN architectures can improve reconstruction quality while preserving computational efficiency.
[AI-119] rustMod-SM: A Multi-Axis Benchmark for Evaluating Trustworthiness of LLM s in Social Media Content Moderation
链接: https://arxiv.org/abs/2611.12970
作者: Aizan Zafar,Sreekantam Sai Venkata,Gathik Jindal,Ritu Raj Sharma,Zishan Ahmad,Tulika Saha,Srinath Srinivasa
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) show strong potential for social media content moderation, but their trustworthiness beyond overall accuracy remains underexplored. We introduce TrustMod-SM, a benchmark of roughly 29,000 instances across eight datasets, to evaluate LLM moderators on five dimensions: trustfulness, fairness, safety, robustness, and context integrity. Evaluating 16 open-weight models (0.5B-284B parameters, spanning text-only and vision-language architectures) and one closed frontier model, we find persistent trustworthiness gaps. Higher detection accuracy often coincides with greater demographic disparity, and models frequently comply with concealment or exaggeration prompts, assign high confidence to wrong predictions, rarely escalate ambiguous cases, and struggle to separate hate speech from counter-speech or reclaimed language (especially below 8B parameters). Scaling does not uniformly resolve these issues, and no single model excels across all five dimensions.
[AI-120] BuySideFlow: A Point-in-Time Benchmark for LLM Agents in Investment Research
链接: https://arxiv.org/abs/2611.12966
作者: Dong Bo,Fanjin Zhang,Ke Cheng,Xinxin Zhao,Xiaowei Su,Yang Wang,Jinghang Xu,Cuiping Li,Hong Chen,Jing Zhang
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) can generate SQL and SQL–Python workflows for investment research, but ensuring point-in-time (PIT) validity remains challenging. Agents must enforce disclosure cutoffs and select the appropriate historical states and record versions throughout data retrieval and financial analysis. We introduce \textscBuySideFlow, a benchmark of 408 practitioner-derived Chinese-language tasks spanning equities, funds, fixed income, and macroeconomics over a fixed snapshot from a licensed production financial database. We provide executable references and a taxonomy of temporal errors, pairing result checks with expert audits of executed workflows. We evaluate nine agent configurations on all tasks, with result accuracy ranging from 16.2% to 36.8%. An audit of 900 runs on 100 shared tasks confirms PIT violations in 193 of the 380 runs that pass result checks (50.8%), showing that output agreement alone does not establish temporal validity.
[AI-121] RBLA: Efficient Rank-Specific LoRA Adapter Extraction for Federated Fine-Tuning with Heterogeneous Clients
链接: https://arxiv.org/abs/2611.12961
作者: Shuaijun Chen,Omid Tavallaie,Xin Chen,Wentao Gao,Nguyen Tran,Albert Y. Zomaya
类目: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Federated fine-tuning with low-rank adaptation (LoRA) reduces communication and computational overhead by training and exchanging compact trainable LoRA factors. Clients may use different LoRA ranks to meet adapter communication budgets and accommodate different local adaptation needs. The server must process these differences during aggregation and construct adapters at each client’s rank for local training. We distinguish pre-aggregation alignment, which handles factor dimensions and scaling conventions before aggregation and rank-specific LoRA adapter extraction, which constructs client-sized pairs from the aggregated global model. Two common approaches to rank-specific extraction are direct rank truncation and Singular Value Decomposition (SVD) of the dense update. Direct prefix truncation keeps the leading columns of one aggregated factor and the corresponding rows of the other. It is cheap but can increase reconstruction error. The dense SVD approach first forms the product of aggregated LoRA factors. It then decomposes this matrix and retains the leading singular components to construct adapters at the target rank. This gives the optimal rank-constrained approximation in Frobenius norm but incurs significant computational overhead. To address this trade-off, we propose RBLA+ as an extension of RBLA. It uses established compact QR/core-SVD decomposition to minimize Frobenius error for the extracted LoRA update without forming the dense update. It also accounts for client and server scaling conventions during upload and broadcast. Across vision and language tasks, RBLA+ improves accuracy over RBLA in multiple heterogeneous settings. In CPU benchmarks with synthetic adapters, RBLA+ has lower extraction time than the dense partial-SVD baseline. RBLA+ preserves each client’s prescribed adapter size and per-round communication payload.
[AI-122] IIT Patna at “ICPR 2026 TVRID Competition on Privacy-Preserving Person Re-Identification from Top-View RGB-Depth Camera”
链接: https://arxiv.org/abs/2611.12960
作者: Abhinav Kumar,Swagatam Pati,Advait Deshmukh,Aditya Mehrotra,Bibek Das,Kumari Priya,Utsav Kumar Nareti,Saba Akram,Chandranath Adak
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Privacy-aware person re-identification (Re-ID) from top-view cameras replaces frontal appearance with anthropometric shape, while depth sensing further reduces color-based identity cues. The ICPR 2026 TVRID competition evaluates this setting using 86 identities captured by four synchronized Intel RealSense D455 cameras with RGB and Depth streams across flat, ascent, descent, and oblique viewpoints. Performance is measured using mAP and CMC@1 across within-camera in/out, ascent-descent, and flat-oblique scenarios. The competition had 147 registered participants and 33 final submissions. Three teams from IIT Patna participated: ‘abhinavkumar03’ and ‘sjkdgfhl_’ ranked 5th and 7th among 31 ranked participants, and ‘vivek katuri’ reached 77.67% CMC@1 / 85.20% mAP on the RGB track. This paper presents the methods of the two top-10 teams. Methodology 1 (abhinavkumar03) achieves 93.51% mAP and 90.42% CMC@1 on Depth, ranking 4th of 28, while Methodology 2 (sjkdgfhl_) reaches 99.63%/99.35% on RGB and 86.26%/80.34% on Depth. Both substantially outperform the 57.19% track-average Depth mAP, with Methodology 1 yielding a 7.3-point gain over Methodology 2. These results demonstrate the importance of depth representation and supervision for privacy-preserving Re-ID.
[AI-123] Monitoring One Channel Is Not Communication Control: Causal Evidence for Adaptive Reallocation of Joint Message–Route Codes
链接: https://arxiv.org/abs/2611.12959
作者: Liu Hung Ming(PARRAWA AI, cyril.liu@gmail.com, equal contribution),Tian Xia(PAII, TianXia0209@gmail.com, equal contribution)
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 23 pages total: 8-page main paper plus 15-page supplementary material; 1 main-paper figure and 3 main-paper tables. Equal contribution. Code available at: this https URL
Abstract:Monitoring a visible channel can change how learned agents communicate without removing why communication is useful. We test this distinction in an end-to-end game where a sender and receiver learn a discrete protocol and route policy without communication supervision. In two independent 20-seed batches, 20/20 and 19/20 protocols form. Online partial coverage increases shadow-route share over no monitoring by .701 [.574,.826] and .575 [.412,.737], while task accuracy remains .905/.913 and signed frozen message contribution remains .592/.563. Complete coverage removes most selective migration without removing communication. Frozen interventions, including a fresh pre-outcome confirmation, show that route is semantic: joint message–route shuffling changes .668 of receiver predictions and route alone retains .388 accuracy versus .25 chance. A corrected explicit-silence cost makes 18/20 policies fully silent and lowers mean sending to .050, but its strict complete-suppression gate fails because two seeds retain communication. Failed semantic-evasion and operational gates further bound the result. The evidence supports adaptive reallocation of a joint message–route code under partial monitoring, not open-world route discovery, emergent encryption, or a universal defense.
[AI-124] Embodied Science Agents : Making Embodied Experimental Design Learnable
链接: https://arxiv.org/abs/2611.12958
作者: Yihang Che,Zhongwei Yu,Yang Li,Xingyu Lu,Hongxia Yu,Jun Wang
类目: Artificial Intelligence (cs.AI)
备注: Accepted by DAI 2026
Abstract:Recent advances in large models have accelerated AI for Science toward agentic science, where AI agents autonomously conduct scientific discovery. Realising this vision requires closing the loop between computational reasoning and physical experimentation, positioning self-driving laboratories (SDLs) as critical enablers. However, existing SDLs exhibit a fundamental paradox: foundation model integration has increased linguistic sophistication while severing physical grounding. By treating models as disembodied planners that output symbolic workflows for separate robotic systems, current architectures inherently block the development of embodied experimental intuition. We argue that overcoming this paradox requires embedding \emphlearnability along three interdependent axes: cognitive learnability (learning what to investigate), experimental learnability (learning where to search), and embodied learnability (learning how to act). Building on this view, we introduce the Embodied Science Agent (ESA), which unifies digital intelligence with physical embodiment through three synergistic components: a Digital Agent that provides cognitive and experimental learnability via memory-based reasoning and Bayesian optimisation, an Embodied Agent that provides embodied learnability via generalisable instrument control, and an Embodied Unified Interface for seamless coordination. We validated ESA in autonomous wastewater treatment experiments, navigating over 200 million possible conditions. Through 15 continuous rounds, ESA raised the final solution transmittance from 82% to 99.7%, corresponding to a 98.5% reduction in residual dye concentration and near-complete decolourisation, while its learned visuomotor policies generalised across varying instrument placements, a promising step toward next-generation SDLs for autonomous scientific discovery.
[AI-125] ADIndex: Layer-Adaptive Sparse Attention Indexing via Group-Level Distillation
链接: https://arxiv.org/abs/2611.12956
作者: Jin-Fei Qi,Yiduo Li,Jiang-Xin Shi,Kan Liu,Tao Lan,Hanlin Tang,Yu-Feng Li
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages,13 figures
Abstract:DeepSeek Sparse Attention (DSA) reduces the computational cost of attention through token-level selection. However, its indexer still scores the entire causal prefix for each query, making indexing a dominant source of latency in long-context inference. Notably, the indexer alone can account for more than 50% of total prefill GPU time at long context lengths. To address this bottleneck, we propose ADIndex, an Adaptive and Distillation-driven Indexing method for token-level sparse attention. ADIndex integrates three key components: (i) a layer-wise calibration strategy that adaptively accommodates cross-query retrieval overlap, motivated by our observation of pronounced inter-layer heterogeneity; (ii) a group-level distillation scheme that leverages this heterogeneity to efficiently train adaptive layer-wise group indexers, capturing salient retrieval signals from individual queries; and (iii) a low-latency inference strategy that reduces repeated full-prefix scans through token bank sharing within query groups while retaining query-specific refinement. Experiments on DeepSeek-V3.2 and GLM-5.1 demonstrate that ADIndex preserves near-lossless task performance on LongBench, LongBench v2, and RULER while delivering substantial efficiency gains over standard DSA, including prefill and decoding kernel speedups of up to 6.22x and 3.40x, respectively, at a context length of 256K across the layer-wise reuse configurations, as well as an end-to-end serving speedup of 1.54x at a context length of 128K.
[AI-126] Predictive Surprise as Self-Grounding Concept Bottleneck for Interpretable Time Series NEURIPS2026
链接: https://arxiv.org/abs/2611.12955
作者: Sachith Abeywickrama,Emadeldeen Eldele,Min Wu,Xiaoli Li,Chau Yuen
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Machine Learning (stat.ML)
备注: Accepted at NeurIPS 2026, Main Track, Poster
Abstract:Time-series classifiers deployed in risk-sensitive domains must be (1) accurate, (2) interpretable, and (3) able to abstain when evidence is weak. Existing methods do not deliver all 3 at once, and the obstacle is structural, rather than logistical. Meaningful temporal patterns cannot be defined without knowing segment boundaries, yet meaningful boundaries cannot be placed without knowing which patterns to expect. Existing approaches break this circularity by using fixed windows or expert-annotated vocabularies, thereby sacrificing at least one of the 3 properties. We introduce ConceptTime, which closes the loop through a single self-supervised signal. A frozen probabilistic forecaster predicts a distribution over the next observation at every timestep of the sequence. We call the mismatch between its prediction and the realized signal the predictive surprise. This signal both locates segment boundaries and characterizes each resulting segment through the forecaster. Segments are thus grounded by how they behaved relative to expectation, not by what they look like. These summaries cluster into a vocabulary of dynamical regimes that we call concepts. A lightweight head classifies from the concept sequence, and the distance to the nearest concept yields reliability by design. Across 17 public benchmarks, ConceptTime is competitive with state-of-the-art black-box accuracy, beats every interpretable baseline, and retains near-98% accuracy on Epilepsy with 1% of labels. It further remains competitive with softmax, prototype-distance, and SHAP/TimeX/TimeX++ on OOD detection and deletion faithfulness, without a single annotation, language model call, or domain expert. Code is available at: this https URL.
[AI-127] Physics2Weight: Direct Neural Network Architecture and Weight Generation from Physical Laws Using Large Language Models
链接: https://arxiv.org/abs/2611.12954
作者: Shogo Sato,Tomoyasu Shimada,Kazuhiko Murasaki,Takashi Sano
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in large language models (LLMs) have enabled weight-space programming (WSP), which directly generates neural network weights without gradient-based optimization. However, extending WSP to physical problems remains challenging, particularly in achieving both solution accuracy and physical consistency. In this paper, we propose Physics2Weight (PtW), a framework that extends WSP to solving partial differential equations by directly generating neural network weights and editing architectures based on governing equations and initial and boundary conditions. PtW introduces physics-constrained prompting and physics-guided architecture editing, combined with iterative weight refinement from WSP, to improve solution accuracy and physical consistency without gradient-based optimization. Experiments on four PDEs, namely the heat, advection, Burgers’, and Allen–Cahn equations, demonstrate lower solution errors than conventional physics-informed neural networks, while revealing limitations in satisfying the governing equations for nonlinear PDEs.
[AI-128] Agent Architecture Cost-Success Trade-offs: A Controlled Evaluation across Enterprise-Style SOPs
链接: https://arxiv.org/abs/2611.12953
作者: Subhrangshu Nandi,Hitesh Arora,Yunfei Bai,Udita Patel,Chirag Shah,Hannah Kwon,Wei Zhang,Weian Chen,Chloe Ruoqi Li,Alberto Sconfienza,Ayush Goyal,Yasmine Lee,Ming Xiong,Prathap Ramachandra
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Enterprises increasingly use large language model agents to execute standard operating procedures (SOPs): long, branching, tool-intensive workflows. Yet there is little controlled evidence for choosing among competing agentic architectures. Holding the model, tasks, and tools fixed and varying only the architecture, we compare four designs (a function-calling single agent, a skill-augmented agent, centralized multi-agent orchestration, and a triage swarm; eight arms) across 13 enterprise SOPs, and introduce a skill-definition quality rubric to build matched high and low-quality skill corpora. Three takeaways emerge. (1)~\textbfThere is no universal best architecture; the right choice is SOP-dependent. On a given SOP, task success ranges from under 10% on the hardest to near 100% on the easiest; no architecture is statistically separable in aggregate, and added structure only plausibly pays off on complex, branching SOPs (a directional, not yet significant, trend). (2)~\textbfAdded architecture cost must be justified by a task-success gain. Every coordinated arm (multi-agent or skill-augmented) spends 1.4 – 4.7\times the tokens and 2.6 – 5.8\times the latency of the single agent for gains that are usually small and rarely significant, making the single agent the efficient default; high-quality skills do lift success (to 73.1% from 67.4% ) while degraded skills fall below it. (3)~\textbfImplementation quality outweighs the single- versus multi-agent choice. A plausible multi-agent implementation loses to the single agent, yet a targeted redesign of it becomes the only arm to significantly beat the baseline ( 75.6% vs.\ 67.4% ).
[AI-129] SetPrint: Black-Box LLM Fingerprinting via Setwise Correspondence of Response Changes
链接: https://arxiv.org/abs/2611.12950
作者: Jinmo Han,Ju Yeon Kang,Jimin Hong,Sunghwan Ahn,Yonghyeon Jun,Nam Soo Kim
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Black-box LLM fingerprinting aims to determine whether a target model was derived from a source model when only the output responses to input queries are observable. One way to reveal this relationship is to perturb each query and observe the resulting responses of both models, yielding two corresponding sets of response variations. These sets provide two complementary sources of information: the relations among response changes within each set, and the correspondence of response changes between the two sets. Existing approaches, however, have not fully exploited both. We introduce \textbfSetPrint, a training-free method that jointly captures both within-set relations and between-set correspondence. SetPrint expresses each response change relative to the mean change of its own set, and then compares changes from the two models that were induced by the same perturbation. On LeaFBench, SetPrint consistently outperforms existing training-free black-box fingerprinting methods. Further analyses attribute these gains to both within-set relations and between-set correspondence.
[AI-130] When Proofs Are Abundant What Is a Paper For?
链接: https://arxiv.org/abs/2611.12943
作者: Charles-Albert Lehalle
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI)
备注:
Abstract:AI systems now produce formally verified proofs of celebrated open problems, and the cost of obtaining a correct proof is falling toward zero. If correctness becomes cheap, what is left for a research paper to communicate? I argue that a paper is not exhausted by a verified statement. It is a narrative exposed to the criticism of a community, made of an intension (the author’s choice of problem and perspective), a scientific chain of thought (the reasoning steps leading to the result), and the arguments supporting each step. Most published mathematics is judged on these two elements rather than on correctness alone, and machine-generated formal scripts convey neither. Borrowing from logic and type theory, I formalise the first as a Derivation-Level Intension and the second as a Directional Extension: a decomposition of a proof into cut-points whose axiomatic footprints reveal its weak links, where the result could be strengthened, and its strong links, which can be transported to other theories. Moreover, I examine five roles AI can play in the research process, including bibliographical search and critical judgment, and show that none of them removes the need to declare these two elements. I mention ways these two concepts could be useful for Journals and for training of young mathematicians in a AI era. If proofs become abundant, the scarce resource is not correctness but a community’s shared understanding of why a result matters and how it could be pushed further.
[AI-131] abular Foundation Models vs Tuned Gradient Boosting for Telecom Churn: A Profit- and Calibration-Aware Evaluation
链接: https://arxiv.org/abs/2611.12942
作者: Ismail Hossain Polas
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 2 figures, 14 tables. Code, per-split predictions and reproducibility audit: this https URL (archived at this https URL )
Abstract:Tabular foundation models (TFMs) predict from a labelled context in a single forward pass. A recent benchmark reported that they decisively outperform tree ensembles for customer churn, but it compared them with untuned gradient-boosted trees (GBDTs) and scored only ranking metrics, while a retention campaign is judged in money. We re-evaluate TabPFN-2.5, TabPFN-3.5, TabPFN-3.5-Fast and TabICL v2 against Optuna-tuned GBDTs on five public telecom churn datasets over 10 seeded splits. We separate three questions: whether a model ranks churners well, whether that ranking earns more expected maximum profit (EMP), and whether its probabilities can drive a threshold policy. With a split-aware test and Holm correction over 20 pre-declared comparisons, TabPFN-3.5 earns significantly more EMP than the best tuned GBDT on Cell2Cell (1.9%) and Iranian (7.6%), and these two gains also hold against each of the three tuned GBDTs separately. On Orange it is ahead in all 10 splits (14.3%), but the gain is not significant after correction (Holm p = 0.155). The splits resample the same datasets, so these results do not stand for independent operators. TabICL v2, the earlier benchmark’s leader, beats the best tuned GBDT only on Iranian, not all three of them, and underpredicts Orange churn by 2.1 points on a 7.3% base rate. On the datasets and splits where we tested them, a substantially larger tuning budget and a duplicate-aware split leave the main conclusions unchanged. Retuning the baseline cuts the earlier headline gap from 6.1 to 4.0 PR-AUC points without closing it. The licence of the TabPFN-3.5 weights we used (License v1.0), not scoring cost, is what stands between its gains and production use.
[AI-132] AI Governance Should Prioritize Control and Knowledge Boundaries Over Limiting Intelligence NEURIPS2026
链接: https://arxiv.org/abs/2611.12941
作者: Hendrik Luuk
类目: Artificial Intelligence (cs.AI)
备注: Accepted at the 40th Conference on Neural Information Processing Systems (NeurIPS 2026), Position Paper Track
Abstract:The rapid proliferation of autonomously acting AI agents makes the governance of agentic systems urgent. We propose the competence circuit, summarized as IKC + KC + C, that decomposes an agent’s real-world impact into three pathways: intelligence-mediated discovery, procedural know-how, and direct control. Using the canonical fast-takeoff debate as a stress test, we formalize the conditions for intelligence-mediated fast takeoff as a conjunction jointly sufficient for fast catastrophic supremacy, showing that most rely on rapid knowledge acquisition or control attainment rather than intelligence alone. We operationalize epistemic intelligence as hypothesis-selection efficiency and validate this framework via stochastic simulation on NK fitness landscapes across six scenarios spanning the conjunctive chain. An analytic bound supported by the simulations shows that intelligence-driven speedup is capped by the ratio of competing intelligence levels regardless of task difficulty. The simulations also expose a fast-takeoff dilemma: a large intelligence-driven speedup multiplier materializes only when the hypothesis space is noisy, but the absolute timescale remains constrained by the time required for capability-critical tests, so a large multiplier need not imply rapid progress. Simulations also indicate that institutional chokepoints disproportionately slow down superintelligent agents when mandatory authorization delays are large relative to the negligible time these agents spend on unproductive search. We argue that the dominant safety levers are restricting control and enforcing knowledge boundaries rather than limiting intelligence alone, and this applies to agentic systems at any capability level. Purely digital domains weaken these bottlenecks, strengthening the case for architectures that intentionally “put physics in the loop.”
[AI-133] Reinforcement Learning based control of Cancer Chemotherapy treatments
链接: https://arxiv.org/abs/2611.12940
作者: Jayakody Kankanamalage Chamani Shiranthika
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Masters thesis at the National Taipei University. Received the IEEE Taipei Section, Best Masters thesis award in 2021
Abstract:Reinforcement Learning has achieved tremendous success in recent years. Extending its scope from the traditional complex games such as Atari, Go, Pokeman, and Chess, reinforcement learning has caught researchers’ attention on modelling real-life scenarios. Personalized medicine and the dynamic treatment regimen are two such preliminary research fields; researchers are focusing their mindsets currently. Estimating the optimal dynamic treatment regimens from the observational real clinical data is being popularized among the researchers, widening its scope towards more advanced versions of reinforcement learning algorithms. This research is about a research study that focused on reinforcement learning techniques that have been approached in the context of cancer chemotherapy treatments. Primarily this thesis addresses the result of the study by means of the state-of-the-art on reinforcement learning techniques used in generating the optimal policy for sequential treatments, including the existing online reinforcement learning approaches. Thereby a simulation for cancer chemotherapy via traditional Q-learning would be presented. Secondly, the study will extend towards using offline reinforcement learning in the optimal policy generation. Thus, the proposed solution follows a data-driven approach, which works on the real human colon cancer clinical data. The novelty of the proposed solution resides in the implementation of an offline reinforcement learning approach by means of a model-based architecture using the conservative Q-learning and the model-based offline policy optimization algorithm. As per the experimental results and the evaluations, it was revealed that the proposed Chemo RL model caters well the problem of cancer chemotherapy treatment scheduling and serves as a supportive model for the junior oncologists in deciding their treatment decisions. Comments: Masters thesis at the National Taipei University. Received the IEEE Taipei Section, Best Masters thesis award in 2021 Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2611.12940 [cs.AI] (or arXiv:2611.12940v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.12940 Focus to learn more arXiv-issued DOI via DataCite
[AI-134] Graph Visualizations in Prompts for Knowledge Graph Question Answering AACL
链接: https://arxiv.org/abs/2611.12938
作者: Luiz do Valle Miranda,Andreas L. Opdahl,Grzegorz J. Nalepa
类目: Artificial Intelligence (cs.AI)
备注: Accepted at AACL-IJCNLP conference
Abstract:We study when and how multimodal input improves knowledge graph question answering (KGQA) in large vision–language models. Using a controlled evaluation over 1,500 WebQSP and CWQ questions and six VLMs within a fixed KGQA pipeline, we compare text-only prompting against text-plus-graph visualization at the per-question level and analyze performance changes as a function of reasoning complexity, model size, and image layout. Adding a graph image yields an average gain of 1.85 F1 points, driven by strong precision improvements on low-complexity questions (+4.65 F1). Mixed-effects and regression analyses show that these multimodal gains do not depend strongly on model scale, but are highly sensitive to graph layout: image-only settings sharply degrade performance, hierarchical Graphviz layouts outperform less organized renderings, and decomposing large subgraphs into separated paths yields the largest improvements. Taken together, the results indicate that visual prompting primarily aids KGQA by enhancing prediction precision in low-complexity contexts, provided that visual design respects cognitive and perceptual constraints. To optimize both accuracy and inference efficiency, we argue for adaptive visual prompting policies that selectively append diagrams to small, visually sparse subgraphs with well-structured layouts, avoiding unnecessary visual clutter and token overhead on complex queries. We conclude by relating these findings to a visual notation design theory, outlining how principled diagram encoding can guide future multimodal VLM prompting.
[AI-135] LandslideFM: A Foundation Model Towards Unified Landslide Perception and Understanding
链接: https://arxiv.org/abs/2611.12936
作者: Shanwen Wang,Sirui Wang,Xin Sun,Jungong Han
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Intelligent landslide detection is essential for disaster prevention. However, existing models primarily focus on isolated visual tasks, require extensive labeled data, and lack deep analytical and scene-understanding capabilities. To address these limitations, we propose LandslideFM, the first unified foundation model that connects visual perception tasks with contextual interpretation of landslides. Specifically, LandslideFM adapts to multiple visual tasks via a Source-Agreement Graduated Trust semi-supervised learning framework that leverages limited labeled data alongside abundant unlabeled data. Furthermore, predicted landslide regions provide explicit spatial guidance to a Vision-Language Model adapted through parameter-efficient fine-tuning, enabling comprehensive landslide reasoning. To support training and evaluation in this domain, we also present LandslideVL, the first benchmark for intelligent landslide analysis, combining visual imagery and newly introduced textual prompts. Comprehensive experiments across public datasets and LandslideVL demonstrate that LandslideFM achieves superior quantitative and qualitative performance. Most importantly, LandslideFM surpasses general-purpose VLMs in specialized landslide reasoning and scene analysis. Our code is released at this https URL.
[AI-136] rain on prefill monitor at decode: Using sparse autoencoders for topic monitoring
链接: https://arxiv.org/abs/2611.12935
作者: Rishikesh Mallagundla,Pranav Patel
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 22 pages
Abstract:Topic monitoring is the task of labelling the topic of each token in a model’s response during generation. Activation-based monitors that run at generation time have so far been trained to detect harmful content and are limited to two or three classes. We introduce TopicScope, a framework for training causal topic classifiers on SAE features that assign a topic to every token, so that the label can change partway through a response. We train three detector architectures on synthetic multi-topic conversations covering seven topics. Across Gemma-3 1B and 4B, monitors trained on features extracted from prefill hidden states were able to generalise to decoding. We also found that the classifiers are robust to perturbations, losing at most 5 macro-F1 points when off-topic asides are inserted, topics are removed or sections are reordered. Ablations conducted to understand the contribution of the SAE revealed that classifiers reaching baseline performance without it were several times larger. Our findings show that compact topic monitors can be trained inexpensively in prefill and deployed during generation. More broadly, our work shows where SAEs are useful beyond safety classification. Our code, dataset and checkpoints are available at this https URL and this https URL
[AI-137] Causal Time Series Modeling of Suprag lacial Lake Evolution in Greenland under Distribution Shift ICML
链接: https://arxiv.org/abs/2510.15265
作者: Emam Hossain,Muhammad Hasan Ferdous,Devon Dunmire,Aneesh Subramanian,Md Osman Gani
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted as full paper in ICMLA 2025 (Special Session 1: Deep Learning and Applications)
Abstract:Causal modeling offers a principled foundation for uncovering stable, invariant relationships in time-series data, thereby improving robustness and generalization under distribution shifts. Yet its potential is underutilized in spatiotemporal Earth observation, where models often depend on purely correlational features that fail to transfer across heterogeneous domains. We propose RIC-TSC, a regionally-informed causal time-series classification framework that embeds lag-aware causal discovery directly into sequence modeling, enabling both predictive accuracy and scientific interpretability. Using multi-modal satellite and reanalysis data-including Sentinel-1 microwave backscatter, Sentinel-2 and Landsat-8 optical reflectance, and CARRA meteorological variables-we leverage Joint PCMCI+ (J-PCMCI+) to identify region-specific and invariant predictors of supraglacial lake evolution in Greenland. Causal graphs are estimated globally and per basin, with validated predictors and their time lags supplied to lightweight classifiers. On a balanced benchmark of 1000 manually labeled lakes from two contrasting melt seasons (2018-2019), causal models achieve up to 12.59% higher accuracy than correlation-based baselines under out-of-distribution evaluation. These results show that causal discovery is not only a means of feature selection but also a pathway to generalizable and mechanistically grounded models of dynamic Earth surface processes.
[AI-138] wo Communities One Problem: Branch-and-Bound vs. A* for Resource-Constrained Project Scheduling
链接: https://arxiv.org/abs/2611.14426
作者: Izack Cohen,Dominik Kress,Lara Nehrke,Erwin Pesch,Shimrit Shtern
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注:
Abstract:Branch-and-Bound (BB) and A^* are popular exact search methods originating from Operations Research and Artificial Intelligence, respectively. Although the two methods share fundamental search principles, their different ways of exploring the search space can lead to substantially different computational performance. We investigate these differences using the Resource-Constrained Project Scheduling Problem (RCPSP) as an exemplary problem setting. We first establish the relationships between mixed-integer linear programming, state-action, and timed Petri-net representations. Since BB and A^* admit numerous algorithmic enhancements, we focus on relatively basic implementations. This allows us to examine the fundamental differences between the methods rather than compare the performance of state-of-the-art solvers. By using the same lower bound and best-first node selection, we isolate the effects of the search mechanisms, particularly the identification of equivalent states reached through different scheduling decision sequences. Experiments on 480 benchmark RCPSP instances show that graph-based A^* substantially outperforms the BB variants considered. With additional pruning rules, A^* proves optimality for all 480 instances, compared with 445 for best-first BB under the same time and memory limits. Depth-first BB uses less memory and can find feasible solutions even when the search does not finish within the time limit. Stronger linear programming relaxations reduce the search space but introduce considerable computational overhead. Our findings highlight the importance of duplicate-state detection and clarify the computational trade-offs between the search approaches. These insights can help researchers select an appropriate method and identify opportunities to incorporate effective mechanisms from one approach into the other.
[AI-139] Achieving Robust Channel Estimation Neural Networks on Doppler effects by Designed Training Data
链接: https://arxiv.org/abs/2611.14267
作者: Dianxin Luan,John Thompson
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Channel estimation is crucial in wireless communication systems. Many existing works train and test their models using the same or similar channel. However, practical channels are infinitely diverse and time-variant while data-driven algorithms often degrade on new and previously unseen channels. This prohibits the practical implementation of neural networks for channel estimation, particularly in high speed scenarios characterized by rapidly channel profile changes and very high Doppler shifts. Online training can fine-tune models to adapt to new channels, but requires high computation resource and incurs significant latency for physical-layer devices. Moreover, online training cannot exhaustively cover all possible channels because there are infinitely many possible channels. Therefore, we investigate the ability of neural networks to generalize to wireless channels. This paper proposes design criteria to generate synthetic training data, which guaranty that the resulting networks perform robustly across wireless channels, especially for very high Doppler shifts and different Doppler spectra. It ensures that offline-trained neural networks require no prior information or online training to precisely estimate the new channels. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of architecture. From simulation results, neural networks achieve a certain performance across wireless channels with different unseen power delay profiles (PDP) and Doppler shifts.
[AI-140] Bridging Graph and Pairwise Learning for Gene Regulatory Network Inference from scRNA-seq Data
链接: https://arxiv.org/abs/2611.13801
作者: Hang Li,Jiaxin Qi,Yan Cui,Yuhua Zheng,Jianqiang Huang
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI)
备注: Accepted to Bioinformatics
Abstract:Motivation: Gene regulatory networks (GRNs) capture the underlying interactions through which transcription factors (TFs) regulate genes. Based on gene expression data, existing GRN inference approaches generally fall into two categories: graph-based methods, which model the GRN as a whole graph, and pairwise-based methods, which decompose the GRN into individual TF-target gene pairs for modeling. However, each approach exhibits limitations that are precisely the strengths of its counterpart. Graph-based methods tend to overfit due to their reliance on a single training graph, whereas pairwise-based methods benefit from abundant TF-target training pairs. In contrast, pairwise-based methods overlook the global topological structure, which is essential for comprehensively capturing regulatory mechanisms. Results: To address these limitations, we propose scGPL, an end-to-end unified framework that jointly models global regulatory topology and local interactions. scGPL extracts macro-level features from the global regulatory graph and introduces a dynamic, importance-guided subgraph sampling mechanism to capture fine-grained local dependencies. By fusing global representations with subgraph-enhanced local features, our framework bridges the gap between the two paradigms, incorporating essential whole-graph topological dependencies while retaining the data abundance advantage of pairwise methods. Extensive experiments on seven benchmark datasets demonstrate that scGPL consistently achieves state-of-the-art performance, with up to 13.7% improvement over the strongest baseline, validating the effectiveness of our integrative design. Availability and implementation: The source code is maintained on GitHub (this https URL), and the datasets and versioned releases are archived at this https URL for reproducibility.
[AI-141] A Hardware-Trained Generative Quantum Eigensolver for Sample-Based Quantum Diagonalization of Organotin Hydroxides up to 70 Qubits
链接: https://arxiv.org/abs/2611.13755
作者: Kunal Kumar,Kimberlee Keithley,Toshiya Iwamori,Shu Kanno,Ikko Hamamura,Takao Kobayashi,Akira Koseki,Qi Gao,Naoki Yamamoto,Hiroshi Horii,Yuri Kobayashi,Borja Peropadre,Kouhei Nakaji,Toru Imai,Takayuki Osogami
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Chemical Physics (physics.chem-ph)
备注: 76 pages (26 pages main text, 50 pages appendices), 24 figures, 38 tables
Abstract:Quantum hardware can provide feedback for training circuit generators beyond the reach of exact classical simulation. We train generative quantum eigensolver (GQE) policies, transformers conditioned on molecular descriptors, to generate the state-preparation circuits for sample-based quantum diagonalization (SQD). Training runs first on a noiseless simulator and then with the IBM Heron processor ibm_kobe inside the loop, at 46 and 70 qubits ( 1.2 \times 10^8 shots for the four main training runs). A classical CCSD(T) reference energy of the training molecules enters only an admission gate and two auxiliary reward terms. During hardware training, a reward penalty prevents collapse to circuits that sample only the Hartree-Fock determinant. Policies trained on transition-metal complexes are applied without retraining to Sn(OH) _3 -Me and its hydrates, minimal models of organotin photoresists for extreme-ultraviolet lithography, from 32 to 70 qubits. After one round of hardware sampling, followed by classical ranking and recovery of the cached counts with the molecular Hamiltonian and no further QPU calls, 27 of 28 runs at 46-70 qubits are within 15 mHa of the density-matrix renormalization group (DMRG) reference (median 4.5 mHa), and in all eight runs with an untrained control the trained policy is more accurate. At 56-70 qubits the selected circuits use 34-72 times fewer two-qubit gates than the local unitary cluster Jastrow (LUCJ) circuit transpiled for the same device. These results demonstrate that circuit generators can be trained with quantum hardware at 70 qubits.
[AI-142] he Dose Makes the Poison: Exposure-Aware Evaluation of Generative Drug Design NEURIPS2026
链接: https://arxiv.org/abs/2611.13398
作者: Maria Franz,N. Benjamin Erichson
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 3 figures. Accepted at the AI4DD workshop (AI for Drug Discovery: Bridging the Translation Gap), NeurIPS 2026
Abstract:Generative models are increasingly used to propose molecules for drug discovery. They can produce far more candidates than can be experimentally tested, making computational selection essential. Most selection methods rely on predicted molecular properties, such as potency and toxicity, but overlook how these properties translate into drug exposure at a given dose. As a result, molecules that appear promising on paper may require impractical doses or raise safety concerns. We investigate this gap by using physiologically based pharmacokinetic (PBPK) simulations to re-evaluate molecules selected from three PARP1-targeted generative pipelines. Of 60 shortlisted molecules, seven fail to achieve sufficient target exposure, while 28% of the remaining candidates show predicted safety risks at their required dose. Moreover, 85% of candidates with an achievable dose are outperformed by molecules overlooked during static selection. These findings suggest that accounting for dose and exposure can substantially change which generated molecules are considered promising for drug development.
[AI-143] ProTEUS: A mutation-resolved world model for closed-loop protein evolution
链接: https://arxiv.org/abs/2611.13348
作者: Yikun Zhang,Minghao Guo,Guangxue Xu,Peter Yichen Chen,Wojciech Matusik,Wengong Jin,Jingjing Chai,Kaiyi Jiang
类目: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI)
备注: 64 pages, 5 main figures, 5 supplementary figures
Abstract:Protein engineering requires navigating rugged fitness landscapes, where small sequence changes can produce large functional differences, but experimental screening can only sample a small fraction of sequence space. Thus, a central challenge is to efficiently traverse the large combinatorial space of mutations in silico. We introduce ProTEUS, a mutation-resolved world model for closed-loop protein evolution. Pretrained on deep mutational scanning assays, ProTEUS learns a joint representation of sequence, evolutionary, and structural information using MutationFormer, a mutation-biased attention architecture. Variants are represented as states in this joint latent space, and mutations define actions whose functional consequences are predicted. To support exploration of many mutants, we developed a fast, physics-constrained all-atom predictor that utilizes explicit geometric constraint enforcement to achieve 10 fold increase in speed than AlphaFold3 with guaranteed physical validity. Lastly, ProTEUS refines its prediction and understanding of protein activity through closed loop experimental feedback to guide evolution toward a desired phenotype. ProTEUS surpasses established models in both zero-shot and supervised fine-tuning mutation prediction tasks. We experimentally demonstrate its utility in two closed-loop multi-objective engineering campaigns on aldehyde dehydrogenase and levansucrase, yielding variants with 10 fold higher activity than wild type. ProTEUS provides a world-model framework that connects virtual evaluation of mutational outcomes with experimental learning for efficient protein evolution.
[AI-144] MRFoundry: Knowledge-guided agent loops for end-to-end MRI research
链接: https://arxiv.org/abs/2611.13289
作者: Ke Wang,Atakan Topcu,Hossein Momeni,Molin Zhang
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI)
备注: 18 pages, including 6 pages of Supporting Information. Code: this https URL
Abstract:AI agents can write code, use scientific software, and revise computational workflows, opening new possibilities for MRI research. Effective assistance requires connecting acquisition physics, data conventions, specialized tools, and scientific validation. We present MRFoundry, an open-source, extensible package that connects MRI knowledge to agent actions and output checks. Project-local memory preserves decisions and evidence, while reviewed contributions allow researchers to improve shared guidance. Researcher-supervised examples demonstrate skill-guided sequence auditing and acquired-data reconstruction, connecting tool use, output checks, and documented decisions. Alongside MRFoundry, we introduce MRI Agent Bench to compare agents with and without the package across models and MRI task domains. Across 240 paired Core evaluations spanning 24 task types and ten models, all-checks-passed completion increased from 67.5% without MRFoundry to 78.3% with it. Across 134 Hard pairs, completion increased from 36.6% to 44.8%. MRFoundry makes MRI workflow expertise available as reusable guidance that researchers can inspect, adapt, and extend within existing agent and scientific software environments. MRFoundry is openly available at this https URL.
[AI-145] STAR: Semantic-Criticality Tiered Allocation of Radio Resources via Zero-Shot Learning
链接: https://arxiv.org/abs/2611.13238
作者: Albert Shaju,Christo Kurisummoottil Thomas,Mingzhe Chen
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: 6 pages, 9 figures, 1 table. Submitted to IEEE ICC 2027
Abstract:In remote inference, edge devices transmit high-resolution images over wireless links to a receiver, where an artificial intelligence (AI) model answers task-specific questions about these images to enable reliable decisions. These answers are often determined by a small set of task-causal pixels, such as those of text, whose corruption changes the answer, so these pixels must be delivered with high fidelity. Conventional transmission protects every pixel equally, so it either consumes many resource elements (REs) or corrupts these regions at low signal-to-noise ratio (SNR). To overcome these limitations, in this paper, a zero-shot, domain-agnostic framework, termed semantic-criticality tiered allocation of resources (STAR), is proposed. In particular, a per-pixel semantic-criticality indicator (SCI) is derived from the attention of a frozen vision-language model, so new tasks and domains are handled by changing only the prompt, without retraining. The resulting task causality-aware resource allocation problem over protection tiers is solved exactly by a threshold rule on the SCI, realized by a coded tier for the task-causal pixels and an uncoded, downsampled base layer for the full image. Simulations over a 5G New Radio link demonstrate that STAR attains a semantic accuracy of at least 0.98 for E_s/N_0 \geq -4 dB and, at comparable semantic reliability, reduces the REs of robust coded transmission by 59.5% and 91.5% on factory- and vehicle-monitoring datasets, respectively, the latter without any change to the models. Although a deep joint source-channel coding baseline uses fewer REs at comparable semantic accuracy, it must be retrained with labeled data for every new task or domain.
[AI-146] Everything in its right place: attention is a radio head
链接: https://arxiv.org/abs/2611.13146
作者: Lluís Arola-Fernández,Aissam Khadraoui,Alex Arenas
类目: Physics and Society (physics.soc-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Adaptation and Self-Organizing Systems (nlin.AO)
备注: 6 pages, 3 figures. Supplemental Material included (10 pages). Code: this https URL
Abstract:Attention, the core of language models, is a digital calculation over tokens: an exponential selects them, a division normalizes and a positional code sets the order. We show that attention can instead emerge from the synchronization of a receiver with its context tokens. Each past token is an oscillator whose state carries its content and whose frequency drifts with its age. The present token selects them by content, aligning an antenna with its query, and by position, setting the frequency of a receiver with its dial. Solving Kuramoto’s problem for this receiver yields an attention layer in closed form with two phases: above a critical coupling set by the context, the receiver locks and its weights normalize; below it, it stays silent and its weights sum to less than one. Trained networks use both phases to select, abstain and count, fail where the theory predicts and keep reading far beyond the training length. Each part of attention finds its place in one device, a radio head: it tunes in to what to hear and when, and stays silent when nothing relevant is on air.
[AI-147] Autonomous Optimization of Numerical Algorithms by an AI Agent : Application to Atomistic Simulation
链接: https://arxiv.org/abs/2611.13143
作者: Jeheon Woo
类目: Chemical Physics (physics.chem-ph); Artificial Intelligence (cs.AI)
备注: 29 pages, 5 figures, 1 table; Supplementary Information included
Abstract:Coding agents built on large language models, run in automated improvement loops, can discover more efficient algorithms. We present AIOA, a framework that brings this approach to the numerical algorithms of atomistic simulation: an agent improves an algorithm against a pre-specified metric in a loop guarded by verifiers it cannot alter. Applied to ASE, a widely used atomistic simulation library, AIOA targets two facets of an algorithm: its implementation, rewritten to run faster with unchanged results, and the method itself, redesigned to converge with fewer force evaluations. Agents made six kernels 22-24 times faster on average in Python with NumPy and SciPy. On unseen problems, optimizers from the best configuration needed 3.1-3.4 times fewer force evaluations than L-BFGS for structure relaxation and 4.6-17 times fewer than standard climbing-image nudged elastic band calculations for reaction paths. The agents’ optimizers also contain two algorithmic components we did not find in the literature.
[AI-148] oken Importance-Aware Semantic Communication Enabled Robust Video Transmission
链接: https://arxiv.org/abs/2611.12928
作者: Xiwen Zhang,Yubo Peng,Luping Xiang,Kun Yang
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注:
Abstract:Video applications are increasingly prevalent, yet their high data volume and temporal dependency make robust wireless transmission challenging under limited communication resources. To address this issue, we propose a token importance-aware semantic communication (Ti-SemCom) framework for bandwidth-efficient video transmission. Specifically, object and motion information is jointly extracted to identify communication-relevant regions, which are hierarchically associated with semantic tokens according to their importance. The transmitted tokens are then dynamically selected based on both token importance and the available bandwidth, prioritizing more informative video content under resource constraints. At the receiver, untransmitted regions reuse recently reconstructed references to further reduce redundant transmission. Experiments on the CAVIAR dataset show that Ti-SemCom can achieve an 88.3% reduction in communication cost while maintaining competitive reconstruction quality.
[AI-149] AFNO: Universal Certifiably-Arbitrag e-Free Operator Learning Framework for European Call-Price Surfaces
链接: https://arxiv.org/abs/2611.12899
作者: Anastasis Kratsios,Yannick Limmer,Hans Buehler,Blanka Horvath,Raeid Saqur
类目: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注: 63 pages, 9 figures, 7 tables
Abstract:Neural operators (NOs) have been introduced as deep learning-powered solutions to the computational bottleneck of solving families of problems. Once trained, an NO can rapidly output approximate solutions, avoiding the need to rerun classical solvers. However, standard NO architectures cannot guarantee static no-arbitrage when predicting European call-price surfaces from market or model features and, as we verify experimentally, can yield negative PnL in downstream trading tasks. We introduce a certifiably arbitrage-free (AF) and universal encoder-decoder NO framework, continuously mapping inputs from a reproducing kernel Hilbert space (RKHS) \mathcalH over any fixed subset of [0,\infty)^2 to a convex combination of arbitrage-free European call-price surfaces by continuously solving an approximate nearest-neighbour problem. We show that a nearest arbitrage-free correction of an unconstrained predicted surface need not exist, implying that no-arbitrage cannot, in general, be restored through post-processing. Further, we show that standard NOs, such as the FNO and certain encoder-decoder NOs, admit parameter configurations whose outputs violate static no-arbitrage. Thus, out-of-the-box NOs generally cannot be certifiably restricted to arbitrage-free configurations, but may only be guided towards them through PINN-type regularization during training. Comments: 63 pages, 9 figures, 7 tables Subjects: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2611.12899 [q-fin.CP] (or arXiv:2611.12899v1 [q-fin.CP] for this version) https://doi.org/10.48550/arXiv.2611.12899 Focus to learn more arXiv-issued DOI via DataCite
机器学习
[LG-0] orchCurves: Differentiable Parametric Curves
链接: https://arxiv.org/abs/2611.14516
作者: Alex Shtoff
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Software paper for a library I’ve been working on for more than a year
Abstract:We present TorchCurves, a PyTorch library that treats vector-valued parametric curves as learnable primitives. Its central operation maps batches of scalar inputs to points on curves represented in B-spline or Legendre polynomial bases, with differentiation through both inputs and coefficients. Curve coefficients can be optimized jointly with the surrounding model or produced by another network. The library provides trainable modules, reusable basis modules, and a functional interface, supporting applications ranging from continuous feature embeddings to Kolmogorov-Arnold networks and functional regression. Its implementation combines classical evaluation algorithms with vectorized PyTorch operations, emphasizing readability and maintainability without custom hardware kernels. Timing experiments compare curve evaluation against NumPy and SciPy and KAN training against EfficientKAN across a range of problem sizes. The results illustrate where this generality remains computationally practical and where overhead is substantial. Library is available on GitHub at this https URL.
[LG-1] Pyrite: Building and Benchmarking Protein-Ligand Pose Ranking Agents NEURIPS2026
链接: https://arxiv.org/abs/2611.14511
作者: Megha Srivastava,Michael H. Lim,Allison M. Keys,Pranav Murugan,Nate Gruver
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: 32 pages, Agentic AI for Biological Discovery Workshop (NeurIPS 2026)
Abstract:Predicting how a ligand binds to its target is central to small-molecule drug discovery. Modern cofolding models can generate diverse candidate poses, but reliably ranking these candidates remains challenging. We introduce Pyrite, a pairwise pose-ranking benchmark comprising 1,500 pose pairs across diverse protein-ligand complexes. Expert chemists equipped with 3D visualization tools and physics-based scores perform near chance, highlighting the difficulty of interpreting available chemical signals. To investigate automated ranking, we introduce PyriteRep, a structured representation of molecular geometry, protein-ligand interactions, and physicochemical scores, and develop two LLM-based agents: PyriteReasoner, which directly compares poses through text-based reasoning, and PyriteFeaturizer, which generates executable code to construct features for logistic regression, optionally incorporating features derived from LLM subcalls. These agents achieve up to 67% and 74% accuracy, respectively, exceeding observed expert performance. LLM-guided feature discovery generally outperforms direct reasoning, while Group Relative Policy Optimization (GRPO) improves open-weight models, enabling them to match or surpass closed-source frontier models in our evaluations. These results establish Pyrite as a challenging test of pose-ranking ability and highlight LLM-guided feature discovery as a promising approach to interpret and combine the outputs of computational chemistry tools.
[LG-2] When Does the Real Wiring Win? Testing connectome structure-function claims against unmeasured physiology
链接: https://arxiv.org/abs/2611.14508
作者: Yeyuan Liu
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注: 23 pages, 5 figures, 10 tables. Code, data and prediction files are included as ancillary files
Abstract:Connectome-constrained models are often used to argue that a measured wiring diagram outperforms shuffled or rewired wiring, and hence that structure shapes function. Such comparisons can confound wiring with how the models are tuned: a connectome fixes who connects to whom, not the excitability, gains or plasticity rates the model also needs, and modellers often tune these until the real-wiring model works. We separate three estimands: physiology tuned on the real wiring and transferred to nulls (P1), each wiring tuned on its own (P2), and P2 restricted to physiology consistent with independent data (P3). The biases involved are known in machine learning (under-tuned baselines, inherited weights, winner’s curse); we measure what they do to biological conclusions, using synthetic ground truth, placebo nulls and three Drosophila circuit models. Some claims survive every protocol we test: segregated reward and punishment dopamine improves learning, and head-direction topography is required for a persistent bump. Others do not. In the mushroom body, the real Kenyon-cell output wiring beats a cross-subtype identity shuffle by 0.09 under P1 and P2 but loses to it by 0.09 under P3 (95% CIs exclude zero; held-out runs confirm). The verdict is set by the share of alpha’beta’ cells among active Kenyon cells, which P1 selects low; at the share implied by imaging (about 44%) the real-wiring model fails to learn, exposing a model inadequacy rather than a deficit of the fly’s wiring. In flyvis, P1 penalises a mirrored connectome, which has a constructed solution within 0.07 (median) of the real error, more than a spatially shuffled one in 50 of 50 members; the ranking survives ten-fold longer retraining but depends on the checkpoint at fifty-fold. A credible structural claim needs matched optimisation, data-constrained physiology and a model that still works there.
[LG-3] Continual-ARC: Measuring Skill Acquisition over a Working Lifetime
链接: https://arxiv.org/abs/2611.14505
作者: Daniel Lenton
类目: Machine Learning (cs.LG)
*备注: 51 pages, 13 figures. Code and run records: this https URL
Abstract:Agent harnesses have emerged as a promising mechanism for continual learning, alongside test-time parametric learning. However, the current benchmarks have several limitations: they generally focus on a single applied domain, serve each instance only once, and assume a single family of learner, so memory-based and weight-based systems are rarely compared on the same scale. We propose Continual-ARC, a lifelong stream built from the three ARC-AGI editions, whose tasks need no domain knowledge. The same tasks return after dormant gaps with fresh instances, while new tasks keep arriving. On each instance, the learner may pay for demonstrations, submit answers, or act inside the harness, with retries permitted on incorrect submissions, which are paid for. Each instance is scored based on the share of this feedback budget consumed. Since a learner may keep anything between instances, memory-based and weight-based systems are scored on the same scale. After the stream, the learner is frozen and tested on fresh instances of the tasks it met, which measures memorization, and on unseen tasks, which measures generalization. We run an agent harness with frozen weights, with and without persistence, on Qwen3.5-9B and GLM-5.3 Flash, comparing them with two prompting floors, a program-induction method and Qwen3.5-9B’s weights trained to imitate GLM-5.3 Flash. Persistence in the harness generally does not improve memorization unless provided with explicit expert guidance. Most harness-based methods still learn less than a floor that simply caches each task’s demonstrations, across all models we tested. No harness-based method gains on unseen tasks, whereas weight-based learning shows substantial gains. This opens the door to future weight-based continual learning that could teach models to drive harnesses in new domains.
[LG-4] Emergent Biological Capabilities in a Foundation Model for Molecular Interactions NEURIPS2026
链接: https://arxiv.org/abs/2611.14504
作者: Adi Mashiach
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: Accepted at NeurIPS 2026 (Main Track, Oral)
Abstract:Foundation models trained on large unlabeled corpora develop emergent capabilities, tasks that go beyond the training objective. We extend this paradigm to multimodal biology with Bonbon, a foundation model for molecular interactions trained on approximately 3.2 trillion tokens of paired protein-ligand sequences. From a single representation space, Bonbon exhibits three zero-shot emergent capabilities at hierarchical levels of resolution: functional (mechanism of action), residue (binding site localization, including orthosteric/allosteric discrimination), and bond (covalent versus non-covalent engagement). All three capabilities generalize to out-of-distribution complexes. Bonbon was trained on protein-ligand sequences alone, without any task-specific supervision and without structural coordinates. We conducted controlled scaling experiments across six model scales from 42M to 1.6B parameters; every capability evaluated shows a sharp jump at the largest scale on continuous metrics, even as pretraining loss decreases smoothly. This demonstrates that loss-based scaling laws alone are insufficient to predict when zero-shot emergent capabilities become available. The capabilities reported here emerge after training at a 1:2,000 parameter-to-token ratio to accommodate the sparsity of paired biological interaction data. The same frozen representations achieve state-of-the-art binding and affinity prediction at five orders of magnitude greater speed than co-folding methods. In prospective wet-lab validation across nine disease targets spanning seven protein families, Bonbon achieves a 61.5% dose-response hit rate on de novo compounds, yielding structurally novel molecules not previously reported to inhibit these proteins, including for out-of-distribution targets.
[LG-5] meSeek: Multi-Task and Multi-Domain Pretraining for Generalized Time Series Analytics
链接: https://arxiv.org/abs/2611.14491
作者: Peng Chen,Yihang Wang,Zhongwen Rao,Yang Shu,Yuying Qiu,Yuxuan Chen,Lujia Pan,Bin Yang,Chenjuan Guo
类目: Machine Learning (cs.LG)
*备注: Accepted by IEEE Transactions on Pattern Analysis and MachineIntelligence
Abstract:Time series analytics has been widely applied across various domains. While existing time series foundation models (TSFMs) have demonstrated promising results, they still face two major challenges: 1) Most TSFMs mainly rely on stacking Transformer layers to increase parameter size, lacking the ability to adaptively model complex time series patterns from multi-domain data. 2) Current TSFMs are primarily pre-trained for specific tasks, e.g., forecasting or anomaly detection, and a few models support multiple tasks, but they fail to fully leverage task specificity and commonality, resulting in suboptimal performance. To address these challenges, we propose TimeSeek, a novel generalized time series analytics model that adaptively models time series patterns from multi-domain data and unifies multiple tasks in pre-training. Conceptually, TimeSeek is guided by selective information sharing under domain and task heterogeneity. From the perspective of multi-domain data, we propose a Multi-Domain Feature Extractor that adaptively captures temporal dependencies and channel correlations based on domain-specific characteristics, including progressive multi-scale modeling and adaptive slot channel modeling. From the perspective of the diversity of tasks, we propose multi-task collaborative training, which leverages task specificity and task commonality through task customization and cross-task training, effectively unifying multiple tasks in pre-training and better adapting to diverse downstream scenarios. After pre-training on large-scale time series data, TimeSeek achieves state-of-the-art performance in forecasting, anomaly detection, and classification tasks across both few-shot learning and zero-shot situations, together with favorable inference efficiency.
[LG-6] A Theory of Policy Optimization in Post-Training: Coverag e Collapse and Reference Resets
链接: https://arxiv.org/abs/2611.14467
作者: Audrey Huang,Nan Jiang,Sham M. Kakade,Sadhika Malladi,Akshay Krishnamurthy,Dylan J. Foster
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 141 pages including appendices, 3 figures
Abstract:Policy optimization is central to language model post-training, yet principled guidance remains limited. We revisit its theoretical foundations to identify algorithms that learn near-optimal policies using total samples and gradient steps governed by the base model’s response coverage, a measure of how readily it generates optimal responses. We call such algorithms coverage-preserving. This guarantee captures the goal of amplifying capabilities accessible through sampling from the base model. (1) Coverage collapse. REINFORCE, the basis for PPO and GRPO, can drive the probability of optimal responses to near zero, even as reward improves. This coverage collapse stalls optimization, and convergence can require exponentially many steps despite good initial coverage. The failure arises even with exact gradients and persists under KL regularization to the base model. (2) Reference resets. We show that repeatedly resetting the KL reference to the most recent policy mitigates coverage collapse. We formalize this intervention in Approximate Policy Mirror Descent (APMD), a meta-algorithm that approximately solves a sequence of KL-regularized RL subproblems. APMD enables stronger regularization, making each subproblem easier to solve, while reference resets allow convergence to global optimality. (3) Coverage-preserving algorithms. For softmax linear policies over pretrained features, we use APMD to establish the first global convergence guarantees showing that widely used families of post-training algorithms are coverage-preserving. These include stylized PPO and GRPO variants with explicit KL regularization and reference resets, and squared-loss algorithms that implement them implicitly, including those used to train the Kimi models. These results clarify how regularization, clipping, and objective choice affect the sampling and optimization costs of post-training. Comments: 141 pages including appendices, 3 figures Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2611.14467 [cs.LG] (or arXiv:2611.14467v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.14467 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Audrey Huang [view email] [v1] Tue, 10 Nov 2026 18:31:36 UTC (2,003 KB) Full-text links: Access Paper: View a PDF of the paper titled A Theory of Policy Optimization in Post-Training: Coverage Collapse and Reference Resets, by Audrey Huang and 5 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 stat stat.ML 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-7] Accurate predictions of properties of energetic materials with tabular foundation models
链接: https://arxiv.org/abs/2611.14462
作者: Vagn Krister Nes Glomsrud,Mathias Bynke,Lars Henry Berge Olsen,Dennis Christensen
类目: Machine Learning (cs.LG)
*备注:
Abstract:We use TabPFN, a recently developed tabular foundation model, to predict three properties of energetic materials: impact sensitivity, decomposition temperature and crystal density. Without any hyperparameter tuning, TabPFN outperforms the best published models for all three properties. It also substantially outperforms a wide range of established machine-learning models, including tuned gradient boosting (XGBoost), random forests and Gaussian processes. This holds both when the models are given hand-crafted molecular descriptors and when they are given learned representations from Chemprop’s message-passing neural network. In particular, for all three properties, TabPFN applied to Chemprop’s own embedding is more accurate than Chemprop’s predictions. TabPFN is also among the fastest models we consider, more than an order of magnitude faster than XGBoost. As TabPFN predicts the entire distribution of the target rather than just a single value, it offers built-in uncertainty quantification, which we compare with the empirical distributions of compounds with repeated measurements. Finally, we interpret the predictions using Shapley values, which rank the features by their importance.
[LG-8] DnNP: Denoising Input Uncertainty in Neural Processes NEURIPS2026
链接: https://arxiv.org/abs/2611.14424
作者: Fatemeh Tohidian,Chengzhi Shi,Soomi Lee,Matthew Elia,Amy Mueller,Stratis Ioannidis,Jennifer Dy
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026. 20 pages, 3 figures, 3 tables. Code: this https URL
Abstract:Neural Processes (NPs) are flexible meta-learning models capable of uncertainty quantification across diverse applications. However, the standard NP framework assumes access to noise-free inputs. This is unrealistic in many practical settings, where sensor noise or measurement errors are unavoidable. This discrepancy between model assumptions and real-world data can significantly degrade prediction quality and uncertainty estimates. We introduce Denoising Neural Processes (DnNPs), a principled extension of the NP framework that explicitly models input uncertainty. We train DnNPs via variational inference. DnNPs jointly learn to denoise corrupted inputs and perform meta-learning, making them robust to noisy training data. We demonstrate DnNPs’ effectiveness on both synthetic and real-world datasets, showing consistent improvements in prediction and uncertainty quantification compared to standard NPs. On PM2.5 spatial interpolation from real-world air quality sensors, DnNP reduces RMSE by up to 30% and NLL by up to 73% relative to standard NPs. We also provide theoretical grounding: we show that the Bayes-optimal predictor under noisy inputs belongs to the DnNP model class, and that plug-in models with fixed observation variance incur an irreducible KL divergence gap that DnNP provably avoids.
[LG-9] Exploring Post-Hoc Disentanglement of Audio Foundation Models for Music Similarity
链接: https://arxiv.org/abs/2611.14415
作者: Lucas de Souza Brandão,Gabriel da Costa Merlin,Diego Furtado Silva
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 5 pages
Abstract:Audio foundation models like CLAP, MuQ, and MERT excel at capturing general musical similarity. On the other hand, their entangled latent spaces limit interpretability and fine-grained attribute control. Conditional Similarity Networks (CSN) address this by pairing a backbone representation model with learned masks that gate the embedding space into attribute-specific subspaces. However, existing methods train the backbone from scratch, rather than leveraging pretrained foundation models. We instead present a systematic study of post-hoc disentanglement, adapting CSN to gate frozen pretrained embeddings. We evaluate adding trainable parameters to adapt the embedding under different masking and pooling regimes across two datasets. Our findings show that minimal adaptation capacity is sufficient for post-hoc gating to improve both attribute-specific disentanglement and overall retrieval performance, while pooling, masking, and depth choices yield limited additional gains.
[LG-10] Discovering Equations for the Thermodynamics of South Asian Monsoon Precipitation
链接: https://arxiv.org/abs/2611.14401
作者: Savannah L. Ferretti,Jane W. Baldwin,Michael S. Pritchard,Tom Beucler
类目: Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: Main Text (23 pages, 6 figures, 1 table) and Supporting Information (6 pages, 2 figures, 3 tables)
Abstract:South Asian monsoon precipitation depends on the thermodynamic structure of the lower troposphere, but how its ingredients combine to shape precipitation remains unclear, partly because this dependence is nonlocal in the vertical. We use symbolic distillation, which combines neural networks and symbolic regression, to discover interpretable precipitation equations from 21 years of June-August reanalysis data. Neural networks first learn the vertical thermodynamic structure most relevant to precipitation, condensing each variable into a single interpretable feature not tied to a particular vertical grid. Then, symbolic regression derives closed-form equations from these features and the surface state. Our best equation explains 1.5 times as much precipitation variance as a widely used buoyancy-based diagnostic and recovers about 85% of neural network skill while using about a thousand times fewer parameters. Unlike the buoyancy diagnostic, which adds moisture and instability, the equations treat them as competing controls, with the heaviest precipitation occurring when the lower free troposphere is humid. Over ocean, sensible heating is associated with additional precipitation mainly when near-surface air is warm and moist, improving predicted precipitation and its diurnal cycle. Although the learned features were optimized only for precipitation prediction, the equations largely respond to each feature as expected from convective physics, consistent with the reanalysis. These responses reduce the dependence of monsoon precipitation on the entire vertical column to a few simple, testable constraints that could guide data-driven precipitation models to make them more physically consistent.
[LG-11] A Margin Perspective on LoRA: Robustness to Catastrophic Forgetting and Adapter Merging (MaLoRA)
链接: https://arxiv.org/abs/2611.14397
作者: Ziqing Xu,Hancheng Min,Lachlan Ewen MacDonald,Salma Tarmoun,Enrique Mallada,Weijie Su,Rene Vidal
类目: Machine Learning (cs.LG)
*备注:
Abstract:Low-rank adaptation (LoRA) is the de facto method for parameter-efficient fine-tuning of large neural networks, achieving strong task adaptation with minimal trainable parameters and low optimization cost. Beyond this efficiency, practitioners have observed two striking properties: \textitrobustness to catastrophic forgetting and the ability to \textitmerge independently trained adapters into a single model that performs competitively across multiple tasks. These properties are central to continual and multi-task learning, yet remain poorly understood. In this work, we provide a theoretical explanation through a unified \textitmargin-based perspective. We analyze LoRA in multiclass linear classification under a near-orthogonal task regime with \ell_2 -regularization, and characterize the optimal LoRA adapter across regularization regimes. Our analysis shows that, in an intermediate regime of regularization parameters, the optimal adapter aligns with the \emphmax-margin solution on the fine-tuning data. Building on this characterization, we derive two key consequences. First, we obtain closed-form expressions for the margins on pre-training and fine-tuning data, revealing a precise margin trade-off: the regularization parameter controls the balance between retention and adaptation. Second, we analyze adapter merging, proving that merged models achieve positive margin on each task and deriving optimal mixing coefficients that balance margins across tasks and maximize the margin over their union. These results lead to a simple, training-free merging rule, which we term \textitmargin-based LoRA merging (MaLoRA). Experiments on modern architectures and real datasets validate our theoretical predictions, showing that MaLoRA matches or outperforms several adapter-merging baselines across a range of vision and language classification tasks.
[LG-12] Uncertainty-Aware Hybrid Offline Reinforcement Learning with Online Action Selection Enabled by Sparse Gaussian-Process Critics
链接: https://arxiv.org/abs/2611.14374
作者: Marco Chan,Christos Evripidou,Liqiu Dong,Benoit Chachuat,Mehmet Mercangöz(Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London)
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 8 pages, 5 figures, 1 table. Submitted to the 2027 European Control Conference (ECC 2027)
Abstract:Start-ups and grade changes remain largely operator-driven, while developing and maintaining dynamic models for model-based control requires substantial engineering effort. Hybrid Offline Learning and Online Optimisation (HOFLON) uses one-step offline reinforcement learning to select actions from historical transition data. In its original implementation, the online objective combines the predicted return from a tree-ensemble critic with an adversarial-autoencoder (AAE) penalty for unfamiliar state-action pairs. We examine the effect of this penalty and replace the critic and AAE with a sparse variational Gaussian-process (sGP) critic. Actions maximise \mu - \kappa\sigma , an uncertainty-adjusted estimate of the return from one action followed by the historical operating practice. On the HOFLON polymerisation-reactor benchmark, removing the AAE from HOFLON led to 4 failed start-ups in 15 runs over five critics. At its published weight, the variation in the AAE penalty is four orders of magnitude smaller than the variation in the tree-ensemble critic. Nevertheless, it changes actions when the predicted returns of competing candidates are nearly equal, and paired runs with and without the penalty diverged from the first move. With a Matérn-5/2 sGP critic, \kappa = 0 led to failure in 13 of 15 runs, whereas with \kappa = 3 none of 15 runs (five critics, three noise realisations) failed. The median and lowest returns were 13,464 and 13,398 (HOFLON: 13,393 and 12,411). A squared-exponential sGP critic with learned inducing inputs needed \kappa \geq 3 to remain within the empirical data support in steady state, and then settled with a temperature offset of 2.0 K.
[LG-13] he Unused Input: The Initial State as a Conditioning Interface for Recurrent TSFMs NEURIPS2026
链接: https://arxiv.org/abs/2611.14357
作者: Bernhard Voggenberger,Christian Ganhör,Levente Zólyomi,Sebastian Böck,Sepp Hochreiter
类目: Machine Learning (cs.LG)
*备注: Accepted at the NeurIPS 2026 Workshop on Foundation Models for Temporal Systems (FMTS). 21 pages
Abstract:Time series foundation models (TSFMs) forecast unseen series without task-specific training. Increasingly they are built on recurrent backbones, whose constant per-step cost makes long-context inference cheap, and which, unlike transformers, carry a state. A recurrent backbone computes its prediction from two inputs: the observed context window and the initial recurrent state. In practice only the first is utilised and the initial state is unused by convention. We ask whether the initial state can be utilised in recurrent TSFMs. Using the xLSTM-based zero-shot forecaster TiRex as a case study, we freeze the backbone and train only a small head that writes a context vector into the initial state, so that any change in accuracy is attributable to the injected state rather than to adapted weights. A spectral summary of the input window, learned once and applied unchanged to every series, lowers mean GiftEval MASE by 0.175%, with every seed improving, which neither a parameter-matched LoRA adapter nor a learnable static state reproduces. An embedding of text attached to each series, evaluated on six MoTime datasets, reveals two roles for the state. As a per-dataset adaptation surface, a static state lowers MASE by 0.04-6.96% of the zero-shot baseline (8-70% of the gain of full fine-tuning). As a channel for exogenous information, the text-conditioned state lowers it by a further 0.15-0.41% of the zero-shot baseline on four of six datasets and, combined with a LoRA adapter, exceeds full fine-tuning, which has no access to the text, on one of them. In TiRex, the effect concentrates in the mLSTM blocks and is strongest at short context. The results are preliminary and the improvements small, but they point to the initial state as a direction for further research.
[LG-14] Auditing the Judge: Internal Signals for LLM Reasoning Assurance
链接: https://arxiv.org/abs/2611.14323
作者: Mayank Gulati,Gerhard Wunder
类目: Machine Learning (cs.LG)
*备注: The 8th IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications
Abstract:Language-model systems use self-judgment to decide which answers to release, review, or recompute. Because intervention is costly, an assurance score must rank errors well at the available budget, not merely over the full ranking. We compare the judge’s continuous verdict-token score with a supervised correctness probe trained on attention-head activations from the same prompt. Exact identities link fixed-budget error recall to residual risk and show that AUROC averages recall over all budgets. Across three open 7-12B models on GSM8K and MATH, the probe finds 1.3-22.5 additional percentage points of errors at 5% review in all six model-dataset settings (median 7.4 points). At 10% routing, probe-guided allocation improves accuracy over equal-cost random allocation in both GSM8K experiments, but beats judge-guided routing only for the weaker judge. Cross-dataset transfer lowers probe AUROC for every model in both directions relative to target-domain training. Because dataset, weight precision, and generation budget change together, this measures a joint protocol shift rather than a dataset-only effect. Internal signals can improve supervised, in-domain budget allocation, but are not domain-general confidence estimators.
[LG-15] MammothDP: Private Boosted Decision Trees Ready for Trusted Hardware and Leakage-free Hyperparameters
链接: https://arxiv.org/abs/2611.14317
作者: Moritz Kirschte,Thorsten Peinemann,Kari Kostiainen,Jan Wichelmann,Thomas Eisenbarth,Esfandiar Mohammadi
类目: Cryptography and Security (cs.CR); Databases (cs.DB); Machine Learning (cs.LG)
*备注:
Abstract:Data spaces like the European Health Data Space (EHDS) require the protection of sensitive data in studies during the data’s processing and the study’s release alike. In this work, we investigate how to implement this mandate using established trusted execution environments (TEEs) and timing side-channel hardening to protect the processing even under a compromised server, and the state-of-the-art differential privacy (DP) notion to protect the release of studies on tabular data. This combined setup could seem to complete the picture, but in fact raises unexpected security challenges that we mitigate by introducing MammothDP: a significant milestone towards a holistic machine learning system based on the recent gradient boosted decision tree learner S-BDT that is provably DP if used within a privacy filter, timing side-channel hardened, and deployed on a TEE. To achieve a strong privacy-utility tradeoff, we introduce two novel components: an early stopping criterion that adaptively selects the number of trees without any privacy overhead, and an improved rollback protection that is no longer vulnerable under timing side-channel attacks.
[LG-16] PSL-RGHA: Parallel Split Learning with Reference Gradient Half-Space Alignment for Mitigating Gradient Divergence
链接: https://arxiv.org/abs/2611.14312
作者: Han Li,Zheng Lin,Miao Yang,Zehang Lin,Jing Yang,Zihan Fang,Qiyang Zhang,Zhaolu Kang,Jianmin Li,Lin Li,Mingwei Hong,Shunzhi Zhu
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 10 pages, 13 figures
Abstract:The escalating complexity of deep neural networks hinders the deployment of federated learning (FL) on resource-constrained devices, especially for multimedia applications such as on-device image classification and speech recognition. Parallel split learning (PSL) has emerged as a promising solution by offloading a substantial portion of the computational workload to the server through layer-wise model partitioning, making it particularly suitable for resource-limited visual and speech intelligence scenarios. However, the absence of client-side aggregation renders PSL sus ceptible to severe training divergence and potential convergence failure under heterogeneous data distributions. To address this challenge, we propose PSL-RGHA, a gradient-aligned PSL framework that employs reference gradient half-space alignment mechanism to mitigate training divergence among client devices. PSL-RGHA comprises two novel components: reference gradient progressive construction (RGPC) and gradient half-space alignment (GHA). Specifically, RGPC evaluates the directional consistency of client gradients and progressively synthesizes a robust reference gradient via Weiszfeld updates, effectively approximating the global descent direction. Then, GHA constrains client gradients within the reference half-space defined by RGPC to mitigate gradient conflicts and rectify local updates. Extensive experiments demonstrate that our PSL-RGHA framework outperforms existing baselines in various settings.
[LG-17] Federated Learning for Cross-Regional Epidemic Forecasting with Asynchronous Outbreaks
链接: https://arxiv.org/abs/2611.14272
作者: Ruibin Liu,Francisco Fernandes,Xuanfeng Li,Zige Liu,André Salgado,Chitin Hon,Zifeng Yang,Arlindo Oliveira,Ana Teresa Freitas
类目: Machine Learning (cs.LG)
*备注:
Abstract:Objective: Epidemic outbreaks are often asynchronous across regions, yet collaborative forecasting is hampered by privacy laws and heterogeneous public health interventions. We developed and evaluated, as a proof of concept, a federated learning (FL) framework for cross-regional COVID-19 forecasting under asynchronous outbreaks among Guangdong, Macau, and Portugal, while maintaining data sovereignty. Methods: Using daily case data from WHO and China CDC for the three regions, whose outbreaks were asynchronous and interventions heterogeneous, we trained Long Short-Term Memory networks via FL for short-term forecasting, benchmarked against localized and centralized training using Root Mean Square Error, Mean Absolute Error, and Symmetric Mean Absolute Percentage Error. All experiments used the Flower FL framework with consistent hyperparameters across sites. Publicly available, aggregated case-count data were used so that the centralized benchmark could be computed as a reference. Results: The dataset comprised 1,233 daily observations (411 days) of new confirmed COVID-19 cases from November 24, 2021, to January 8, 2023. The earlier Omicron wave in Portugal supplied transmission dynamics that improved forecasts in the later-onset regions of Macau and Guangdong. The FL model achieved a normalized Root Mean Square Error of 0.028, an 88.3% improvement over the local model (0.240) and comparable to the centralized model, with network overhead mainly determined by model size and aggregation rounds. Conclusion: FL achieved short-term forecasting accuracy close to centralized training while maintaining data residency, with acceptable overhead. Leveraging asynchronous outbreaks across regions may offer a practical path to privacy-preserving collaborative forecasting, though prospective validation is required. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2611.14272 [cs.LG] (or arXiv:2611.14272v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.14272 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ruibin Liu [view email] [v1] Tue, 10 Nov 2026 16:14:49 UTC (1,438 KB)
[LG-18] Physics-Constrained Autoencoder for Interpretable Health Representation Learning in Turbofan Engines
链接: https://arxiv.org/abs/2611.14266
作者: Leonardo Baldo,Jokin Alcibar Ibarzabal,Daniel Cisneros Acevedo,Kai Goebel,Manuel Arias Chao
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Health Indices (HIs) summarize degradation from operational data and support diagnostic and prognostic tasks in Prognostics and Health Management (PHM). Their usefulness depends on capturing degradation and interpreting system condition. Autoencoders learn such representations from unlabelled monitoring data; however, when training is driven only by reconstruction, the physical meaning of latent variables remains undefined, limiting their interpretation and diagnostic value. This work proposes a physics-constrained autoencoder that embeds system knowledge as an inductive bias in the decoder architecture and as a learning bias in the loss function. The decoder contains differentiable physical equations that receive latent variables and operating conditions as inputs, structurally guiding each latent variable to represent a predefined physical quantity and enabling component-level analysis before aggregation into a system-level this http URL methodology is benchmarked on the N-CMAPSS dataset against alternative representations. Reconstruction reaches an R^2 of 0.9993 and an MAE of 0.0036, comparable to fully data-driven models. After operating-condition correction, the inferred physical quantities track degradation, and their aggregation produces an HI with a Spearman correlation of 0.9791 with the ground truth, an RMSE of 0.0286, and an MAE of 0.0233. The methodology is further evaluated using a single model trained across four datasets representing distinct degradation scenarios. This model preserves component-level signatures that distinguish compression-side from expansion-side degradation without diagnostic this http URL, the proposed framework enables degradation trends to be visualized on component maps, providing a health representation in a form familiar to engine operators and domain experts.
[LG-19] Chain of Physics: Supporting Fault Diagnosis with Engineering-Inspired Large Language Model Reasoning
链接: https://arxiv.org/abs/2611.14263
作者: Wenqi Zhou,Leonardo Baldo,Kai Goebel,Manuel Arias Chao
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Fault diagnosis in complex systems requires linking observed changes in physical quantities to the failure mechanisms that produce them. Experienced engineers perform this task by combining sensor information with knowledge of system behavior and fault propagation. Large Language Models (LLMs) can support this process, but their diagnostic reasoning depends on the availability of asset-specific engineering knowledge and on how this knowledge is represented and used during inference. This work introduces Chain of Physics (CoP), an LLM-based fault-diagnosis framework built around a Constrained Knowledge Graph (CKG) generated automatically from asset-specific reference documentation. CoP addresses failure-sensor association from textual engineering knowledge. The CKG stores diagnostic chains connecting failure modes, changes in physical quantities, and monitoring sensors. During inference, semantically similar graph paths are retrieved for each question and used as both information and in-context examples for the reasoning process. Candidate failure modes are extracted from the question and analysed independently through dedicated LLM calls, which generate plausible failure-to-observation chains. A final decision module compares these chains with the available answer choices. CoP is evaluated on the FailureSensorIQ benchmark across four industrial asset classes and three LLMs. Compared with direct prompting and conventional Retrieval-Augmented Generation (RAG), CoP improves diagnostic accuracy across all evaluated models, with gains ranging from 7.8 to 19.6 percentage points and from 11.7 to 18.2 percentage points, respectively. The results show that structured engineering knowledge can improve LLM-based diagnostic reasoning when graph retrieval is combined with in-context examples of fault propagation.
[LG-20] SinkState: On-the-Fly SVD of Recurrent States for Efficient Hybrid LLM Serving
链接: https://arxiv.org/abs/2611.14246
作者: Haolin Jiang,Weihang Li,Zhongzhu Zhou,Jinwen Pan,Santosh Pandey,Hang Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Hybrid models combine softmax attention with linear attention, which compresses history into fixed-size recurrent states to balance model quality and inference efficiency. However, linear attention has not pushed this compression to its limit: we find that the queries, keys, and values of linear layers concentrate in a stable, low-dimensional subspace, a phenomenon we call the linear attention sink, so each state is effectively low-rank, with a rank that varies across states. Yet existing serving systems keep these redundant states dense, reading and rewriting them in full at every decoding step, so their memory traffic grows with the batch size and dominates the decoding cost. Thus SinkState stores each recurrent state in an adaptive low-rank form throughout inference with buffered updates and a local SVD restricted to the top subspace. SinkState further consolidates the remaining components asynchronously on the CPU. On four hybrid models with Mamba-2 and Gated DeltaNet (GDN) layers, from 4B to 120B parameters, SinkState reduces the peak memory of recurrent states by up to 2.6x (1.9x on average) and speeds up decoding by up to 1.58x (1.25x on average) over Mamba-SSM and FLA, while maintaining accuracy.
[LG-21] ree-Ising: Repairing Language Models with Bit-Level Ising Edits
链接: https://arxiv.org/abs/2611.14210
作者: Hadi Salloum,Ammar Ali
类目: Machine Learning (cs.LG)
*备注:
Abstract:Weight-space programming, introduced in recent MIT work by Takehi et al., enables a frontier LLM agent to read a network’s weights and write a repair. Their GPT-6 Astra agent corrected the reasoning loops of Qwen3.5-0.8B without gradients, using 158 agent turns, 89.7 million tokens, and 50 forward-pass requests. Our method, Tree-Ising, casts this repair as a sparse discrete optimization problem over the model’s own BF16 weights. Forward statistics define an objective that weakens a loop direction, strengthens a concluding direction, and preserves the layer’s output on productive tokens. Within a state-tree representation, where lossless BF16 storage acts as a special case, the admissible edits are sign flips and exponent shifts that preserve all shared group exponents and mantissas. We decompose this objective exactly into independent QUBO (Ising) problems, one per output row, which we solve on a GPU using simulated annealing or quantum-inspired simulated bifurcation. On the agent’s two layers, Tree-Ising achieves 54.6% on MATH-500, outperforming the agent’s edit (51.7%) under the same evaluation protocol, while the base model scores 7.9% as reported. The simulated bifurcation solver yields a statistically indistinguishable repair at 55.1%. Both solvers outperform the agent edit across all five math benchmarks, with statistically significant improvements on MATH-500 and GSM8K after correcting for multiple comparisons. Our approach modifies 13% to 17% fewer weights and reduces BF16 bit flips by a factor of 2.2 to 2.3. It requires only four forward-pass requests and zero LLM tokens, with the annealing recipe costing about 0.90 in GPU time. On Qwen3.5-2B, with layers and dose selected using only the base model, the same recipe increases greedy GSM8K accuracy from 21.6% to 69.4%. As a proof of concept, we execute an eight-variable repair subproblem using QAOA on an IBM Heron processor.
[LG-22] Nonadaptive One-Sided Testing of Binary Rank
链接: https://arxiv.org/abs/2611.14163
作者: Nader H. Bshouty
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG)
*备注:
Abstract:The binary rank of a binary matrix is the minimum number of disjoint all-one rectangles that partition its one-entries. We study entry-query testing of binary rank at most d , with one-sided error and nonadaptive queries. We give a tester using \tilde O(d^4/\epsilon^2) queries, independent of the matrix dimensions and a general lower bound of \tilde \Omega(d^3/2/\epsilon) .
[LG-23] Unipolar Neuron Networks Trained by Recirculation for Reward-Based Continual Learning
链接: https://arxiv.org/abs/2611.14153
作者: Florin Leon
类目: Machine Learning (cs.LG)
*备注: 37 pages, 11 figures, 8 tables
Abstract:Unipolar neurons are the predominant type of nerve cell in the octopus and in most other invertebrates. A single neurite (a fiber extending from the cell body) branches into a tree that carries both input and output synapses. The branches send graded local outputs to nearby cells, and one site on the main neurite sends a global output to distant targets. This paper proposes a network of such neurons and a learning rule for it. Each neuron is modeled as a tree of passive segments, whose activations are computed exactly as the steady state of a linear system. The network learns from reward alone, with a rule called recirculation. After each forward pass, a second pass sends requested changes of the outputs back through the same connections. Each synapse then adjusts its weight using only quantities available at its own location. Because the system of each neuron is symmetric, this update closely follows the gradient of the error in the predicted reward. Two mechanisms limit forgetting when tasks are learned in sequence: a task code inhibits a different subset of branches in each task, while consolidation lowers the learning rate of synapses that changed strongly in the recent past. The model is evaluated on a sequence of MNIST and Iris tasks. The network maintains high accuracy with low forgetting, whereas gradient-based training of the same network forgets earlier tasks almost completely.
[LG-24] Deviation-Aware Pretraining for Cardiovascular Disease Detection from Electrocardiograms
链接: https://arxiv.org/abs/2611.14141
作者: Siyuan Yan,Haibo Zheng,Feng Lu,Wei Li
类目: Machine Learning (cs.LG)
*备注:
Abstract:Electrocardiogram (ECG) signals are critical for cardiovascular disease (CVD) detection. Most current deep learning models rely on supervised, task-specific training, which limits their ability to generalize across diseases, patient populations, and acquisition settings. This paper presents a self-supervised ECG feature extraction framework that learns from nearly one million unlabeled 12-lead ECG recordings. The framework includes two modules: Deviation Localization Embedding (DLE), which detects waveform-level deviations from normal patterns, and Association Reconstruction Embedding (ARE), which models cross-lead associations through deviation-guided reconstruction to capture abnormal patterns expressed across multiple leads. These modules enable the framework to capture subtle deviation-related patterns and their cross-lead context. The framework achieves up to 6.5% AUROC and 12.8% F1 improvements over prior methods on multiple public datasets, even without additional fine-tuning the pretrained feature extractor. Embedding visualizations show that the learned features support meaningful disease grouping and anomaly identification. The approach reduces the need for labeled data and can support clinicians in diverse healthcare settings. The framework has potential to improve early detection and monitoring of cardiovascular diseases, contributing to more accessible and equitable heart care.
[LG-25] Right for the Wrong Reason s? Evaluating Plausibility and Behavioral Change in Explanation-Guided Learning
链接: https://arxiv.org/abs/2611.14134
作者: David Dembinsky,Adriano Lucieri,Andreas Dengel
类目: Machine Learning (cs.LG)
*备注: Accepted at EXPLAINS 2026 (Best Student Paper Award). Pre-review manuscript
Abstract:Explanation-Guided Learning (EGL) promises models that are not only accurate but also rely on task-relevant evidence. However, whether improved agreement between model explanations and human-provided rationales also implies a changed decision strategy remains unclear. We study this tension under controlled synthetic shortcut introduction using a constructed shortcut dataset. Inspired by real-world applications, our benchmark covers shortcuts of different kinds, including localized, background-based, and global cues. This allows us to evaluate whether different EGL objectives produce behavioral changesand explanatory changes. Across these axes, we find that the effects of EGL are strongly objective-specific. Rationale-based data augmentation provides the clearest robustness gains when shortcut evidence can be separated from task-relevant foreground information, reaching average accuracy improvements of up to +4.8 percentage points (pp) together with increased foreground reliance. Attribution-constraining objectives primarily improve explanation plausibility, increasing Importance Mass by up to +14 pp on average, but provide weaker and less consistent behavioral correction, with average accuracy gains of at most +1.6 pp. Moreover, neither improved robustness nor improved plausibility reliably implies higher perturbation-based fidelity. Overall, our results show that EGL can be useful, but its success cannot be inferred from rationale agreement alone and should instead be evaluated along complementary behavioral and explanation-level axes. We publicly release all experimental results to enable further analysis (this https URL).
[LG-26] Hierarchical Graph Representation Learning with Pooling-Induced Substructures NEURIPS2026
链接: https://arxiv.org/abs/2611.14126
作者: Luca Sbicego,Xiaowen Dong,Dorina Thanou
类目: Machine Learning (cs.LG)
*备注: Accepted to NeurIPS 2026
Abstract:Hierarchical graph-level tasks that require reasoning over substructures that extend beyond local neighborhoods are posing challenges for standard Graph Neural Networks (GNNs). While graph pooling aims to address this by coarsening graphs into hierarchical representations, existing methods often fail to explicitly preserve the substructures they induce, leading to inconsistent empirical performance. Moreover, evaluating such approaches remains challenging due to the lack of benchmarks with explicit hierarchical structure. We propose Pooled-Substructure-Aware GNNs, an approach to graph pooling that explicitly preserves and processes substructures uncovered by structure-based pooling schemes. We show that this design enhances the expressive power of pooling-based GNNs in the sense of the Weisfeiler-Lehman (WL) test. To enable principled evaluation, we introduce two new synthetic datasets with controllable hierarchical structure and propose a metric to characterize the prevalence of hierarchical patterns in real-world graph-level benchmarks. On tasks with relevant hierarchical structure, our approach outperforms matched pooling baselines, while remaining competitive on structurally simpler benchmarks. Code and datasets are publicly available at this https URL.
[LG-27] Opportunity-Cost Option-Critic: Adaptive Switching Costs for Robust Hierarchical Reinforcement Learning
链接: https://arxiv.org/abs/2611.14099
作者: Khaled Ammoura,Aya Kayal,Naseem Daher
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deliberation costs are commonly used in hierarchical reinforcement learning (HRL) to prevent options from collapsing to primitive actions. Existing approaches rely on fixed penalties or duration-based adaptations, both of which are indirect proxies for an option’s utility. This paper argues that option persistence should instead depend on the opportunity cost of abandoning the current behavior. We propose the Opportunity-Cost Option-Critic ( OC^2 ) framework, which defines a state-dependent deliberation cost using the positive value gap between the active option and its best alternative. This encourages persistence when the current option is clearly preferable and allows rapid switching when a better option emerges. We show that directly learning such costs leads to a degenerate zero-cost solution and introduces an auxiliary objective that aligns the learned cost with the value-gap target. The approach is evaluated in Four Rooms and Pinball domains under stationary, goal-shift, and geometry-shift conditions. In the Pinball goal-shift task, the OC^2 framework achieves a 52% first-ten-episode success rate compared with 18% for a fixed-cost baseline. The results suggest that opportunity-cost-based persistence improves adaptation in non-stationary environments.
[LG-28] What a CAGE 4 Score Measures: An Audit of a Learned Cyber-Defense Benchmark Its Reference Defenders and the Tables Built on Them
链接: https://arxiv.org/abs/2611.14068
作者: James Rice,Ji Hwan Park,Andres Kwasinski
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 47 pages. Code at this https URL (tag arxiv-v1), data at this https URL
Abstract:CAGE Challenge 4 is the benchmark on which learned multi-agent cyber defenders are compared. We reproduce its two reference defenders, KEEP and H-MARL Expert, in a pinned container and show that three things read from it measure less than they are taken to measure. First, the simulator silently discards any action asserted for an agent whose previous action is still running, and charges one point for a discarded Restore as for one that runs. The documented observation does not expose the busy flag. Both reference defenders read it in their own wrappers, and switching that check off at evaluation costs KEEP’s released policy 99.58 points per episode and H-MARL’s 145.98, in both cases the charge for Restores sent to busy agents, with H-MARL’s executed actions unchanged step for step. On a supervisory harness, holding interventions until the agent is idle is worth 25.3 and 27.6 points per episode at the published setting. In training, the check decides how a busy step enters the trajectory. Trained without it, KEEP leads at a hundred updates in all four paired seeds and trails from two hundred on in all four. Continued to two thousand updates on three seeds, the repaired recipe ends inside the released interval on one seed and short on two, by 27 and 125 points, a spread across training seeds far wider than the released reproduction interval. Second, the evaluation seeds the environment but not the policy, so a reported mean is one draw, and 88.9% of per-episode outcome variance is the policy’s own sampling draw. Third, recovery precision depends on the truth it is scored against. Across five published comparison tables, none states the artifact, sampling, criterion and adversary that would make its numbers comparable. We release the instrument, our patches to both defenders, the traces and checkpoints behind every number, and a five-item disclosure checklist.
[LG-29] Generalizing Causal Decision-Making: Markov Equivalence Transportability and Counterfactuals
链接: https://arxiv.org/abs/2611.14066
作者: Min Woo Park,Sanghack Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Structural causal bandits use causal knowledge to guide exploration when reward mechanisms are unknown. This paper extends the approach to partially known causal structure, information from other environments, and realizable counterfactual actions. First, we develop graphical criteria for minimal intervention sets under Markov equivalence and characterize possibly-optimal minimal intervention sets for maximal ancestral graphs. A search algorithm finds sets that are possibly optimal in at least one graph represented by a partial ancestral graph. Second, we derive target reward bounds from source distributions through partial transportability, accounting for non-manipulable variables. Dominance relations provide further bounds for action pruning and restricted exploration, with a regret guarantee under valid prior bounds. Third, we formulate counterfactual structural causal bandits over actions supported by specified experimental capabilities. Graphical characterizations of minimality and possible optimality yield algorithms that remove equivalent or dominated choices while preserving an optimal action. Experiments with specified structural causal models examine how these reductions and transferred bounds affect learning. Together, these results clarify how causal knowledge and experimental capabilities determine which actions merit exploration and guide learning within the retained action space.
[LG-30] ScaleBrain: A Scale-Consistent Framework for EEG Foundation Models
链接: https://arxiv.org/abs/2611.14035
作者: Minyu Zhou,Yajing Wu,Yongqiang Tang,Wang Jia,Wensheng Zhang
类目: Machine Learning (cs.LG); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:Foundation models pre-trained on large unlabeled electroencephalography (EEG) corpora have emerged as a transformative paradigm for Brain-Computer Interface (BCI) applications. Recent EEG foundation models have shown that multi-scale representations derived from fixed-length patches can improve EEG representation learning. Existing frameworks commonly construct temporal scales through feature-level operations. However, these operations leave the correspondence between learned features and scale-specific characteristics of the original EEG signals implicit. Moreover, when the self-supervised objective is not aligned with the multi-scale input representations, information at some temporal scales may receive insufficient supervision during pre-training. In light of this, we propose ScaleBrain, a scale-consistent pre-training framework that explicitly coordinates multi-scale representations of EEG from both the model input and optimization objective sides. Specifically, ScaleBrain introduces multi-scale EEG tokenization to represent the same EEG segment through analysis windows with different temporal spans and shared temporal anchors, and pairs these representations with scale-specific reconstruction to align with the multi-scale input structure and provide supervision across temporal resolutions during self-supervised learning. We further develop an axial spatio-temporal attention mechanism to contextualize the fused multi-scale representation along spatial and temporal dimensions. Extensive evaluations on six public BCI benchmarks against 12 representative baselines show that ScaleBrain achieves the highest mean Balanced Accuracy on five datasets and ranks third on the remaining one, with only 5.0M parameters, demonstrating strong performance and favorable parameter efficiency.
[LG-31] BioXArena-Pro: a research-grade benchmark for autonomous biomedical machine learning agents UAI
链接: https://arxiv.org/abs/2611.13994
作者: Loka Li,Duzhen Zhang,Xingbo Du,Leonard Song,Kun Zhang,Le Song
类目: Machine Learning (cs.LG)
*备注: 51 pages (22 main text, 29 supplementary information), 5 figures, 7 supplementary tables. Code: this https URL ; Data: this https URL ; Leaderboard: this https URL
Abstract:Coding agents are increasingly proposed as autonomous builders of biomedical models, but existing benchmarks are small, near saturation or open to leaked information. We present BioXArena-Pro, 64 research-level modelling tasks from eight biomedical domains, each rebuilt from a published study; most hold out whole biological units such as species or cohorts. Across 22 agent configurations, the best scores 55.1 of 100, and the scaffold matters almost as much as the model. We re-ran a published method for every task: where the published recipe applies unchanged, the best agent and the methods tie; on rebuilt tasks the methods trail by 8.2 points, suggesting that agents gain by adapting to new data. Pretrained models (including biological foundation models) matter where the strongest agent uses them: forbidding them decreases its score on those tasks by 9 points. Every task was audited in several AI-assisted rounds and a final human review.
[LG-32] mporal Behavior Trees for Reinforcement Learning: Specifications as Rewards Curricula and Diagnostics
链接: https://arxiv.org/abs/2611.13993
作者: Vladimir Krsmanovic,Bernd Finkbeiner
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning from formal specifications often separates task definition, reward shaping, curriculum design, and policy diagnostics into different mathematical objects. This fragmentation requires additional conversion steps and discards structure already present in the specification, such as subtask boundaries, ordering constraints, and quantitative measures. We introduce TBTCL (Temporal Behavior Tree Centric Learning), a framework that uses Temporal Behavior Trees (TBTs) as a unified interface for specification-guided reinforcement learning. A single TBT specifies the task, induces dense robustness-based rewards, defines a curriculum, and supports better understanding of learned policies. By adapting TBT syntax and semantics for specification-guided reinforcement learning, we provide a formalism that is more expressive than popular specification languages such as LTL and inherently hierarchical. We present algorithms to automatically extract a curriculum DAG and derive dense, robustness-based rewards directly from TBT semantics. Furthermore, we utilize TBT trace segmentation to build understandable competence profiles and automatically adapt the reward toward bottlenecks. We evaluate TBTCL on six specifications across discrete and continuous domains and show that Temporal Behavior Trees can serve as a specification, a reward, a curriculum, and a diagnostic.
[LG-33] Universal Approximation Theorem for Metaplectic Neural Operators
链接: https://arxiv.org/abs/2611.13991
作者: Ahmed Abdeljawad,Malek Yacine Rezoug
类目: Machine Learning (cs.LG); Functional Analysis (math.FA)
*备注:
Abstract:We prove universal approximation for metaplectic neural operators on nonempty bounded Lipschitz domains. The nonlocal branches are whole-space metaplectic multipliers with zero extension and restriction, associated with any fixed free symplectic matrix. Assuming a smooth, globally Lipschitz, nonpolynomial activation, we obtain compact-uniform approximation of continuous operators from C^s to C^r and from H^s to H^r in the respective output norms, for arbitrary nonnegative integers s,r and finite input and output channel dimensions. In the Sobolev setting, compact input sets must additionally be uniformly bounded in L^\infty . One hidden operator layer with one nonlocal branch suffices. A single scalar symbol atom and finitely many positional features are prescribed by the transform and domain independently of the target operator, compact set and accuracy. The proof combines averaging neural operator theory with an exact chirp-conjugated averaging identity and derivative estimates. The result includes Fourier and fixed nondegenerate fractional Fourier multiplier constructions.
[LG-34] Gradient Inversion through Linear and Affine Subspace Constraints
链接: https://arxiv.org/abs/2611.13976
作者: Konstantinos Bouros,Athanasios P. Liavas
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 26 pages, 10 figures
Abstract:Federated learning enables multiple clients to collaboratively train a machine-learning model without directly sharing their local datasets. However, as prior works have shown, the gradients exchanged during training may reveal sensitive information about client data. Most existing attacks usually optimize dummy inputs to match the true client gradients (Gradient Inversion Attacks), while some attempt to reconstruct them based on the observation that aggregate gradients from a fully connected (FC) layer are a linear combination of its inputs. In this work, we extend this observation by showing that aggregate gradients from an FC layer with the correct scaling are actually an affine combination of its inputs. We evaluate a gradient inversion attack that leverages either known linear-subspace information or its affine-subspace counterpart at each FC layer to improve reconstruction accuracy. Our results show that subspace-based methods can substantially improve reconstruction quality over the chosen baselines in certain settings. We further demonstrate that moderate overestimation of the batch size can generally improve or preserve reconstruction quality, even when the true batch size is unknown. We additionally examine reconstruction under gradient perturbations introduced by a differential privacy (DP) mechanism, providing insight into the effectiveness of the considered formulations under noisy conditions.
[LG-35] Geometry-Dependent Approximation and Stability Properties of Shallow ReLUk Networks on Bounded Euclidean Domains
链接: https://arxiv.org/abs/2611.13952
作者: Yuqing Liu,Tong Mao,Jinchao Xu
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:On bounded Euclidean domains, we study how hidden-parameter geometry affects shallow ReLU ^k networks through results on approximation rates, saturation-type lower bounds, Gram and stiffness matrix spectra, Bernstein and Marcinkiewicz–Zygmund inequalities, discrete least-squares recovery, and inverse approximation. Our analysis uses smooth localization and dyadic Fourier estimates. We quantify parameter geometry by the fill distance h and antipodal separation distance \underline h . For the prescribed network space L_n^k , representative approximation estimates take the form \beginequation* \beginaligned \inf_g_n\in L_n^k|f-g_n|\mathcal L^2(\Omega’)\lesssim h^r|f|\mathcal H^r(\Omega),\ \inf_g_n\in L_n^k|f-g_n|\mathcal L^2(\Omega)\gtrsim \underline h^\fracd+2k+12|f|\mathcal L^2(\Omega’). \endaligned \endequation* The upper bound exploits target regularity up to the critical order \fracd+2k+12 , while the lower bound applies asymptotically to fixed targets smoother than this order. The upper estimate holds on an interior subdomain \Omega’ , whereas the lower estimate concerns the larger domain \Omega . We also obtain geometry-dependent spectral estimates that quantify conditioning and coefficient stability. Further results establish norm discretization and regularized recovery from function values, and infer target regularity from approximation rates under coefficient or separation conditions. Altogether, these results describe how parameter geometry governs approximation accuracy, the stability of coefficient representations, discrete recovery, and the regularity information contained in convergence rates. Subjects: Machine Learning (cs.LG); Numerical Analysis (math.NA) Cite as: arXiv:2611.13952 [cs.LG] (or arXiv:2611.13952v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.13952 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-36] PARDIG: Global Precipitation Estimates Learned from Sparse Satellite Observations
链接: https://arxiv.org/abs/2611.13941
作者: Guy Shalev,Stephan Rasp,Boris Babenko,Andrew El-Kadi,Mohammed Alewi Hassen,Samier Merchant,Fred Zyda,Dominic Masters,Ferran Alet,Salva Rühling Cachay,Ilan Price,Akib Uddin,Nofar Peled Levi,Ben Gaiarin,Aaron Bell,Peter Battaglia,Alvaro Sanchez-Gonzalez
类目: Machine Learning (cs.LG)
*备注:
Abstract:Estimating precipitation at the global scale is vital for Earth system science and disaster response, yet observational coverage and measurement precision remain constrained. Existing solutions provide options that trade off desirable traits such as skill, latency, geographical coverage, temporal coverage, and spatial and temporal resolution. We present PARDIG, an artificial intelligence framework based on a spherical graph-mesh transformer architecture that produces near-real-time global precipitation estimates. PARDIG is trained on dense multi-modal inputs, including atmospheric state analyses and multi-spectral geostationary satellite imagery, to predict sparse, high-precision precipitation measurements from spaceborne radar. At inference, the trained model is applied densely across the globe at 0.1^\circ spatial and 15-minute temporal resolution, producing complete near-real-time precipitation fields from 2016 to the present, alongside a historical variant extending the record back to 1999. Evaluation against three independent radar networks (over the US, Europe, and Japan) and against over 36,000 surface stations worldwide shows that PARDIG consistently outperforms IMERG V07 (Final and Early), MSWEP V3, ERA5, and HRES across precipitation thresholds and regions, with the largest improvements at extreme intensities. Case studies of extreme precipitation events demonstrate that PARDIG successfully captures their spatial structure and timing. The improvements also extend to forecasting: WeatherNext 3, a global weather model, produces more skillful precipitation forecasts when trained to predict PARDIG. Both the historical and near-real-time PARDIG datasets are openly available under a CC BY 4.0 license.
[LG-37] Dual-Core Tensor Wheel Decomposition with Structure-Cached Acceleration for Tensor Recovery
链接: https://arxiv.org/abs/2611.13938
作者: Wenchao Xie,Qingsong Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Tensor wheel decomposition captures complex correlations in high-dimensional data through interactions between outer-ring factors and a high-order central core. However, updating these components requires the explicit construction and manipulation of large-scale matrices, which limits computational efficiency and incurs particularly high computational and memory costs for the central-core update. To address these limitations, we propose dual-core tensor wheel decomposition (DCTW), which factorizes the central core into two lower-order cores linked by a shared latent bond. Each core is coupled to a different group of outer-ring factors. We further propose a structure-cached acceleration strategy that avoids explicit large-scale matrix multi- plications and caches intermediate tensors generated during tensor network contractions for reuse across block updates. This reuse reduces redundant computation and avoids the explicit construction of large matrices. Complexity analysis and experiments show that the strategy based on structural caching accelerates optimization compared with explicit and implicit up- date schemes while limiting the increase in peak memory as the ranks grow. Experiments on color video completion and robust denoising show that DCTW matches or outperforms representative tensor network methods in reconstruction quality, computational efficiency, and scalability.
[LG-38] Learning from First Discrepancies with Joint Reset and Pilot Guarantees
链接: https://arxiv.org/abs/2611.13913
作者: Michal Čertík
类目: Machine Learning (cs.LG)
*备注:
Abstract:A run that stops at its first wrong decision shows the queries and their correct labels up to and including that decision, and nothing after it. We study how many such runs are needed for learning. Each run tests a hypothesis, the pilot, chosen beforehand from a fixed binary class of VC dimension at most d , on a fresh record of at most D queries drawn from one unknown distribution that does not depend on the pilot. The terminal information of a record is shown only if the pilot makes no discrepancy, and a bounded loss, fixed in advance, is attached to the position where a hypothesis stops. If the discrepancy probabilities of members of the class have infimum at most f and their risks have infimum at most r , one learner uses \widetilde O((d+1)[D(f+\varepsilon)+r]/\varepsilon^2) runs and \widetilde O(D(1+f/\varepsilon)) distinct pilots on every history. It returns a member of the class and an interval of width at most \varepsilon containing, with probability at least 1-\delta , both the output’s risk and the optimal risk. We also treat unknown ceilings and expected costs. Lower bounds for finite classes show that each order is necessary in the worst case, possibly on different instances, at positive dimension, small accuracy and fixed confidence. The learner enumerates label patterns of the class on displayed queries, possibly in exponential time; the results, under standard measurability conditions, concern statistical resources only. The proof combines a gain computable from stopped displays, exponential weights, a sparse depth-robust graph and a harmonic-mean bound.
[LG-39] Conformal Risk Control for QoT-Aware Lightpath Feasibility Under Technology-Driven Domain Shift
链接: https://arxiv.org/abs/2611.13893
作者: Kiarash Rezaei,Omran Ayoub,Paolo Monti,Carlos Natalino
类目: Machine Learning (cs.LG)
*备注: 8 pages; 4 figures; accepted for publication in the Journal of Lightwave Technology (JLT)
Abstract:Artificial intelligence and machine learning (AI/ML)-based regression models support operational decision-making in optical networks but typically expose only point predictions, which carry no statistically controlled information about their reliability. This paper proposes a post-hoc conformal risk control (CRC) framework that keeps a pretrained AI/ML regressor fixed, provides a finite-sample guarantee on uncertainty band miscoverage, and maps the band to operational decisions through policy rules. The guarantee applies to interval miscoverage, while decision-error rates are evaluated empirically. We instantiate the framework for quality of transmission (QoT)-aware lightpath (LP) feasibility assessment, in which a regressor predicts optical signal-to-noise ratio (OSNR) under domain shifts caused by fiber, amplifier, and switching upgrades. Feature shift contribution scores identify the LP features whose relation to QoT changes the most across a technology transition, and two policies translate the bands into feasibility decisions, allowing operators to tune the trade-off between conservatively rejecting or aggressively accepting LP realizations. Experiments on open space-division multiplexing (SDM) QoT datasets over the CONUS topology show that, for a target coverage of 90%, the uncertainty bands meet the target in all 170 trials spanning 17 domain shifts and ten calibration-test partitions, whereas a source-only quantile regression (QR) baseline meets it in none. Under severe shifts, the efficiency-oriented (EO) policy empirically improves zero-shot (ZS) feasibility-decision accuracy from about 85% to above 99%, approaching a transfer learning (TL) policy while avoiding retraining. The framework provides finite-sample control of uncertainty band miscoverage for regression-based optical network decision support, with potential applications beyond QoT-aware LP feasibility assessment.
[LG-40] Where to Approximate in Neurosymbolic Inference? NEURIPS2026
链接: https://arxiv.org/abs/2611.13862
作者: Samy Badreddine,Emile van Krieken,Luciano Serafini,Antonio Vergari
类目: Machine Learning (cs.LG)
*备注: Accepted to NeurIPS 2026
Abstract:Probabilistic neurosymbolic methods rely on weighted model counting (WMC) to combine neural predictors with symbolic constraints. Exact WMC is #P-hard and typically scales poorly, so many methods resort to approximations. By unifying existing approaches under a three-step bottom-up approximate compilation pipeline, we study where approximation budget is best allocated. Inspired by tensor networks, we instantiate two steps with tensor train decompositions and derive new pipelines that exactly multiply approximated factors and recompress their products via SVD- or interpolation-based schemes. Across six tasks in Sudoku, arithmetic reasoning, and scene understanding, and across increasing problem sizes, our pipelines produce WMC estimates orders-of-magnitude more accurate than prior methods. This translates into stronger downstream learning on tasks where reasoning relies on tight interactions between symbolic factors, where cruder approximations ignore these dependencies. Our pipelines open novel design space for effective approximate compilation of challenging probabilistic reasoning tasks.
[LG-41] JAX-WAVE: A Differentiable Physics-Supervised Framework for Inverse Wave Design in Heterogeneous Media
链接: https://arxiv.org/abs/2611.13826
作者: Honghui Du,Binyao Guo,Yushu Pan
类目: Machine Learning (cs.LG)
*备注:
Abstract:Designing a prescribed wave field in a heterogeneous medium requires an initial pressure distribution that compensates for the medium’s propagation response. Repeated numerical inversion solves this task afresh for each target and environment. We introduce JAX-WAVE for amortized inverse wave design: a shared neural network maps desired terminal fields and known wave-speed maps directly to bounded initial pressures. Training through a differentiable finite-difference time-domain solver uses the physical design objective without requiring optimized control labels. Linear propagation and symmetric pressure bounds imply an exact sign-reversal relation for the regularized optimum. A shared-weight projection enforces this relation, preserves feasibility, and satisfies an exact identity for the paired-sign objective. We characterize the discrete propagator and its weighted adjoint to connect learning, numerical inversion, and optimality assessment. On an independent confirmation set, three trained models achieve unseen-material errors of 25.24% for geometric targets and 4.77% for reachable targets. Matched-capacity comparisons show improved reconstruction with the sign parameterization. Within a 5 ms median online budget, the learned map reduces mean error by 45% relative to accelerated numerical inversion on the heterogeneous benchmark; longer numerical solves recover greater accuracy. The results establish a reusable inverse-design procedure and show how its accuracy and computational value depend on target structure and query workload.
[LG-42] AnchorMTS: Multi-View Signal Representation and Forecast-Anchored Fusion for Long-Term Time-Series Forecasting
链接: https://arxiv.org/abs/2611.13794
作者: Gao Wu,Tingting Xu,Changning Wu,Tengfei Long,Yong Liu,Jiabin Jin,Kexin Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Long-term time-series forecasters typically optimize a single numerical representation, even though the same history exposes complementary geometry, recurrence, and semantic structure. We propose AnchorMTS, which derives self-aligned visual and textual views from each observed window and uses them only to refine a complete numerical forecast. A trainable evidence path produces a candidate forecast, while a parameter-free output projection enforces an exact per-variable bound on its displacement from the numerical anchor. Across six benchmarks and four horizons, AnchorMTS achieves the best macro MSE/MAE (0.336/0.367) among seven supervised baselines and wins 18 of 24 MSE settings against the cellwise baseline envelope; removing the auxiliary path raises macro MSE to 0.340. The projection leaves 95.8% of candidate refinements unchanged while yielding zero bound violations, showing that complementary multi-view evidence can improve forecasting while its final influence remains explicitly controlled.
[LG-43] Omni-JEV: Text and Dual-Stream Speech Decisions
链接: https://arxiv.org/abs/2611.13777
作者: Shengzhou Kong
类目: Machine Learning (cs.LG)
*备注: 23 pages, 5 figures, 11 tables
Abstract:A user utterance does not uniquely determine the next action in full-duplex dialogue: its consequences depend on the assistant’s response state and interaction history. Speech activity, utterance completion, and interruption intent provide related evidence, whose implications for a response require an explicit action policy. We formulate this task at collected-segment endpoints and introduce Omni-JEV, which maps text or role-indexed user and assistant-reference speech to probabilities over five interaction actions. An aligned speech-to-speech understanding backbone encodes the acoustic roles separately and shares its candidate readout with text inputs. Bilingual weak supervision supports cross-entropy fine-tuning followed by reinforcement learning for probability-sensitive decisions (RLCD). We compare Gaussian-logit and categorical Choice RLCD from the same supervised initialization under matched training exposure, observing different trade-offs between classification and probability quality. Gaussian Omni-JEV achieves test accuracies of 84.08% for audio and 96.55% for text. This common action interface supports decisions about when a full-duplex voice assistant should respond, preserve ongoing speech, interrupt, or resume an interrupted response.
[LG-44] Beyond Interaction Scores: Selecting Feature Interactions via Independent Replication
链接: https://arxiv.org/abs/2611.13765
作者: Jincheng Gao,Shuo Sun
类目: Machine Learning (cs.LG); Databases (cs.DB); Machine Learning (stat.ML)
*备注: 20 pages. Code and results: this https URL
Abstract:Learning feature interactions is essential for accurate prediction and interpretable modeling, yet a high interaction score may reflect fitting noise rather than a reproducible pattern. Ranking methods score feature pairs but leave the cutoff to the user; testing methods set the cutoff but rely on repeated model refits or a model of the joint feature distribution. We present LENS, a model-agnostic framework for selecting feature interactions through independent replication. LENS trains two models with the same learning algorithm on disjoint data and accesses them only through predictions. For each feature pair, it removes main effects from the joint-effect surface to isolate an interaction pattern, ranks pairs by its strength, and tests whether the second model reproduces it at a meaningful strength. A single multiple-testing correction determines which pairs to retain, using only two model fits regardless of the number of features. We prove that a replication ratio alone can retain arbitrarily weak patterns shared by both fits, whereas a strength floor restores selection consistency, and we characterize when normalization in Friedman’s H^2 reverses interaction rankings. On 16 synthetic designs, LENS matches leading model-specific ranking methods (e.g., TreeSHAP) and recovers 91% of material interactions, against 45% for the knockoff-based DIAMOND, without selecting false pairs; with 1,225 candidate pairs and no interactions, it makes substantially fewer false discoveries. On large-scale French motor insurance data, it identifies interactions documented in the actuarial literature and recovers 90% of the deviance gain obtained using all pairs. On two credit datasets, LENS selects few pairs while maintaining or slightly improving predictive performance over the additive baseline.
[LG-45] When Covariance Is Not Enough: Kernel-Based Regularization for Single-Cell Counterfactuals
链接: https://arxiv.org/abs/2611.13748
作者: Korbinian Traeuble,Edward de Brouwer,Archit Verma,Doron Haviv,Kipper Fletez-Brant
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注: 17 pages, 3 figures, 5 tables
Abstract:Counterfactual editing of single cells, predicting how an observed cell would look had one factor been different, enables in-silico screening where experiments are infeasible, but is useful only if the edit is faithful to the individual and not merely to the condition. Concept bottleneck generative models make the effects of interventions interpretable by decomposing a cell’s representation into known and unknown factors. Previous work uses a linear cross-covariance penalty to disentangle known and unknown dimensions. We demonstrate that this first-order criterion does not fully separate information when non-linear interactions are present. We first demonstrate the inadequacy of linear cross-factor penalization with simulation experiments with controlled edits and known non-linear covariate effects. We then demonstrate that adding a nonlinear kernel criterion, the normalized Hilbert-Schmidt Independence Criterion, is able to recover the true intervention effects on simulation data. Finally, we apply the non-linear concept bottleneck model to a complex single-cell RNA-seq dataset and find that it improves the ability to generate counterfactual expression profiles of cells under different tissue environments.
[LG-46] Plug-and-Play ADMM for Inverse Problems with Flow Matching Denoiser NEURIPS2026
链接: https://arxiv.org/abs/2611.13742
作者: Dibyanshu Kumar,Magda Gregorova
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Plug-and-Play (PnP) methods with generative priors have achieved strong performance in inverse problems, with recent flow matching models enabling fast and scalable reconstruction. However, a key challenge in PnP methods is maintaining consistency between data fidelity updates and the learned generative prior, as data-consistency steps can move iterates away from the flow trajectory. We propose an ADMM-based PnP framework for flow matching priors that enforces stronger consistency between the measurement model and the prior through auxiliary variables and dual updates. In addition, we introduce a trajectory correction mechanism that restores consistency with the flow via a combination of deterministic and stochastic projections. Our method provides a more principled integration of flow priors within PnP frameworks, achieving high-quality reconstructions with reduced computational cost.
[LG-47] Deterministic Reproducible Inference in Exact Integer Arithmetic and Verifiable Distributed Inference as Its Consequence
链接: https://arxiv.org/abs/2611.13730
作者: Sayed Amir Karim(Pelloran Inc.)
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Software Engineering (cs.SE)
*备注:
Abstract:Optimized floating-point inference is not reproducible across environments: the same model on different hardware, compilers or thread counts can produce different bits. We present tritify, an open-source runtime that executes the forward pass of real models – decoder-only language models, Mixture-of-Experts decoders, and the Segment Anything 2.1 image/video stack – with the entire run-time path in exact integer arithmetic; floating point is confined to offline quantization and the float reference used for verification. We make three separate claims and verify each. (1) Cross-hardware reproducibility: the integer path produces bit-identical output run to run, across CPU instruction-set architectures, and across CPU/GPU, carried by a reproducing SHA-256 of the output. (2) Decision fidelity: the integer path reproduces the float reference’s decision on the models and inputs tested, with the numeric exceptions measured; per-layer fidelity through sixteen layers of OLMoE-1B-7B and of a 106-billion-parameter MoE shows no fidelity cliff. (3) Location independence: a model’s computation can be split across instruction sets, CPU and GPU, and networked machines with zero effect on the output bits. GLM-4.5-Air (106,852,251,264 parameters, 427 GB of integer codes) generated its answer across three machines, none of which can hold its integer codes in RAM, bit-identically to a single-host reference. The consequence is verifiable distributed inference: an untrusted server or a fleet of devices runs the model while an open scalar oracle re-derives the exact bytes; a guarantee against a dishonest server is future work. We do not claim a per-device speed-up, bit-equality of floating-valued intermediates, or confidentiality. Code: this https URL
[LG-48] Weighted-AoI-Driven UAV Trajectory and Reporting for Dynamic CKM Tracking
链接: https://arxiv.org/abs/2611.13726
作者: Zeyu Zhao,Yueling Che,Sheng Luo
类目: Machine Learning (cs.LG)
*备注: 6 pages, 2 figures, 2 tables, submitted to the WCNC conference
Abstract:Existing channel knowledge map (CKM) construction assumes quasi-static environments, failing when channels rapidly decorrelate. This paper investigates continuous online dynamic CKM tracking over an airborne 2D plane via a UAV fleet. We formulate a long-term error minimization problem under UAV mobility and reporting constraints. This problem, however, is intractable with unobservable errors for unmeasured cells. To tackle this, we propose a metric-driven scheduling approach featuring the Weighted-Age of Information (W-AoI). W-AoI evaluates cells by combining exact temporal staleness with spatial uncertainty predicted by a 2D U-Net. We prove that top- K W-AoI selection maximizes the one-slot error reduction bound. Guided by this guarantee, we develop a lightweight greedy trajectory policy incorporating continuous along-path sensing. Simulations demonstrate our method reduces steady-state error by 3.8 dB ^2 and 4.8 dB ^2 over AoI-only and FIFO baselines, closely approaching an idealized oracle.
[LG-49] RUER: Test-Time Reward Correction Using an Error-Aware Rectifier for Reinforcement Learning
链接: https://arxiv.org/abs/2611.13714
作者: Mingxiang Shen,Jinhao Li,Jiaming Xu,Yibo Han,Guohao Dai,Yijia Zhang,Ningyi Xu
类目: Machine Learning (cs.LG)
*备注: 26 pages, 14 figures
Abstract:Test-time reinforcement learning enables language models to adapt using rewards derived from their own answers. However, majority voting can reinforce incorrect solutions even when correct alternatives are available. Our training audit shows that false positives (incorrect responses receiving positive rewards) dominate reward errors. We focus on a practical regime where ground-truth rewards are difficult to obtain at scale, while a limited labeled source set is available. We introduce TRUER, a framework that uses limited supervision to train a reward rectifier that distinguishes correctness from consensus. To achieve this goal, we train the rectifier with an emphasis on incorrect majorities and correct minorities, targeting cases where consensus and correctness disagree. After that, we freeze the rectifier and use it to correct rewards during subsequent test-time adaptation. By improving reward accuracy and thereby model performance, TRUER consistently outperforms naive TTRL in both metrics across all evaluated configurations.
[LG-50] FedVP: Federated Learning across Heterogeneous Models by Pooling Locally Trained Verifiers
链接: https://arxiv.org/abs/2611.13708
作者: Yifan Li,Xi Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Conventional federated learning relies on the shared model backbone to aggregate client updates, limiting collaboration among institutions that deploy large language models (LLMs) from different families and at different scales. We propose Federated Verifier Pool (FEDVP), a new federated learning paradigm that enables heterogeneous clients to collaborate through shared verification rather than parameter aggregation. Each client trains a verifier using local data and responses generated by its chosen LLM backbone. The server pools these independently trained verifiers and combines their judgments to select answers without merging model parameters. This allows both LLM backbones and verifiers to differ in architecture and size, while keeping the LLM backbones frozen and deriving collaborative gains from improved answer selection. We evaluate FEDVP across five public medical question-answering datasets treated as federated clients, using Qwen and LLaMA backbones ranging from 3B to 14B parameters. FEDVP consistently improves performance across all five clients and outperforms the strongest conventional federated baselines by 1.46-5.79 percentage points in mean accuracy.
[LG-51] Decision-Focused Learning via Neural Optimization Layers for Battery-Assisted Grid-Import Smoothing in AI Data Centers
链接: https://arxiv.org/abs/2611.13694
作者: Xinda Zheng,Canchen Jiang,Frits de Nijs,Edward Lam,Markus Wagner
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Rapid AI data-center power fluctuations challenge grid integration. Sustained grid-import smoothing requires allocating limited battery power and energy under uncertain demand. We formulate risk-aware BESS scheduling that coordinates advance reserve commitments and demand-responsive dispatch, accounting for operating costs, grid-ramp penalties, and their conditional value-at-risk. We develop battery scheduling decision-focused learning (BS-DFL) with a constraint-aware neural optimization layer trained directly from this objective without optimal-solution labels. The trained layer is then frozen to guide demand-predictor learning through scheduling costs and ramping risk, enabling solver-free rolling-horizon scheduling. In a 10,000-minute simulation using loads derived from measured AI workloads, BS-DFL reduces mean absolute minute-to-minute grid-import changes by 71.70% and total scheduling cost by 18.01% relative to an MSE-trained predictor coupled with a mixed-integer linear programming solver, with an average decision time of 18.77 ms.
[LG-52] SepsisFlow: an event-stream foundation model for continuous risk warning and trajectory modelling in ICU sepsis
链接: https://arxiv.org/abs/2611.13661
作者: Yawen Zhang,Wanzhe Xu,Zhenhua Zhang,Jinyu Wu,Yizhou Zhuang,Yitao Yang,Kenta Nakai
类目: Machine Learning (cs.LG)
*备注: 50 pages, 6 figures, 2 tables
Abstract:Sepsis is a leading cause of ICU death with heterogeneous onset and course, yet most warning models predict only whether sepsis will occur, leaving clinicians uncertain of its onset context and early trajectory once an alarm sounds. To answer these questions on one hourly clock from ICU admission, we built SepsisFlow: before onset, it combines a Mamba model pretrained on 2.57 million EHRs with about 1,700 hourly features in gradient-boosted trees to predict 6/12/24-h risk; at onset, it fuses Mamba and tabular encoders via a gated network into continuous patient states defining onset contexts; after onset, it encodes 72-h organ-support and physiological trajectories via an attention bidirectional recurrent network into early courses. For pre-onset warning in MIMIC-IV, SepsisFlow detected more cases than NEWS2, qSOFA, and SIRS at equal alarms, gaining about 8 cases per 100 from hourly updating, while outperforming 16 retrained algorithms and maintaining risk ranking in eICU. At onset, its contexts separated 2% cardiovascular-ICU from 15%-17% other 28-day mortality, and within each context after onset, persistent support carried fivefold higher mortality than de-escalation. SepsisFlow establishes a foundation-model paradigm for continuous risk and trajectory stratification in critical care.
[LG-53] CP-MLPs: A Tensor-Rank Theory of Tied and Untied MLP Blocks
链接: https://arxiv.org/abs/2611.13647
作者: Md Rifat Arefin,Farzaneh Heidari,Irina Rish,Guillaume Rabusseau
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Transformer feed-forward blocks combine several design choices: squared activations, gating, and tied versus untied parameter sharing, whose separate contributions to expressivity remain poorly understood. We introduce CP-MLPs, a tensor framework in which each multiplicative unit contributes a rank-1 interaction with three roles: detecting an input feature, reading a value, and writing to the output. Tied gated blocks such as ReLU ^2 force the detector and value roles to share the same direction; untied gated blocks such as ReGLU and SwiGLU let them differ. We show that this distinction yields exact, degree-wise tensor-rank separations: untied detector-value units are strictly more width-efficient for indefinite quadratic interactions and role-asymmetric monomials, while tied units are structurally matched to symmetric pure-power targets. The width advantage of untying therefore depends on the target structure. Experiments show loss plateaus below the exact-representation width, but these plateaus alone do not establish a positive approximation gap: arbitrarily accurate approximation is governed by border rank. In trained gated MLPs, untied blocks retain an advantage in selected comparisons at matched input-projection budget. Code is available at this https URL.
[LG-54] MouldIt: Data Efficiency of Multi-Task Temporal Representation Learning in Injection Moulding
链接: https://arxiv.org/abs/2611.13646
作者: Abdullah Ayad
类目: Machine Learning (cs.LG); Robotics (cs.RO); Signal Processing (eess.SP)
*备注: 14 pages, 10 figures, 4 tables
Abstract:Smart industrial solutions that can address multiple tasks require models that understand the underlying processes. We examine representation learning for modeling the injection moulding process as an example of such solutions. We consider its data efficiency and multi-modal benefit compared to classical machine learning alternatives. To this end, we synthesize a process-informed dataset based on a real reference cycle of an Erlanger Träger part. We then introduce MouldIt, a temporal convolution and attention based variational autoencoder that reconstructs in-mould signals, while classifying parts as acceptable or as one of our synthetic failure modes. We evaluate MouldIt on two tasks, known-class classification and unknown-class detection, across nine dataset sizes in single-modal and multi-modal settings. We compare it with an accept-all baseline and the classical methods k-nearest neighbors, logistic regression, random forests, and principal component analysis, which we adapt to support both tasks. MouldIt outperforms classical methods across both tasks from 4000 cycles in the single-modal setting and from 2000 cycles in the multi-modal setting. It achieves part sorting losses of 6.0 \pm 0.7% and 1.0 \pm 0.3% in the single-modal and multi-modal settings respectively, ranking first across all baselines and a combination of principal component analysis and random forests.
[LG-55] Curriculum proof repair with learned counterexamples NEURIPS2026
链接: https://arxiv.org/abs/2611.13627
作者: Yuechun Sun,Jun Yang,Kexin Pei
类目: Programming Languages (cs.PL); Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026. 28 pages
Abstract:Language models (LMs) have substantially lowered the barrier to applying formal verification for building reliable and secure software. Existing approaches commonly follow the verifier-in-the-loop paradigm, but the verifier often provides only coarse, binary pass/fail feedback, which offers little actionable signal on how the proof should be repaired. Recent work shows that counterexamples provide more actionable feedback by grounding proof failures in concrete program states, but generating useful ones at inference time requires costly iterative refinement. We present TopoVerus, an LM-based framework for Verus proof repair that internalizes counterexample-guided diagnosis as a model capability, producing useful counterexamples in a single pass. Our key insight is to train counterexample generation not as a standalone validation task, but as a data-driven diagnostic objective: a counterexample is useful when conditioning on it helps the model produce a safe, verifier-passing proof. Given a failed Verus proof and verifier error, TopoVerus generates a structured source-level counterexample, repair rationale, and repaired proof in one trajectory, optimized with reinforcement learning from Verus feedback and a proof-code safety gate. To guide the order of invariant repairs, TopoVerus introduces a curriculum with a topological graph reward based on invariant dependencies derived from inductiveness tests, which encourages counterexample generation to follow the same topological order. Across seven Verus benchmarks with 471 problems, TopoVerus achieves 61.6%/72.2% weighted-average Safe-Pass@1/Safe-Pass@3, outperforming the strongest baseline, Claude Sonnet 4.5, by 5.7/6.3 points. We also show that TopoVerus’s learned counterexamples can directly augment the reasoning of existing LM-based proof repair approaches, improving their success rate by 64.3% compared to chain-of-thought diagnostics.
[LG-56] Multifidelity Operator Inference for Non-intrusive Model Reduction under Data Scarcity
链接: https://arxiv.org/abs/2611.13624
作者: Vivian Zhang,Tomoki Koike,Atticus Rex,Elizabeth Qian
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Computation (stat.CO)
*备注: 21 pages, AIAA SciTech Forum 2027
Abstract:Many-query analyses, in which a computational model is repeatedly evaluated, play an important role in uncertainty quantification and optimization for engineering design. However, many-query analyses can be prohibitively expensive when considering high-fidelity numerical models, especially those governed by partial differential equations (PDEs). Surrogate modeling replaces costly simulations with cheap approximations, enabling practical uncertainty quantification and design workflows. Machine learning (ML) approaches to surrogate modeling have gained significant traction in the development of surrogate models due to their non-intrusive nature; they do not require access to the underlying simulation code. The Operator Inference (OpInf) approach, in particular, fits reduced operators to state and corresponding time derivative data. However, high-fidelity training data is scarce due to limited computational budgets, and OpInf models trained on such scarce data often exhibit high variance, a critical liability in high-risk engineering design. We propose Multifidelity Operator Inference (MF-OpInf), which reduces this variance by supplementing scarce high-fidelity snapshots with abundant low-fidelity data drawn from coarser discretizations of the same problem. MF-OpInf handles data across varying fidelity levels by utilizing a multifidelity linear regression framework to estimate the reduced operators. Numerical experiments on model PDE problems demonstrate that MF-OpInf predicts PDE solution fields with higher accuracy compared to the high-fidelity-only OpInf with the same computational budget.
[LG-57] Unified Optimization of Traffic Signal and Lane Channelization for an Isolated Intersection Using Reinforcement Learning
链接: https://arxiv.org/abs/2611.13599
作者: Jiwon Kang,Jiyoung Kim,Hyunseok Jun,Keemin Sohn
类目: Machine Learning (cs.LG)
*备注:
Abstract:Optimizing traffic signal control has been a long-lasting critical issue in traffic engineering, and a number of robust methodologies have emerged to solve the problem so far. On the other hand, the road design for lane channelization is a static problem that is difficult to solve jointly with the real-time traffic signal control. If traffic volume does not vary, it is possible that both problems can be solved in an integrated manner as a single static optimization problem. Recently, researchers have devised a mixed integer linear problem (MILP) to jointly determine the traffic signal and road width designs. However, this joint optimization problem must be solved repeatedly whenever the traffic flow pattern varies. Solving the MILP may also take impractical computing time as the problem size gets larger. We present a robust methodology for solving the joint optimization problem using reinforcement learning (RL) on a real-time basis. Abrupt changes in lane markings with an incompatible signal phase can jeopardize drivers. To sidestep such a dangerous action change, we train a model with human feedback to filter infeasible action transitions in a rule-based manner. We devised a deep Q-network that can accommodate joint actions for traffic signal and lane marking plans. To ensure the implementation of the proposed operation, a variable sign for lane markings is necessary at each stop line of an intersection. The experimental results confirm that the proposed methodology enhances the intersection performance by increasing throughput.
[LG-58] Investigating Composite Modeling in Multi-Objective Bayesian Optimization for Scientific Design
链接: https://arxiv.org/abs/2611.13565
作者: Ricky Ye,Sahas Goli,Muhammad Ali,Poompol Buathong,Peter I. Frazier
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 20 pages, 6 tables
Abstract:Scientific design often requires balancing competing objectives that are expensive to evaluate through experiments or simulations. Multi-objective Bayesian optimization (MOBO) addresses this challenge by using probabilistic models to guide the selection of promising designs. In many applications, tunable inputs map to observable intermediate responses, which are then combined through known functions to compute the final objectives. Prior work on Bayesian optimization of composite functions shows that learning from these intermediate responses, which we call Composite modeling, can substantially improve sampling efficiency in single-objective optimization, compared to methods that condition only on black-box observations of the objective function. The benefits of Composite modeling in MOBO, however, remain understudied. In this work, we study the benefits of Composite modeling in MOBO and investigate the factors driving variation in its gains. We first establish single-objective conditions under which intermediate observations provide no additional predictive information beyond objective values, where composite and black box methods use the same warped Gaussian process prior. Controlled numerical experiments with versions of four MOBO methods across eight synthetic and application-inspired scientific-design benchmarks examine how these conditions relate to optimization gains in the multi-objective setting, where the black box approach models the objective functions with a standard multi-output Gaussian process. Both investigations support the claim that composite modeling tends to help more when the function mapping intermediate responses to final objectives is non-bijective and non-affine.
[LG-59] Discovering Structurally Plausible and Interpretable Cognitive Models with Large Language Models
链接: https://arxiv.org/abs/2611.13561
作者: Boyoung Kwon,Jiyoon Seo,Sang Ho Lee
类目: Machine Learning (cs.LG)
*备注:
Abstract:Computational cognitive modeling has traditionally relied on manual model construction, which is a time-intensive process that requires substantial domain expertise. Although recent LLM-based approaches have begun to automate model generation, they often prioritize predictive accuracy at the expense of structural plausibility and parameter identifiability. We introduce DisCo, an LLM-guided framework that discovers interpretable symbolic cognitive models through evolutionary search with two structural components: a Validator that filters structurally invalid models, and a Cleaner that promotes parameter identifiability by removing redundant parameters. Across three behavioral tasks, DisCo consistently discovers models that achieve performance competitive with task-specific baselines. Ablation analyses show that the Validator is important for recovering psychologically meaningful structure. Without this component, models tend to violate behavioral constraints and show less consistent associations with external cognitive measures. These results suggest that LLM-driven model discovery, when coupled with appropriate structural constraints, can yield cognitively plausible models that go beyond predictive fit.
[LG-60] abular Foundation Models in Financial Predictions
链接: https://arxiv.org/abs/2611.13550
作者: Ed deHaan,Allen H. Huang
类目: Machine Learning (cs.LG); Computational Finance (q-fin.CP); Statistical Finance (q-fin.ST)
*备注:
Abstract:This technical note introduces tabular foundation models (TFMs) to accounting research and evaluates their performance in one common benchmark task: predicting firms’ next-year earnings changes. TFMs produce numeric predictions and learn in context, without post-release training or tuning, making them natural and easy to use for forecasting. The TFMs we study are pretrained on synthetic tabular data, so they avoid look-ahead bias from memorizing firms’ outcomes. We compare Google Research’s TabFM with a fine-tuned open-weight large language model developed in recent accounting research and with several tree-based models. TabFM is more accurate than all comparison models. It is also faster and less computationally intensive than the large language model. Several tree-based specifications produce predictions more quickly than TabFM, so choosing between them involves a trade-off between accuracy and speed. We conclude that TFMs have promise as a new tool for forecasting in accounting and finance. However, our evaluation is based on a single benchmark task, and no class of models is likely to dominate in all settings.
[LG-61] Closing the BF16-FP16 Convergence Speed Gap in RL Post-Training with Stochastic Rounding
链接: https://arxiv.org/abs/2611.13545
作者: Mahdi Nazemi,Oleg Rybakov,Tijmen Blankevoort,Asit Mishra,Carlo del Mundo
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:BF16 is widely used to pretrain large language models because it has the same dynamic range as FP32 and converges similarly to FP32 training. However, in reinforcement learning with verifiable rewards, where learning rates are typically low, we find that standard mixed-precision BF16 training converges more slowly than FP16 training at the same learning rate. We attribute this gap to weight sticking: when FP32 shadow weights are quantized to BF16 using round-to-nearest, small updates often leave the BF16 weights used for rollout generation and training unchanged. Consequently, several optimizer steps may be needed before updates to the shadow weights are reflected in the BF16 weights. Raising the BF16 learning rate reduces weight sticking and matches FP16’s convergence speed, but it also increases the KL divergence from the reference model. We instead apply stochastic rounding when quantizing the FP32 shadow weights to BF16. This approach matches FP16’s convergence speed across different models and datasets while reducing the KL divergence relative to BF16 training with a larger learning rate. Unlike FP16, it keeps BF16’s wider dynamic range and needs no loss scaling.
[LG-62] Adaptive catalyst discovery emerges from composable scientific intelligence
链接: https://arxiv.org/abs/2611.13536
作者: Wentao Li,Qilong Cai,Qi Lei,Jinxing Chen,Ruixuan Chen,Jiangjie Qiu,Botian Wang,Kai Zhao,Honghao Chen,Leyi Zhao,Zemeng Wang,Yijun Li,Jun Yin,Fangfang Li,Hongxuan Liu,Yingdong Lu,Yawen Ouyang,Manu Suvarna,Wei-Ying Ma,Ya-Qin Zhang,Kostya S. Novoselov,Hao Zhou,Jiong Lu,Xiaonan Wang
类目: Machine Learning (cs.LG)
*备注: 29 pages, 8 figures
Abstract:Catalysis underpins chemical manufacturing, yet knowledge for catalyst design remains dispersed across experimental observations, computational data and catalysis subdisciplines. To overcome this challenge, we present Composable Catalytic Intelligence (CatAI), a physics-grounded modular AI framework that learns 1.8 million structure-property relations and organizes them into 30 physicochemical encoders. This reusable intermediate layer enables task-specific models to guide catalyst discovery for targeted reactions and objectives. Across benchmark tasks, CatAI achieves a 7.8% lower task-averaged prediction error than the next-best models. In experimental validation, CatAI identified a new confined-passable pocket catalyst applicable in industrial homogeneous polymer synthesis with 9 times the activity of the commercial benchmark. In parallel, it guided autonomous closed-loop optimization of a highly active Cu-based geminal-atom photocatalyst for C-O coupling reactions surpassing the traditional Ni-based systems. This approach connects property-level data to catalytic objectives, supporting physicochemical knowledge reuse across mechanistically distinct systems through computational screening and iterative model-experiment feedback.
[LG-63] Beyond the Log-Linear Frontier: Pre-Asymptotic Analysis of Finite-Budget Neural Scaling Laws
链接: https://arxiv.org/abs/2611.13534
作者: Tianyu Zhao,Zhixiang Ren
类目: Machine Learning (cs.LG)
*备注: 72 pages, 8 figures, 4 tables, including appendices
Abstract:Classical neural scaling laws model excess risk as a separable power law in model capacity N and dataset size D . At finite budgets, however, empirical learning curves and compute-optimal allocations depart from this log-linear frontier. This paper analyzes how spectral and domain heterogeneity govern these finite-scale departures. Along the capacity axis, Mellin–Barnes analysis of covariance resolvents yields a three-term expansion where competing spectral and regularization modes produce finite-scale curvature and logarithmic transitions. Because fitting multiple power laws on narrow pilot grids is ill-conditioned, we eliminate the loss floor via first-order Aitken differencing to obtain a well-conditioned closed-form initializer. Along the data axis, KKT duality reduces K -domain mixing to scalar water-filling, yielding finite-budget bottleneck bounds, an online sampling curriculum, and a simplex Hessian metric for cross-domain transfer. Along the iso-compute frontier C=6ND , we prove that the optimal allocation exponent drifts in proportion to capacity and data exponent variances, while Schur-complement reduction separates intrinsic drift from frozen-hyperparameter bias. Across 45 operator spectra, Pythia (70M–12B), Chinchilla (245 runs), and RegMix (17 domains), our estimators achieve 5.89% holdout MAPE, cut high-compute allocation regret by up to 76.4% , and reduce cross-scale mixture RMSE by 59.4% with zero selection regret.
[LG-64] he RSI Tax: When Recursive Self-Improvement Is Not Worth the Cost
链接: https://arxiv.org/abs/2611.13524
作者: Yitong Zhang,Ang Li,Yisen Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recursive self-improvement (RSI) seeks to improve an agent using its own task experience. Its practical value depends on whether performance gains justify the full cost of self-improvement and task execution. We evaluate this trade-off on continual task streams, scoring each task before its feedback is used for subsequent updates. We define the \emphRSI Tax as the token cost of the self-improvement process and include it alongside task-execution costs in end-to-end evaluation. Across six methods and two language models, a bounded-note baseline matches or exceeds the observed accuracy of Meta-Harness, EvoSkill, and RRSI, with mean total-token savings of 65.6-71.0%. We decompose the RSI Tax into verification, exploration, and writing costs, and track subsequent use of the associated state or artifact. For these three RSI methods, verification accounts for 74.8–93.6% of the aggregate RSI Tax. A substantial share of the RSI Tax is also associated with states or artifacts that are not used later in the evaluated streams. These results expose a practical limitation of costly RSI procedures: improvements in the deployed agent need not justify the expense of the improvement process. They motivate RSI objectives that jointly optimize the RSI Tax and expected task-execution cost while ensuring continued growth in agent capabilities across the task stream.
[LG-65] Parallel Broyden Methods for Efficiently Evaluating Nonlinear State Space Models NEURIPS2026
链接: https://arxiv.org/abs/2611.13523
作者: Ian Christopher Tanoh,Scott W. Linderman
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: Accepted at NeurIPS 2026
Abstract:Nonlinear state space models are typically evaluated sequentially, limiting their ability to exploit modern parallel hardware. Recent work has shown that state space models can be evaluated in parallel by reformulating evaluation as a root-finding problem, which can be solved with a parallel form of Newton’s method. However, exact Newton methods require expensive multiplication of dense Jacobian matrices, and quasi-Newton methods based on diagonal approximations, while more scalable, are often slow to converge because they neglect interactions across state dimensions. We introduce a novel quasi-Newton approach based on Broyden’s method, which captures coupling terms with diagonal-plus-low-rank approximations to blocks of the Jacobian. We construct the approximations from trajectory secants, avoiding automatic differentiation while retaining the favorable scaling of diagonal approximations and remaining compatible with parallel scan through rank compression. We prove finite-step recovery of the exact trajectory under the compressed scan, and show that convergence is tracked by the compression error and by the block approximation error along the current trajectory error, rather than in spectral norm. Empirically, we show that our method is advantageous when interactions are approximately low rank or when Jacobian evaluation is expensive: on low-rank RNNs, parallel MCMC algorithms, and denoising diffusion models, it converges in fewer iterations than other quasi-Newton methods and is the fastest parallel method when Jacobian evaluation is expensive or state dimensions are large.
[LG-66] Deep Personalization: Continuous Context Observation for Local Proactive Agents
链接: https://arxiv.org/abs/2611.13457
作者: Yijia Shao,Song Bian,Junxian Guo,Jindong Jiang,Zhen Dong,Huizi Mao,Diyi Yang,Song Han
类目: Machine Learning (cs.LG)
*备注: 27 pages, 7 figures
Abstract:As AI agents become increasingly capable, users must still recognize when and how to ask for help, leaving many opportunities for useful support unrealized. Proactive agents hold the promise to close this gap, but effective proactivity requires understanding users’ real-time activities and personalizing intervention timing so that assistance is offered when it is likely to be helpful rather than disruptive. We introduce deep personalization, a closed-loop process that connects continuous context observation with continual agent adaptation: an agent observes user activity, selectively retains long-term context, and adapts intervention decisions from implicit feedback. We initiate deep personalization in Coco, a proactive desktop agent, by having a separate Observer Agent that monitors desktop activity, maintains compact long-term user modeling context, and learns when assistance is desirable from implicit signals such as engagement, dismissal, explicit requests, and repeated work patterns. We further develop efficient on-device strategies to enable the Observer Agent to run on local AI compute platforms without streaming raw activity data off-device. For evaluation at scale without collecting sensitive real-user activity, we introduce a data-synthesis pipeline that transforms existing computer-use trajectories into OSWorld-Persona, a benchmark with persona-conditioned intervention labels. On OSWorld-Persona, deep personalization improves macro F1 across three model backbones from 0.12-0.19 under direct prompting to 0.35-0.48 after adaptation, a 2.4-3.0x relative gain. In a week-long user study, we observe that Coco improves its macro-averaged F1 score from 0.199 to 0.515. These results suggest that on-device deep personalization can make proactive agents more timely and increasingly aligned with each user’s everyday work.
[LG-67] SLIC: Reinforcement Fine-Tuning Small LMs for Multi-Turn Analog Circuit Optimization NEURIPS2026
链接: https://arxiv.org/abs/2611.13452
作者: Yan Xu,Zhengqi Gao,Ching-Yun Ko,Zhang-Wei Hong,Wenjie Lu,Tao Yu,Xin Zhang,Duane S. Boning,Ruonan Han
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026. 43 pages, 12 figures. Project page: this https URL
Abstract:Analog integrated circuit (IC) design is a multi-turn, simulator-grounded engineering task that demands iterative reasoning over coupled performance tradeoffs, yet proprietary process design kits (PDKs) make hosted large language model workflows difficult to deploy in practical design settings. We present SLIC (Small LM Iterating on Circuits), a reinforcement fine-tuning framework that trains a 4B-scale, locally deployable language model for closed-loop analog circuit optimization. SLIC pairs structured circuit representations with discretized relative-update actions, domain- and success-gated multiobjective rewards, and Reward-Guided Rollout Steering (RGRS), which turns per-step simulator feedback into signals for both policy learning and online rollout construction in this sparse, highly constrained domain. We further release an open, goal-conditioned benchmark of 41 amplifier topologies with standardized protocols for in-distribution validation, held-out topology evaluation, and cross-technology-node transfer. Trained solely with open source 65 nm CMOS predictive technology models (PTM), SLIC-Random achieves 87.1% in-distribution four-specification Pass@1 within eight turns, surpassing the 763B DeepSeek-V4.1-Flash Think by 48.5 percentage points. Beyond the training distribution, SLIC-Softmax reaches 72.6% Pass@1 on held-out topologies, and the trained policies consistently transfer across five unseen technology nodes without additional fine-tuning. This transfer extends to a commercial TSMC 65 nm CMOS PDK, where SLIC-Random achieves 49.9% held-out-topology Pass@1, demonstrating practical deployment through local inference without transmitting proprietary data to external LLM services. Performance further scales with test-time simulator interactions, reaching up to 83.2% held-out-topology Pass@1 at a 32-turn budget.
[LG-68] Fermat ProbCover: Coverag e-Based Active Learning on Fermat Distance Graphs
链接: https://arxiv.org/abs/2611.13448
作者: Gokul Bhusal,Delanna Do,Kevin Miller,Ekaterina Merkurjev,Stephan Wojtowytsch
类目: Machine Learning (cs.LG)
*备注:
Abstract:Active learning aims to reduce annotation cost by selecting the most informative examples for labeling. In the low-budget regime, where uncertainty-based methods often fail, coverage-based active learning methods such as ProbCover have been shown to be effective. However, these methods typically rely on Euclidean distance, which may not adequately capture the underlying geometry of data. In this article, we propose PC-Fermat, a coverage-based active learning method that replaces Euclidean coverage neighborhoods with neighborhoods defined by a Fermat distance, i.e., a data-adaptive distance that reflects the underlying distribution of the data points. To facilitate computation, we construct a weighted Euclidean neighborhood graph and use a truncated shortest-path algorithm to compute the Fermat coverage graph. We also establish sufficient conditions under which this construction exactly preserves the Fermat coverage computed on the complete graph. Finally, we evaluate PC-Fermat on eight classification datasets across varying labeling budgets, with particular emphasis on the very low-budget regime. Our experimental results demonstrate that PC-Fermat outperforms several existing active learning methods on the majority of the datasets.
[LG-69] Plumbline: When the Loss Lies Validating LLM Training Correctly
链接: https://arxiv.org/abs/2611.13393
作者: Ruochen Wen,Hongyang Wang,Siwei Li,Wenbo Chen,Xian Li,Jingguo Liu,Leo Fan,Yiming Yan,Qian Qu,Zheshen Wang,Benoit Dumoulin
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 16 pages, 6 figures, 3 tables
Abstract:Correctness is a prerequisite for training large language models (LLMs) at scale, yet it is hard to ensure: a fault can sit in any part of a run, and how it shows differs across frameworks and the libraries they depend on. Some failures are loud and some are silent, and both take time to detect and trace. A single fault can waste a long run: the run crashes late, or the model scores lower only at the final evaluation. We present Plumbline, a correctness validation system for LLM training, used in production at Amazon. Plumbline checks each part of the training lifecycle, from the data a run trains on to the model it serves, down to the new layers each model brings and up to whole runs. Its diagnosis reuses these checks to trace failures to their root causes. Both rest on a systematic correctness methodology designed to carry across models and frameworks. Plumbline supports two agentic use cases: Day-0 onboarding, which validates the training of a newly released model before teams rely on it, and root-cause tracing of training failures. Across 3 frameworks and 5 models, it found 61 problems, 32 of them silent. Of these, 42 are fixed or reported upstream, 8 of them first raised by us. We further group the 61 into seven failure patterns that show where training breaks, and report how problems recur across frameworks and models.
[LG-70] AdjointFlow: Accelerating Scientific Inverse Problems with Physics-Based Transport
链接: https://arxiv.org/abs/2611.13389
作者: Ethan Hanold,Nicholas M. Boffi,Qin Li,Leonardo Zepeda-Núñez
类目: Machine Learning (cs.LG)
*备注: 51 pages, 17 figures, including appendices
Abstract:Inverse problems are ubiquitous across scientific applications ranging from seismic imaging to astrophysics to nondestructive testing, yet classical solvers often struggle with nonconvexity and repeated evaluations of expensive physical models. Diffusion models can amortize this computation offline, but largely data-driven conditional models underutilize known physics and suffer from ill-conditioned transport maps when the target distribution is concentrated. Plug-and-play (PnP) methods explicitly leverage the physical model to guide a learned prior, improving data consistency but reintroducing costly iterative computations. We instead propose AdjointFlow, a framework built on stochastic interpolants that injects the known physics into the generative transport itself. We design a data-dependent, adjoint-based initialization to reduce transport distance to the target, modify the transport velocity to remain regular as the endpoint distributions concentrate, and parameterize the network to build the initialization directly into the learned velocity. Together, these enable accurate reconstruction at substantially lower cost than generative models starting from Gaussian noise. Experiments on inverse scattering, seismic imaging, and black hole imaging show that AdjointFlow consistently produces more accurate reconstructions than conditional diffusion models while being up to five orders of magnitude faster at inference than PnP methods. Optional inference-time refinement that queries the physical model can further improve data consistency, matching or exceeding the accuracy of PnP methods without retraining.
[LG-71] Settling the Burn-in Gap: Sharp Risk Bounds for Tail-Averag ed SGD in Linear Regression
链接: https://arxiv.org/abs/2611.13383
作者: Qingyue Zhao,Xuheng Li,Quanquan Gu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: 33 pages
Abstract:We study the excess risk of constant-stepsize online stochastic gradient descent (SGD) with tail-averaging for high-dimensional linear regression. Building on the instance-dependent spectral framework of Zou et al. (2021), we establish matching initialization-explicit upper and lower bounds on the total excess risk of tail-averaged SGD in terms of the population covariance spectrum \mathbfH and initialization \mathbfw_0-\mathbfw^* whenever a trajectory spectral feedback condition holds. Existing analyses exhibit a fundamental gap in the effective variance: the upper bound of Zou et al. (2021) retains an undamped contribution from the initial error in the head eigenspace \mathbfI_0:k^\dagger , whereas their lower bound omits the variance induced by the head initialization altogether. We show that both the existing upper and lower bounds are loose: the single-hop fast-mode variance contribution from the head initialization is exponentially damped during the burn-in phase, whereas fourth-moment energy scattered from the head initialization into slow spectral modes during the early burn-in steps survives undamped into the averaging window. We prove that our spectral feedback condition is satisfied in two concrete constant-stepsize regimes: (i) for arbitrary covariance spectra \mathbfH and any finite horizon, under an explicit horizon-dependent stepsize chosen prior to execution; and (ii) for polynomially decaying spectra \lambda_i\asymp i^-a ( a1 ), under a single horizon-independent positive stepsize. Finally, we complement these positive results with explicit one- and two-dimensional separation constructions proving an exponential-versus-polynomial gap in the lower bound of Zou et al. (2021) and showing why fourth-moment assumptions alone cannot determine the asymptotic exponential decay rate without additional structural conditions.
[LG-72] Discrete Model-Based Diffusion for Combinatorial Optimization in Multi-Robot Task Allocation
链接: https://arxiv.org/abs/2611.13382
作者: Haejoon Lee,Thomas Marshall Vielmetti,Xinyi Wang,Dimitra Panagou
类目: Robotics (cs.RO); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 11 pages, 3 figures
Abstract:Decision making in multi-agent and multi-robot systems often involves discrete decision variables, leading to NP-hard combinatorial optimization problems that must be solved within seconds for near real-time implementation. Denoising diffusion has emerged as a powerful tool for such problems, but existing approaches face a trade-off: Model-Based Diffusion (MBD) incorporates diffusion in sampling-based optimization (SBO) without training but only over continuous spaces, while existing diffusion-based combinatorial solvers operate over discrete spaces but require offline training. To this end, we propose Discrete Model-Based Diffusion (Discrete MBD), a training-free SBO method that directly optimizes discrete variables by approximating categorical reverse transition distributions through Monte Carlo sampling. We establish a finite-step lower bound on the probability of finding a global minimizer and show that this bound converges to one as the number of denoising steps increases. Extensive experiments on generic integer programs and heterogeneous multi-robot coalition formation and scheduling demonstrate that Discrete MBD outperforms SBO baselines in nearly all settings while achieving solution quality competitive with that of mixed-integer programming and reinforcement learning methods at a fraction of their computational cost.
[LG-73] Looping Deeper Without Growing the KV Cache: Recurrent Depth for Causal Encoder-Decoders
链接: https://arxiv.org/abs/2611.13378
作者: Jiaqi Leng,Minshen Zhang,Zhaofeng Sun,Yu-Yang Qian,Peng Zhao,Hao Zhang
类目: Machine Learning (cs.LG)
*备注: 23 pages (11 pages main text), 10 figures, 14 tables
Abstract:Looped language models deepen computation without adding parameters, yet caching full-context key-value (KV) states across layers and rounds severely bounds serving batch size. We design a looped causal encoder-decoder (CED) that achieves recurrent depth with zero round-wise KV growth, addressing where and how to loop an asymmetric stack in which only sparse encoder layers generate global KV. We find that recurrence must be placed in the encoder to iteratively refine global KV representations rather than re-reading static decoder projections, which both drives language-modeling gains and preserves long-context scaling. Furthermore, training the encoder loop under stochastic recurrence depth induces a fixed-point equilibrium where every round losslessly shares the last round’s KV cache. Compounding cross-layer and cross-round KV sharing, the resulting Looped CED matches an unlooped CED’s minimal memory footprint and recovers 86-93% of a looped Transformer’s gain with 1/6 of its global KV. To test serving at production scale, we loop the 552B-parameter DeepSeek-V4.1-Flash for 8 rounds on one 4xGB200 node: with per-layer and per-round KV the loop cannot fit a single 128K-token request, whereas Looped CED holds 36.8x less KV per token, fits 38 such requests (as many as the unlooped model), and serves 3.6x higher throughput on 8K-token prompts.
[LG-74] Reconstructing Boundary Layer Wind Profiles with Machine Learning: A Comparative Study on IGRA Radiosonde Data ICDM2026
链接: https://arxiv.org/abs/2611.13359
作者: Fedor Buzaev(1),Dmitry Efremenko(1),Daria Pugacheva(1 and 2),Fedor Ratnikov(1),Egor Bugaev(1),Vladimir Gordin(1 and 3),Denis Derkach(1) ((1) HSE University, (2) Generalizable AI Systems, (3) MIRAI)
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Atmospheric and Oceanic Physics (physics.ao-ph)
*备注: 10 pages, 8 figures. Accepted at the 2nd Workshop on AI-Ready Data for Science Discovery (ADSD), IEEE ICDM 2026
Abstract:Wind profiles in the atmospheric boundary layer (ABL) drive short-range forecasting and wind-energy siting, yet sparse radiosonde coverage forces practitioners to interpolate them from a handful of physical predictors. We benchmark modern tabular learners against analytical parameterizations on 158,097 IGRA soundings. CatBoost, TabM, and FT-Transformer reduce RMSE to 1.3-1.6 m/s, against approximately 3 m/s for power-law and Monin-Obukhov fits; once the predictor set is fixed the three architectures differ by less than 0.05 m/s, so accuracy is driven by predictors rather than architecture. A cohort analysis of within-bin profile dispersion places an empirical floor of approximately 1.77 m/s on any point-wise predictor that uses standard radiosonde features, consistent with prior eddy-viscosity inversions. A one-dimensional Fourier Neural Operator applied as a post-hoc corrector to frozen tabular predictions reduces near-surface RMSE on U.S. stations from 1.5 to 1.2 m/s, while leaving upper-layer error unchanged. Further gains should come from new predictors rather than larger models.
[LG-75] DEPUTYBENCH: Evaluating Contextual Safety in Enterprise Agents
链接: https://arxiv.org/abs/2611.13355
作者: Hao Zhao,Minghao Lin
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:
Abstract:Enterprise agents must determine whether ordinary requests are permitted by organizational records and policies while continuing legitimate work. We introduce DEPUTYBENCH, an executable benchmark with 79 base items, 375 instances, paired permission contexts, and graded evidence requirements. Across 12 models, revised audits give 88.0% pooled safety and 79.1% paired success. A controlled study of 28 derived tasks and six models tests authorization-fact flips, evidence masking, and irrelevant rewrites. Outcomes track backend permissions, yet restricted execution is more frequent with missing evidence than explicit non-permission: 10.3% versus 3.5% on 27 matched tasks, a 6.8-percentage-point difference (95% interval: 1.9-13.0; secondary analysis). A post-hoc analysis locates 61 of 100 masked-condition executions in four privacy tasks requiring complete cumulative counts or person-specific consent. The difference persists when the most error-prone model or the largest contributing task is excluded, but is much smaller outside those four tasks. Thus high safety with available evidence does not establish reliable stopping when permission cannot be verified. Mean paired success is equivalent within a prespecified five-point margin under irrelevant rewrites, while strong-model safety remains near ceiling. Scorer validation improves from 178 to 266 correctly scored cases out of 284 held-out counterexamples. Two security experts manually verified task ground truth over a two-week period; stage labels remain LLM-generated. The concentration analysis describes task structure and audited outcomes, not an identified internal mechanism.
[LG-76] Advancing Mass-Conserving Deep Learning for Distributed Hydrologic Modeling: A Case Study in a Snow-Dominated Watershed
链接: https://arxiv.org/abs/2611.13346
作者: Yihan Wang,Lujun Zhang,N. Benjamin Erichson,Yang Hong,Tiantian Yang
类目: Machine Learning (cs.LG); Geophysics (physics.geo-ph)
*备注:
Abstract:Recent progress in physics-aware deep learning (DL) has embedded mass conservation within Long Short-Term Memory (LSTM) architecture for hydrologic modeling, improving physical consistency and enabling diagnostics of internal states. However, existing mass-conserving neural networks are primarily developed in lumped formulations. Consequently, previous studies provide only fragmented and often indirect evidence of when and why imposed physical constraints may lead to unrealistic hydrologic behavior. To address this limitation, we propose Physics-Enhanced Conservation-Augmented Net (PECAN), a mass-conserving convolutional LSTM-based framework for distributed hydrologic modeling and diagnosing structural limitations of physics-aware DL models. PECAN is implemented in two variants: (1) PECAN-MC with a strict precipitation-based conservation constraint and (2) PECAN-MCR with a relaxed conservation constraint allowing additional water inputs beyond precipitation. Both variants are trained solely on gauge streamflow and evaluated in a snow-dominated watershed in Colorado. We benchmark PECAN against lumped DL baselines and the distributed process-based Variable Infiltration Capacity (VIC) model. Results show that PECAN achieves competitive outlet streamflow simulation accuracy while producing spatially explicit latent states for interpretation. Comparisons with reference soil moisture indicate that PECAN cell states resemble meaningful temporal soil moisture dynamics. However, analyses also reveal that PECAN misrepresents snow processes by prematurely removing winter snow water storage from the system rather than retaining it for spring release. PECAN demonstrates accurate streamflow simulation at gauges and shows diagnostic potential to expose structural limitations in hydrologic process representation, thereby informing future physics-aware neural network development for hydrology.
[LG-77] One Trajectory Two Requirements: Accuracy and Local Stability in Euler Sampling for Flow Matching Policies
链接: https://arxiv.org/abs/2611.13311
作者: Mahindra Rautela
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注:
Abstract:Flow matching robot policies commonly use a fixed Euler step count selected from offline accuracy sweeps. At inference time, however, each policy input (visual observation, robot state, and language instruction) together with a source-noise draw defines a different ODE trajectory and sequence of local Jacobians. We ask whether a step count that accurately follows the nominal noise-to-action trajectory can still be too coarse for strongly contracting directions around it. An exact two-coordinate system and a controlled augmentation of six trained flow models show that trajectory accuracy and local Euler stability can impose different step requirements even when the original learned transport is unchanged. We then study a frozen \pi_0.5 -LIBERO checkpoint without modifying its weights or action dynamics. Across 30 policy inputs from 10 LIBERO-10 tasks, the Jacobian exhibits strong contracting directions near the action endpoint, yet no sampled mode violates the Euler non-expansion condition. On five of those inputs, accuracy sweeps require 4–9 Euler steps at 5% terminal error and 87–112 at 1%. These five paired cases are therefore consistent with accuracy-limited rather than stability-limited sampling under this local criterion, while the controlled experiments show why endpoint accuracy alone cannot rule out a separate stability requirement.
[LG-78] Neural Network-Assisted CLEAN for Efficient Multipath Parameter Estimation in MIMO Systems
链接: https://arxiv.org/abs/2611.13298
作者: Chaofan Deng,Linyu Sun,Jaeho Lee,Zishen Wan,Arijit Raychowdhury
类目: Machine Learning (cs.LG)
*备注:
Abstract:High-resolution multipath estimation in wideband multiple-input multiple-output (MIMO) channels requires repeated searches over angle of arrival, angle of departure, and propagation distance. This paper presents neural network-assisted CLEAN (NN-CLEAN), which replaces the multidimensional search at each extraction step with a multi-head classifier while retaining least-squares (LS) estimation of the complex path gain, parametric reconstruction, and residual cancellation. The same classifier is applied to successive residuals, allowing the extraction depth to vary without changing its output structure. With the number of iterations prescribed by the reference multipath-component (MPC) count, off-grid synthetic tests show that NN-CLEAN slightly exceeds grid-search CLEAN (GS-CLEAN) in path accuracy for one and two MPCs, although its accuracy declines more rapidly at larger path counts. For complete three-MPC extraction at a batch size of eight, NN-CLEAN achieves an approximately 27\times speedup and a 52 -fold reduction in peak GPU memory relative to GS-CLEAN. Applied without retraining to two-MPC line-of-sight (LOS) and non-line-of-sight (NLOS) Sionna ray-tracing channels within a coded MIMO link, NN-CLEAN substantially reduces channel-reconstruction mean-squared error (MSE) and bit error rate (BER) relative to interpolated LS channel estimation. GS-CLEAN retains an accuracy advantage on these channels, indicating the estimation cost associated with the computational savings.
[LG-79] An Analytical Model of Compute-limited Multistage Training Pipelines NEURIPS2026
链接: https://arxiv.org/abs/2611.13276
作者: Nishil Patel,Jin Hwa Lee,Basile Confavreux,Andrew Saxe
类目: Machine Learning (cs.LG)
*备注: Accepted at NeurIPS 2026
Abstract:Modern foundation models are trained through multiple stages, yet how these stages interact and how compute should be allocated across them remain poorly understood. We introduce an analytically tractable high-dimensional model combining unsupervised pretraining with a linear autoencoder, supervised fine-tuning (SFT) with noisy stepwise labels, and reinforcement learning (RL) with sparse episodic rewards. Using random matrix theory and an operator-projection closure, we derive approximate low-dimensional learning dynamics that closely match simulations. We show that finite pretraining data imposes a representation-dependent performance limit, with Bayes classification error scaling as the inverse square root of the pretraining sample ratio, and observe the same exponent in asymptotic SFT and RL errors. Better pretraining also reduces sensitivity to noisy supervision. SFT initialization shortens the initial RL plateau induced by sparse rewards, an effect we also observe in OLMo-2 1B on GSM8K. Longer SFT warm starts additionally improve asymptotic RL performance. Optimizing compute across all three stages reveals that the optimal allocation depends on supervision quality and total budget, with a non-monotonic dependence of the RL compute share on budget. Finally, a complementary compositional model shows how RL selectively strengthens task-relevant pretrained skills while largely preserving unused skills. Together, these results identify mechanisms linking representation quality, feedback structure, and compute allocation in multistage training.
[LG-80] Which Acoustic Features Survive Compression? Tracker-Estimated First-Formant Statistics for Codec-Robust Audio DeepfakeDetection
链接: https://arxiv.org/abs/2611.13258
作者: Yash Sukhdeve(1),Aaron Storey(2),Masudul H. Imtiaz(1) ((1) Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA, (2) Department of Computer Science, Clarkson University, Potsdam, NY 13699, USA)
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:
Abstract:Audio deepfake detectors that work well on clean lab recordings often degrade after telephone or platform compression. This paper asks a specific question: which low-dimensional acoustic descriptors stay informative after codec degradation? We screen 36 utterance-level features(formants, PNCCs, LFCCs, prosody) on ASVspoof 2019 data and test on the official ASVspoof 2021 LA set across seven codec conditions. Of 33 statistically significant descriptors, only four show large practical separation (|r| 0.5, bootstrap 95% CIs), all first-formant (F1) measures, highly redundant (PC1: 79% variance). The payoff is stability: the 36-feature SVM degrades 14 pp under codec mismatch (9.5%-23.5% EER), while the four-feature RBF-SVM degrades only 5.9 pp (17.0%-22.9%). The F1 subset varies 3.4 pp across conditions versus 13.1 pp for the full set. F1 differences persist after controlling for speaker identity (p greater than 10^10, mixed-effects model). The results suggest that physically interpretable F1 statistics offer a compact, codec-stable diagnostic signal that may complement higher accuracy neural detectors.
[LG-81] GigaEdge Spench: An SDK for Practical Streaming Speech Enhancement
链接: https://arxiv.org/abs/2611.13243
作者: Artem Sokolov,Artem Khaidarov,Yana Lysova,Alexander Gladkikh,Vitaliy Yulis
类目: Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注: full version. draft
Abstract:We present GigaEdge Spench, an open-source SDK for streaming speech enhancement on CPU servers, mobile devices, and in browsers. Applications can build enhancement channels from neural denoising, normalization and dynamics blocks, sample-rate conversion, and acoustic echo cancellation where supported. These stages can be combined, while normalization can also run without a neural model. The release includes an 8 kHz INT8 denoiser and discriminative and hybrid 48 kHz FP32 models. A shared C++ core supports ONNX Runtime and TensorFlow Lite through native interfaces, Android and iOS SDKs, and WebAssembly integration. Independent stream contexts share model resources, and consecutive frames from one stream can be processed in a single inference call. We evaluate the released models on EARS-WHAM v2 and compare their quality and processing costs with RNNoise and DPDFNet2. All tested native and Web configurations process files faster than real time, although larger chunks require more time to collect input. We also measure how many streams a shared model can process on a CPU. Our contribution is a configurable deployment system and an experimental study of how model choice, chunk size, and execution environment affect its use. All reported SDK timing uses ONNX Runtime. Index Terms: speech enhancement, streaming inference, cross-platform deployment, temporal frame grouping, computational efficiency
[LG-82] Block-Sparse Gauss-Newton Optimization of Finite Basis Physics-Informed Neural Networks: Sparse Automatic Differentiation Approach
链接: https://arxiv.org/abs/2611.13231
作者: Denis Korolev,Adrian Hill,Jeeeun Lee
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:In this work, we develop an efficient Gauss-Newton optimization approach for finite basis physics-informed neural networks (FBPINNs). The key ingredient is the overlapping domain decomposition architecture of FBPINNs, which localizes the influence of each subnetwork and induces block sparsity in both the residual Jacobian and the corresponding Gauss-Newton matrix. We characterize this structure and exploit it through sparse automatic differentiation, using graph coloring to partition structurally orthogonal Jacobian columns or rows. This reduces differentiation costs and memory requirements while preserving all structurally nonzero derivative information. The resulting compressed Jacobian representations allow us to assemble the Gauss-Newton system directly in block-sparse form, enabling the use of sparse direct solvers and preconditioned iterative methods. In this way, the otherwise expensive second-order optimization problem is reduced to scalable sparse linear algebra. We demonstrate the approach on multiscale PDEs, where accurate approximation requires rich neural network parametrizations and large numbers of collocation points. Numerical experiments show that our block-sparse Gauss-Newton method attains high accuracy and substantially accelerates optimization relative to first-order baselines while remaining computationally tractable for large problem sizes.
[LG-83] Audited Gradients Anchored Policies: Model-Based Reinforcement Learning for Efficient Flow Control
链接: https://arxiv.org/abs/2611.13203
作者: Ruige Kong,Stefania Fresca
类目: Machine Learning (cs.LG)
*备注: 46 pages, 16 figures, 12 tables. Code and result files: this https URL
Abstract:Feedback controllers for flows are expensive to design, since each candidate must be evaluated with a high-fidelity solver. Differentiable surrogates reduce that cost, but they also introduce a failure mode that prediction error cannot detect. Policy optimization uses the derivative of the objective with respect to the action, which we call the action gradient and which can be computed either through the surrogate or through the high-fidelity solver. Optimization through the surrogate uses only its action gradient, and a surrogate can follow the flow trajectory closely while misestimating this derivative. The surrogate action gradient is compared against a central finite-difference estimate from the high-fidelity solver on held-out states, under acceptance criteria fixed before the comparison, and we call that comparison an audit. A low-dimensional feedback law is then fitted to the surrogate action gradient when the surrogate passes the audit, and to the solver estimate when it does not. This law initializes a neural policy, and a quadratic penalty holds the policy near it during optimization. The proposed approach is validated on four test cases. The resulting controllers outperform or match a model-free baseline, while using 3 to 156 times fewer high-fidelity solver interactions across the four test cases. One surrogate predicts the objective at correlation 0.9996 but its action gradient at only 0.459, so prediction accuracy does not identify a usable surrogate. Another surrogate, whose action gradient agrees with the solver estimate at correlation 0.996, makes the objective 0.52% worse when the policy is optimized through it without the anchor penalty, so an accurate gradient alone does not provide a working controller. Finally, a surrogate that passes the audit at one fixed action fails it around the actions the deployed controller applies, so a pass is local.
[LG-84] Prediction-Powered LLM Ranking with Adaptively Collected Human Feedback
链接: https://arxiv.org/abs/2611.13186
作者: Gabriel Sargent,Will Wei Sun,Zhengwu Zhang,Yufeng Liu
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 35 pages, 4 figures
Abstract:Ranking large language models (LLMs) through human evaluations is essential for downstream decision-making. Because human feedback is expensive, evaluation platforms often collect it adaptively, prioritizing informative model comparisons using previously observed feedback. To further improve precision with limited human feedback, evaluators can draw on abundant unrated responses and relatively inexpensive LLM judges; however, judge bias and adaptive dependence challenge standard i.i.d. inference frameworks. We propose Prediction-Powered Ranking (PP-Rank), a framework that combines LLM-judge predictions with human feedback to improve precision while providing valid inference under adaptive sampling. Data-driven prediction weights minimize the asymptotic covariance trace within the proposed class. In theory, we establish asymptotic normality for general evaluation targets and simultaneous ranking inference. A joint martingale analysis handles dependence between adaptive human feedback and auxiliary data. We further derive a rule for reducing exploration that prioritizes lower-cost comparisons while matching a conservative variance bound, and characterize allocation limits for regular \sqrtT -rate estimation of pairwise means. Semi-synthetic experiments on UltraFeedback-Extended show improvements over human-only baselines in score estimation, confidence interval width, and ranking certification. Synthetic experiments demonstrate robustness to judge quality and adaptive allocation, while showing that PP-Rank can substantially reduce evaluation costs without sacrificing precision.
[LG-85] Making Strides in Symbolic Imitation Learning: Offline Demonstrations Online Tuning
链接: https://arxiv.org/abs/2611.13182
作者: Bryce MacInnis,Malcolm I. Heywood
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: 16 pages + 17 pages of supplementary, 9 tables, 7 figures
Abstract:Deep reinforcement learning (DRL) methods have been the dominant paradigm in continuous control tasks due to their efficacy in function approximation, while interpretable evolutionary methods have lagged largely behind for the opposite reason. In this work, we introduce Symbolic Imitation Learning, a framework for distilling an oracle policy through an offline dataset into a decision list with linear model motion primitives. We show that this, in conjunction, with online Augmented-Random Search (ARS) tuning of the constants and motion primitives approximates the performance of the oracle across 20 benchmark RL tasks and exceeds the oracle in 11 of those 20 tasks while simultaneously producing parsimonious, interpretable policies that domain experts can better understand.
[LG-86] RadRef: A Reference-Free Metric for Radiology Report Generation
链接: https://arxiv.org/abs/2611.13168
作者: Shelly Schwartz,Alan Ropp,Michael B. Sneider,Nur Yildirim,Cree M. Gaskin,Chirag Agarwal
类目: Machine Learning (cs.LG)
*备注:
Abstract:AI-generated radiology reports are usually evaluated by comparison with a radiologist-authored reference, leaving no way to score them when no reference exists. We introduce RadRef, a reference-free metric that scores report claims against image-derived beliefs over a clinical finding vocabulary, weighted by relevance to the clinical indication. On ReXVal, RadRef achieves 73.1% agreement on non-tied same-radiograph report pairs with radiologist rankings, comparable to leading reference-based metrics overall. It is also competitive on false findings and has its clearest relative result on omissions (missed findings). We further evaluate 100 model-generated and 100 radiologist-authored reports from 100 MIMIC-CXR studies, all independently annotated by two board-certified radiologists. On omissions, RadRef attains Kendall’s tau-b = 0.295, against 0.386 between the two readers themselves, and no compared metric differs significantly from RadRef on this target. The same image-grounded representation provides a false-finding readout, RadRef-FF, with tau-b = 0.32 on ReXVal and 0.29 in the reader study, without requiring a reference. Controlled perturbations confirm that RadRef depends on the radiograph, report, and clinical indication. RadRef enables image-grounded evaluation before a radiologist-authored reference exists, supporting quality monitoring of report-generation systems in clinical settings, and complements reference-based metrics when one is available.
[LG-87] High Probability Risk Control for Online Policy Learning
链接: https://arxiv.org/abs/2611.13163
作者: Yihong Guo,Drew Prinster,Suchi Saria,Anqi Liu
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study risk control for online policy learning, where an agent adaptively collects data and aims to learn a new policy whose risk stays below a tolerance threshold at every round with high probability. Such policy updates and data collection induce feedback covariate shift (FCS): observed data depend on history through evolving policies and the data-collection process. FCS brings two substantive problems for risk control: 1) efficient risk evaluation for each candidate policy and 2) valid policy selection with high-probability risk control. To address these, we propose a permutation-based weighted risk evaluator with weights efficiently approximated by the Sinkhorn algorithm and select policies whose estimated risk upper confidence bounds (UCB) fall below the target threshold, with the UCB obtained through a proposed Gaussian bootstrap method. Theoretically, we prove that our risk evaluator has a no-larger variance upper bound than that of the standard importance-weighted evaluator. Our policy control method also yields an asymptotically valid simultaneous risk bound and provides asymptotic high-probability risk control for the selected policy. Empirically, we evaluate our method on the online contextual bandit problem, safe question answering benchmark, and Medical Claim Filtering tasks. Our method maintains the target risk level, achieves lower risk than baseline methods, has lower variance in risk estimation, and achieves reliable policy selection with high-probability risk control. Further ablation studies support our theoretical claims.
[LG-88] Byzantine-Robust Muon: Resilient Momentum Orthogonalization for Distributed Learning
链接: https://arxiv.org/abs/2611.13137
作者: Grigory Malinovsky,Yury Demidovich,Peter Richtárik
类目: Machine Learning (cs.LG)
*备注: 51 pages
Abstract:Optimization methods, including the Muon optimizer based on the Linear Minimization Oracle (LMO) and momentum orthogonalization, have shown strong empirical performance in large-scale and distributed learning, yet their robustness to adversarial behavior and data heterogeneity is not well understood. We study these methods within a unified first-order trust-region framework and analyze Muon-based updates with robust aggregation for Byzantine-robust distributed and federated learning. Our analysis does not rely on the i.i.d. assumption and introduces a non-Euclidean notion of heterogeneity consistent with the trust-region geometry. Under this framework, we establish convergence guarantees for both general non-convex and star-convex objectives. Our results provide a theoretical characterization of Byzantine-robust Muon-based optimization, with an additive heterogeneity error floor consistent with known impossibility results, under explicit aggregation assumptions. Under an explicit additive oracle-error condition, the guarantees also cover inexact orthogonalization, with the non-convex Byzantine terms in the bound’s numerator unchanged but a smaller progress denominator. In simulated federated training with ResNet-18 and ViT-Tiny on CIFAR-10, additional image benchmarks, and a 38M-parameter byte-level language model on TinyStories, the tested Muon configuration attains higher attacked accuracy (lower perplexity) than the tested SGD and normalized-SGD configurations, and than sign updates on CIFAR-10. Fixed-honest controls further show a substantial RFA advantage over averaging under a bounded RFA-query attack, higher attacked accuracy for Muon-NS than tensorwise Euclidean normalization from shared checkpoints, and lower held-out TinyStories perplexity than a tuned AdamW baseline.
[LG-89] IC-GNN: Modeling Individualized Brain Community Organization for Disease Prediction
链接: https://arxiv.org/abs/2611.13070
作者: Yang Zhang,Xiao Zhou,Jonathan Warrell,Avram Holmes,Xuan Zhang,Mark Gerstein, for theAlzheimer’s Disease Neuroimaging Initiative
类目: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
*备注:
Abstract:Brain networks exhibit modular organization, where community structures vary substantially across individuals and diseases. Understanding these individual differences is crucial for advancing both basic neuroscience research and precision medicine. However, widely used predefined group-level brain atlases often overlook this heterogeneity. While individualized functional parcellations capture subject-specific variability in community boundaries, they often require lengthy scan times to achieve fine-grained resolution and complicate cross-subject comparisons. To address this trade-off and investigate how individualized community organization captures disease-associated changes across individuals, we propose IC-GNN, an atlas-anchored model that forms distinct, individualized functional communities directly over atlas-defined brain regions. IC-GNN derives discrete, individualized multiscale communities from resting-state functional magnetic resonance imaging (fMRI) data, refines community-guided connections, and adapts shared regional weights to each subject’s community context to enable both individualized modeling and group-level comparisons. Across more than 30,000 participants from ABIDE (autism spectrum disorder), ADNI (Alzheimer’s disease), and the UK Biobank (depression), IC-GNN achieves the highest performance compared with 15 baseline methods. We further demonstrate that these individualized communities are reproducible within scans and across sessions, and carry predictive power on their own. Furthermore, IC-GNN identifies disease-related shifts in community organization that align with established literature, highlighting a promising approach for modeling individualized community structure for disease prediction and biomarker discovery.
[LG-90] Explanation-Aware Fairness Beyond Output Parity: Joint Output-Attribution Regularization for Medical Image Classification
链接: https://arxiv.org/abs/2611.13052
作者: Gökhan Özbulak,Oscar Jimenez-del-Toro,Lilian Berton,André Anjos
类目: Machine Learning (cs.LG)
*备注: Submitted to IEEE ISBI 2027. Under review
Abstract:Output-level fairness does not constrain whether demographic groups rely on similar predictive contributions in representation space. For a nonlinear encoder with a linear head, we show that outcome-conditioned output score disparity equals the signed sum of feature attribution differences across subgroups by allowing small prediction gaps to arise through cancellation effect. We test whether an attribution disparity penalty complements output-level equalized odds based fairness surrogates. Across 960 DINOv2-LoRA models on HAM10000 and Harvard-GF datasets for gender and race fairness, attribution augmentation increases utility-fairness trade-off performance for hypervolume in six different settings with paired 95% confidence intervals and relative gains of 1.49-10.57%. Under a predefined utility constraint, augmented model families also achieve 9.0-38.7% lower equalized odds difference, lower attribution disparity, and reduced cancellation ratio while preserving predictive utility. These results support attribution fairness as a complementary explanation-aware constraint on output fairness.
[LG-91] Permutation-Based Pre-trained Model Merging via Reciprocal Orthogonality
链接: https://arxiv.org/abs/2611.13016
作者: Akira Ito,Daiki Chijiwa,Atsutoshi Kumagai
类目: Machine Learning (cs.LG)
*备注:
Abstract:Ito et al. (2026) showed that sufficiently wide, independently trained neural networks can exhibit linear mode connectivity (LMC) with softmax temperature calibration, even without permutation. LMC means that a low-loss linear path connects the models’ parameters. This behavior is explained by layerwise exponentially weighted connectivity (LEWC), whereby each merged layer outputs an exponentially weighted sum of the original models’ corresponding representations, yielding ensemble-like predictions. However, prior work observed LEWC with substantial widening, such as 8192 units per hidden layer for MLPs and 16\times width for VGG-11, raising the question of whether such widths are necessary. We therefore search for permutations that encourage reciprocal orthogonality (RO), which suppresses interference between each model’s weights and the other model’s representations. This allows both models’ computations to coexist in the merged network, promoting LEWC at smaller widths. We formulate the search as independent layerwise quadratic assignment problems and approximately solve their continuous relaxations using Frank–Wolfe. Experiments show that our method facilitates LMC with less width expansion. It also merges models trained on different data distributions, where weight matching, which maximizes weight similarity through permutations, struggles. The merged predictions approximate those of a logit ensemble of the original models.
[LG-92] FaST: Factored Synthesis of Transitions for Offline Reinforcement Learning
链接: https://arxiv.org/abs/2611.13013
作者: Daniel Campa,Spandan Das,Srinjoy Das
类目: Machine Learning (cs.LG)
*备注: 18 pages, 6 figures, 10 tables
Abstract:Synthetic data augmentation promises to scale offline reinforcement learning beyond fixed logged datasets, but leading diffusion-based generators model the transition tuple (state, action, next state, reward, terminal) as a monolith: no single factor can be natively redrawn in isolation. We present FaST (Factored Synthesis of Transitions), which factors transitions via the chain rule into state, action, dynamics, reward, and terminal stages, fitting each with an estimator matched to its statistical properties. Across twelve D4RL locomotion and maze benchmarks, TD3+BC and IQL agents trained on FaST transitions match the SynthER diffusion baseline while generating transitions over 90x faster. The explicit action-given-state conditional can also be re-queried for a fixed state, enabling targeted action synthesis: drawing N candidate actions per generated state and keeping the highest-advantage one under a value function fit to the same data, thereby synthesizing improved behavior during generation rather than filtering for it post-hoc. Best-of-10 synthesis beats training on real data on 11 of 12 medium and medium-replay settings (+8.9 D4RL points on average, up to +28.4), and is competitive with advantage-filtered SynthER at a fraction of its generation cost. On expert mixtures both mechanisms degrade identically, tracing the failure to the advantage estimate itself rather than to either targeting mechanism.
[LG-93] Branch-and-Commit for Frozen RL Agents : When and Where to Spend Test-Time Compute
链接: https://arxiv.org/abs/2611.13003
作者: Sahar Admoni,Assaf Hallak,Yftah Ziser,Ofra Amir
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reinforcement learning agents are often deployed with policies that cannot be retrained or modified. In digital environments whose states can be copied and resumed, an operator has another option: sample several continuations from the current state, evaluate them, and continue from the endpoint of the selected branch. We call this procedure branch-and-commit. We study when it improves return, what produces the improvement, and where its computational budget should be spent. Across four environments, two frozen policies, five continuation verifiers, and a model-free selection rule, we find that the benefit of branching depends on dispersion: the difference between the best candidate return and the mean candidate return. Branching provides no benefit in low-dispersion settings, while increasing return by 12.8% in our main Craftax-Medium setting. Most of this gain comes not from selecting better actions, but from selecting among different stochastic realizations of the same action. Several competent verifiers achieve similar episode returns, suggesting that verifier quality is not the main constraint at our operating budget. Instead, computation is better allocated to states where useful dispersion is likely. On held-out seeds, gating branching using predicted dispersion retains 69% of the improvement from uniform branching while using 48% as many branching calls. These results characterize when branch-and-commit is useful and clarify what its gains represent.
[LG-94] One Table Many Queries: Benchmarking Generalist Tabular Models Across Generation Imputation and Prediction
链接: https://arxiv.org/abs/2611.13001
作者: Yuanrui Wang,Kaifang Li,Xuanyue Li,Ziyun Li,Yan Lu,Xingxuan Zhang,Peng Cui
类目: Machine Learning (cs.LG)
*备注:
Abstract:Generation, imputation, and prediction query different aspects of the same tabular distribution, but are usually evaluated separately. This leaves unclear which capabilities they share, how their training objectives interact, and whether one model can perform all three well. We introduce the One Table Benchmark (\ours), spanning 56 datasets, 46 registered methods, and 20 capability axes under aligned data splits and explicit task contracts. We compare task ranks on aligned support, train five backbones under all seven non-empty objective combinations on five datasets with three seeds, and contrast fixed-model profiles with specialist portfolios. Rank comparisons connect imputation and prediction through numerical recovery and dependence preservation, and link generation to both through predictive utility; distributional fidelity follows different, sometimes opposing, rankings. Objective interventions show the largest pooled gains from including the recipient’s own objective. Generation has the broadest coverage of positive pooled auxiliary effects, while prediction shows potential to improve imputation and synthetic-data utility, with responses that vary across backbones. Cross-addition effects reveal that objective benefits need not add and depend on the objectives already present. Fixed-model comparisons expose corresponding utility–fidelity trade-offs: some generalists surpass specialist portfolios on utility, while fidelity continues to favor specialization. Frozen foundation models show strong prediction and numerical point imputation, together with promising synthetic-data utility, highlighting their potential across all three tasks. These findings motivate unified tabular models that share useful conditional information while improving categorical reconstruction and faithful joint sampling.
[LG-95] Multimodal Graph Learning: A Survey of Models Benchmarks and Frontiers
链接: https://arxiv.org/abs/2611.12990
作者: Rui Xue,Tianfu Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Multimodal attributed graphs (MAGs), in which nodes carry text, images, and other modalities while edges encode relations among entities, underlie e-commerce, social media, content platforms, and creative applications. Learning on MAGs has developed along two complementary lines: graph-centric methods redesign how modalities are fused, propagated, and connected within message passing, while foundation-model-centric methods pretrain transferable multimodal graph encoders or bring graph context into multimodal large language models for prediction and generation. In parallel, several benchmarks have appeared with different datasets and evaluation protocols. This survey takes an algorithm-centric and evaluation-grounded view of the field and makes three contributions. Modeling: we introduce a unified message-passing formulation and organize methods into five integration levels (feature, propagation, topology, learning, and foundation model), covering discriminative and graph-conditioned generative models; within this formulation we derive representative methods as special cases, compare their assumptions, expressiveness, and cost, and identify cross-modal sensitivity as a property that separates otherwise similar architectures. Datasets and evaluation: we review recent benchmarks, summarize their datasets and metrics, define model-independent descriptors (label homophily, average degree, clustering, and edge provenance), and map each evaluation protocol to the baselines it uses. Frontiers: we connect multimodal graph learning to AI agents, covering the multimodal graphs that agents perceive and remember and graph engineering, the practice of organizing agent workflows as explicit graphs; we then discuss open problems, concrete efforts for the community, and research directions, including when graph structure helps in the presence of strong multimodal foundation models.
[LG-96] StatPrompt-LLM : A Lightweight Statistical Refinement for Foundation-Style Time-Series Forecasting
链接: https://arxiv.org/abs/2611.12974
作者: Aniket Sinha,Veronica W. Tsou
类目: Machine Learning (cs.LG)
*备注: 10 pages, 1 figure, 7 tables
Abstract:Time-series forecasting has recently seen a paradigm shift with the introduction of foundation-style models that adapt large pretrained language architectures to sequential data. However, effectively exposing fundamental temporal structures, such as trend, scale, and temporal dependence, to these models remains an open challenge, particularly when the available observations are limited. In this paper, we conduct an exploratory empirical study across different generations of forecasting architectures and propose StatPrompt-LLM, a lightweight statistical refinement of Time-LLM. StatPrompt-LLM augments the existing Time-LLM prompting mechanism with dataset-level statistical information and introduces a zero-initialised autoregressive bypass as a complementary linear forecasting pathway. We evaluate our framework on electricity and meteorological time-series benchmarks, including ETTh1, ETTh2, and Weather, under full-prefix and reduced-prefix configurations. Under the 10% reduced-prefix configuration, StatPrompt-LLM achieved lower in-sample prediction errors than Time-LLM, with unweighted average relative reductions across datasets of 40.2% in MSE and 21.4% in MAE. Full-prefix results were more dataset-dependent. Ablation analyses revealed relatively modest and dataset-dependent changes when selected additional statistical descriptor groups were masked within the combined architecture. Computational profiling indicated additional model parameters and runtime overhead relative to Time-LLM.
[LG-97] Exploration via Exploitation: The Blessing of Reward Diversity in Personalized Federated RL
链接: https://arxiv.org/abs/2611.12964
作者: Jiin Woo,Gauri Joshi,Yuejie Chi
类目: Machine Learning (cs.LG)
*备注:
Abstract:While federated reinforcement learning (RL) has traditionally guaranteed high efficiency in homogeneous environments, collaboration is often hindered when agents possess distinct goals. In this paper, we flip this paradigm, framing reward heterogeneity not as a hurdle to consensus, but as a structural asset that catalyzes exploration. We first propose Personalized Federated Upper Confidence Bound Value Iteration (PF-UCBVI), which achieves optimal linear speedup by decoupling shared dynamics from personalized objectives. To eliminate the risks of explicit exploration, we then introduce Personalized Federated Exploration-Free Value Iteration (PF-EFVI), a purely greedy algorithm that leverages reward diversity to ensure state-action coverage. We prove that PF-EFVI attains logarithmic regret without explicit exploration bonuses under sufficient reward diversity. Our results reveal that agent disagreement shifts the exploration burden from temporal complexity to spatial diversity, enabling efficient collective learning without explicit exploration.
[LG-98] An ensemble of graph generative models for assisted history matching of the Brugge field case
链接: https://arxiv.org/abs/2611.12927
作者: Gleb Shishaev,Anton Shirokov,Shadfar Davoodi,Mohammed Al-Shargabi,Vasily Demyanov,Daniel Arnold
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注:
Abstract:Assisted history matching remains non-unique because many geological realisations can reproduce equivalent production responses with substantial differences between individual well matches. This ambiguity increases when a field comprises several depositional settings and modelling strategies. The paper applied a graph autoencoder approach to history matching of a benchmark Brugge field that comprises different depositional zones. Each stratigraphic zone is modelled with its dedicated generative model that comprises a joint latent space for optimisation history matching loops. Graph-based generators operate directly on the active cells of the corner-point grid, so its irregular geometry is handled without conversion to a regular lattice. The optimisation search in the 160-dimensional joint latent space is done with the Covariance Matrix Adaptation Evolution Strategy. The multi-component objective function includes static well data mismatch, dynamic production mismatch, and a geological realism penalty based on the Decoder induced latent metric. Latent space diagnostics with topological data analysis are applied prior to history matching to test whether one generator is sufficient or whether a multiple generator formulation is required. The results show that the zone-based formulation gives coherent latent spaces, preserves the main porosity and permeability relations that vary from one zone to another, and supports history matching while keeping the solutions within the learned geological manifold. The matched models reproduce field oil and water production within the prior uncertainty range and provide a comparable well-level match to previously published Brugge studies based on Ensemble Kalman Filter, Particle Swarm Optimisation with principal component analysis of parameterisation, and Multiple Kernel Learning with Particle Swarm Optimisation.
[LG-99] Bayesian inverse problems and epidemic-threshold uncertainty for a heterogeneous SIS reaction-diffusion model
链接: https://arxiv.org/abs/2611.14468
作者: Duc-Lam Duong
类目: Analysis of PDEs (math.AP); Machine Learning (cs.LG); Number Theory (math.NT); Statistics Theory (math.ST); Populations and Evolution (q-bio.PE)
*备注:
Abstract:We study Bayesian inference of persistence from early observations of a heterogeneous susceptible–infected–susceptible (SIS) reaction–diffusion model with standard incidence. We show that the leading prevalence dynamics depend on the unknown coefficients only through the difference between the transmission and recovery rates and the infected diffusivity. The same pair determines the principal eigenvalue whose sign governs persistence, so early prevalence can inform the threshold even when the individual rates remain uncertain. We prove global weak well-posedness and quantitative parameter stability for the SIS system and use the resulting observation-map estimates to establish Bayesian well-posedness. For an initial infection of size \varepsilon , we derive a uniform second-order expansion that describes how the separate rates enter prevalence. Incidence depends explicitly on transmission at leading order. When the noise standard deviation is proportional to \varepsilon , we prove O(\varepsilon) convergence in Hellinger distance of the nonlinear posteriors to their low-prevalence limits for both prevalence alone and joint prevalence and incidence data. We bound the principal eigenvalue using the growth rate of the infected L^2 norm between two times. This rate gives a lower bound for the full nonlinear system, and the upper bound separates spatial transients from nonlinear saturation. Uniform spectral estimates and bounds on infection size and measurement error give sufficient conditions for persistence and extinction. We also bound the error in posterior persistence probabilities under forward and eigenvalue approximation, accounting for posterior mass near the threshold. Numerical examples illustrate threshold inference and the predicted (O(\varepsilon)) convergence of the nonlinear posteriors to their low-prevalence limits in Hellinger distance.
[LG-100] A Theory of Cross-Validation Variance
链接: https://arxiv.org/abs/2611.14463
作者: Nathaniel S. O’Connell,Jaime L. Speiser
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 27 pages main text, 57 pages supplementary material with 6 sections, 1 figure
Abstract:Cross-validation estimates predictive performance by repeatedly splitting the data into training and validation sets, fitting the model on the first and evaluating it on the second. Every observation influences the estimate in two roles, through its own evaluation when it is held out and through the models it helps fit when it trains. The random splits determine its weight in each role. From this representation, we prove an exact finite-sample identity for the variance of the cross-validated estimate. For a given splitting scheme with a common training set size, the variance is a function of these weights, with constants of the model, the metric, and the population. It covers metrics averaged over observations, such as mean squared error, and over case–control pairs, such as the AUC. The identity shows which schemes to use. The variance decomposes into sampling variance, which depends only on the training size, and variance due to the random fold splitting, which arises when the weights change from one draw of the splits to the next. K-fold cross-validation, whose weights are equal across observations by design, is therefore variance optimal at first order; Monte Carlo and bootstrap cross-validation are not. The first-order variance of K-fold does not depend on K, and under reasonable conditions on the model leave-one-out has the smallest variance of any K-fold scheme. For the AUC the same holds for leave-pair-out, which evaluates every case–control pair. Finally, we show the cross-validated estimate is asymptotically normal with the identity’s full variance. Together, these results provide a general theoretical foundation for inference on cross-validated performance and establish principles for selecting variance-optimal cross-validation designs.
[LG-101] Compositional random forest
链接: https://arxiv.org/abs/2611.14275
作者: Khaled Masoumifard,Stephan van der Westhuizen,Sugnet Gardner-Lubbe
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Compositional data have been analysed in the literature from three main points of view: directly on the simplex, in log-ratio coordinates, and as directional data on the unit sphere. In this paper, we construct a random forest framework for compositional responses that accommodates all three. Random forests are ensembles of decision trees, and the split criterion determines how each tree grows. We use the log-likelihood gain as the split criterion and consider distributions defined on the different spaces in which compositions can be represented. The Dirichlet likelihood has been studied in the literature, but its covariance is fixed by its mean up to a single scalar, and its maximum likelihood estimator has no closed form. To obtain more flexible criteria, we use two well-known transformations. First, the square-root transformation maps compositions onto the unit sphere, where existing directional distributions require numerical estimation. We introduce a tangent-normal (TN) distribution, which shares its parameters with the Kent distribution, and construct a split criterion whose gain is computed from additive sufficient statistics: the shape parameters are maximised out in closed form, up to an exponentially small truncation constant, so that no parameter is estimated iteratively. Second, the log-ratio transformation maps compositions to Euclidean space, where the multivariate logistic-normal distribution has a flexible covariance and a closed-form maximum log-likelihood, also based on additive sufficient statistics. Both criteria respond to a shift of the mean, measured relative to the covariance, and to a change in the covariance itself. In simulations, the most accurate forest depended on the covariance structure of the composition. The TN forest was the most accurate in two of the three designs.
[LG-102] On Federated Averag ing with Partial Participation under a General Variance Condition
链接: https://arxiv.org/abs/2611.14239
作者: Ziming Wang,Kun Huang,Karl Henrik Johansson,Shi Pu
类目: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 20 pages, 3 figures
Abstract:In this paper, we study the convergence of Federated Averaging (FedAvg) with partial participation under a general variance condition. In each communication round, the server samples S of the n clients uniformly at random without replacement. The stochastic gradients are assumed to satisfy the so-called ABC condition, which allows their variance to grow with the local objective gaps. For smooth nonconvex objectives, we establish a finite-time stationarity bound in terms of the heterogeneity measure \sigma_f^\star:=f^\star-\frac1n\sum_i=1^n f_i^\star and the client sampling factor I_S:=(n-S)/(n-1) . Here, f^\star and f_i^\star denote the infima of the global average objective and client i 's local objective, respectively. The bound exhibits a linear speedup in the number of sampled clients S and recovers the full-participation result when S=n . Under diminishing stepsizes, we further prove that the gradient norm converges to zero both in expectation and almost surely. Notably, compared with existing analyses of FedAvg with partial participation, our results require no additional data heterogeneity assumption such as bounded gradient dissimilarity (BGD), and the sampling-induced term in our bound is not amplified by the number of local updates at the leading order. The importance of removing the BGD assumption is illustrated by an example that is smooth and lower bounded but violates BGD, yet is covered by our analysis.
[LG-103] Normalizing Flows for Classical Simulation of Quantum Circuits in Phase Space
链接: https://arxiv.org/abs/2611.14006
作者: Edward Jiang,Timothy Heightman,Marcin Płodzień
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 9 pages, 3 figures
Abstract:We present a novel framework to learn a representation of quantum circuits based on the phase space formulation of many-body systems. Given that N -qubit states can be represented as probability distributions on a smooth 2N -dimensional manifold, we show how unitary gates can be parameterized as normalizing flows between these distributions, opening a new avenue for quantum circuit simulation using generative models. We evaluate our method on three common families of quantum circuits, and show that its accuracy exceeds that of current state-of-the-art neural network methods for large system sizes.
[LG-104] On the Spectral Estimation of VARMA Processes
链接: https://arxiv.org/abs/2611.13973
作者: Bin Zhu,Mattia Zorzi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 12 pages, 2 figures with the IEEE Transactions template
Abstract:We consider the problem of estimating the power spectral density of a Vector-valued Autoregressive Moving-Average (VARMA) process. A well-established approach to accomplishing this task consists of formulating a moment problem that selects the spectral density maximizing an entropy-like index while matching certain moments of the underlying process. However, existing methods assume that the moving-average (MA) component is either fixed or scalar, thereby reducing the class of ARMA models over which the optimal spectral density is sought. In this paper, we propose a new moment problem, based on the notion of transportation entropy, for learning the spectral density of a VARMA process without fixing the MA component or restricting it to be scalar.
[LG-105] Dual-Space Preconditioning for Variational Inequalities and Root-Finding Problems NEURIPS2026
链接: https://arxiv.org/abs/2611.13916
作者: Jan Quan,Konstantinos Oikonomidis,Alexander Bodard,Panagiotis Patrinos
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: NeurIPS 2026 poster
Abstract:This paper develops a dual-space preconditioning framework for variational inequalities and root-finding problems beyond classical cocoercivity and Lipschitz continuity assumptions. To this end, we introduce both a novel cocoercivity-type condition and a relaxed Lipschitz continuity condition, and derive convergence of deterministic and stochastic methods under these generalized conditions. For isotropic preconditioners, the resulting algorithms can be interpreted as variable-stepsize variants of classical methods, extending recent analyses based on (L_0, L_1) -type growth conditions while allowing simpler proofs and larger stepsize ranges. We also show that the framework naturally handles variational inequalities through weak Minty-type arguments. Finally, we study the associated continuous-time dynamics and derive a variational and optimal control interpretation of the preconditioned flow. Overall, the results show that dual-space preconditioning provides a flexible mechanism for designing and analyzing operator methods beyond the standard Euclidean Lipschitz setting.
[LG-106] Outliers and the Hard-Margin Linear SVM in High-Dimension Low-Sample-Size Settings
链接: https://arxiv.org/abs/2611.13869
作者: Yugo Nakayama
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study how outliers affect the hard-margin linear support vector machine when the dimension grows while the sample sizes remain fixed. For one-sided contamination, the centered Gram matrix converges to a constant inlier block plus an outlier-diagonal component, and the limiting dual problem has a closed-form solution. All outliers act through one effective distance. This representation yields explicit thresholds for loss of inlier support, margin shrinkage. intercept displacement, and asymptotic misclassification. We then treat contamination in both classes. The limiting intercept is the multiplier of the class-balance constraint, and the two classes pull it in opposite directions. Consequently, at most one class can collapse or be asymptotically misclassified, whereas symmetric contamination leaves the limiting discriminant function unchanged even though the margin shrinks. Seven numerical experiments verify the dual allocation, phase transitions, and discriminant limits.
[LG-107] Optimal Last-Iterate Bounds for Nonexpansive Stochastic Approximation
链接: https://arxiv.org/abs/2611.13846
作者: Ramanan Tamizholi,Siddharth Chandak,Rahul Singh
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 23 pages
Abstract:We establish last-iterate mean-square residual bounds for nonexpansive stochastic approximation (SA), or stochastic Krasnoselskii-Mann (KM) iterations. In particular, we consider fixed-point mappings that are nonexpansive with respect to the Euclidean norm, and focus on polynomially decaying stepsizes. We study three conditions on the conditional second moment of the noise, and obtain bounds that are optimal up to logarithmic factors in each case. When it scales linearly with the squared residual, the best rate is O(1/k^1-\epsilon) for any \epsilon0 . In this case, for all considered stepsizes, our bounds match those of the noiseless KM iteration. When it is bounded by a constant, the best rate is O(\log^2 k/\sqrtk) . When it scales affinely with the squared norm of the iterate, the best rate is O(1/k^1/2-\epsilon) , and the mean-square residual can diverge if the stepsizes are not square-summable. Thus, unlike for contractive SA, square-summable stepsizes are necessary for nonexpansive SA. We demonstrate how various algorithms with cocoercive maps, such as stochastic gradient descent, linear SA, and learning in cocoercive games, fit into our framework, and how the different noise conditions arise in each of them.
[LG-108] Adaptive Policy-Guided Error Mitigation (APGEM): A Unified Learning-Based Framework for Noise Resilience in Quantum Machine Learning
链接: https://arxiv.org/abs/2611.13815
作者: Bisma Majid,Shabir Ahmad Sofi,Mir Mohammad Yousuf
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 28 pages, 22 figures, 17 tables
Abstract:Error mitigation stands between variational quantum algorithms and useful results on today’s noisy hardware, yet in practice a mitigation method is usually chosen once, by hand, and applied unchanged for the life of an experiment. This is a poor fit for quantum machine learning (QML), where the circuit and the noise drift as training proceeds, so the best strategy in one regime is often wrong in another. We present a generalized form of Adaptive Policy-Guided Error Mitigation (APGEM), which treats the choice of mitigation technique as an online decision made from an algorithm’s own feedback. A single contextual-bandit controller observes signals any variational algorithm already produces, such as fidelity, entropy, reward or loss, and gradient behavior, and selects among Zero-Noise Extrapolation, Probabilistic Error Cancellation, Measurement Error Mitigation, Dynamical Decoupling, and circuit optimization per step. Because it depends only on this shared feedback interface, the framework applies unchanged across supervised, unsupervised, reinforcement, and hybrid quantum-classical learning. Evaluated on five paradigms across 80 algorithm-and-noise conditions, adaptive selection reaches 95.5% of an oracle that always picks the best technique and significantly outperforms all six fixed strategies (Friedman \chi^2=92.18 , p\approx1.1\times10^-17 ; pairwise Wilcoxon p4\times10^-6 ). The learned policy is noise-aware: its favored technique shifts as depolarizing and readout error rise. On noise models from eight real IBM devices, APGEM tracks the oracle almost exactly (99.9% of oracle utility) at a flat cost of roughly 1.3\times10^4 shots from three to eight qubits.
[LG-109] Machine-learning decoders for fusion-based quantum computing in real time
链接: https://arxiv.org/abs/2611.13630
作者: Daniel Barter,Jacob C. Bridgeman,Chris Dawson,Terry Farrelly,Shane Fleming,Mercedes Gimeno-Segovia,Paul Hartke,Timothy Hein,Sean Howell,Gregory Hyegang Jun,Siddhartha Kamalakara,Yehua Liu,Jose M. Monsalve Diaz,Eddie Richter,Sam Roberts
类目: Quantum Physics (quant-ph); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: 22+21 pages, 20 figures, 4 tables, comments welcome
Abstract:Fault-tolerant quantum computation requires decoders that combine accuracy, throughput, low latency, and scalability to large numbers of physical qubits. In photonic fusion-based architectures, erasures from photon loss and probabilistic fusion measurements are a dominant error mechanism, motivating decoders that remain accurate at high erasure rates. We introduce PRISM, a transformer-based machine-learning decoder designed to exploit the known locations of erasures in protocols subject to both Pauli and erasure errors. We benchmark PRISM across a wide range of fault-tolerant protocols based on surface codes, color codes, and quantum low-density parity-check codes, with distances up to 24. Across these benchmarks, PRISM matches the logical error rates of integer-programming-based decoders while being significantly faster, including at error rates up to 50% of threshold. We further optimize the decoder and its real-time hardware implementation to reduce latency while maintaining accuracy. Our optimized implementation on an AMD VP1902 FPGA can decode logical blocks of distance 8 in both space and time, demonstrating a latency of 6.59 microseconds and an amortized decoding time of 1.56 microseconds per logical block is possible. These results show that machine-learning decoding can provide accurate, low-latency inference for erasure-dominated fault-tolerant quantum architectures.
[LG-110] Simultaneous Near-Optimal Region Identification in Continuous Simulation Optimization: Minimax Complexity and Adaptive Sampling
链接: https://arxiv.org/abs/2611.13489
作者: Yilin Zheng,Haowei Wang,Wenjia Wang,Szu Hui Ng
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Simulation optimization typically returns a single estimated optimal point. When decisions also depend on criteria evaluated independently of simulation, such as resource cost, the decision maker instead needs the set of designs whose expected performance lies within a tolerance of the optimum, often choosing the tolerance only after seeing the tradeoffs. We study how to identify these near-optimal regions on a continuous design space, simultaneously for all tolerances up to a prescribed maximum T , with region estimation error at most \eta . We show that region identification is not a by-product of optimization. Specifically, when \alpha\nu , any procedure, even one given the optimal design in advance, needs at least \Omega\bigl(\eta^-(2+d/\nu)\min\1,KT^d/\alpha\bigr) simulation evaluations, where d is the dimension, \nu the smoothness, and (K,\alpha) the covering parameters that describe the geometry of the near-optimal designs. We propose SASORI, which returns an estimated optimal point and the regions for all tolerances from the same evaluations, matching this lower bound up to logarithmic factors. SASORI also achieves the optimal tradeoff between region estimation error and cumulative regret, and reduces to a minimax-optimal simulation optimization procedure at zero tolerance. Numerical experiments illustrate the effectiveness of our proposed method.
[LG-111] Differentially Private Statistical Inference for Rankings from Pairwise Comparisons
链接: https://arxiv.org/abs/2611.13485
作者: Xintao Xia,Jia Gu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Reliable inference for rankings of large language models based on human pairwise comparisons must preserve the privacy of individual preference records. To meet this need, we develop methods for score and rank inference under the Bradley-Terry model with edge differential privacy. For an objective-perturbed estimator, a new leave-one-out analysis establishes finite-sample bounds on Euclidean and entrywise estimation errors. For sufficiently dense comparison graphs, the error bounds extend to smaller privacy budgets, including regimes in which its privacy term dominates its statistical term. A uniform asymptotic expansion yields asymptotically valid confidence intervals for individual scores and pairwise score differences. To improve finite-sample performance, we account for both regularization bias and privacy-induced randomness by combining a plug-in ridge-bias correction with Gaussian-Laplace calibration. We further establish a bootstrap procedure for simultaneous rank inference, providing asymptotically valid confidence sets for individual ranks and tests of top- K membership. When the comparison probability is public and known, all proposed inferential procedures require no additional privacy budget. We evaluate finite-sample performance through simulations and apply the proposed methods to rank large language models for creative writing.
[LG-112] VC-Dimension and Generalization in Geometric Machine Learning with Applications to Active Learning
链接: https://arxiv.org/abs/2611.13337
作者: Gokul Bhusal,Delanna Do,Ekaterina Merkurjev,Kevin Miller,Stephan Wojtowytsch
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Differential Geometry (math.DG)
*备注: 19 pages + appendices
Abstract:We compute the VC-dimension of metric ball classifiers in the model spaces of Riemannian geometry (Euclidean, spherical, hyperbolic) and present upper and lower bounds on the VC-dimension of metric balls in several other situations, including general normed vector spaces (potentially infinite VC-dimension), vector spaces with \ell^p -norms (VC-dimension grows at least linearly in the dimension, faster than Euclidean for p=1 ), flat tori, and general Riemannian manifolds (arbitrarily large VC-dimension after a conformal change). In several situations where the VC-dimension is large or even infinite, we prove generalization bounds under additional assumptions on the data distribution using covering arguments. Both in the VC-dimension and covering setting, we consider data on low-dimensional subsets of high-dimensional Euclidean spaces. As an application, we consider coverage-based algorithms in active learning.
[LG-113] me Series Change Point Detection with Gaussian Processes
链接: https://arxiv.org/abs/2611.13295
作者: Olga Isupova,Danil Kuzin,Fredrik Gustafsson,Lyudmila Mihaylova
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:
Abstract:This paper proposes a novel general Gaussian process framework for change point detection in time series. A change is defined as an alteration of the hyperparameters of the Gaussian process prior. We formulate the problem in the context of statistical hypothesis testing and demonstrate its performance over both offline and online multiple change points detection. The approach provides theoretical guaranties of its performance. An important advantage of the developed framework is that it does not need a detailed specification of the time series function before and after a change. The hyperparameter values are learned from the data. Comprehensive evaluation of the proposed framework is provided both on synthetic and real data and its efficiency is demonstrated.
[LG-114] Continuous Horizon Forecasting
链接: https://arxiv.org/abs/2611.13228
作者: Malcolm Wolff,Elliott Zackrone,Souhaib Ben Taieb,Michael W. Mahoney,Kin G. Olivares
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: International Journal of Forecasting (Work in progress paper)
Abstract:Multi-horizon forecasting has been dominated by discrete forecast strategies that treat model outputs as fixed-vectors or recurrently create them. In this paper, we introduce the Continuous Horizon (CH) strategy that generalizes upon recursive, direct, and multi-step strategies by redefining forecasts as a continuous operator and the forecast horizon as a query variable rather than an index. We present the theoretical foundations for the CH strategy, a Universal Operator Approximation Theorem, and a continuous-horizon bias-variance decomposition that formally proves the estimation variance improvements of CH over the discrete strategies. We validate the CH strategy empirically through a simulation study, and on 16 large-scale datasets from the M-series benchmark, showing how CH achieves competitive RMSE accuracy, while uniquely enabling parallel inference, targeted horizon querying, and interpolation and extrapolation across unseen horizons.
[LG-115] MediPFN: Amortized In-Context Nuisance Learning and Inference for Causal Mediation Analysis
链接: https://arxiv.org/abs/2611.13219
作者: Shi Bo,Jiaming Hu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 27 pages, 7 figures
Abstract:Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways through other processes. Tabular foundation models perform classification and regression through in-context learning, providing estimates of conditional means and propensity scores used in causal mediation analysis. Constructing confidence intervals from these estimates requires accounting for uncertainty introduced by fitting and combining several nuisance functions. We introduce MediPFN, an amortized inference framework that adapts a pretrained tabular foundation model to nuisance prediction and combines its predictions through a cross-fitted influence-function estimator. A separate head learns quantiles of the estimator’s standardized error from simulated tables with known indirect effects. Supervised fine-tuning, symmetrization, and independent calibration then produce a confidence interval. We establish coverage bounds for the resulting intervals under standard assumptions. Across experiments, MediPFN attains coverage closer to the nominal level than several comparators. It also reduces the computational cost of uncertainty quantification, running 59.1 times faster than bootstrap in matched comparisons.
[LG-116] Near-Optimal Sample Complexity of Deterministic Agnostic PAC Learning
链接: https://arxiv.org/abs/2611.13210
作者: Shai Ben-David,Steve Hanneke,Farnam Mansouri,Amirreza Shaeiri
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:We study the problem of agnostic PAC learning under deterministic labels, where the examples are labeled by an arbitrary deterministic function, which need not belong to the concept class C . This model captures misspecification without stochastic label noise and lies between the realizable and fully agnostic settings. Prior work showed that, when C has finite diameter K and VC dimension d , deterministic labels can improve the sample complexity upper bound from the classical agnostic guarantee O(d/\varepsilon^2) to \widetildeO(K/\varepsilon) , but left a large gap between upper and lower bounds. Our main result sharpens this picture. We prove that every class of VC dimension d and finite diameter K is learnable with sample complexity \widetildeO\left( \fracK^1/3 , d^2/3\varepsilon \right) , and we construct classes for which this bound is tight up to logarithmic factors. We also study the online analogue of this problem and show that the minimax regret is again partly controlled by the diameter of the class. Technically, our upper bounds are obtained via memorization-based learning algorithms that exploit the deterministic label structure.
[LG-117] EvoSurv: Evolution-Aware Multimodal Survival Modeling Reveals Complementary Prognostic Signals
链接: https://arxiv.org/abs/2611.13190
作者: Wei Wang,Po-Yu Liang,Jun Bai
类目: Quantitative Methods (q-bio.QM); Machine Learning (cs.LG)
*备注: 11 pages, 3 figures
Abstract:Accurate cancer survival prediction remains challenging due to tumor heterogeneity. Although multimodal survival models integrate clinical and molecular data, they often overlook evolutionary information encoded in genomic alterations. To address this gap, we developed eTime, a 38-dimensional evolutionary representation comprising eight feature groups that characterize clonal and subclonal mutation burden, mutational spectra, functional consequences, copy-number alterations, and anoikis-related genomic changes. We further developed EvoSurv, a multimodal survival framework integrating eTime with clinical variables and RNA expression. Across six TCGA cancer cohorts comprising 2,782 patients, EvoSurv achieved the highest mean concordance index (C-index) in all six cancers, averaging 0.663 and outperforming three state-of-the-art survival prediction methods. More importantly, we assessed whether evolutionary features provide additional prognostic information beyond clinical and transcriptomic predictors. eTime remained significantly associated with survival in head and neck squamous cell carcinoma (HNSC) and liver hepatocellular carcinoma (LIHC) after adjustment for clinical variables and RNA-derived risk scores. Feature-group analyses further revealed cancer-specific prognostic patterns. In HNSC, TG substitutions were associated with increased mortality risk after clinical and molecular adjustment, including in HPV-negative patients, with supporting evidence from an independent CPTAC-HNSCC cohort. Overall, these findings demonstrate that eTime captures survival-associated evolutionary information and highlight its potential to provide prognostic value beyond clinical and transcriptomic predictors.
[LG-118] Generative Learning on Manifolds for Probabilistic Seismic Performance Assessment and Structural Identifiability
链接: https://arxiv.org/abs/2611.12908
作者: Dimitris G Giovanis,Ioannis G Kevrekidis,Roger G Ghanem
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
*备注:
Abstract:Seismic performance assessment relies on computationally expensive nonlinear simulations, limiting uncertainty quantification and inference of structural properties from observed responses. We develop a manifold-based generative framework that learns the joint distribution of uncertain structural parameters and seismic-response trajectories from limited high-fidelity simulations. Combining Diffusion Maps, score-based Diffusion Modeling, and Geometric Harmonics, the framework enables both forward probabilistic prediction and inverse inference. Applied to a nine-story steel moment-resisting frame, the results reveal which structural properties are identifiable from seismic performance and how uncertainty in these properties influences response. In particular, the learned representation captures post-capping degradation characteristics more effectively than individual strength parameters. Inverse inference identifies distinct structural configurations that produce similar seismic performance, with representative solutions independently validated using nonlinear simulations. These findings demonstrate how generative learning can support uncertainty-aware seismic assessment while revealing structural non-identifiability and alternative mechanisms underlying observed performance
[LG-119] Alchemically Transferable Parameters for Variational Quantum Eigensolvers
链接: https://arxiv.org/abs/2611.11445
作者: Jonas Motyl,Jakob S. Kottmann
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
*备注: 14 pages, 8 figures
Abstract:Machine-learned circuit parameters could remove one of the main bottlenecks of variational quantum eigensolvers: the classical optimization loop, with its typical barren plateaus, local minima, and costly energy and gradient evaluations, is replaced by a direct prediction. Prior work showed that machine-learning models paired with a structured circuit design can transfer across system size and structure within hydrogenic systems. The next step: Extending them to organic molecules would seem to require re-training on use-case-specific training sets. In this work, we show that this is not necessary by establishing an alchemical approach. A geometric projection places surrogate hydrogen atoms along the bond directions of the target molecule and so maps its local bonding environment onto small hydrogenic fragments. Existing hydrogenic solvers can then be reused as they are. The only new quantity is a single projection radius per bond type, which is fitted once on a small reference molecule and reused unchanged thereafter. We demonstrate the approach on the separable-pair circuit designs that have been used in prior work and extend the approach to multi-circuit approaches that span effective bases for quantum subspaces.
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